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719dc5e9-1ffc-4b97-b8e7-d939d4275369/7e5c5ade-0ce7-4aab-992f-c0ecdbe306a8/0/0 | Webit Face | Face Detection, Verification, Similarity, Age, Gender, Race, Emotion, Landmarks and Parts analysis. | 6 | Detection | Perform A.I. powered Face Detection over photos with multiple faces. | 200 | Response | {"status": "success", "data": {"input_source": "url", "faces": [{"face_id": 1, "confidence": 0.9999762773513794, "bounding_box": {"start": {"x": 60.262008733624455, "y": 48.873873873873876}, "end": {"x": 72.63464337700145, "y": 59.909909909909906}}}, {"face_id": 2, "confidence": 0.9999254941940308, "bounding_box": {"start": {"x": 9.606986899563319, "y": 49.43693693693694}, "end": {"x": 21.542940320232898, "y": 60.92342342342342}}}, {"face_id": 3, "confidence": 0.9999184608459473, "bounding_box": {"start": {"x": 25.036390101892284, "y": 51.013513513513516}, "end": {"x": 37.11790393013101, "y": 61.82432432432432}}}, {"face_id": 4, "confidence": 0.9999114274978638, "bounding_box": {"start": {"x": 77.14701601164484, "y": 50.56306306306306}, "end": {"x": 89.95633187772926, "y": 61.82432432432432}}}, {"face_id": 5, "confidence": 0.9992819428443909, "bounding_box": {"start": {"x": 44.832605531295485, "y": 52.13963963963964}, "end": {"x": 57.35080058224163, "y": 63.06306306306306}}}], "faces_count": 5}, "message": null} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"status": {"type": "string"}, "data": {"type": "object", "properties": {"input_source": {"type": "string"}, "faces": {"type": "array", "items": {"type": "object", "properties": {"face_id": {"type": "integer"}, "confidence": {"type": "number"}, "bounding_box": {"type": "object", "properties": {"start": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}, "end": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}}, "required": ["end", "start"]}}, "required": ["bounding_box", "confidence", "face_id"]}}, "faces_count": {"type": "integer"}}, "required": ["faces", "faces_count", "input_source"]}, "message": {"type": "null"}}, "required": ["data", "message", "status"]} |
719dc5e9-1ffc-4b97-b8e7-d939d4275369/eddf4e77-faec-4f67-bd4b-8ee5fd52cd7b/0/0 | Webit Face | Face Detection, Verification, Similarity, Age, Gender, Race, Emotion, Landmarks and Parts analysis. | 6 | Similarity | Perform A.I. powered Face Similarity comparison over a source face against multiple target faces. | 200 | Response | {"status": "success", "data": {"input_source": "url", "target_source": "url", "input_face": {"face_id": 1, "confidence": 0.99979, "bounding_box": {"start": {"x": 43.04761904761905, "y": 38.142857142857146}, "end": {"x": 56.476190476190474, "y": 61.57142857142857}}}, "matches": [{"face_id": 2, "similarity": 0.98243, "confidence": 0.99993, "bounding_box": {"start": {"x": 9.606986899563319, "y": 49.43693693693694}, "end": {"x": 21.542940320232898, "y": 60.92342342342342}}}, {"face_id": 5, "similarity": 0.765, "confidence": 0.99928, "bounding_box": {"start": {"x": 44.832605531295485, "y": 52.13963963963964}, "end": {"x": 57.35080058224163, "y": 63.06306306306306}}}, {"face_id": 1, "similarity": 0.12163, "confidence": 0.99998, "bounding_box": {"start": {"x": 60.262008733624455, "y": 48.873873873873876}, "end": {"x": 72.63464337700145, "y": 59.909909909909906}}}, {"face_id": 3, "similarity": 0.05647, "confidence": 0.99992, "bounding_box": {"start": {"x": 25.036390101892284, "y": 51.013513513513516}, "end": {"x": 37.11790393013101, "y": 61.82432432432432}}}, {"face_id": 4, "similarity": 0.05071, "confidence": 0.99991, "bounding_box": {"start": {"x": 77.14701601164484, "y": 50.56306306306306}, "end": {"x": 89.95633187772926, "y": 61.82432432432432}}}], "matches_count": 5}, "message": null} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"status": {"type": "string"}, "data": {"type": "object", "properties": {"input_source": {"type": "string"}, "target_source": {"type": "string"}, "input_face": {"type": "object", "properties": {"face_id": {"type": "integer"}, "confidence": {"type": "number"}, "bounding_box": {"type": "object", "properties": {"start": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}, "end": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}}, "required": ["end", "start"]}}, "required": ["bounding_box", "confidence", "face_id"]}, "matches": {"type": "array", "items": {"type": "object", "properties": {"face_id": {"type": "integer"}, "similarity": {"type": "number"}, "confidence": {"type": "number"}, "bounding_box": {"type": "object", "properties": {"start": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}, "end": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}}, "required": ["end", "start"]}}, "required": ["bounding_box", "confidence", "face_id", "similarity"]}}, "matches_count": {"type": "integer"}}, "required": ["input_face", "input_source", "matches", "matches_count", "target_source"]}, "message": {"type": "null"}}, "required": ["data", "message", "status"]} |
719dc5e9-1ffc-4b97-b8e7-d939d4275369/153d421d-d46f-49ba-91c6-a890bab03596/0/0 | Webit Face | Face Detection, Verification, Similarity, Age, Gender, Race, Emotion, Landmarks and Parts analysis. | 6 | Analysis | Perform A.I. powered Face Analysis in order to extract Age, Gender, Race and Emotion insights and attributes. | 200 | Response | {"status": "success", "data": {"input_source": "url", "result": {"face_id": 1, "emotion": "happy", "score": 0.9999915361404419, "emotions_scores": {"angry": "0.0000000000000000", "disgust": "0.0000000000000000", "fear": "0.0000000000000000", "happy": "0.9999915361404419", "neutral": "0.0000085076790128", "sad": "0.0000000000000000", "surprise": "0.0000000000000086"}, "bounding_box": {"start": {"x": 31.2, "y": 34.53333333333333}, "end": {"x": 81.8, "y": 79.46666666666667}}}}, "message": null} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"status": {"type": "string"}, "data": {"type": "object", "properties": {"input_source": {"type": "string"}, "result": {"type": "object", "properties": {"face_id": {"type": "integer"}, "emotion": {"type": "string"}, "score": {"type": "number"}, "emotions_scores": {"type": "object", "properties": {"angry": {"type": "string"}, "disgust": {"type": "string"}, "fear": {"type": "string"}, "happy": {"type": "string"}, "neutral": {"type": "string"}, "sad": {"type": "string"}, "surprise": {"type": "string"}}, "required": ["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"]}, "bounding_box": {"type": "object", "properties": {"start": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}, "end": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}}, "required": ["end", "start"]}}, "required": ["bounding_box", "emotion", "emotions_scores", "face_id", "score"]}}, "required": ["input_source", "result"]}, "message": {"type": "null"}}, "required": ["data", "message", "status"]} |
719dc5e9-1ffc-4b97-b8e7-d939d4275369/428a490e-3bbd-4361-8770-3931e0a537ee/0/0 | Webit Face | Face Detection, Verification, Similarity, Age, Gender, Race, Emotion, Landmarks and Parts analysis. | 6 | Verification | Perform A.I. powered Face Verification of a source face against multiple target faces. | 200 | Response | {"status": "success", "data": {"input_source": "url", "target_source": "url", "input_face": {"face_id": 1, "confidence": 0.99979, "bounding_box": {"start": {"x": 43.04761904761905, "y": 38.142857142857146}, "end": {"x": 56.476190476190474, "y": 61.57142857142857}}}, "matches": [{"face_id": 1, "closeness": 0.98243, "confidence": 0.99993, "bounding_box": {"start": {"x": 9.606986899563319, "y": 49.43693693693694}, "end": {"x": 21.542940320232898, "y": 60.92342342342342}}}], "matches_count": 1}, "message": null} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"status": {"type": "string"}, "data": {"type": "object", "properties": {"input_source": {"type": "string"}, "target_source": {"type": "string"}, "input_face": {"type": "object", "properties": {"face_id": {"type": "integer"}, "confidence": {"type": "number"}, "bounding_box": {"type": "object", "properties": {"start": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}, "end": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}}, "required": ["end", "start"]}}, "required": ["bounding_box", "confidence", "face_id"]}, "matches": {"type": "array", "items": {"type": "object", "properties": {"face_id": {"type": "integer"}, "closeness": {"type": "number"}, "confidence": {"type": "number"}, "bounding_box": {"type": "object", "properties": {"start": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}, "end": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}}, "required": ["end", "start"]}}, "required": ["bounding_box", "closeness", "confidence", "face_id"]}}, "matches_count": {"type": "integer"}}, "required": ["input_face", "input_source", "matches", "matches_count", "target_source"]}, "message": {"type": "null"}}, "required": ["data", "message", "status"]} |
93f8ed86-61cc-46bf-bbe2-3a3fca930578/a09a1e14-b7ec-42cb-ad61-5454233cd189/0/0 | Biometric FingerPrint Recognition | This API facilitates fingerprint recognition, allowing users to efficiently identify and discover optimal matches among fingerprints. It serves as a powerful tool for applications requiring precise biometric authentication and verification, such as security systems, law enforcement databases, and access control solutions. | 6.6 | fingerprint recognition from file upload | Effortlessly initiate the analysis process by uploading and preparing your dataset along with the target file. Simply create a dataset by uploading a group of images, and seamlessly process them alongside the target image for comprehensive results. | 200 | New Example | {"response": {"file": "LEFT_INDEX_FINGER.bmp", "match": 85, "matches": [{"file": "LEFT_MIDDLE_FINGER.bmp", "score": 76}], "key1": "", "key2": ""}} | {"type": "object", "properties": {"response": {"type": "object", "properties": {"file": {"type": "string"}, "match": {"type": "integer"}, "matches": {"type": "array", "items": {"type": "object", "properties": {"file": {"type": "string"}, "score": {"type": "integer"}}}}, "key1": {"type": "string"}, "key2": {"type": "string"}}}}} |
97a7459a-4dd4-4aba-9c24-c1e5345d432f/557949ae-2ac9-4482-81a7-10732823619e/0/0 | Japanese Driver License OCR | Extract the text of your name, home address, date of birth, date of issuance, expiration date, and ID number from the front and back of your Japanese driver's license. | 6.6 | Japanese Driver License OCR | Support jpg, png, bmp, pdf, tiff, single-frame gif and other formats, the image size does not exceed 10M. | 200 | Response | {"code": "200", "status": "SUCCESS", "date": "11/22/2023 06:57:40 PM", "result": {"rotated_image_height": 648, "image_angle": 0, "rotated_image_width": 1022, "item_list": [{"value": "\u65e5\u672c \u82b1\u5b50\u6771\u4eac\u82b1\u5b50", "key": "name"}, {"value": "\u6771\u4eac\u90fd\u5343\u4ee3\u7530\u533a\u971e\u304c\u95a22-1-2", "position": {"bottom": 170, "left": 53, "right": 104, "top": 145}, "key": "address"}, {"value": "\u4ee4\u548c01\u5e7405\u670807\u65e5", "position": {"bottom": 220, "left": 153, "right": 491, "top": 188}, "key": "date_of_birth"}, {"value": "\u4ee4\u548c01\u5e7405\u670807\u65e5", "position": {"bottom": 214, "left": 57, "right": 101, "top": 193}, "key": "the_delivery_date"}, {"value": "2024\u5e74(\u4ee4\u548c06\u5e74)06\u670801\u65e5", "position": {"bottom": 275, "left": 43, "right": 627, "top": 232}, "key": "valid_term"}, {"value": "012345678900", "position": {"bottom": 479, "left": 175, "right": 525, "top": 441}, "key": "number"}], "type": "driverlicence_front"}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"code": {"type": "string"}, "status": {"type": "string"}, "date": {"type": "string"}, "result": {"type": "object", "properties": {"rotated_image_height": {"type": "integer"}, "image_angle": {"type": "integer"}, "rotated_image_width": {"type": "integer"}, "item_list": {"type": "array", "items": {"type": "object", "properties": {"value": {"type": "string"}, "key": {"type": "string"}, "position": {"type": "object", "properties": {"bottom": {"type": "integer"}, "left": {"type": "integer"}, "right": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}}, "required": ["key", "value"]}}, "type": {"type": "string"}}, "required": ["image_angle", "item_list", "rotated_image_height", "rotated_image_width", "type"]}}, "required": ["code", "date", "result", "status"]} |
859601e0-9a4c-4fb6-b633-370e58ea21ea/3a99f1cb-5e8e-4084-979f-f139b25c7106/0/0 | Face Detection | PresentID Face detection API can detect the face(s) in your image and retrieve some features such as Age, Gender, Landmarks, etc. | 8.4 | Send image by image file | Detect face(s) in your image and retrieve some features such as Age, Gender, Land Marks and etc.
Image size should not exceed 8 MB
Also, the images must not be larger than 5000 pixels and smaller than 50 pixels | 200 | FaceDetectionResponse | {"data": [{"age": {"maxAge": 46, "minAge": 41}, "blur": {"level": "medium", "value": 0.18274683425000002}, "faceID": "62fbc589-8709-40e2-967c-42989ee1082f", "faceLandMarks": [{"title": "CenterRightEye", "x": 282, "y": 193}, {"title": "CenterLeftEye", "x": 285, "y": 218}, {"title": "NoseTip", "x": 293, "y": 242}, {"title": "LipsRightCorner", "x": 313, "y": 258}, {"title": "LipsLeftCorner", "x": 338, "y": 263}], "gender": {"index": 1, "title": "Male"}, "rectangle": {"lb": {"x": 381, "y": 251}, "lt": {"x": 381, "y": 146}, "rb": {"x": 276, "y": 251}, "rt": {"x": 276, "y": 146}}, "rotateAngel": 0, "status": 200, "statusMessage": "OK"}], "hasError": false, "statusCode": 200, "statusMessage": "OK"} | {"properties": {"data": {"items": {"properties": {"age": {"properties": {"maxAge": {"type": "integer"}, "minAge": {"type": "integer"}}, "type": "object"}, "blur": {"properties": {"level": {"type": "string"}, "value": {"type": "number"}}, "type": "object"}, "faceID": {"type": "string"}, "faceLandMarks": {"items": {"properties": {"title": {"type": "string"}, "x": {"type": "integer"}, "y": {"type": "integer"}}, "type": "object"}, "type": "array"}, "gender": {"properties": {"index": {"type": "integer"}, "title": {"type": "string"}}, "type": "object"}, "rectangle": {"properties": {"lb": {"properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}, "type": "object"}, "lt": {"properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}, "type": "object"}, "rb": {"properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}, "type": "object"}, "rt": {"properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}, "type": "object"}}, "type": "object"}, "rotateAngel": {"type": "integer"}, "status": {"type": "integer"}, "statusMessage": {"type": "string"}}, "type": "object"}, "type": "array"}, "hasError": {"type": "boolean"}, "statusCode": {"type": "integer"}, "statusMessage": {"type": "string"}}, "type": "object"} |
99aa5e80-f65b-460f-961f-4e47d5456200/1ba6e516-7b4b-40ec-a5c2-22613d9ac6db/0/0 | Signature Recognition | Powered by AWS Textract - accelerate document verification and enhance security by incorporating signature finding and recognition. This technology locates signatures within documents streamlining processes and ensuring the authenticity of critical transactions. Improve efficiency and trust in your workflows with seamless signature identification. | null | signature | Signature recognition | 200 | Example_1 | [{"Type": "SIGNATURE", "Confidence": 34.75679016113281, "BoundingBox": {"Height": 0.02957404963672161, "Left": 0.12615138292312622, "Top": 0.2997671365737915, "Width": 0.15474019944667816}}, {"Type": "SIGNATURE", "Confidence": 33.538169860839844, "BoundingBox": {"Height": 0.021493464708328247, "Left": 0.6277494430541992, "Top": 0.3625658452510834, "Width": 0.16780896484851837}}, {"Type": "SIGNATURE", "Confidence": 39.22246551513672, "BoundingBox": {"Height": 0.019732262939214706, "Left": 0.11536668241024017, "Top": 0.8878365159034729, "Width": 0.1498892903327942}}] | {"type": "string", "title": "FormResult"} |
c37c1272-4eb4-40c5-a5fc-835046232ff6/6bcba152-fa34-4615-8484-394ca6f7daf1/0/0 | Promity Facial Emotion | API for recognizing facial emotions in images in real time. | 7.7 | Emotions by URL | This endpoint recognize 7 emotions:
1. angry
2. disgust
3. fear
4. happy
5. sad
6. surprise
7. neutral
Check out our tutorials for example of usage in Python. | 200 | New Example | {"detections": [{"crop": {"x1": 0.49029475450515747, "y1": 0.5199255347251892, "x2": 0.5589765906333923, "y2": 0.38276559114456177, "score": 0.9992923736572266}, "emotions": {"angry": 0.01486795861274004, "disgust": 5.832673923578113e-05, "fear": 0.04769697040319443, "happy": 0.007116216700524092, "sad": 0.041543327271938324, "surprise": 0.05854588747024536, "neutral": 0.8301714062690735}}, {"crop": {"x1": 0.11643223464488983, "y1": 0.5169875025749207, "x2": 0.1809975504875183, "y2": 0.39529597759246826, "score": 0.998594343662262}, "emotions": {"angry": 0.04906865209341049, "disgust": 0.00010269907943438739, "fear": 0.006792327389121056, "happy": 0.00701629463583231, "sad": 0.0014710179530084133, "surprise": 0.010464858263731003, "neutral": 0.9250841736793518}}, {"crop": {"x1": 0.8563294410705566, "y1": 0.24784332513809204, "x2": 0.917901873588562, "y2": 0.1223781406879425, "score": 0.9980091452598572}, "emotions": {"angry": 0.07673224806785583, "disgust": 0.0034004771150648594, "fear": 0.0696248933672905, "happy": 0.3008084297180176, "sad": 0.4222947955131531, "surprise": 0.00031910650432109833, "neutral": 0.12682004272937775}}, {"crop": {"x1": 0.1482899785041809, "y1": 0.3003842234611511, "x2": 0.20176897943019867, "y2": 0.20043990015983582, "score": 0.9973342418670654}, "emotions": {"angry": 0.018076175823807716, "disgust": 2.274739927088376e-06, "fear": 0.01710568182170391, "happy": 0.13184937834739685, "sad": 0.003582003293558955, "surprise": 0.015439125709235668, "neutral": 0.8139452934265137}}, {"crop": {"x1": 0.598613440990448, "y1": 0.3124071955680847, "x2": 0.6528789401054382, "y2": 0.20392271876335144, "score": 0.9954931735992432}, "emotions": {"angry": 0.012377092614769936, "disgust": 5.150173819856718e-06, "fear": 0.04596496745944023, "happy": 0.0037105451337993145, "sad": 0.1039978489279747, "surprise": 0.00806606374680996, "neutral": 0.8258783221244812}}, {"crop": {"x1": 0.7280519008636475, "y1": 0.5563205480575562, "x2": 0.7874197363853455, "y2": 0.4102036952972412, "score": 0.7963489294052124}, "emotions": {"angry": 0.24640542268753052, "disgust": 0.07081300020217896, "fear": 0.05385291948914528, "happy": 0.03003399632871151, "sad": 0.2724117636680603, "surprise": 0.00690825330093503, "neutral": 0.31957465410232544}}]} | {"type": "object", "properties": {"detections": {"type": "array", "items": {"type": "object", "properties": {"crop": {"type": "object", "properties": {"x1": {"type": "number"}, "y1": {"type": "number"}, "x2": {"type": "number"}, "y2": {"type": "number"}, "score": {"type": "number"}}}, "emotions": {"type": "object", "properties": {"angry": {"type": "number"}, "disgust": {"type": "number"}, "fear": {"type": "number"}, "happy": {"type": "number"}, "sad": {"type": "number"}, "surprise": {"type": "number"}, "neutral": {"type": "number"}}}}}}}} |
c37c1272-4eb4-40c5-a5fc-835046232ff6/acfd7485-9390-41ea-96e9-f82c6a2bcc2f/0/0 | Promity Facial Emotion | API for recognizing facial emotions in images in real time. | 7.7 | Emotions by file | This endpoint recognize 7 emotions:
1. angry
2. disgust
3. fear
4. happy
5. sad
6. surprise
7. neutral
Check out our tutorials for example of usage in Python. | 200 | New Example | {"detections": [{"crop": {"x1": 0.49029475450515747, "y1": 0.5199255347251892, "x2": 0.5589765906333923, "y2": 0.38276559114456177, "score": 0.9992923736572266}, "emotions": {"angry": 0.01486795861274004, "disgust": 5.832673923578113e-05, "fear": 0.04769697040319443, "happy": 0.007116216700524092, "sad": 0.041543327271938324, "surprise": 0.05854588747024536, "neutral": 0.8301714062690735}}, {"crop": {"x1": 0.11643223464488983, "y1": 0.5169875025749207, "x2": 0.1809975504875183, "y2": 0.39529597759246826, "score": 0.998594343662262}, "emotions": {"angry": 0.04906865209341049, "disgust": 0.00010269907943438739, "fear": 0.006792327389121056, "happy": 0.00701629463583231, "sad": 0.0014710179530084133, "surprise": 0.010464858263731003, "neutral": 0.9250841736793518}}, {"crop": {"x1": 0.8563294410705566, "y1": 0.24784332513809204, "x2": 0.917901873588562, "y2": 0.1223781406879425, "score": 0.9980091452598572}, "emotions": {"angry": 0.07673224806785583, "disgust": 0.0034004771150648594, "fear": 0.0696248933672905, "happy": 0.3008084297180176, "sad": 0.4222947955131531, "surprise": 0.00031910650432109833, "neutral": 0.12682004272937775}}, {"crop": {"x1": 0.1482899785041809, "y1": 0.3003842234611511, "x2": 0.20176897943019867, "y2": 0.20043990015983582, "score": 0.9973342418670654}, "emotions": {"angry": 0.018076175823807716, "disgust": 2.274739927088376e-06, "fear": 0.01710568182170391, "happy": 0.13184937834739685, "sad": 0.003582003293558955, "surprise": 0.015439125709235668, "neutral": 0.8139452934265137}}, {"crop": {"x1": 0.598613440990448, "y1": 0.3124071955680847, "x2": 0.6528789401054382, "y2": 0.20392271876335144, "score": 0.9954931735992432}, "emotions": {"angry": 0.012377092614769936, "disgust": 5.150173819856718e-06, "fear": 0.04596496745944023, "happy": 0.0037105451337993145, "sad": 0.1039978489279747, "surprise": 0.00806606374680996, "neutral": 0.8258783221244812}}, {"crop": {"x1": 0.7280519008636475, "y1": 0.5563205480575562, "x2": 0.7874197363853455, "y2": 0.4102036952972412, "score": 0.7963489294052124}, "emotions": {"angry": 0.24640542268753052, "disgust": 0.07081300020217896, "fear": 0.05385291948914528, "happy": 0.03003399632871151, "sad": 0.2724117636680603, "surprise": 0.00690825330093503, "neutral": 0.31957465410232544}}]} | {"type": "object", "properties": {"detections": {"type": "array", "items": {"type": "object", "properties": {"crop": {"type": "object", "properties": {"x1": {"type": "number"}, "y1": {"type": "number"}, "x2": {"type": "number"}, "y2": {"type": "number"}, "score": {"type": "number"}}}, "emotions": {"type": "object", "properties": {"angry": {"type": "number"}, "disgust": {"type": "number"}, "fear": {"type": "number"}, "happy": {"type": "number"}, "sad": {"type": "number"}, "surprise": {"type": "number"}, "neutral": {"type": "number"}}}}}}}} |
2ed09f28-5839-4f52-b712-80ed8a021e0b/21c5c54c-a951-4e47-8d30-8219d20aa06d/0/0 | Parking places | Analize image to get info about parking places using machine learning! | 8.6 | Detect by upload file (form data) | Method returns busy/free parking places and some additional info. | 200 | Example_1 | {"pplaces": [{"isFree": false, "box": [], "confidence": 0}], "state": {}, "info": {}} | {"title": "Detect method responce", "type": "object", "properties": {"pplaces": {"title": "Pplaces", "type": "array", "items": {"title": "Pplace", "type": "object", "properties": {"isFree": {"title": "Isfree", "type": "boolean", "description": "Parking place is Busy/False or Free/True", "default": false}, "box": {"title": "Box", "type": "array", "items": {}, "description": "Box coordinates: (x1,y1,x2,y2)", "default": [0, 0, 0, 0]}, "confidence": {"title": "Confidence", "type": "number", "description": "Confidence percentage", "default": 0}}}, "default": []}, "state": {"title": "State", "type": "object", "default": {}}, "info": {"title": "Info", "type": "object", "default": {}}}} |
2ed09f28-5839-4f52-b712-80ed8a021e0b/21c5c54c-a951-4e47-8d30-8219d20aa06d/1/0 | Parking places | Analize image to get info about parking places using machine learning! | 8.6 | Detect by upload file (form data) | Method returns busy/free parking places and some additional info. | 422 | Example_1 | {"detail": [{"loc": [], "msg": "", "type": ""}]} | {"title": "HTTPValidationError", "type": "object", "properties": {"detail": {"title": "Detail", "type": "array", "items": {"title": "ValidationError", "required": ["loc", "msg", "type"], "type": "object", "properties": {"loc": {"title": "Location", "type": "array", "items": {"type": "string"}}, "msg": {"title": "Message", "type": "string"}, "type": {"title": "Error Type", "type": "string"}}}}}} |
2ed09f28-5839-4f52-b712-80ed8a021e0b/9be6d553-f6d8-4cdb-aad8-ad245d0fc583/0/0 | Parking places | Analize image to get info about parking places using machine learning! | 8.6 | Detect by base64 encoded file (string) | Method returns busy/free parking places and some additional info.
imageb64 - base64 encoded image. Valid values are:
1. plain base64 text, like `/9j/4AAQSkZJRgABAQ...`
2. data-uri with base64 text, like `data:image/jpeg;base64,/9j/4AAQSkZJRgABAQ...`
3. `bytes to string` like `b'/9j/4AAQSkZJRgABAQ...`
| 200 | Example_1 | {"pplaces": [{"isFree": false, "box": [], "confidence": 0}], "state": {}, "info": {}} | {"title": "Detect method responce", "type": "object", "properties": {"pplaces": {"title": "Pplaces", "type": "array", "items": {"title": "Pplace", "type": "object", "properties": {"isFree": {"title": "Isfree", "type": "boolean", "description": "Parking place is Busy/False or Free/True", "default": false}, "box": {"title": "Box", "type": "array", "items": {}, "description": "Box coordinates: (x1,y1,x2,y2)", "default": [0, 0, 0, 0]}, "confidence": {"title": "Confidence", "type": "number", "description": "Confidence percentage", "default": 0}}}, "default": []}, "state": {"title": "State", "type": "object", "default": {}}, "info": {"title": "Info", "type": "object", "default": {}}}} |
2ed09f28-5839-4f52-b712-80ed8a021e0b/9be6d553-f6d8-4cdb-aad8-ad245d0fc583/1/0 | Parking places | Analize image to get info about parking places using machine learning! | 8.6 | Detect by base64 encoded file (string) | Method returns busy/free parking places and some additional info.
imageb64 - base64 encoded image. Valid values are:
1. plain base64 text, like `/9j/4AAQSkZJRgABAQ...`
2. data-uri with base64 text, like `data:image/jpeg;base64,/9j/4AAQSkZJRgABAQ...`
3. `bytes to string` like `b'/9j/4AAQSkZJRgABAQ...`
| 422 | Example_1 | {"detail": [{"loc": [], "msg": "", "type": ""}]} | {"title": "HTTPValidationError", "type": "object", "properties": {"detail": {"title": "Detail", "type": "array", "items": {"title": "ValidationError", "required": ["loc", "msg", "type"], "type": "object", "properties": {"loc": {"title": "Location", "type": "array", "items": {"type": "string"}}, "msg": {"title": "Message", "type": "string"}, "type": {"title": "Error Type", "type": "string"}}}}}} |
8eceda12-02b0-4786-9027-96e4d0a9d807/d4f3c366-29ad-4c99-95d3-62af670c05cb/0/0 | Face Detection and Analysis | Our comprehensive Face Detection & Analysis API offers a wide range of functionalities. It accurately detects and provides the precise location of faces within images. Additionally, it incorporates advanced features such as liveness detection, age estimation, gender identification, analysis of facial expressions (emotions), and detection of facemasks. With our API, you can efficiently analyze and extract valuable insights from facial data. | 7.6 | Detect & Analyze | supported image format is JPEG and PNG
Output detected faces, with each face's :
Bounding box coordinate (0 to 1 relative to image dimension).
0, 0 is top left corner of the image while 1, 1 is bottom right corner of the image
x1 & y1 is bbox top left point coordinate
x2 & y2 is bbox bottom right point coordinate
Face Liveness (is_real_face)
Estimated age
Estimated gender
Estimated face expression (emotion)
["neutral", "happy", "sad", "surprise", "anger"]
wear_facemask estimation
| 200 | Success Example | {"status": "success", "image_file_name": "omar-lopez-T6zu4jFhVwg-unsplash.jpg", "face_count": 10, "analysis_result": [{"bbox": {"x1": 0.2699, "y1": 0.24103, "x2": 0.31314, "y2": 0.30961}, "liveness": {"is_real_face": true, "liveness_probability": 0.88112}, "age": 35.95, "gender": "male", "gender_probability": 0.86915, "emotion": "happy", "emotion_probability": 0.99404, "wear_facemask": false}, {"bbox": {"x1": 0.8732, "y1": 0.30382, "x2": 0.91393, "y2": 0.37269}, "liveness": {"is_real_face": true, "liveness_probability": 0.85638}, "age": 44.63, "gender": "male", "gender_probability": 0.96848, "emotion": "happy", "emotion_probability": 0.96287, "wear_facemask": false}, {"bbox": {"x1": 0.73992, "y1": 0.28733, "x2": 0.77376, "y2": 0.34317}, "liveness": {"is_real_face": false, "liveness_probability": 0.92994}, "age": 20.52, "gender": "female", "gender_probability": 0.95935, "emotion": "happy", "emotion_probability": 0.99934, "wear_facemask": false}, {"bbox": {"x1": 0.17861, "y1": 0.239, "x2": 0.21914, "y2": 0.30874}, "liveness": {"is_real_face": true, "liveness_probability": 0.93495}, "age": 20.81, "gender": "female", "gender_probability": 0.98389, "emotion": "happy", "emotion_probability": 0.99927, "wear_facemask": false}, {"bbox": {"x1": 0.56862, "y1": 0.33709, "x2": 0.6033, "y2": 0.39207}, "liveness": {"is_real_face": true, "liveness_probability": 0.99558}, "age": 25.07, "gender": "female", "gender_probability": 0.846, "emotion": "neutral", "emotion_probability": 0.515, "wear_facemask": false}, {"bbox": {"x1": 0.0963, "y1": 0.23814, "x2": 0.14038, "y2": 0.31481}, "liveness": {"is_real_face": true, "liveness_probability": 0.58593}, "age": 20.55, "gender": "female", "gender_probability": 0.97251, "emotion": "happy", "emotion_probability": 0.99784, "wear_facemask": true}, {"bbox": {"x1": 0.64487, "y1": 0.30122, "x2": 0.67934, "y2": 0.36198}, "liveness": {"is_real_face": false, "liveness_probability": 0.83463}, "age": 23.35, "gender": "female", "gender_probability": 0.98556, "emotion": "happy", "emotion_probability": 0.99152, "wear_facemask": false}, {"bbox": {"x1": 0.48861, "y1": 0.29282, "x2": 0.52517, "y2": 0.35388}, "liveness": {"is_real_face": false, "liveness_probability": 0.85941}, "age": 20.95, "gender": "female", "gender_probability": 0.97329, "emotion": "happy", "emotion_probability": 0.99948, "wear_facemask": false}, {"bbox": {"x1": 0.77773, "y1": 0.31279, "x2": 0.82118, "y2": 0.37558}, "liveness": {"is_real_face": true, "liveness_probability": 0.99404}, "age": 24.23, "gender": "female", "gender_probability": 0.98527, "emotion": "happy", "emotion_probability": 0.97869, "wear_facemask": false}, {"bbox": {"x1": 0.36014, "y1": 0.27604, "x2": 0.40025, "y2": 0.33767}, "liveness": {"is_real_face": true, "liveness_probability": 0.99911}, "age": 22.42, "gender": "female", "gender_probability": 0.97946, "emotion": "happy", "emotion_probability": 0.75895, "wear_facemask": false}], "latency_ms": 389.49} | {"type": "object", "properties": {"status": {"type": "string"}, "image_file_name": {"type": "string"}, "face_count": {"type": "integer"}, "analysis_result": {"type": "array", "items": {"type": "object", "properties": {"bbox": {"type": "object", "properties": {"x1": {"type": "number"}, "y1": {"type": "number"}, "x2": {"type": "number"}, "y2": {"type": "number"}}}, "liveness": {"type": "object", "properties": {"is_real_face": {"type": "boolean"}, "liveness_probability": {"type": "number"}}}, "age": {"type": "number"}, "gender": {"type": "string"}, "gender_probability": {"type": "number"}, "emotion": {"type": "string"}, "emotion_probability": {"type": "number"}, "wear_facemask": {"type": "boolean"}}}}, "latency_ms": {"type": "number"}}} |
8eceda12-02b0-4786-9027-96e4d0a9d807/b3dc144a-7a8d-4f85-aaa5-a54f67f85acb/0/0 | Face Detection and Analysis | Our comprehensive Face Detection & Analysis API offers a wide range of functionalities. It accurately detects and provides the precise location of faces within images. Additionally, it incorporates advanced features such as liveness detection, age estimation, gender identification, analysis of facial expressions (emotions), and detection of facemasks. With our API, you can efficiently analyze and extract valuable insights from facial data. | 7.6 | Detect & Analyze 2 | Same as above, but has another method of input
This endpoint consume JSON input (image url path) | 200 | Success Example | {"status": "success", "image_file_name": "omar-lopez-T6zu4jFhVwg-unsplash.jpg", "face_count": 10, "analysis_result": [{"bbox": {"x1": 0.2699, "y1": 0.24103, "x2": 0.31314, "y2": 0.30961}, "liveness": {"is_real_face": true, "liveness_probability": 0.88112}, "age": 35.95, "gender": "male", "gender_probability": 0.86915, "emotion": "happy", "emotion_probability": 0.99404, "wear_facemask": false}, {"bbox": {"x1": 0.8732, "y1": 0.30382, "x2": 0.91393, "y2": 0.37269}, "liveness": {"is_real_face": true, "liveness_probability": 0.85638}, "age": 44.63, "gender": "male", "gender_probability": 0.96848, "emotion": "happy", "emotion_probability": 0.96287, "wear_facemask": false}, {"bbox": {"x1": 0.73992, "y1": 0.28733, "x2": 0.77376, "y2": 0.34317}, "liveness": {"is_real_face": false, "liveness_probability": 0.92994}, "age": 20.52, "gender": "female", "gender_probability": 0.95935, "emotion": "happy", "emotion_probability": 0.99934, "wear_facemask": false}, {"bbox": {"x1": 0.17861, "y1": 0.239, "x2": 0.21914, "y2": 0.30874}, "liveness": {"is_real_face": true, "liveness_probability": 0.93495}, "age": 20.81, "gender": "female", "gender_probability": 0.98389, "emotion": "happy", "emotion_probability": 0.99927, "wear_facemask": false}, {"bbox": {"x1": 0.56862, "y1": 0.33709, "x2": 0.6033, "y2": 0.39207}, "liveness": {"is_real_face": true, "liveness_probability": 0.99558}, "age": 25.07, "gender": "female", "gender_probability": 0.846, "emotion": "neutral", "emotion_probability": 0.515, "wear_facemask": false}, {"bbox": {"x1": 0.0963, "y1": 0.23814, "x2": 0.14038, "y2": 0.31481}, "liveness": {"is_real_face": true, "liveness_probability": 0.58593}, "age": 20.55, "gender": "female", "gender_probability": 0.97251, "emotion": "happy", "emotion_probability": 0.99784, "wear_facemask": true}, {"bbox": {"x1": 0.64487, "y1": 0.30122, "x2": 0.67934, "y2": 0.36198}, "liveness": {"is_real_face": false, "liveness_probability": 0.83463}, "age": 23.35, "gender": "female", "gender_probability": 0.98556, "emotion": "happy", "emotion_probability": 0.99152, "wear_facemask": false}, {"bbox": {"x1": 0.48861, "y1": 0.29282, "x2": 0.52517, "y2": 0.35388}, "liveness": {"is_real_face": false, "liveness_probability": 0.85941}, "age": 20.95, "gender": "female", "gender_probability": 0.97329, "emotion": "happy", "emotion_probability": 0.99948, "wear_facemask": false}, {"bbox": {"x1": 0.77773, "y1": 0.31279, "x2": 0.82118, "y2": 0.37558}, "liveness": {"is_real_face": true, "liveness_probability": 0.99404}, "age": 24.23, "gender": "female", "gender_probability": 0.98527, "emotion": "happy", "emotion_probability": 0.97869, "wear_facemask": false}, {"bbox": {"x1": 0.36014, "y1": 0.27604, "x2": 0.40025, "y2": 0.33767}, "liveness": {"is_real_face": true, "liveness_probability": 0.99911}, "age": 22.42, "gender": "female", "gender_probability": 0.97946, "emotion": "happy", "emotion_probability": 0.75895, "wear_facemask": false}], "latency_ms": 389.49} | {"type": "object", "properties": {"status": {"type": "string"}, "image_file_name": {"type": "string"}, "face_count": {"type": "integer"}, "analysis_result": {"type": "array", "items": {"type": "object", "properties": {"bbox": {"type": "object", "properties": {"x1": {"type": "number"}, "y1": {"type": "number"}, "x2": {"type": "number"}, "y2": {"type": "number"}}}, "liveness": {"type": "object", "properties": {"is_real_face": {"type": "boolean"}, "liveness_probability": {"type": "number"}}}, "age": {"type": "number"}, "gender": {"type": "string"}, "gender_probability": {"type": "number"}, "emotion": {"type": "string"}, "emotion_probability": {"type": "number"}, "wear_facemask": {"type": "boolean"}}}}, "latency_ms": {"type": "number"}}} |
7b7bf492-a026-4a6f-8224-c0937d44af22/76f1c90c-75c7-485e-b1be-d2d4d8419ab2/0/0 | VIN Recognition/Decoder | This API extract vin from a ID cards, labels, receipts, invoices, documents, barcodes, etc and returns the information such as the manufacturer, model, year, country of origin and much more.. | 1.7 | Get Info From VIN | This endpoint returns the information of a vin such as country of origin, model, year, model, car seats, etc. | 200 | Response | [{"Manufacturer": "American Honda Motor Co Inc"}, {"Adress line 1": "100 W Alondra Blvd"}, {"Adress line 2": "Gardena CA 90248-2702,"}, {"Region": "North America"}, {"Country": "United States"}, {"Note": "Manufacturer builds more than 500 vehicles per year"}, {"Entered VIN": "19UYA42601A019296"}, {"Corrected VIN": "19UYA42601A019296"}, {"Squish VIN": "19UYA4261A"}, {"WMI": "19U"}, {"VIS identifier": "N/A"}, {"VDS": "YA42601A"}, {"Year identifier": "1"}, {"Serial number": "019296"}, {"VIN type": "normal"}, {"Check digit": "valid"}, {"Make": "Acura"}, {"Model": "CL"}, {"Model year": "2001"}, {"Body style": "2 Doors Coupe"}, {"Engine type": "3.2L V6 SOHC 24V FWD"}, {"Fuel type": "Gasoline"}, {"Transmission": "Automatic"}, {"Manufactured in": "United States"}, {"Body type": "Coupe"}, {"Number of doors": "2"}, {"Number of seats": "4"}, {"Displacement SI": "3228"}, {"Displacement CID": "197"}, {"Displacement Nominal": "3.2"}, {"Engine type": "V6"}, {"Engine head": "SOHC"}, {"Engine valves": "24"}, {"Engine cylinders": "6"}, {"Engine aspiration": "Naturally"}, {"Compression ratio": "10.5"}, {"Engine HorsePower": "260"}, {"Engine KiloWatts": "194"}, {"Automatic gearbox": "5AT"}, {"Fuel type": "Gasoline"}, {"MPG city": "19"}, {"MPG highway": "29"}, {"Driveline": "FWD"}, {"Anti-Brake System": "4-Wheel ABS"}, {"Front brake type": "Disc"}, {"Rear brake type": "Disc"}, {"Front suspension": "Independent"}, {"Rear suspension": "Independent"}, {"Front spring type": "Coil"}, {"Rear spring type": "Coil"}, {"Tire front": "215/50R17"}, {"Tire rear": "215/50R17"}, {"GVWR range": "4001-5000"}, {"Equipment 0": "Cruise control"}, {"Equipment 1": "Traction control"}, {"Equipment 2": "Stability control"}, {"Equipment 3": "Engine immobilizer"}, {"Equipment 4": "Tachometer"}, {"Equipment 5": "Low fuel level indicator"}, {"Equipment 6": "Vehicle anti-theft"}, {"Equipment 7": "Clock"}, {"Equipment 8": "AM/FM radio"}, {"Equipment 9": "Cassette player"}, {"Equipment 10": "CD player"}, {"Equipment 11": "CD changer"}, {"Equipment 12": "Manual sunroof"}, {"Equipment 13": "Power Steering"}, {"Equipment 14": "Tilt steering wheel"}, {"Equipment 15": "Daytime running lights"}, {"Equipment 16": "Power door locks"}, {"Equipment 17": "Power windows"}, {"Equipment 18": "Power seats"}, {"Equipment 19": "Bucket seats"}, {"Equipment 20": "Leather seats"}, {"Equipment 21": "Heated mirrors"}, {"Equipment 22": "Rear defogger"}, {"Equipment 23": "Front air conditioning"}, {"Equipment 24": "ABS brakes"}, {"Equipment 25": "Alloy wheels"}, {"Equipment 0": "Power sunroof"}] | {"$schema": "http://json-schema.org/schema#", "type": "array", "items": {"type": "object", "properties": {"Manufacturer": {"type": "string"}, "Adress line 1": {"type": "string"}, "Adress line 2": {"type": "string"}, "Region": {"type": "string"}, "Country": {"type": "string"}, "Note": {"type": "string"}, "Entered VIN": {"type": "string"}, "Corrected VIN": {"type": "string"}, "Squish VIN": {"type": "string"}, "WMI": {"type": "string"}, "VIS identifier": {"type": "string"}, "VDS": {"type": "string"}, "Year identifier": {"type": "string"}, "Serial number": {"type": "string"}, "VIN type": {"type": "string"}, "Check digit": {"type": "string"}, "Make": {"type": "string"}, "Model": {"type": "string"}, "Model year": {"type": "string"}, "Body style": {"type": "string"}, "Engine type": {"type": "string"}, "Fuel type": {"type": "string"}, "Transmission": {"type": "string"}, "Manufactured in": {"type": "string"}, "Body type": {"type": "string"}, "Number of doors": {"type": "string"}, "Number of seats": {"type": "string"}, "Displacement SI": {"type": "string"}, "Displacement CID": {"type": "string"}, "Displacement Nominal": {"type": "string"}, "Engine head": {"type": "string"}, "Engine valves": {"type": "string"}, "Engine cylinders": {"type": "string"}, "Engine aspiration": {"type": "string"}, "Compression ratio": {"type": "string"}, "Engine HorsePower": {"type": "string"}, "Engine KiloWatts": {"type": "string"}, "Automatic gearbox": {"type": "string"}, "MPG city": {"type": "string"}, "MPG highway": {"type": "string"}, "Driveline": {"type": "string"}, "Anti-Brake System": {"type": "string"}, "Front brake type": {"type": "string"}, "Rear brake type": {"type": "string"}, "Front suspension": {"type": "string"}, "Rear suspension": {"type": "string"}, "Front spring type": {"type": "string"}, "Rear spring type": {"type": "string"}, "Tire front": {"type": "string"}, "Tire rear": {"type": "string"}, "GVWR range": {"type": "string"}, "Equipment 0": {"type": "string"}, "Equipment 1": {"type": "string"}, "Equipment 2": {"type": "string"}, "Equipment 3": {"type": "string"}, "Equipment 4": {"type": "string"}, "Equipment 5": {"type": "string"}, "Equipment 6": {"type": "string"}, "Equipment 7": {"type": "string"}, "Equipment 8": {"type": "string"}, "Equipment 9": {"type": "string"}, "Equipment 10": {"type": "string"}, "Equipment 11": {"type": "string"}, "Equipment 12": {"type": "string"}, "Equipment 13": {"type": "string"}, "Equipment 14": {"type": "string"}, "Equipment 15": {"type": "string"}, "Equipment 16": {"type": "string"}, "Equipment 17": {"type": "string"}, "Equipment 18": {"type": "string"}, "Equipment 19": {"type": "string"}, "Equipment 20": {"type": "string"}, "Equipment 21": {"type": "string"}, "Equipment 22": {"type": "string"}, "Equipment 23": {"type": "string"}, "Equipment 24": {"type": "string"}, "Equipment 25": {"type": "string"}}}} |
907d28d6-d1ad-4f42-a75c-f9c074fc3365/34a27e9f-a9fe-490d-95c2-2b354808b572/0/0 | Passport Recognition | Easily find and extract passport information | 8.3 | Extract_Passport_MRZ_v1_passport_post | Extract Machine Readable Zone (MRZ) on passports | 200 | Example_1 | {"confidence": "100", "mrz_valid": true, "filename": "", "expiration_date": "", "number": "", "surname": "", "names": [], "gender": "", "birth": "", "country": "", "nationality": "", "text": ""} | {"title": "MRZData", "required": ["confidence", "mrz_valid", "filename", "expiration_date", "number", "surname", "names", "gender", "birth", "country", "nationality", "text"], "type": "object", "properties": {"confidence": {"title": "Confidence", "maximum": 100, "minimum": 0, "type": "integer", "description": "Confidence score evaluation"}, "mrz_valid": {"title": "Mrz Valid", "type": "boolean", "description": "True if MRZ extraction is correct"}, "filename": {"title": "Filename", "type": "string", "description": "Reference filename"}, "expiration_date": {"title": "Expiration Date", "type": "string", "description": "Expiration date (YYMMDD format)"}, "number": {"title": "Number", "type": "string", "description": "Passport ID number"}, "surname": {"title": "Surname", "type": "string", "description": "Lastname"}, "names": {"title": "Names", "type": "array", "items": {"type": "string"}, "description": "List of surname"}, "gender": {"title": "Gender", "type": "string", "description": "Gender (M/F)"}, "birth": {"title": "Birth", "type": "string", "description": "Date of birth (YYMMDD format)"}, "country": {"title": "Country", "type": "string", "description": "Passport country code (alpha-3 ISO 3166-1)"}, "nationality": {"title": "Nationality", "type": "string", "description": "Nationality code (alpha-3 ISO 3166-1)"}, "text": {"title": "Text", "type": "string", "description": "Full recognized text"}}} |
907d28d6-d1ad-4f42-a75c-f9c074fc3365/34a27e9f-a9fe-490d-95c2-2b354808b572/1/0 | Passport Recognition | Easily find and extract passport information | 8.3 | Extract_Passport_MRZ_v1_passport_post | Extract Machine Readable Zone (MRZ) on passports | 422 | Example_1 | {"detail": [{"loc": [], "msg": "", "type": ""}]} | {"title": "HTTPValidationError", "type": "object", "properties": {"detail": {"title": "Detail", "type": "array", "items": {"title": "ValidationError", "required": ["loc", "msg", "type"], "type": "object", "properties": {"loc": {"title": "Location", "type": "array", "items": {"type": "string"}}, "msg": {"title": "Message", "type": "string"}, "type": {"title": "Error Type", "type": "string"}}}}}} |
8199ca99-9a6a-41a6-a2cb-c1d4f55cd120/57ece31f-c328-41ad-a53f-373d61301a32/0/0 | Easy SFW | An API to detect NSFW images easily by url! | null | Classify Image by URL | Classify Image by URL with this endpoint. This classify images as following classes with probability :
Sexy
Neutral
Porn
Hentai
Drawing
| 200 | New Example | {"url": "https://i.imgur.com/k6EGPzN.jpg", "success": true, "results": {"Porn": 0.6529322862625122, "Sexy": 0.18656782805919647, "Neutral": 0.1286962926387787, "Hentai": 0.02629004418849945, "Drawing": 0.005513431504368782}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"url": {"type": "string"}, "success": {"type": "boolean"}, "results": {"type": "object", "properties": {"Porn": {"type": "number"}, "Sexy": {"type": "number"}, "Neutral": {"type": "number"}, "Hentai": {"type": "number"}, "Drawing": {"type": "number"}}, "required": ["Drawing", "Hentai", "Neutral", "Porn", "Sexy"]}}, "required": ["results", "success", "url"]} |
d3a886da-3ea0-4b50-9b3d-7995266f79fc/52173bd4-feb2-4596-89b4-0885c3842267/0/0 | Taiwan ID Card OCR | Extract all key fields from Taiwan ID card, including name, date of birth, gender, ID number, date of issue, etc. | 5.6 | Taiwan ID Card OCR | Support jpg, png, bmp, pdf, tiff, single-frame gif and other formats, the image size does not exceed 10M. | 200 | Response | {"code": "200", "status": "SUCCESS", "date": "11/23/2023 01:47:26 AM", "result": {"rotated_image_height": 1809, "image_angle": 0, "rotated_image_width": 2839, "item_list": [{"value": "\u5deb\u653f\u80b2", "position": {"bottom": 1090, "left": 508, "right": 1603, "top": 902}, "key": "name", "confidence": 0.992}, {"value": "\u6c11\u570b90\u5e748\u670827\u65e5", "position": {"bottom": 1365, "left": 490, "right": 1623, "top": 1273}, "key": "date_of_birth", "confidence": 0.983}, {"value": "\u7537", "position": {"bottom": 1417, "left": 2352, "right": 2470, "top": 1325}, "key": "sex", "confidence": 0.995}, {"value": "\u6c11\u570b105\u5e743\u670814\u65e5(\u5f70\u7e23)\u521d\u767c", "position": {"bottom": 1619, "left": 486, "right": 1568, "top": 1551}, "key": "date_of_issue", "confidence": 0.991}, {"value": "N126537755", "position": {"bottom": 1691, "left": 1945, "right": 2717, "top": 1591}, "key": "id", "confidence": 0.992}], "type": "taiwan_idcard_front"}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"code": {"type": "string"}, "status": {"type": "string"}, "date": {"type": "string"}, "result": {"type": "object", "properties": {"rotated_image_height": {"type": "integer"}, "image_angle": {"type": "integer"}, "rotated_image_width": {"type": "integer"}, "item_list": {"type": "array", "items": {"type": "object", "properties": {"value": {"type": "string"}, "position": {"type": "object", "properties": {"bottom": {"type": "integer"}, "left": {"type": "integer"}, "right": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}, "key": {"type": "string"}, "confidence": {"type": "number"}}, "required": ["confidence", "key", "position", "value"]}}, "type": {"type": "string"}}, "required": ["image_angle", "item_list", "rotated_image_height", "rotated_image_width", "type"]}}, "required": ["code", "date", "result", "status"]} |
ecd2e3b9-58da-43b4-a11e-6cf975dbe082/7a0091ba-118c-4a08-89ad-3468e891c9c2/0/0 | ALPR&MRR | Automatically extract single or multiple lines of license plate numbers, truck ADR plate numbers, special characters, and identify the country code, while also recognizing the vehicle category, brand, model, generation, color, and angle. | 7.3 | alpr-mrr | Please note that vehicles without license plates will not be recognized | 200 | Response | {"code": "200", "date": "11/03/2023 12:33:25 AM", "photo": {"height": 675, "width": 1200}, "service": "ALPR&MMR", "status": "SUCCESS", "vehicles": [{"angle": "rear", "category": "CAR", "color": "WHITE", "country": "USA-OH", "generation": "Mk III (2015)", "license_plate": "PKJ9742", "license_plate_position": {"bottom": 364.7837829589844, "left": 1077.7156982421875, "right": 1145.3675537109375, "top": 332.9396667480469}, "make": "Ford", "model": "Expedition"}, {"angle": "frontal", "category": "CAR", "color": "GRAY", "country": "USA-OH", "generation": "Mk II (2005)", "license_plate": "DXY1679", "license_plate_position": {"bottom": 390.35955810546875, "left": 456.8094177246094, "right": 508.3710632324219, "top": 370.35577392578125}, "make": "Honda", "model": "CR-V"}]} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"code": {"type": "string"}, "date": {"type": "string"}, "photo": {"type": "object", "properties": {"height": {"type": "integer"}, "width": {"type": "integer"}}, "required": ["height", "width"]}, "service": {"type": "string"}, "status": {"type": "string"}, "vehicles": {"type": "array", "items": {"type": "object", "properties": {"angle": {"type": "string"}, "category": {"type": "string"}, "color": {"type": "string"}, "country": {"type": "string"}, "generation": {"type": "string"}, "license_plate": {"type": "string"}, "license_plate_position": {"type": "object", "properties": {"bottom": {"type": "number"}, "left": {"type": "number"}, "right": {"type": "number"}, "top": {"type": "number"}}, "required": ["bottom", "left", "right", "top"]}, "make": {"type": "string"}, "model": {"type": "string"}}, "required": ["angle", "category", "color", "country", "generation", "license_plate", "license_plate_position", "make", "model"]}}}, "required": ["code", "date", "photo", "service", "status", "vehicles"]} |
3c730999-3ddf-4479-ad5d-287f8d505e31/dfe9f1cf-9110-4741-9262-aa8bdf059fd3/0/0 | Mantis Object Detection | Reliable and Accurate Face and Object Detection via ResNet-50 | 6.6 | Detection Objects as Json by Base64 encoded Text | Detection Objects as JSON by Base64 encoded Text.
To try this with an existing image, you can use this website: https://elmah.io/tools/base64-image-encoder/ and POST the Base64 tp this endpoint. | 200 | Response | {"number-of-objects": 1, "detected-objects": [{"label": "person", "confidence": 98.9824116230011, "bounding-box": [{"x": 0.07455971837043762, "y": 0.04716154932975769}, {"x": 0.9225395917892456, "y": 0.04716154932975769}, {"x": 0.9225395917892456, "y": 0.9939415454864502}, {"x": 0.07455971837043762, "y": 0.9939415454864502}]}]} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"number-of-objects": {"type": "integer"}, "detected-objects": {"type": "array", "items": {"type": "object", "properties": {"label": {"type": "string"}, "confidence": {"type": "number"}, "bounding-box": {"type": "array", "items": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}}}, "required": ["bounding-box", "confidence", "label"]}}}, "required": ["detected-objects", "number-of-objects"]} |
3c730999-3ddf-4479-ad5d-287f8d505e31/393b654d-bc2b-48b2-a85c-7788c21c077f/0/0 | Mantis Object Detection | Reliable and Accurate Face and Object Detection via ResNet-50 | 6.6 | Detection Objects as Json by Image URL | Any valid image URL and return JSON | 200 | Response | {"number-of-objects": 1, "detected-objects": [{"label": "person", "confidence": 98.09197783470154, "bounding-box": [{"x": 0.06937640905380249, "y": 0.022285550832748413}, {"x": 0.884888231754303, "y": 0.022285550832748413}, {"x": 0.884888231754303, "y": 1.013501763343811}, {"x": 0.06937640905380249, "y": 1.013501763343811}]}]} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"number-of-objects": {"type": "integer"}, "detected-objects": {"type": "array", "items": {"type": "object", "properties": {"label": {"type": "string"}, "confidence": {"type": "number"}, "bounding-box": {"type": "array", "items": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}}}, "required": ["bounding-box", "confidence", "label"]}}}, "required": ["detected-objects", "number-of-objects"]} |
32a0f0cd-a4e6-4ee3-90cf-5e7ed79cbe4f/5048f3fb-c1c9-41db-a19f-9ec004c6ec2b/0/0 | Emotion Detection | Humans are used to taking in non-verbal cues from facial emotions. Now, AI can also get better at reading emotions. The model detects the following emotions on a human face: neutral, joy, sadness, surprise, anger, fear, disgust. | 8.9 | Emotion Detection for image |
For more details contact us | 200 | Response | [{"probability": 0.9997902512550354, "rectangle": {"left": 175.14981957674027, "top": 175.63757234811783, "right": 454.1455734014511, "bottom": 576.8297291278839}, "emotion": {"value": "happy", "probability": 0.9988462924957275, "sentiments": {"angry": 1.882492688309867e-05, "disgust": 5.586654879152775e-05, "fear": 3.724266207427718e-05, "happy": 0.9988462924957275, "sad": 8.374534081667662e-05, "surprise": 0.0007656816160306334, "neutral": 0.00019238727691117674}}}] | {"$schema": "http://json-schema.org/schema#", "type": "array", "items": {"type": "object", "properties": {"probability": {"type": "number"}, "rectangle": {"type": "object", "properties": {"left": {"type": "number"}, "top": {"type": "number"}, "right": {"type": "number"}, "bottom": {"type": "number"}}, "required": ["bottom", "left", "right", "top"]}, "emotion": {"type": "object", "properties": {"value": {"type": "string"}, "probability": {"type": "number"}, "sentiments": {"type": "object", "properties": {"angry": {"type": "number"}, "disgust": {"type": "number"}, "fear": {"type": "number"}, "happy": {"type": "number"}, "sad": {"type": "number"}, "surprise": {"type": "number"}, "neutral": {"type": "number"}}, "required": ["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"]}}, "required": ["probability", "sentiments", "value"]}}, "required": ["emotion", "probability", "rectangle"]}} |
5527c661-8534-4c28-a927-d0db16a7c29d/01cc0bcd-6172-4c5b-87d8-8b0afc0657f8/0/0 | Image Caption / Description Generator | This service is designed to generate appropriate captions for an input image, which can be used to provide context, create more accessible content, or aid in content discovery. | 8.3 | /api-v1.0/ImageCaption | Request Body
The request body should be a JSON object with the following keys:
{
"api_key" : "Your assigned API key"
""base64_image": "The Base64 encoded string of the image you wish to analyze"
} | 200 | 0 | {"error_code": 0, "image_caption": "a sexy woman in a black bikini and black lingerie"} | {"type": "object", "properties": {"image_caption": {"type": "string"}, "error_code": {"type": "integer"}}} |
e15a7c51-1850-48af-9c85-007f355d795d/b24f4582-2119-41e9-8341-4e8211776e2a/0/0 | Face Detection | Face Detection | null | Face Detection | Returns an array where each of the elements is a face detected in the provided image. | 200 | New Example | {"Detected_faces": [{"Probability": 0.9488824009895325, "BoundingBox": {"endY": 1916, "endX": 3665, "startY": 1049, "startX": 2798}}, {"Probability": 0.8998819589614868, "BoundingBox": {"endY": 2443, "endX": 1604, "startY": 1447, "startX": 608}}, {"Probability": 0.8902185559272766, "BoundingBox": {"endY": 1821, "endX": 4990, "startY": 917, "startX": 4086}}]} | {"Detected_faces": [{"BoundingBox": {}}, {"BoundingBox": {}}, {"BoundingBox": {}}]} |
0ea5e4cf-154f-4e39-b974-338fa48de57e/324b6474-6abf-43e9-993a-40fb752952af/0/0 | Face Detection | GoldWare provides fast and accurate Face Detection at an unmatched price.
100,000 API calls per month for only $8.99. | null | Detect Faces | Detects faces in the image given. Returns the bounding boxes and confidence ratings of all detected faces. | 200 | Example_1 | {"detected_faces": [{"bbox": {"x1": 410, "y1": 293, "x2": 508, "y2": 424}, "confidence": 0.9999239444732666}, {"bbox": {"x1": 1245, "y1": 209, "x2": 1340, "y2": 341}, "confidence": 0.9997527003288269}, {"bbox": {"x1": 182, "y1": 354, "x2": 283, "y2": 489}, "confidence": 0.99933260679245}, {"bbox": {"x1": 1028, "y1": 386, "x2": 1113, "y2": 501}, "confidence": 0.9987832903862}, {"bbox": {"x1": 1432, "y1": 310, "x2": 1529, "y2": 418}, "confidence": 0.9985816478729248}, {"bbox": {"x1": 575, "y1": 378, "x2": 676, "y2": 507}, "confidence": 0.997880220413208}, {"bbox": {"x1": 781, "y1": 282, "x2": 876, "y2": 407}, "confidence": 0.9977992177009583}, {"bbox": {"x1": 1635, "y1": 420, "x2": 1721, "y2": 531}, "confidence": 0.9950201511383057}]} | {"type": "object", "properties": {"detected_faces": {"type": "array", "items": {"type": "object", "properties": {"bbox": {"type": "object", "properties": {"x1": {"type": "integer"}, "y1": {"type": "integer"}, "x2": {"type": "integer"}, "y2": {"type": "integer"}}}, "confidence": {"type": "number"}}}}}} |
0ea5e4cf-154f-4e39-b974-338fa48de57e/324b6474-6abf-43e9-993a-40fb752952af/1/0 | Face Detection | GoldWare provides fast and accurate Face Detection at an unmatched price.
100,000 API calls per month for only $8.99. | null | Detect Faces | Detects faces in the image given. Returns the bounding boxes and confidence ratings of all detected faces. | 422 | Example_1 | {"detail": [{"loc": [], "msg": "", "type": ""}]} | {"title": "HTTPValidationError", "type": "object", "properties": {"detail": {"title": "Detail", "type": "array", "items": {"title": "ValidationError", "required": ["loc", "msg", "type"], "type": "object", "properties": {"loc": {"title": "Location", "type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "integer"}]}}, "msg": {"title": "Message", "type": "string"}, "type": {"title": "Error Type", "type": "string"}}}}}} |
c9ac2c90-ecb7-4160-8f12-0014dbdffead/24f04a2f-9981-474f-ad2f-119f3df31379/0/0 | Text Captcha Solver | There are some captchas that are made by TEXT patterns. This API solve them. | 7.8 | /txtCaptcha | Solves a text captcha by providing the input text. | 400 | Example_1 | {"error": ""} | {"type": "object", "properties": {"error": {"type": "string"}}} |
c9ac2c90-ecb7-4160-8f12-0014dbdffead/24f04a2f-9981-474f-ad2f-119f3df31379/1/0 | Text Captcha Solver | There are some captchas that are made by TEXT patterns. This API solve them. | 7.8 | /txtCaptcha | Solves a text captcha by providing the input text. | 200 | Example_1 | {"solvedtext": ""} | {"type": "object", "properties": {"solvedtext": {"type": "string"}}} |
0f6213d7-f60f-4657-a47a-aa9ac12c69a2/bca75c15-4f7e-49b0-acfb-8f5bc6f9ba75/0/0 | OCR Extract text | Unlock precise text extraction from images and PDFs. Our API ensures exceptional accuracy and high performance, swiftly processing data with reliability. Seamlessly integrate with Base64 encoding for flexible workflow support. Ideal for detailed document analysis, efficient data indexing, and streamlined workflows. Integrate now for seamless, accurate text extraction. | 5.8 | File-Based Text Extraction | For optimal performance, keep these points in mind:
Size Recommendation:
Ensure your file size stays below 10MB. This recommendation helps maintain efficient processing.
Supported Formats:
We support the following file types: WEBP, JPEG, PNG, and PDF.
Adhering to these guidelines ensures a smooth and effective experience. Questions? Check our documentation or contact support. | 200 | success | {"response": {"error": 0, "execution_time": 123.45, "text": "Example text", "type": "exampleType"}} | {"type": "object", "properties": {"response": {"type": "object", "properties": {"error": {"type": "integer"}, "execution_time": {"type": "number"}, "text": {"type": "string"}, "type": {"type": "string"}}}}} |
0f6213d7-f60f-4657-a47a-aa9ac12c69a2/5377b084-b567-4d2c-93a1-3e301ff3ff69/0/0 | OCR Extract text | Unlock precise text extraction from images and PDFs. Our API ensures exceptional accuracy and high performance, swiftly processing data with reliability. Seamlessly integrate with Base64 encoding for flexible workflow support. Ideal for detailed document analysis, efficient data indexing, and streamlined workflows. Integrate now for seamless, accurate text extraction. | 5.8 | Text Extraction from URL | Ensure a seamless text extraction process:
Valid Input:
Provide a valid link to a supported document or use a Base64 data URL (max length: 1MB).
File Size Limit:
Recommended file size is under 10MB for optimal performance.
Supported Types:
WEBP
JPEG
PNG
PDF | 200 | New Example | {"response": {"error": 0, "execution_time": 123.45, "text": "Example text", "type": "exampleType"}} | {"type": "object", "properties": {"response": {"type": "object", "properties": {"error": {"type": "integer"}, "execution_time": {"type": "number"}, "text": {"type": "string"}, "type": {"type": "string"}}}}} |
13a2056b-a04d-4074-836b-171f11194c70/7f284e6c-fd16-44dc-aa48-5fe0cd02ad28/0/0 | Nude Detect (Video) | Use NetSpark's Nude and pornography image processing engine for high accuracy detection of nudity, sexuality and pornography in videos. Use the confidence rating to define an acceptable risk level for your audience. If you don't have tolerance for false positives we recommend defining 2% confidence as your limit for acceptable content. Supports most video hosting sites [YouTube, Vimeo, Facebook etc.). For support for larger volumes or for inspection of video content, contact us for a custom ... | null | Detect Nudity in Web-Hosted Video | Detect Nudity in Web-Hosted Video | 200 | New Example | {"frames": {"is minimal clothing": {"Possible": "0%", "Very Likely": "0%"}, "nude": {"Possible": "0%", "Very Likely": "0%"}}, "is minimal clothing": "Very Unlikely", "is nude": "Very Unlikely", "result code": "14", "status": "success", "summary_frames": "119", "video duration": "00:02:59.32", "video url": "https://sample.video.url/1.avi"} | {"properties": {"frames": {"properties": {"is minimal clothing": {"properties": {"Possible": {"type": "string"}, "Very Likely": {"type": "string"}}, "type": "object"}, "nude": {"properties": {"Possible": {"type": "string"}, "Very Likely": {"type": "string"}}, "type": "object"}}, "type": "object"}, "is minimal clothing": {"type": "string"}, "is nude": {"type": "string"}, "result code": {"type": "string"}, "status": {"type": "string"}, "summary_frames": {"type": "string"}, "video duration": {"type": "string"}, "video url": {"type": "string"}}, "type": "object"} |
3cfa1e95-1095-4d94-b747-73666e68d5e8/c1463168-e94a-45c1-ab83-fe966a3dbb51/0/0 | Cameralyze | Cameralyze is a no-code platform that has lots of AI components and algorithms. | null | Face Bluring Endpoints | Detects faces and blurs all of them. | 200 | New Example | {"image_url": "https://cmz-temp-files.s3.amazonaws.com/620f25b7-30d9-4fef-98af-a64af305a93c.png?AWSAccessKeyId=ASIATJFBOSWIZISSUDWA&Signature=z3uQtJRydTI8NwkpG97LiLvmjY8%3D&x-amz-security-token=IQoJb3JpZ2luX2VjEGcaCWV1LXdlc3QtMSJHMEUCIGBUsDSOABCUgfGjlrRkE7ZvPHeD%2FPfF8RKgmhpHHIwQAiEAm56NyEEtIUv2IvDIh45chzRekCiffyPeZruYk8scg3Qq6gIIQBACGgwyMjU4MjQzODAzMDUiDIcPsL%2FXBEW5XEcMvSrHAkw%2BPngNLjUlgBQLYg0LoYrCh6EtU3GKjPjC877Sv3sooWrQrnepACtghZp85LzN5FTi14lX3hYwIjLFINn1huaTE9MfE7kFU5VGQklxsSqoZ9Zoy8O0GHIcBXoY9kFcSOH%2B5bj%2F6E0Z1njMDnikpALydns%2BcTOd34yW3VqJFKNczzLyoNlXw4jvCv41PZhWm8U30eufEFXJNHHopmq555WnRcDIm0ihxrvpeDNfpGC1RH4z%2Fy%2FdDc83tHurWcjEnEbJUfYxnDWzQkQUPE3YFF7XP9U%2FkKmM86JE%2B1bOE4t%2BOg3SO7dcWrHyaT%2FE%2F%2BQNPfPeT6%2FHCCzwj11tLto%2FVChv8fXyKChmWnmCEhZ%2Bi%2FfgGH6IMYjyqi%2BVOXlHgzgl7Z5ZWJMZbykdEddNqR1hj9mIQQ1tRv%2BVfw8GTrs6JGecjY4WtGDyMDChvr6aBjqeAY81CK0QHUM9L0c2SymW3rCWJjD4smJ1hlPB2EsjzC8svpixY%2F6SqCcK12HKhTHfcFLfbrl3iu4CDd2e1440pHbeGxZwD0i9OU5FAsN1hPwfvCnCq3ZQgQ4N7OCnatXdLs207FP6qZUxg3awipexR6f8RjFpMZPA58Nyd0xnkZeCGRipVVqquP%2Bzu8H6QumFBOasn5L4Iww%2BUY1JEkkQ&Expires=1666164267", "date": "2022-10-19T06:54:27.508996", "order": 1666162467} | {"type": "object", "properties": {"image_url": {"type": "string"}, "date": {"type": "string"}, "order": {"type": "integer"}}} |
3cfa1e95-1095-4d94-b747-73666e68d5e8/164b9453-6c10-4734-ac51-7780852e2ff9/0/0 | Cameralyze | Cameralyze is a no-code platform that has lots of AI components and algorithms. | null | Age & Gender Detection | Detects age and gender. | 200 | New Example | {"detections": [{"confidence": 99.99011993408203, "left": 119, "top": 54, "width": 48, "height": 69, "gender": {"gender": "Male", "confidence": 99.9594955444336}, "age": {"minimum": 37, "maximum": 45}}, {"confidence": 99.93456268310547, "left": 249, "top": 17, "width": 49, "height": 67, "gender": {"gender": "Male", "confidence": 99.9895248413086}, "age": {"minimum": 40, "maximum": 48}}, {"confidence": 99.81285095214844, "left": 472, "top": 73, "width": 46, "height": 63, "gender": {"gender": "Male", "confidence": 99.99279022216797}, "age": {"minimum": 42, "maximum": 50}}, {"confidence": 99.98607635498047, "left": 380, "top": 43, "width": 44, "height": 55, "gender": {"gender": "Male", "confidence": 99.99153900146484}, "age": {"minimum": 38, "maximum": 46}}], "date": "2022-10-19T10:10:53.133065", "order": 1666174253} | {"type": "object", "properties": {"detections": {"type": "array", "items": {"type": "object", "properties": {"confidence": {"type": "number"}, "left": {"type": "integer"}, "top": {"type": "integer"}, "width": {"type": "integer"}, "height": {"type": "integer"}, "gender": {"type": "object", "properties": {"gender": {"type": "string"}, "confidence": {"type": "number"}}}, "age": {"type": "object", "properties": {"minimum": {"type": "integer"}, "maximum": {"type": "integer"}}}}}}, "date": {"type": "string"}, "order": {"type": "integer"}}} |
3cfa1e95-1095-4d94-b747-73666e68d5e8/035338de-30f4-479d-a225-bc68d989c872/0/0 | Cameralyze | Cameralyze is a no-code platform that has lots of AI components and algorithms. | null | Background Remover | Removes background of images. | 200 | New Example | {"image_url": "https://cmz-public.s3.eu-west-1.amazonaws.com/images/64c87706-9d6a-4e55-80d7-6aca93798d34.png", "date": "2022-10-19T10:03:30.694465", "order": 1666173810} | {"type": "object", "properties": {"image_url": {"type": "string"}, "date": {"type": "string"}, "order": {"type": "integer"}}} |
3cfa1e95-1095-4d94-b747-73666e68d5e8/47fca8cc-bec2-438d-933e-0045618353d5/0/0 | Cameralyze | Cameralyze is a no-code platform that has lots of AI components and algorithms. | null | Face Detection | Detects faces and return the coordinates. | 200 | New Example | {"detections": [{"confidence": 99.99011993408203, "left": 119, "top": 54, "width": 48, "height": 69}, {"confidence": 99.93456268310547, "left": 249, "top": 17, "width": 49, "height": 67}, {"confidence": 99.81285095214844, "left": 472, "top": 73, "width": 46, "height": 63}, {"confidence": 99.98607635498047, "left": 380, "top": 43, "width": 44, "height": 55}], "date": "2022-10-19T07:17:14.613533", "order": 1666163834} | {"type": "object", "properties": {"detections": {"type": "array", "items": {"type": "object", "properties": {"confidence": {"type": "number"}, "left": {"type": "integer"}, "top": {"type": "integer"}, "width": {"type": "integer"}, "height": {"type": "integer"}}}}, "date": {"type": "string"}, "order": {"type": "integer"}}} |
d60eb263-beee-49f2-9cde-b18ddbe190ee/9f6677a0-cffb-43c3-97ab-f5007e1cb491/0/0 | Malasyia Driving License OCR | Extract all key fields from Malaysia Driving License including Malaysia Driving License Name, Date of Birth, License Number, Date of Issue, Expiry Date and Head Portrait. | 6.5 | Malasyia Driving License OCR | Support jpg, png, bmp, pdf, tiff, single-frame gif and other formats, the image size does not exceed 10M. | 200 | Response | {"code": "200", "status": "SUCCESS", "date": "11/23/2023 12:55:30 AM", "result": {"rotated_image_height": 446, "image_angle": 0, "rotated_image_width": 707, "type": "malaysia_driving_license", "details": {"driving_license_level": {"value": "B2 D", "position": {"bottom": 295, "left": 225, "right": 266, "top": 280}}, "address": {"value": "39TMN SRI PEKULA 2TIKAM BATU08000 SUNGAI PETANIKEDAH", "position": {"bottom": 456, "left": 222, "right": 412, "top": 352}}, "nationality": {"value": "MALAYSIA", "position": {"bottom": 259, "left": 223, "right": 307, "top": 245}}, "expire_date": {"value": "22/02/2015", "position": {"bottom": 333, "left": 319, "right": 405, "top": 317}}, "license_number": {"value": "820222075547", "position": {"bottom": 259, "left": 358, "right": 479, "top": 243}}, "name": {"value": "MOHD NAZRI BIN AHMAD", "position": {"bottom": 206, "left": 221, "right": 457, "top": 185}}, "issue_date": {"value": "18/08/2013", "position": {"bottom": 333, "left": 227, "right": 312, "top": 317}}, "head_portrait": {"value": 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", "position": {"bottom": 0, "left": 0, "right": 0, "top": 0}}}}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"code": {"type": "string"}, "status": {"type": "string"}, "date": {"type": "string"}, "result": {"type": "object", "properties": {"rotated_image_height": {"type": "integer"}, "image_angle": {"type": "integer"}, "rotated_image_width": {"type": "integer"}, "type": {"type": "string"}, "details": {"type": "object", "properties": {"driving_license_level": {"type": "object", "properties": {"value": {"type": "string"}, "position": {"type": "object", "properties": {"bottom": {"type": "integer"}, "left": {"type": "integer"}, "right": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}}, "required": ["position", "value"]}, "address": {"type": "object", "properties": {"value": {"type": "string"}, "position": {"type": "object", "properties": {"bottom": {"type": "integer"}, "left": {"type": "integer"}, "right": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}}, "required": ["position", "value"]}, "nationality": {"type": "object", "properties": {"value": {"type": "string"}, "position": {"type": "object", "properties": {"bottom": {"type": "integer"}, "left": {"type": "integer"}, "right": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}}, "required": ["position", "value"]}, "expire_date": {"type": "object", "properties": {"value": {"type": "string"}, "position": {"type": "object", "properties": {"bottom": {"type": "integer"}, "left": {"type": "integer"}, "right": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}}, "required": ["position", "value"]}, "license_number": {"type": "object", "properties": {"value": {"type": "string"}, "position": {"type": "object", "properties": {"bottom": {"type": "integer"}, "left": {"type": "integer"}, "right": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}}, "required": ["position", "value"]}, "name": {"type": "object", "properties": {"value": {"type": "string"}, "position": {"type": "object", "properties": {"bottom": {"type": "integer"}, "left": {"type": "integer"}, "right": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}}, "required": ["position", "value"]}, "issue_date": {"type": "object", "properties": {"value": {"type": "string"}, "position": {"type": "object", "properties": {"bottom": {"type": "integer"}, "left": {"type": "integer"}, "right": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}}, "required": ["position", "value"]}, "head_portrait": {"type": "object", "properties": {"value": {"type": "string"}, "position": {"type": "object", "properties": {"bottom": {"type": "integer"}, "left": {"type": "integer"}, "right": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}}, "required": ["position", "value"]}}, "required": ["address", "driving_license_level", "expire_date", "head_portrait", "issue_date", "license_number", "name", "nationality"]}}, "required": ["details", "image_angle", "rotated_image_height", "rotated_image_width", "type"]}}, "required": ["code", "date", "result", "status"]} |
9e52b514-f148-4516-b9bf-ca7fd5df7268/d8b6b5f0-d094-4fe4-99b6-5a7ff7a2a4b9/1/0 | Optical character recognition | Powered by AWS Textract - Streamline document handling, accelerate data extraction, and minimize errors. OCR software transforms documents into editable and searchable formats, enhancing productivity and paving the way for a more streamlined and digitized workflow. This powerful service offers exceptional accuracy, easy integration with existing systems, scalability, and a cost-effective. Experience the efficiency and accuracy of AWS Textract to unlock the valuable information within your do... | 6.6 | OcrDetail | Detail example | 200 | Example_1 | [{"Confidence": 99.7843246459961, "Text": "What is OCR (Optical Character Recognition)?", "Type": "line", "BoundingBox": {"Height": 0.015610243193805218, "Left": 0.05871028080582619, "Top": 0.0687444880604744, "Width": 0.40154320001602173}}, {"Confidence": 98.88011169433594, "Text": "document.", "Type": "word", "BoundingBox": {"Height": 0.00960960891097784, "Left": 0.4606381952762604, "Top": 0.8864550590515137, "Width": 0.06749007105827332}}] | {"type": "array", "title": "OcrEntityResponse", "items": {"type": "object", "title": "OcrEntity", "additionalProperties": false, "required": ["Confidence", "text", "type", "BoundingBox"], "properties": {"Confidence": {"type": "number"}, "text": {"type": "string"}, "type": {"type": "string", "enum": ["word", "line"], "title": "OcrEntityType"}, "BoundingBox": {"type": "object", "title": "BoundingBox", "additionalProperties": false, "required": ["Height", "Left", "Top", "Width"], "properties": {"Height": {"type": "number"}, "Left": {"type": "number"}, "Top": {"type": "number"}, "Width": {"type": "number"}}}}}} |
9e52b514-f148-4516-b9bf-ca7fd5df7268/2e3d9f90-fe91-4669-9510-0f4aa6cd4cdf/1/0 | Optical character recognition | Powered by AWS Textract - Streamline document handling, accelerate data extraction, and minimize errors. OCR software transforms documents into editable and searchable formats, enhancing productivity and paving the way for a more streamlined and digitized workflow. This powerful service offers exceptional accuracy, easy integration with existing systems, scalability, and a cost-effective. Experience the efficiency and accuracy of AWS Textract to unlock the valuable information within your do... | 6.6 | OcrWord | Word example | 200 | Example_1 | [{"Confidence": 99.96228790283203, "Text": "What", "Type": "word", "BoundingBox": {"Height": 0.011988956481218338, "Left": 0.058710526674985886, "Top": 0.06919389963150024, "Width": 0.045812174677848816}}, {"Confidence": 99.92723083496094, "Text": "OCR", "Type": "word", "BoundingBox": {"Height": 0.012052597478032112, "Left": 0.12962447106838226, "Top": 0.0693240836262703, "Width": 0.043406013399362564}}] | {"type": "array", "title": "OcrEntityResponse", "items": {"type": "object", "title": "OcrEntity", "additionalProperties": false, "required": ["Confidence", "text", "type", "BoundingBox"], "properties": {"Confidence": {"type": "number"}, "text": {"type": "string"}, "type": {"type": "string", "enum": ["word", "line"], "title": "OcrEntityType"}, "BoundingBox": {"type": "object", "title": "BoundingBox", "additionalProperties": false, "required": ["Height", "Left", "Top", "Width"], "properties": {"Height": {"type": "number"}, "Left": {"type": "number"}, "Top": {"type": "number"}, "Width": {"type": "number"}}}}}} |
9e52b514-f148-4516-b9bf-ca7fd5df7268/2821fe21-7796-44fa-a54e-e85840e20ae3/0/0 | Optical character recognition | Powered by AWS Textract - Streamline document handling, accelerate data extraction, and minimize errors. OCR software transforms documents into editable and searchable formats, enhancing productivity and paving the way for a more streamlined and digitized workflow. This powerful service offers exceptional accuracy, easy integration with existing systems, scalability, and a cost-effective. Experience the efficiency and accuracy of AWS Textract to unlock the valuable information within your do... | 6.6 | OcrLine | Line example | 200 | Example_1 | [{"Confidence": 99.7843246459961, "Text": "What is OCR (Optical Character Recognition)?", "Type": "line", "BoundingBox": {"Height": 0.015610243193805218, "Left": 0.05871028080582619, "Top": 0.0687444880604744, "Width": 0.40154320001602173}}, {"Confidence": 99.79827117919922, "Text": "if you scan a form or a receipt, your computer saves the scan as an image file. You cannot use a text editor to edit, search, or count the", "Type": "line", "BoundingBox": {"Height": 0.012884640134871006, "Left": 0.058557841926813126, "Top": 0.11099738627672195, "Width": 0.8748273849487305}}] | {"type": "array", "title": "OcrEntityResponse", "items": {"type": "object", "title": "OcrEntity", "additionalProperties": false, "required": ["Confidence", "text", "type", "BoundingBox"], "properties": {"Confidence": {"type": "number"}, "text": {"type": "string"}, "type": {"type": "string", "enum": ["word", "line"], "title": "OcrEntityType"}, "BoundingBox": {"type": "object", "title": "BoundingBox", "additionalProperties": false, "required": ["Height", "Left", "Top", "Width"], "properties": {"Height": {"type": "number"}, "Left": {"type": "number"}, "Top": {"type": "number"}, "Width": {"type": "number"}}}}}} |
930de94d-b44b-4a9e-81a1-29c75597f209/40cc52f3-804c-49fe-923b-72426ce350e2/0/0 | Document OCR | Extract structured text and portraits from various ID cards, passports, driver's licenses, etc. Support for 200+ countries and regions and 10,000+ document types.
It can also recognize MRZ and QR/BarCodes. | 9.6 | Document OCR | Not limited to some examples shown in the document. | 200 | Response | {"code": "200", "status": "SUCCESS", "data": {"countryName": "Netherlands", "documentName": "Passport", "errorCode": 0, "image": {"documentFrontSide": "/9j/4AAQSkZJRgABAQEAxwDHAAD/ ", "portrait": "/9j/4AAQSkZJRgABAQEAxwDHAAD/ ", "signature": "/9j/4AAQSkZJRgABAQEAxwDHAAD/ "}, "mrz": {"dateOfBirth": "1965-03-10", "dateOfExpiry": "2016-10-20", "documentClassCode": "P", "documentNumber": "XR1001R58", "givenNames": "WILLEKE LISELOTTE", "issuingStateCode": "NLD", "mrzCode": "P<NLDDE<BRUIJN<<WILLEKE<LISELOTTE<<<<<<<<<<<^XR1001R586NLD6503101F1610202999999990<<<<<82", "name": "DE BRUIJN WILLEKE LISELOTTE", "nationality": "Netherlands", "personalNumber": "999999990", "sex": "F", "surname": "DE BRUIJN", "validState": 0}, "nation": {"authority": "Burgemeester van Stad en^Dorp", "givenNames": "Willeke Liselotte", "name": "De Bruijn Willeke Liselotte", "nationality": "Nederlandse", "placeOfBirth": "Specimen 1001", "sex": "V", "surname": "De Bruijn", "surnameOfSpouse": "e/v Molenaar"}, "ocr": {"authority": "BURGEMEESTER VAN STAD EN^DORP", "dateOfBirth": "1965-03-10", "dateOfExpiry": "2016-10-20", "dateOfIssue": "2011-10-20", "documentClassCode": "P", "documentNumber": "XR1001R58", "givenNames": "WILLEKE LISELOTTE", "height": "175 cm", "issuingStateCode": "NLD", "name": "DE BRUIJN WILLEKE LISELOTTE", "personalNumber": "999999990", "placeOfBirth": "SPECIMEN 1001", "sex": "F", "surname": "DE BRUIJN", "surnameOfSpouse": "E/V MOLENAAR", "validState": 0}, "portrait_rect": {"bottom": 446, "left": 28, "right": 298, "top": 85}, "position": {"bottom": 581, "left": 0, "right": 829, "top": 0}, "score": 0.956498622894287}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"code": {"type": "string"}, "status": {"type": "string"}, "data": {"type": "object", "properties": {"countryName": {"type": "string"}, "documentName": {"type": "string"}, "errorCode": {"type": "integer"}, "image": {"type": "object", "properties": {"documentFrontSide": {"type": "string"}, "portrait": {"type": "string"}, "signature": {"type": "string"}}, "required": ["documentFrontSide", "portrait", "signature"]}, "mrz": {"type": "object", "properties": {"dateOfBirth": {"type": "string"}, "dateOfExpiry": {"type": "string"}, "documentClassCode": {"type": "string"}, "documentNumber": {"type": "string"}, "givenNames": {"type": "string"}, "issuingStateCode": {"type": "string"}, "mrzCode": {"type": "string"}, "name": {"type": "string"}, "nationality": {"type": "string"}, "personalNumber": {"type": "string"}, "sex": {"type": "string"}, "surname": {"type": "string"}, "validState": {"type": "integer"}}, "required": ["dateOfBirth", "dateOfExpiry", "documentClassCode", "documentNumber", "givenNames", "issuingStateCode", "mrzCode", "name", "nationality", "personalNumber", "sex", "surname", "validState"]}, "nation": {"type": "object", "properties": {"authority": {"type": "string"}, "givenNames": {"type": "string"}, "name": {"type": "string"}, "nationality": {"type": "string"}, "placeOfBirth": {"type": "string"}, "sex": {"type": "string"}, "surname": {"type": "string"}, "surnameOfSpouse": {"type": "string"}}, "required": ["authority", "givenNames", "name", "nationality", "placeOfBirth", "sex", "surname", "surnameOfSpouse"]}, "ocr": {"type": "object", "properties": {"authority": {"type": "string"}, "dateOfBirth": {"type": "string"}, "dateOfExpiry": {"type": "string"}, "dateOfIssue": {"type": "string"}, "documentClassCode": {"type": "string"}, "documentNumber": {"type": "string"}, "givenNames": {"type": "string"}, "height": {"type": "string"}, "issuingStateCode": {"type": "string"}, "name": {"type": "string"}, "personalNumber": {"type": "string"}, "placeOfBirth": {"type": "string"}, "sex": {"type": "string"}, "surname": {"type": "string"}, "surnameOfSpouse": {"type": "string"}, "validState": {"type": "integer"}}, "required": ["authority", "dateOfBirth", "dateOfExpiry", "dateOfIssue", "documentClassCode", "documentNumber", "givenNames", "height", "issuingStateCode", "name", "personalNumber", "placeOfBirth", "sex", "surname", "surnameOfSpouse", "validState"]}, "portrait_rect": {"type": "object", "properties": {"bottom": {"type": "integer"}, "left": {"type": "integer"}, "right": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}, "position": {"type": "object", "properties": {"bottom": {"type": "integer"}, "left": {"type": "integer"}, "right": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}, "score": {"type": "number"}}, "required": ["countryName", "documentName", "errorCode", "image", "mrz", "nation", "ocr", "portrait_rect", "position", "score"]}}, "required": ["code", "data", "status"]} |
bd2a03d6-9d50-48dd-80c2-c43a230baf18/fbab5625-96b7-42cd-a23e-243e5de9926d/0/0 | Image To Text OCR | Automated language detection (hybrid approach) and text extraction from diverse types of images, including handwritten ones. Supports a wide range of formats including JPEG, PNG8, PNG24, GIF, BMP, WEBP, RAW, and ICO. | 9.1 | Image To Text OCR | The larger the file, the higher the accuracy of recognition, but smaller sizes can lead to a decrease in recognition accuracy. Therefore, we recommend that the maximum size should not exceed 4MB | 200 | Response | {"status": "SUCCESS", "text": "\u591a\u8a00\u8a9e\u6587\u7ae0\u306e\u4f8b\r\nAn example of multilingual text\r\n\"pabCTeyure\" \u306f\u3001\u82f1\u8a9e\u3067 Hello \u3068\u3044\u3046\u610f\u5473\u306e\u30ed\u30b7\u30a2\u8a9e\u3067\r\n\u3059\u3002\u65e5\u672c\u8a9e\u306a\u3089\u300c\u3053\u3093\u306b\u3061\u306f\u300d\u3068\u3044\u3046\u610f\u5473\u3067\u3059\u306d\u3002 \u30ae\u30ea\u30b7\r\n\u30a2\u306e\u8a00\u8449\u3067\u306f\u3001\"Tel\u00f3 aas\" \u3068\u8a00\u3046\u305d\u3046\u3067\u3059\u3002\u4e2d\u56fd\u306e\u8a00\u8449\r\n\u3067\u306f\u3001\u300c\u4f60\u597d\u300d\u3067\u3059\u3002 \u30cf\u30f3\u30b0\u30eb\u3067\u306f\u3001\u300c\u90e8\u54c1\u300d\u3068\r\n\u3044\u3044\u307e\u3059\u3002 \u30c9\u30a4\u30c4\u8a9e\u3067\u306f\u3001 \u300cGuten Tag\u300d \u307e\u305f\u306f \u300cGr\u00fc\u00df Gott\u300d\r\n\u3068\u8a00\u3044\u307e\u3059\u3002 \u30d8\u30d6\u30e9\u30a4\u8a9e\u3067\u306f\u300c017\u300d \u3068\u3044\u3044\u307e\u3059\u3002\r\nThe term \"ApaBCTBYITE\" means \"hello\" in English,\r\nIn Japanese, it means \"\u3053\u3093\u306b\u3061\u306f\". They say \"Teld ras\"\r\nin Greek language. (It's greek to me!) In Chinese, \"\u4f60\u597d\"\r\nis a word of the same meaning. Korean people say \"2\"\r\nto say hello. In German, they say \"Guten Tag\" or \"Gr\u00fc\u00df Gott\".\r\nIn Hebrew, they say \"d1bw'\"'."} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"status": {"type": "string"}, "text": {"type": "string"}}, "required": ["status", "text"]} |
ed2849ba-56ba-4f1e-a67c-52ff8431e91c/535ca94e-851d-4c9e-a248-0e13ffb376cc/0/0 | Background Remover | PixCleaner offers a REST API that allows you to easily use and interact with our service programmatically. Using our HTTP interface integrate our background removal features into any business or application. | 0.2 | autoRemoveBGSingleImage | removes the background from a single image specified by the user.
Content : Any picture with a separate foreground object from the background (human, animal, product, etc..)
Source : a file upload ( binary file ), url or a base64 format of your image.
returns a url of a semi-transparent PNG image encoded in a json object. | 200 | null | {"image": {"id": "", "originalImage": {"path": "https://cdn.pixcleaner.com/imgs/users/userId/imageId.jpg", "width": "720px", "height": "640px", "mimeType": "image/jpeg"}, "resultImage": {"path": "https://cdn.pixcleaner.com/imgs/users/userId/removedImageId.jpg", "width": "720px", "height": "640px"}}} | {"type": "object", "properties": {"image": {"type": "object", "properties": {"id": {"type": "string"}, "originalImage": {"type": "object", "properties": {"path": {"type": "string", "format": "uri"}, "width": {"type": "number"}, "height": {"type": "number"}, "mimeType": {"type": "string"}}}, "resultImage": {"type": "object", "properties": {"path": {"type": "string", "format": "uri"}, "width": {"type": "number"}, "height": {"type": "number"}}}}}}} |
ed2849ba-56ba-4f1e-a67c-52ff8431e91c/a80bffd4-6c2b-42ce-9061-42f44232d810/0/0 | Background Remover | PixCleaner offers a REST API that allows you to easily use and interact with our service programmatically. Using our HTTP interface integrate our background removal features into any business or application. | 0.2 | getAccount | Returns a description of your user account information.
Useful to get the remaining credits on your account | 200 | null | {"id": "12938782da1ea358761c23", "name": "John Doe", "email": "[email protected]", "credits": 3493} | {"type": "object", "properties": {"id": {"type": "string", "description": "user id as it is stored in our system"}, "name": {"type": "string", "description": "the user's name"}, "email": {"type": "string", "format": "email", "description": "the user's email"}, "credits": {"type": "number", "description": "the remaining credit balance on your account"}}} |
ed2849ba-56ba-4f1e-a67c-52ff8431e91c/a24d8f4d-e06b-46b9-a244-3ab417241d57/0/0 | Background Remover | PixCleaner offers a REST API that allows you to easily use and interact with our service programmatically. Using our HTTP interface integrate our background removal features into any business or application. | 0.2 | improvePixCleaner | Be part of our improve program.If you believe that a picture is not yet managed correctly by our AI algorithms,
you can submit it here. | 200 | null | {"state": "success"} | {"type": "object", "properties": {"state": {"type": "string"}}} |
ed2849ba-56ba-4f1e-a67c-52ff8431e91c/b5b1f7e5-f436-421a-9225-027c0e7b9b7f/0/0 | Background Remover | PixCleaner offers a REST API that allows you to easily use and interact with our service programmatically. Using our HTTP interface integrate our background removal features into any business or application. | 0.2 | getImage | Retuns the result of an image that was previously handled by autoremove route for the given image Id.
accepts two parameters:
- imageId: The Id of the image to get.
- recompute: bollean, if set to true, asks the server to remove the backgorund again for the specified ID (costs credits). | 200 | null | {"image": {"id": "", "originalImage": {"path": "https://cdn.pixcleaner.com/imgs/users/userId/imageId.jpg", "width": "720px", "height": "640px", "mimeType": "image/jpeg"}, "resultImage": {"path": "https://cdn.pixcleaner.com/imgs/users/userId/removedImageId.jpg", "width": "720px", "height": "640px"}}} | {"type": "object", "properties": {"image": {"type": "object", "properties": {"id": {"type": "string"}, "originalImage": {"type": "object", "properties": {"path": {"type": "string", "format": "uri"}, "width": {"type": "number"}, "height": {"type": "number"}, "mimeType": {"type": "string"}}}, "resultImage": {"type": "object", "properties": {"path": {"type": "string", "format": "uri"}, "width": {"type": "number"}, "height": {"type": "number"}}}}}}} |
d82422fe-aa13-4965-95a6-36b74f27e231/76913fcb-6953-4bd4-a335-5fb9ed8cc184/0/0 | Face Detection With Age and Emotions Features | Use our Face Detection API to detect the location of human faces in your images with optional extra features like Age, Gender, Emotions and Facial Features (Sunglasses, Beard etc...). | 5.8 | Face Detection with Age, Gender, Emotions & Facial Features | Use our Face Detection API to detect the location of human faces in your images along with their predicted ages, genders, emotions & facial features
*Image must be a regular JPEG or PNG image. Usually such images have extensions: .jpg, .jpeg, .png. The service checks input file by MIME type … | 400 | Error result | {"request": {"image_url": "https://cdn.pixabay.com/photo/2017/06/20/22/14/man-2425121__340.jp"}, "success": false, "message": "[ERROR]: Please try again with different image url or make sure image is available for download"} | {"type": "object", "properties": {"request": {"type": "object", "properties": {"image_url": {"type": "string"}}}, "success": {"type": "boolean"}, "message": {"type": "string"}}} |
d82422fe-aa13-4965-95a6-36b74f27e231/76913fcb-6953-4bd4-a335-5fb9ed8cc184/1/0 | Face Detection With Age and Emotions Features | Use our Face Detection API to detect the location of human faces in your images with optional extra features like Age, Gender, Emotions and Facial Features (Sunglasses, Beard etc...). | 5.8 | Face Detection with Age, Gender, Emotions & Facial Features | Use our Face Detection API to detect the location of human faces in your images along with their predicted ages, genders, emotions & facial features
*Image must be a regular JPEG or PNG image. Usually such images have extensions: .jpg, .jpeg, .png. The service checks input file by MIME type … | 200 | Success result | {"total_people": 2, "success": true, "message": "Object test analyzed successfully!", "detect_faces": [{"Beard": {"Confidence": 92.97073364257812, "Value": false}, "AgeRange": {"High": 31, "Low": 23}, "Mustache": {"Confidence": 98.25421905517578, "Value": false}, "Gender": {"Confidence": 99.98704528808594, "Value": "Female"}, "Smile": {"Confidence": 95.57243347167969, "Value": true}, "Eyeglasses": {"Confidence": 97.66510772705078, "Value": false}, "Emotions": [{"Type": "HAPPY", "Confidence": 99.89854431152344}, {"Type": "SURPRISED", "Confidence": 6.261795997619629}, {"Type": "FEAR", "Confidence": 5.878974437713623}, {"Type": "SAD", "Confidence": 2.151007652282715}, {"Type": "ANGRY", "Confidence": 0.018151428550481796}, {"Type": "CONFUSED", "Confidence": 0.017904579639434814}, {"Type": "DISGUSTED", "Confidence": 0.01238884124904871}, {"Type": "CALM", "Confidence": 0.00772722065448761}], "Sunglasses": {"Confidence": 99.99668884277344, "Value": false}, "MouthOpen": {"Confidence": 93.236328125, "Value": true}, "Confidence": 99.96906280517578, "Quality": {"Brightness": 85.39833068847656, "Sharpness": 20.927310943603516}, "BoundingBox": {"Left": 0.8350318074226379, "Top": 0.17726090550422668, "Height": 0.186831533908844, "Width": 0.09481673687696457}, "Pose": {"Pitch": -1.5969018936157227, "Roll": 15.738398551940918, "Yaw": -50.15610885620117}, "Landmarks": [{"Type": "eyeLeft", "X": 0.8538621664047241, "Y": 0.2429637610912323}, {"Type": "eyeRight", "X": 0.8784207701683044, "Y": 0.2562246024608612}, {"Type": "mouthLeft", "X": 0.8453412652015686, "Y": 0.3033677637577057}, {"Type": "mouthRight", "X": 0.8654991388320923, "Y": 0.31481754779815674}, {"Type": "nose", "X": 0.8432112336158752, "Y": 0.2739795744419098}, {"Type": "leftEyeBrowLeft", "X": 0.852628231048584, "Y": 0.2272668480873108}, {"Type": "leftEyeBrowRight", "X": 0.8557189106941223, "Y": 0.22650882601737976}, {"Type": "leftEyeBrowUp", "X": 0.8520169258117676, "Y": 0.22101664543151855}, {"Type": "rightEyeBrowLeft", "X": 0.870333731174469, "Y": 0.23372264206409454}, {"Type": "rightEyeBrowRight", "X": 0.8964307308197021, "Y": 0.2501135468482971}, {"Type": "rightEyeBrowUp", "X": 0.8814625144004822, "Y": 0.2357807159423828}, {"Type": "leftEyeLeft", "X": 0.8515511155128479, "Y": 0.2412082850933075}, {"Type": "leftEyeRight", "X": 0.8586652278900146, "Y": 0.24618959426879883}, {"Type": "leftEyeUp", "X": 0.8532898426055908, "Y": 0.23964707553386688}, {"Type": "leftEyeDown", "X": 0.853363573551178, "Y": 0.24544169008731842}, {"Type": "rightEyeLeft", "X": 0.8735968470573425, "Y": 0.25411704182624817}, {"Type": "rightEyeRight", "X": 0.8852437734603882, "Y": 0.25928616523742676}, {"Type": "rightEyeUp", "X": 0.8781784176826477, "Y": 0.252902626991272}, {"Type": "rightEyeDown", "X": 0.8778854608535767, "Y": 0.25869402289390564}, {"Type": "noseLeft", "X": 0.8480846285820007, "Y": 0.28122958540916443}, {"Type": "noseRight", "X": 0.8576096892356873, "Y": 0.2862505316734314}, {"Type": "mouthUp", "X": 0.8488884568214417, "Y": 0.2969021499156952}, {"Type": "mouthDown", "X": 0.8483160138130188, "Y": 0.31505972146987915}, {"Type": "leftPupil", "X": 0.8538621664047241, "Y": 0.2429637610912323}, {"Type": "rightPupil", "X": 0.8784207701683044, "Y": 0.2562246024608612}, {"Type": "upperJawlineLeft", "X": 0.8646963834762573, "Y": 0.24745959043502808}, {"Type": "midJawlineLeft", "X": 0.8555524349212646, "Y": 0.30954718589782715}, {"Type": "chinBottom", "X": 0.8508602976799011, "Y": 0.3471835255622864}, {"Type": "midJawlineRight", "X": 0.9017362594604492, "Y": 0.3333536386489868}, {"Type": "upperJawlineRight", "X": 0.9207292795181274, "Y": 0.27595552802085876}], "EyesOpen": {"Confidence": 67.48162078857422, "Value": true}}, {"Beard": {"Confidence": 99.06673431396484, "Value": true}, "AgeRange": {"High": 30, "Low": 22}, "Mustache": {"Confidence": 91.0721435546875, "Value": false}, "Gender": {"Confidence": 99.9684829711914, "Value": "Male"}, "Smile": {"Confidence": 96.0628433227539, "Value": true}, "Eyeglasses": {"Confidence": 97.70086669921875, "Value": false}, "Emotions": [{"Type": "HAPPY", "Confidence": 99.78570556640625}, {"Type": "SURPRISED", "Confidence": 6.266561985015869}, {"Type": "FEAR", "Confidence": 5.882956027984619}, {"Type": "SAD", "Confidence": 2.154573440551758}, {"Type": "ANGRY", "Confidence": 0.042072489857673645}, {"Type": "CONFUSED", "Confidence": 0.03826736658811569}, {"Type": "DISGUSTED", "Confidence": 0.038090553134679794}, {"Type": "CALM", "Confidence": 0.018622394651174545}], "Sunglasses": {"Confidence": 99.99671936035156, "Value": false}, "MouthOpen": {"Confidence": 94.29051208496094, "Value": true}, "Confidence": 99.99578857421875, "Quality": {"Brightness": 84.01089477539062, "Sharpness": 16.49492073059082}, "BoundingBox": {"Left": 0.1719745397567749, "Top": 0.09375260025262833, "Height": 0.17719531059265137, "Width": 0.08578985184431076}, "Pose": {"Pitch": 1.959987998008728, "Roll": 5.313048362731934, "Yaw": -21.749347686767578}, "Landmarks": [{"Type": "eyeLeft", "X": 0.18334046006202698, "Y": 0.15674783289432526}, {"Type": "eyeRight", "X": 0.21607345342636108, "Y": 0.16320163011550903}, {"Type": "mouthLeft", "X": 0.18269112706184387, "Y": 0.2129240781068802}, {"Type": "mouthRight", "X": 0.20989666879177094, "Y": 0.21853592991828918}, {"Type": "nose", "X": 0.18831408023834229, "Y": 0.1872299164533615}, {"Type": "leftEyeBrowLeft", "X": 0.1753605306148529, "Y": 0.14217928051948547}, {"Type": "leftEyeBrowRight", "X": 0.18786431849002838, "Y": 0.14077870547771454}, {"Type": "leftEyeBrowUp", "X": 0.1802729368209839, "Y": 0.1364988386631012}, {"Type": "rightEyeBrowLeft", "X": 0.20668575167655945, "Y": 0.14414723217487335}, {"Type": "rightEyeBrowRight", "X": 0.2324226051568985, "Y": 0.15307511389255524}, {"Type": "rightEyeBrowUp", "X": 0.2181912660598755, "Y": 0.14343635737895966}, {"Type": "leftEyeLeft", "X": 0.17874568700790405, "Y": 0.15561063587665558}, {"Type": "leftEyeRight", "X": 0.18985441327095032, "Y": 0.15848428010940552}, {"Type": "leftEyeUp", "X": 0.18290503323078156, "Y": 0.15379634499549866}, {"Type": "leftEyeDown", "X": 0.18332096934318542, "Y": 0.159114807844162}, {"Type": "rightEyeLeft", "X": 0.20963285863399506, "Y": 0.1623236984014511}, {"Type": "rightEyeRight", "X": 0.22314505279064178, "Y": 0.16432596743106842}, {"Type": "rightEyeUp", "X": 0.21579529345035553, "Y": 0.16019096970558167}, {"Type": "rightEyeDown", "X": 0.21575485169887543, "Y": 0.16552132368087769}, {"Type": "noseLeft", "X": 0.18771861493587494, "Y": 0.19322218000888824}, {"Type": "noseRight", "X": 0.19988973438739777, "Y": 0.19557952880859375}, {"Type": "mouthUp", "X": 0.192181795835495, "Y": 0.20734697580337524}, {"Type": "mouthDown", "X": 0.19256596267223358, "Y": 0.22426658868789673}, {"Type": "leftPupil", "X": 0.18334046006202698, "Y": 0.15674783289432526}, {"Type": "rightPupil", "X": 0.21607345342636108, "Y": 0.16320163011550903}, {"Type": "upperJawlineLeft", "X": 0.1787378191947937, "Y": 0.15739111602306366}, {"Type": "midJawlineLeft", "X": 0.1800091415643692, "Y": 0.21775583922863007}, {"Type": "chinBottom", "X": 0.19510747492313385, "Y": 0.25379860401153564}, {"Type": "midJawlineRight", "X": 0.23840345442295074, "Y": 0.22888748347759247}, {"Type": "upperJawlineRight", "X": 0.25053560733795166, "Y": 0.1707964390516281}], "EyesOpen": {"Confidence": 81.56422424316406, "Value": true}}]} | {"type": "object", "properties": {"total_people": {"type": "integer"}, "success": {"type": "boolean"}, "message": {"type": "string"}, "detect_faces": {"type": "array", "items": {"type": "object", "properties": {"Beard": {"type": "object", "properties": {"Confidence": {"type": "number"}, "Value": {"type": "boolean"}}}, "AgeRange": {"type": "object", "properties": {"High": {"type": "integer"}, "Low": {"type": "integer"}}}, "Mustache": {"type": "object", "properties": {"Confidence": {"type": "number"}, "Value": {"type": "boolean"}}}, "Gender": {"type": "object", "properties": {"Confidence": {"type": "number"}, "Value": {"type": "string"}}}, "Smile": {"type": "object", "properties": {"Confidence": {"type": "number"}, "Value": {"type": "boolean"}}}, "Eyeglasses": {"type": "object", "properties": {"Confidence": {"type": "number"}, "Value": {"type": "boolean"}}}, "Emotions": {"type": "array", "items": {"type": "object", "properties": {"Type": {"type": "string"}, "Confidence": {"type": "number"}}}}, "Sunglasses": {"type": "object", "properties": {"Confidence": {"type": "number"}, "Value": {"type": "boolean"}}}, "MouthOpen": {"type": "object", "properties": {"Confidence": {"type": "number"}, "Value": {"type": "boolean"}}}, "Confidence": {"type": "number"}, "Quality": {"type": "object", "properties": {"Brightness": {"type": "number"}, "Sharpness": {"type": "number"}}}, "BoundingBox": {"type": "object", "properties": {"Left": {"type": "number"}, "Top": {"type": "number"}, "Height": {"type": "number"}, "Width": {"type": "number"}}}, "Pose": {"type": "object", "properties": {"Pitch": {"type": "number"}, "Roll": {"type": "number"}, "Yaw": {"type": "number"}}}, "Landmarks": {"type": "array", "items": {"type": "object", "properties": {"Type": {"type": "string"}, "X": {"type": "number"}, "Y": {"type": "number"}}}}, "EyesOpen": {"type": "object", "properties": {"Confidence": {"type": "number"}, "Value": {"type": "boolean"}}}}}}}} |
d82422fe-aa13-4965-95a6-36b74f27e231/0b922734-a89e-40f0-a972-40547c4509c5/0/0 | Face Detection With Age and Emotions Features | Use our Face Detection API to detect the location of human faces in your images with optional extra features like Age, Gender, Emotions and Facial Features (Sunglasses, Beard etc...). | 5.8 | Face Detection | Use our Face Detection API to detect the location of human faces in your images.
*Image must be a regular JPEG or PNG image. Usually such images have extensions: .jpg, .jpeg, .png. The service checks input file by MIME type … | 400 | Error Result | {"request": {"image_url": "https://cdn.pixabay.com/photo/2017/06/20/22/14/man-2425121__340.jp"}, "success": false, "message": "[ERROR]: Please try again with different image url or make sure image is available for download"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"request": {"type": "object", "properties": {"image_url": {"type": "string"}}, "required": ["image_url"]}, "success": {"type": "boolean"}, "message": {"type": "string"}}, "required": ["message", "request", "success"]} |
d82422fe-aa13-4965-95a6-36b74f27e231/0b922734-a89e-40f0-a972-40547c4509c5/1/0 | Face Detection With Age and Emotions Features | Use our Face Detection API to detect the location of human faces in your images with optional extra features like Age, Gender, Emotions and Facial Features (Sunglasses, Beard etc...). | 5.8 | Face Detection | Use our Face Detection API to detect the location of human faces in your images.
*Image must be a regular JPEG or PNG image. Usually such images have extensions: .jpg, .jpeg, .png. The service checks input file by MIME type … | 200 | Success result | {"total_people": 2, "success": true, "message": "Object test analyzed successfully!", "detect_faces": [{"Confidence": 99.96906280517578, "Quality": {"Brightness": 85.39833068847656, "Sharpness": 20.927310943603516}, "BoundingBox": {"Left": 0.8350318074226379, "Top": 0.17726090550422668, "Height": 0.186831533908844, "Width": 0.09481673687696457}, "Pose": {"Pitch": -1.5969018936157227, "Roll": 15.738398551940918, "Yaw": -50.15610885620117}, "Landmarks": [{"Type": "eyeLeft", "X": 0.8538621664047241, "Y": 0.2429637610912323}, {"Type": "eyeRight", "X": 0.8784207701683044, "Y": 0.2562246024608612}, {"Type": "mouthLeft", "X": 0.8453412652015686, "Y": 0.3033677637577057}, {"Type": "mouthRight", "X": 0.8654991388320923, "Y": 0.31481754779815674}, {"Type": "nose", "X": 0.8432112336158752, "Y": 0.2739795744419098}]}, {"Confidence": 99.99578857421875, "Quality": {"Brightness": 84.01089477539062, "Sharpness": 16.49492073059082}, "BoundingBox": {"Left": 0.1719745397567749, "Top": 0.09375260025262833, "Height": 0.17719531059265137, "Width": 0.08578985184431076}, "Pose": {"Pitch": 1.959987998008728, "Roll": 5.313048362731934, "Yaw": -21.749347686767578}, "Landmarks": [{"Type": "eyeLeft", "X": 0.18334046006202698, "Y": 0.15674783289432526}, {"Type": "eyeRight", "X": 0.21607345342636108, "Y": 0.16320163011550903}, {"Type": "mouthLeft", "X": 0.18269112706184387, "Y": 0.2129240781068802}, {"Type": "mouthRight", "X": 0.20989666879177094, "Y": 0.21853592991828918}, {"Type": "nose", "X": 0.18831408023834229, "Y": 0.1872299164533615}]}]} | {"type": "object", "properties": {"total_people": {"type": "integer"}, "success": {"type": "boolean"}, "message": {"type": "string"}, "detect_faces": {"type": "array", "items": {"type": "object", "properties": {"Confidence": {"type": "number"}, "Quality": {"type": "object", "properties": {"Brightness": {"type": "number"}, "Sharpness": {"type": "number"}}}, "BoundingBox": {"type": "object", "properties": {"Left": {"type": "number"}, "Top": {"type": "number"}, "Height": {"type": "number"}, "Width": {"type": "number"}}}, "Pose": {"type": "object", "properties": {"Pitch": {"type": "number"}, "Roll": {"type": "number"}, "Yaw": {"type": "number"}}}, "Landmarks": {"type": "array", "items": {"type": "object", "properties": {"Type": {"type": "string"}, "X": {"type": "number"}, "Y": {"type": "number"}}}}}}}}} |
e1147b37-e2c6-451c-8cca-99bd1b2275a2/b9f389b9-faa5-4330-a787-3b37e3b1d3e7/0/0 | PDF OCR | This API extracts texts from pdf with high accuracy | 0.1 | Upload PDF | This endpoint handles pdf files and returns texts as response | 422 | Example_1 | {"detail": [{"loc": [], "msg": "", "type": ""}]} | {"title": "HTTPValidationError", "type": "object", "properties": {"detail": {"title": "Detail", "type": "array", "items": {"title": "ValidationError", "required": ["loc", "msg", "type"], "type": "object", "properties": {"loc": {"title": "Location", "type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "integer"}]}}, "msg": {"title": "Message", "type": "string"}, "type": {"title": "Error Type", "type": "string"}}}}}} |
e1147b37-e2c6-451c-8cca-99bd1b2275a2/b9f389b9-faa5-4330-a787-3b37e3b1d3e7/1/0 | PDF OCR | This API extracts texts from pdf with high accuracy | 0.1 | Upload PDF | This endpoint handles pdf files and returns texts as response | 200 | Example_1 | {"text": "Attention Is All You Need Ashish Vaswani* Noam Shazeer* Niki Parmar* Jakob Uszkoreit* Google Brain Google Brain Google Research Google Research [email protected] [email protected] [email protected] [email protected] Llion Jones* Aidan N. Gomez* FLukasz Kaiser* Google Research University of Toronto Google Brain [email protected] [email protected] [email protected] Illia Polosukhin* * [email protected] Abstract The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English- to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. 1 Introduction Recurrent neural networks, long short-term memory and gated recurrent [7] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and \u201cEqual contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research. tWork performed while at Google Brain. +Work performed while at Google Research. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. "} | {"type": "object", "properties": {"text": {"type": "string"}}} |
5d6735f3-50d1-48e2-bdb9-ebc35d295ef7/9e303983-621b-4829-8207-0f589bf2ba61/0/0 | BioID | Facial Authentication over API! Use our ML model to identify faces! | null | Remove User | Removes user from your application | 200 | Response | {"result": {"message": "User removed"}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"result": {"type": "object", "properties": {"message": {"type": "string"}}, "required": ["message"]}}, "required": ["result"]} |
5d6735f3-50d1-48e2-bdb9-ebc35d295ef7/8b9ef5e0-471f-4afb-83c2-e3f2754e6e26/0/0 | BioID | Facial Authentication over API! Use our ML model to identify faces! | null | Get Information | Returns the amount of users on your application and the current user names. | 200 | Response | {"result": {"users": [], "userCount": 0}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"result": {"type": "object", "properties": {"users": {"type": "array"}, "userCount": {"type": "integer"}}, "required": ["userCount", "users"]}}, "required": ["result"]} |
5d6735f3-50d1-48e2-bdb9-ebc35d295ef7/39af5fa9-33b6-4b87-9a9a-291f7a2219d0/0/0 | BioID | Facial Authentication over API! Use our ML model to identify faces! | null | Check User | Validate a user's photo among the uploaded photos on your application. Supports PNG's and JPG's. | 200 | Response | {"result": {"user": "Bob", "confidence": "0.54"}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"result": {"type": "object", "properties": {"user": {"type": "string"}, "confidence": {"type": "string"}}, "required": ["confidence", "user"]}}, "required": ["result"]} |
5d6735f3-50d1-48e2-bdb9-ebc35d295ef7/a81bafd3-d98e-4eca-a170-505afc8a3690/0/0 | BioID | Facial Authentication over API! Use our ML model to identify faces! | null | Add User Photo | Adds a user photo to your application. PNG's and JPG's are supported. | 200 | Response | {"message": "User added"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"message": {"type": "string"}}, "required": ["message"]} |
01d7f147-dedf-4267-9f3b-1e7e68860c67/c2d96fb2-2cf8-453a-a216-8b813ae07d47/0/0 | Image recognition and prediction | Detect objects on images or predict what is on image. | null | image_prediction_base64image_prediction_post | This endpoint takes an image and return 10 predictions what is on image. | 422 | Example_1 | {"detail": [{"loc": [], "msg": "", "type": ""}]} | {"properties": {"detail": {"items": {"properties": {"loc": {"items": {"type": "string"}, "title": "Location", "type": "array"}, "msg": {"title": "Message", "type": "string"}, "type": {"title": "Error Type", "type": "string"}}, "required": ["loc", "msg", "type"], "title": "ValidationError", "type": "object"}, "title": "Detail", "type": "array"}}, "title": "HTTPValidationError", "type": "object"} |
01d7f147-dedf-4267-9f3b-1e7e68860c67/981bd153-0804-4e81-ac49-57e1a59fc32c/0/0 | Image recognition and prediction | Detect objects on images or predict what is on image. | null | image_recognition_base64image_recognition_post | This endpoint receive image as base64 string and can return you JSON with detected objects and base64 image with draw boxes. | 422 | Example_1 | {"detail": [{"loc": [], "msg": "", "type": ""}]} | {"properties": {"detail": {"items": {"properties": {"loc": {"items": {"type": "string"}, "title": "Location", "type": "array"}, "msg": {"title": "Message", "type": "string"}, "type": {"title": "Error Type", "type": "string"}}, "required": ["loc", "msg", "type"], "title": "ValidationError", "type": "object"}, "title": "Detail", "type": "array"}}, "title": "HTTPValidationError", "type": "object"} |
b1f7cbe4-745c-47f5-a9d4-f720dbaf0bb3/55a8fd6d-3920-4b41-b91f-5fc23b54837d/2/0 | Image Sentiment Analysis | SimpleSent is an image sentiment analysis API. It identifies and interprets emotions portrayed within an image. There are several use cases such as analyzing emotions in editorial illustrations, product reviews, and social media posts. At this time, our models have high accuracy and perform well under edge-case scenarios. | 5.3 | sentiment | Run image sentiment analysis | 200 | Example_1 | {"sentiment": "very positive", "sentiment_score": 0} | {"type": "object", "properties": {"sentiment": {"type": "string", "enum": ["very positive", "very negative", "positive", "neutral", "negative"]}, "sentiment_score": {"type": "number", "minimum": -1, "maximum": 1}}} |
06812c73-6543-4179-a3e5-102b4a5aaedf/0e166a31-5e73-4417-9b8a-73d79971cc6b/0/0 | Redactor Detector | Detect sensitive information inside images, PDFs or raw text, and redact it, for safe upload. | null | Redact only user keywords | Detect and redact only the provided keywords by the user. This can be useful in cases where you want to redact names or information only the user knows about, and doesn't want to deal with the multiple parameters in the redact all API call. | 200 | Response | {"findings": [{"keyword": "military"}, {"keyword": "military"}, {"keyword": "military"}, {"keyword": "against"}, {"keyword": "military"}, {"keyword": "against"}, {"keyword": "against"}], "url": "https://redacted-api-bucket.s3.amazonaws.com/redacted-c3c278bb151f5440973d9dbc35f03a20c12ebda640a2f00493761b2c238b4272.jpeg"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"findings": {"type": "array", "items": {"type": "object", "properties": {"keyword": {"type": "string"}}, "required": ["keyword"]}}, "url": {"type": "string"}}, "required": ["findings", "url"]} |
06812c73-6543-4179-a3e5-102b4a5aaedf/57b921bf-28a4-4efb-9e90-f00a02769561/0/0 | Redactor Detector | Detect sensitive information inside images, PDFs or raw text, and redact it, for safe upload. | null | Redact all with URL | Pass an image URL, and redact all sensitive information in the image. | 200 | Response | {"findings": [{"keyword": "military"}, {"keyword": "military"}, {"keyword": "military"}, {"keyword": "military"}], "url": "https://redacted-api-bucket.s3.amazonaws.com/redacted-c3c278bb151f5440973d9dbc35f03a20c12ebda640a2f00493761b2c238b4272.jpeg"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"findings": {"type": "array", "items": {"type": "object", "properties": {"keyword": {"type": "string"}}, "required": ["keyword"]}}, "url": {"type": "string"}}, "required": ["findings", "url"]} |
06812c73-6543-4179-a3e5-102b4a5aaedf/57b921bf-28a4-4efb-9e90-f00a02769561/1/0 | Redactor Detector | Detect sensitive information inside images, PDFs or raw text, and redact it, for safe upload. | null | Redact all with URL | Pass an image URL, and redact all sensitive information in the image. | 400 | Response | {"detail": "Invalid 'redact_possible_cc' value"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"detail": {"type": "string"}}, "required": ["detail"]} |
4aa109b3-6107-4f59-91c5-48bb9bff00a7/d519ce50-2e58-4fbd-b2de-6e1267aa1ff6/0/0 | Text Plus Recognition | Extract printed, handwritten, slanted, folded, rotated characters from images and PDF files in over 50 different languages. Output includes text content, as well as coordinates and orientation of the text position in the image.View Document | 6.8 | text plus recognition | Supports most formats such as jpg, png, bmp, pdf, tiff, single-frame gif, etc., with a file size of no more than 10M and an image width and height of between 20 and 10000 (pixels). | 200 | Response | {"code": "200", "status": "SUCCESS", "service": "text_ocr", "date": "11/19/2023 10:25:06 PM", "result": {"angle": 0, "width": 1898, "lines": [{"angle": 0, "type": "text", "direction": 1, "handwritten": 0, "score": 0.977, "position": [251, 204, 547, 198, 548, 291, 253, 297], "text": "salut"}, {"angle": 0, "type": "text", "direction": 1, "handwritten": 0, "score": 0.987, "position": [789, 209, 1037, 213, 1035, 297, 787, 293], "text": "hallo"}, {"angle": 0, "type": "text", "direction": 1, "handwritten": 0, "score": 0.999, "position": [49, 335, 539, 331, 539, 421, 50, 424], "text": "merhaba"}, {"angle": 0, "type": "text", "direction": 1, "handwritten": 0, "score": 0.995, "position": [1385, 324, 1564, 324, 1564, 419, 1385, 419], "text": "ol\u00e1"}, {"angle": 0, "type": "text", "direction": 1, "handwritten": 0, "score": 0.993, "position": [710, 338, 1311, 340, 1310, 526, 709, 525], "text": "hello"}, {"angle": 0, "type": "text", "direction": 1, "handwritten": 0, "score": 0.988, "position": [285, 447, 538, 447, 538, 528, 285, 528], "text": "ch\u00e0o"}, {"angle": 0, "type": "text", "direction": 0, "handwritten": 0, "score": 0.297, "position": [1386, 443, 1768, 438, 1769, 523, 1386, 526], "text": "2"}, {"angle": 0, "type": "text", "direction": 1, "handwritten": 0, "score": 0.995, "position": [235, 567, 541, 567, 541, 677, 235, 677], "text": "hola"}, {"angle": 0, "type": "text", "direction": 1, "handwritten": 0, "score": 0.994, "position": [663, 537, 1524, 559, 1519, 729, 658, 707], "text": "\u4f60\u597d"}, {"angle": 0, "type": "text", "direction": 1, "handwritten": 0, "score": 0.999, "position": [1565, 549, 1813, 540, 1816, 618, 1568, 626], "text": "guten"}, {"angle": 0, "type": "text", "direction": 1, "handwritten": 0, "score": 0.998, "position": [508, 711, 999, 715, 998, 832, 507, 829], "text": "bonjour"}, {"angle": 180, "type": "text", "direction": 1, "handwritten": 0, "score": 0.485, "position": [1310, 853, 1042, 853, 1042, 734, 1310, 734], "text": "P\uc18cH\u4e0a"}, {"angle": 0, "type": "text", "direction": 1, "handwritten": 0, "score": 0.97, "position": [1425, 777, 1644, 777, 1644, 858, 1425, 858], "text": "ciao"}, {"angle": 0, "type": "text", "direction": 1, "handwritten": 1, "score": 0.998, "position": [686, 871, 1160, 874, 1160, 975, 686, 972], "text": "\u3053\u3093\u306b\u3061\u306f"}, {"angle": 0, "type": "text", "direction": 1, "handwritten": 0, "score": 0.998, "position": [1253, 932, 1657, 932, 1657, 1025, 1253, 1025], "text": "\uc548\ub155\ud558\uc138\uc694"}, {"angle": 90, "type": "text", "direction": 1, "handwritten": 0, "score": 0.77, "position": [1798, 634, 1798, 1064, 1705, 1064, 1705, 634], "text": "SDO DI3Y"}, {"angle": 270, "type": "text", "direction": 1, "handwritten": 0, "score": 0.998, "position": [1624, 413, 1624, 120, 1707, 120, 1707, 413], "text": "aloha"}, {"angle": 270, "type": "text", "direction": 1, "handwritten": 0, "score": 0.809, "position": [591, 527, 583, 85, 679, 83, 688, 525], "text": "nPNBeT"}], "height": 1106}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"code": {"type": "string"}, "status": {"type": "string"}, "service": {"type": "string"}, "date": {"type": "string"}, "result": {"type": "object", "properties": {"angle": {"type": "integer"}, "width": {"type": "integer"}, "lines": {"type": "array", "items": {"type": "object", "properties": {"angle": {"type": "integer"}, "type": {"type": "string"}, "direction": {"type": "integer"}, "handwritten": {"type": "integer"}, "score": {"type": "number"}, "position": {"type": "array", "items": {"type": "integer"}}, "text": {"type": "string"}}, "required": ["angle", "direction", "handwritten", "position", "score", "text", "type"]}}, "height": {"type": "integer"}}, "required": ["angle", "height", "lines", "width"]}}, "required": ["code", "date", "result", "service", "status"]} |
1fa092bb-a070-4d8e-aab6-3a61adf271a5/51e94bd3-4c08-46d0-82f4-2f2589ca0b9d/0/0 | Vinscanapi.com | An API that scans an image of a V.I.N. (Vehicle Identification Number) Tag or Label and returns the V.I.N. as a validated string. The result can also be extended to provide a fully qualified vehicle information for the scanned V.I.N.. | null | Upload Image for V.I.N. FULL Processing | A POST endpoint that accepts JPEG or PNG file and process the image to find a valid V.I.N. (Vehicle Identification Number) and return a detailed car information in a JSON response for any found V.I.N. | 200 | Response | {"anti_brake_system": "Non-Abs | 4-Wheel ABS", "city_mileage": "21 - 22 miles/gallon", "engine": "2.0L I4 MPI", "highway_mileage": "29 miles/gallon", "made_in": "Japan", "make": "Mitsubishi", "model": "Lancer ES", "optional_seating": null, "overall_height": "58.70 inches", "overall_length": "180.00 inches", "overall_width": "69.40 inches", "process_date": "2020-08-09T22:25:28.4493981-04:00", "standard_seating": "5", "status": "Image Process Completed", "steering_type": "R&P", "style": "Sedan 4D", "tank_size": null, "trim_level": "ES", "vin": "JA3AU26U38U604191", "year": "2008"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"anti_brake_system": {"type": "string"}, "city_mileage": {"type": "string"}, "engine": {"type": "string"}, "highway_mileage": {"type": "string"}, "made_in": {"type": "string"}, "make": {"type": "string"}, "model": {"type": "string"}, "optional_seating": {"type": "null"}, "overall_height": {"type": "string"}, "overall_length": {"type": "string"}, "overall_width": {"type": "string"}, "process_date": {"type": "string"}, "standard_seating": {"type": "string"}, "status": {"type": "string"}, "steering_type": {"type": "string"}, "style": {"type": "string"}, "tank_size": {"type": "null"}, "trim_level": {"type": "string"}, "vin": {"type": "string"}, "year": {"type": "string"}}, "required": ["anti_brake_system", "city_mileage", "engine", "highway_mileage", "made_in", "make", "model", "optional_seating", "overall_height", "overall_length", "overall_width", "process_date", "standard_seating", "status", "steering_type", "style", "tank_size", "trim_level", "vin", "year"]} |
ec410847-e7a3-48dd-a832-8c75c18027f1/c613867f-4a6d-4d21-8c56-7bf5936343f2/0/0 | Skin analysis | Provides multi-dimensional detailed skin analysis on the skin, comprehensive detection and identification of skin color, skin smoothness, acne spots, wrinkles, pores, blackheads, dark circles and eye bags, etc., accurate to such as blood vessel dark circles, acne marks, etc. detail | 8.8 | Skin analysis |
Image requirements
Image format: JPG, PNG, BMP.
Image size: no more than 8 MB.
Image resolution: Below 1920x1080.
| 200 | Success | {"request_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "log_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "error_code": 0, "result": {"face_num": 1, "face_list": [{"face_token": "5b85d6d0f9d4044e0cc829ea4be7fab5", "location": {"left": 313.97, "top": 417.37, "width": 571, "height": 634, "degree": 5}, "skin": {"color": 1, "smooth": 2}, "acnespotmole": {"acne_num": 1, "acne_list": [{"score": 0.17, "left": 497.41, "top": 1008.46, "right": 505.14, "bottom": 1015.24}], "speckle_num": 58, "speckle_list": [{"score": 0.86, "left": 401.54, "top": 662.49, "right": 408.68, "bottom": 669.79}, {"score": 0.14, "left": 411.88, "top": 865.42, "right": 422.07, "bottom": 876.26}, {"score": 0.11, "left": 693.47, "top": 961.55, "right": 697.87, "bottom": 966.09}, {"score": 0.1, "left": 415.18, "top": 792.46, "right": 420.27, "bottom": 797.48}], "mole_num": 1, "mole_list": [{"score": 0.7, "left": 822.29, "top": 436.85, "right": 827.39, "bottom": 445.09}]}, "eyesattr": {"dark_circle_left_type": [0], "dark_circle_right_type": [0], "dark_circle_left": [[{"x": 682, "y": 632}, {"x": 682, "y": 634}, {"x": 681, "y": 635}, {"x": 680, "y": 635}, {"x": 702, "y": 635}, {"x": 698, "y": 635}, {"x": 697, "y": 634}, {"x": 697, "y": 632}]], "dark_circle_right": [[{"x": 402, "y": 612}, {"x": 402, "y": 613}, {"x": 401, "y": 614}, {"x": 415, "y": 614}, {"x": 414, "y": 613}, {"x": 414, "y": 612}]], "eye_bags_left": [[{"x": 694, "y": 644}, {"x": 694, "y": 645}, {"x": 693, "y": 646}, {"x": 692, "y": 646}, {"x": 708, "y": 646}, {"x": 707, "y": 645}, {"x": 707, "y": 644}]], "eye_bags_right": [[{"x": 694, "y": 644}, {"x": 694, "y": 645}, {"x": 707, "y": 645}, {"x": 707, "y": 644}]]}, "blackheadpore": {"poly": [{"class_id": 0, "score": 0.57, "left": 521, "right": 610, "top": 645, "bottom": 792, "point": [{"x": 555, "y": 653}, {"x": 555, "y": 653}, {"x": 560, "y": 653}, {"x": 558, "y": 653}, {"x": 558, "y": 653}, {"x": 557, "y": 653}, {"x": 557, "y": 653}]}, {"class_id": 1, "score": 0.98, "left": 371, "right": 476, "top": 653, "bottom": 813, "point": [{"x": 417, "y": 661}, {"x": 422, "y": 666}, {"x": 422, "y": 666}, {"x": 420, "y": 664}, {"x": 420, "y": 664}, {"x": 420, "y": 662}, {"x": 420, "y": 662}, {"x": 418, "y": 661}, {"x": 418, "y": 661}]}, {"class_id": 1, "score": 0.97, "left": 652, "right": 750, "top": 683, "bottom": 845, "point": [{"x": 666, "y": 689}, {"x": 666, "y": 689}, {"x": 671, "y": 693}, {"x": 669, "y": 693}, {"x": 669, "y": 693}, {"x": 667, "y": 691}, {"x": 667, "y": 691}, {"x": 667, "y": 689}, {"x": 667, "y": 689}]}], "circles": [{"blackhead": [{"x": 573, "y": 777, "r": 1}, {"x": 560, "y": 775, "r": 1}, {"x": 565, "y": 768, "r": 1}, {"x": 565, "y": 764, "r": 1}, {"x": 552, "y": 762, "r": 1}]}, {"pore": [{"x": 697, "y": 835, "r": 1}, {"x": 425, "y": 802, "r": 2}, {"x": 687, "y": 708, "r": 1}]}]}, "skinface": {"skin_health_check_images": {"src_pic": "AD..", "gray_pic": "/9j/4AAQSkZJRgAB", "brown_pic": "AQAAAQABAA", "red_pic": "D/2wBDAAMCAgMCAgMDA"}}, "skinquality": {"skin_dryoil_check": ["0", "1", "0", "0", "0"], "skin_sensitive_check": ["2"]}}]}} | {"request_id": {"type": "string", "required": false, "description": "Request ID."}, "log_id": {"type": "string", "required": false, "description": "Log ID."}, "error_code": {"type": "integer", "required": false, "description": "Error Code."}, "error_code_str": {"type": "string", "required": false, "description": "Error Code."}, "error_msg": {"type": "string", "required": false, "description": "Error Message."}, "result": {"type": "object", "required": false, "properties": {"face_num": {"type": "integer", "required": true, "example": 1, "description": "The number of faces in the picture."}, "face_list": {"type": "array", "required": true, "description": "Face information."}}}} |
ec410847-e7a3-48dd-a832-8c75c18027f1/c613867f-4a6d-4d21-8c56-7bf5936343f2/0/1 | Skin analysis | Provides multi-dimensional detailed skin analysis on the skin, comprehensive detection and identification of skin color, skin smoothness, acne spots, wrinkles, pores, blackheads, dark circles and eye bags, etc., accurate to such as blood vessel dark circles, acne marks, etc. detail | 8.8 | Skin analysis |
Image requirements
Image format: JPG, PNG, BMP.
Image size: no more than 8 MB.
Image resolution: Below 1920x1080.
| 200 | Error | {"request_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "log_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "error_code": 400, "error_code_str": "ERROR_PARAMETERS", "error_msg": "image cannot be empty"} | {"request_id": {"type": "string", "required": false, "description": "Request ID."}, "log_id": {"type": "string", "required": false, "description": "Log ID."}, "error_code": {"type": "integer", "required": false, "description": "Error Code."}, "error_code_str": {"type": "string", "required": false, "description": "Error Code."}, "error_msg": {"type": "string", "required": false, "description": "Error Message."}, "result": {"type": "object", "required": false, "properties": {"face_num": {"type": "integer", "required": true, "example": 1, "description": "The number of faces in the picture."}, "face_list": {"type": "array", "required": true, "description": "Face information."}}}} |
ec410847-e7a3-48dd-a832-8c75c18027f1/c613867f-4a6d-4d21-8c56-7bf5936343f2/1/0 | Skin analysis | Provides multi-dimensional detailed skin analysis on the skin, comprehensive detection and identification of skin color, skin smoothness, acne spots, wrinkles, pores, blackheads, dark circles and eye bags, etc., accurate to such as blood vessel dark circles, acne marks, etc. detail | 8.8 | Skin analysis |
Image requirements
Image format: JPG, PNG, BMP.
Image size: no more than 8 MB.
Image resolution: Below 1920x1080.
| 401 | New Example | {"message": "Invalid API key in request"} | {"type": "object", "properties": {"message": {"type": "string", "required": true, "example": "Invalid API key in request", "description": "Error Message."}}} |
a1ca7aa2-2a91-4573-8ceb-51f3a9bdd0d0/d5fd657b-dcdd-40dd-95d9-062ea0d73375/0/0 | OCR Text Detection | Extract text, boundingBoxes, score, locale and dimensions from image using (google vision text detection) | 4.9 | POST Text Detection | the important thing is that you should use image as a form_data key
Example const data = new FormData(); data.append(‘image’, ‘image_path’); | 200 | New Example | {"success": true, "text": "SMASHING DOCS\nYOUR COMPANY\nSTREET ADDRESS\nCITY, ZIP CODE\nPHONE\nDESCRIPTION\nWEB DESIGN & DEVELOPMENT\nLOGO DESIGN\nSTATIONERY DESIGN\nCONTENT WRITING\nINVOICE NO. #000188\nDATE: NOVEMBER 18TH, 2017\nTO\nCLIENT COMPANY\nSTREET ADDRESS\nCITY, ZIP CODE\nPHONE\nSUB TOTAL\nDISCOUNT\nTOTAL\nACCEPTED PAYMENT METHODS\nPAYPAL, BANK DRAFT\nWE TRULY APPRECIATE YOUR BUSINESS!\nAMOUNT\n$1599.00\n$199.00\n$499.00\n$250.00\n$2547.00\n-$47.00\n$2500.00", "locale": "en", "dimensions": {"height": 643, "width": 500, "type": "jpg"}, "ocr": [{"text": "SMASHING", "confidence": 0, "boundingBox": {"tl": {"x": 81, "y": 82}, "tr": {"x": 149, "y": 82}, "br": {"x": 149, "y": 92}, "bl": {"x": 81, "y": 92}}}, {"text": "DOCS", "confidence": 0, "boundingBox": {"tl": {"x": 152, "y": 82}, "tr": {"x": 187, "y": 82}, "br": {"x": 187, "y": 92}, "bl": {"x": 152, "y": 92}}}, {"text": "YOUR", "confidence": 0, "boundingBox": {"tl": {"x": 55, "y": 117}, "tr": {"x": 94, "y": 117}, "br": {"x": 94, "y": 129}, "bl": {"x": 55, "y": 129}}}, {"text": "COMPANY", "confidence": 0, "boundingBox": {"tl": {"x": 97, "y": 117}, "tr": {"x": 167, "y": 118}, "br": {"x": 167, "y": 130}, "bl": {"x": 97, "y": 129}}}, {"text": "STREET", "confidence": 0, "boundingBox": {"tl": {"x": 56, "y": 135}, "tr": {"x": 88, "y": 135}, "br": {"x": 88, "y": 145}, "bl": {"x": 56, "y": 145}}}, {"text": "ADDRESS", "confidence": 0, "boundingBox": {"tl": {"x": 89, "y": 135}, "tr": {"x": 131, "y": 135}, "br": {"x": 131, "y": 144}, "bl": {"x": 89, "y": 144}}}, {"text": "CITY", "confidence": 0, "boundingBox": {"tl": {"x": 56, "y": 152}, "tr": {"x": 75, "y": 152}, "br": {"x": 75, "y": 161}, "bl": {"x": 56, "y": 161}}}, {"text": ",", "confidence": 0, "boundingBox": {"tl": {"x": 74, "y": 152}, "tr": {"x": 77, "y": 152}, "br": {"x": 77, "y": 160}, "bl": {"x": 74, "y": 160}}}, {"text": "ZIP", "confidence": 0, "boundingBox": {"tl": {"x": 79, "y": 152}, "tr": {"x": 92, "y": 152}, "br": {"x": 92, "y": 160}, "bl": {"x": 79, "y": 160}}}, {"text": "CODE", "confidence": 0, "boundingBox": {"tl": {"x": 96, "y": 152}, "tr": {"x": 121, "y": 152}, "br": {"x": 121, "y": 160}, "bl": {"x": 96, "y": 160}}}, {"text": "PHONE", "confidence": 0, "boundingBox": {"tl": {"x": 56, "y": 168}, "tr": {"x": 88, "y": 168}, "br": {"x": 88, "y": 176}, "bl": {"x": 56, "y": 176}}}, {"text": "DESCRIPTION", "confidence": 0, "boundingBox": {"tl": {"x": 69, "y": 260}, "tr": {"x": 124, "y": 260}, "br": {"x": 124, "y": 267}, "bl": {"x": 69, "y": 267}}}, {"text": "WEB", "confidence": 0, "boundingBox": {"tl": {"x": 69, "y": 291}, "tr": {"x": 91, "y": 291}, "br": {"x": 91, "y": 300}, "bl": {"x": 69, "y": 300}}}, {"text": "DESIGN", "confidence": 0, "boundingBox": {"tl": {"x": 94, "y": 291}, "tr": {"x": 128, "y": 291}, "br": {"x": 128, "y": 300}, "bl": {"x": 94, "y": 300}}}, {"text": "&", "confidence": 0, "boundingBox": {"tl": {"x": 133, "y": 291}, "tr": {"x": 140, "y": 291}, "br": {"x": 140, "y": 300}, "bl": {"x": 133, "y": 300}}}, {"text": "DEVELOPMENT", "confidence": 0, "boundingBox": {"tl": {"x": 143, "y": 291}, "tr": {"x": 214, "y": 291}, "br": {"x": 214, "y": 300}, "bl": {"x": 143, "y": 300}}}, {"text": "LOGO", "confidence": 0, "boundingBox": {"tl": {"x": 69, "y": 325}, "tr": {"x": 97, "y": 325}, "br": {"x": 97, "y": 334}, "bl": {"x": 69, "y": 334}}}, {"text": "DESIGN", "confidence": 0, "boundingBox": {"tl": {"x": 100, "y": 325}, "tr": {"x": 135, "y": 325}, "br": {"x": 135, "y": 333}, "bl": {"x": 100, "y": 333}}}, {"text": "STATIONERY", "confidence": 0, "boundingBox": {"tl": {"x": 69, "y": 360}, "tr": {"x": 126, "y": 360}, "br": {"x": 126, "y": 368}, "bl": {"x": 69, "y": 368}}}, {"text": "DESIGN", "confidence": 0, "boundingBox": {"tl": {"x": 130, "y": 360}, "tr": {"x": 165, "y": 360}, "br": {"x": 165, "y": 368}, "bl": {"x": 130, "y": 368}}}, {"text": "CONTENT", "confidence": 0, "boundingBox": {"tl": {"x": 70, "y": 395}, "tr": {"x": 115, "y": 395}, "br": {"x": 115, "y": 403}, "bl": {"x": 70, "y": 403}}}, {"text": "WRITING", "confidence": 0, "boundingBox": {"tl": {"x": 118, "y": 395}, "tr": {"x": 158, "y": 395}, "br": {"x": 158, "y": 403}, "bl": {"x": 118, "y": 403}}}, {"text": "INVOICE", "confidence": 0, "boundingBox": {"tl": {"x": 274, "y": 54}, "tr": {"x": 338, "y": 54}, "br": {"x": 338, "y": 66}, "bl": {"x": 274, "y": 66}}}, {"text": "NO", "confidence": 0, "boundingBox": {"tl": {"x": 343, "y": 54}, "tr": {"x": 367, "y": 54}, "br": {"x": 367, "y": 66}, "bl": {"x": 343, "y": 66}}}, {"text": ".", "confidence": 0, "boundingBox": {"tl": {"x": 369, "y": 54}, "tr": {"x": 373, "y": 54}, "br": {"x": 373, "y": 66}, "bl": {"x": 369, "y": 66}}}, {"text": "#", "confidence": 0, "boundingBox": {"tl": {"x": 378, "y": 54}, "tr": {"x": 388, "y": 54}, "br": {"x": 388, "y": 66}, "bl": {"x": 378, "y": 66}}}, {"text": "000188", "confidence": 0, "boundingBox": {"tl": {"x": 388, "y": 54}, "tr": {"x": 442, "y": 54}, "br": {"x": 442, "y": 66}, "bl": {"x": 388, "y": 66}}}, {"text": "DATE", "confidence": 0, "boundingBox": {"tl": {"x": 275, "y": 98}, "tr": {"x": 302, "y": 98}, "br": {"x": 302, "y": 109}, "bl": {"x": 275, "y": 109}}}, {"text": ":", "confidence": 0, "boundingBox": {"tl": {"x": 301, "y": 98}, "tr": {"x": 305, "y": 98}, "br": {"x": 305, "y": 109}, "bl": {"x": 301, "y": 109}}}, {"text": "NOVEMBER", "confidence": 0, "boundingBox": {"tl": {"x": 309, "y": 98}, "tr": {"x": 370, "y": 98}, "br": {"x": 370, "y": 109}, "bl": {"x": 309, "y": 109}}}, {"text": "18TH", "confidence": 0, "boundingBox": {"tl": {"x": 373, "y": 98}, "tr": {"x": 393, "y": 98}, "br": {"x": 393, "y": 109}, "bl": {"x": 373, "y": 109}}}, {"text": ",", "confidence": 0, "boundingBox": {"tl": {"x": 394, "y": 98}, "tr": {"x": 398, "y": 98}, "br": {"x": 398, "y": 109}, "bl": {"x": 394, "y": 109}}}, {"text": "2017", "confidence": 0, "boundingBox": {"tl": {"x": 399, "y": 98}, "tr": {"x": 425, "y": 98}, "br": {"x": 425, "y": 109}, "bl": {"x": 399, "y": 109}}}, {"text": "TO", "confidence": 0, "boundingBox": {"tl": {"x": 273, "y": 132}, "tr": {"x": 288, "y": 132}, "br": {"x": 288, "y": 139}, "bl": {"x": 273, "y": 139}}}, {"text": "CLIENT", "confidence": 0, "boundingBox": {"tl": {"x": 275, "y": 160}, "tr": {"x": 321, "y": 160}, "br": {"x": 321, "y": 172}, "bl": {"x": 275, "y": 172}}}, {"text": "COMPANY", "confidence": 0, "boundingBox": {"tl": {"x": 325, "y": 160}, "tr": {"x": 394, "y": 160}, "br": {"x": 394, "y": 171}, "bl": {"x": 325, "y": 171}}}, {"text": "STREET", "confidence": 0, "boundingBox": {"tl": {"x": 274, "y": 179}, "tr": {"x": 306, "y": 179}, "br": {"x": 306, "y": 188}, "bl": {"x": 274, "y": 188}}}, {"text": "ADDRESS", "confidence": 0, "boundingBox": {"tl": {"x": 308, "y": 179}, "tr": {"x": 350, "y": 179}, "br": {"x": 350, "y": 187}, "bl": {"x": 308, "y": 187}}}, {"text": "CITY", "confidence": 0, "boundingBox": {"tl": {"x": 274, "y": 195}, "tr": {"x": 293, "y": 195}, "br": {"x": 293, "y": 204}, "bl": {"x": 274, "y": 204}}}, {"text": ",", "confidence": 0, "boundingBox": {"tl": {"x": 292, "y": 195}, "tr": {"x": 295, "y": 195}, "br": {"x": 295, "y": 203}, "bl": {"x": 292, "y": 203}}}, {"text": "ZIP", "confidence": 0, "boundingBox": {"tl": {"x": 297, "y": 195}, "tr": {"x": 311, "y": 195}, "br": {"x": 311, "y": 203}, "bl": {"x": 297, "y": 203}}}, {"text": "CODE", "confidence": 0, "boundingBox": {"tl": {"x": 314, "y": 195}, "tr": {"x": 338, "y": 195}, "br": {"x": 338, "y": 203}, "bl": {"x": 314, "y": 203}}}, {"text": "PHONE", "confidence": 0, "boundingBox": {"tl": {"x": 274, "y": 211}, "tr": {"x": 308, "y": 211}, "br": {"x": 308, "y": 220}, "bl": {"x": 274, "y": 220}}}, {"text": "SUB", "confidence": 0, "boundingBox": {"tl": {"x": 304, "y": 427}, "tr": {"x": 321, "y": 427}, "br": {"x": 321, "y": 434}, "bl": {"x": 304, "y": 434}}}, {"text": "TOTAL", "confidence": 0, "boundingBox": {"tl": {"x": 324, "y": 427}, "tr": {"x": 351, "y": 427}, "br": {"x": 351, "y": 434}, "bl": {"x": 324, "y": 434}}}, {"text": "DISCOUNT", "confidence": 0, "boundingBox": {"tl": {"x": 306, "y": 451}, "tr": {"x": 352, "y": 451}, "br": {"x": 352, "y": 457}, "bl": {"x": 306, "y": 457}}}, {"text": "TOTAL", "confidence": 0, "boundingBox": {"tl": {"x": 315, "y": 476}, "tr": {"x": 351, "y": 477}, "br": {"x": 351, "y": 487}, "bl": {"x": 315, "y": 486}}}, {"text": "ACCEPTED", "confidence": 0, "boundingBox": {"tl": {"x": 184, "y": 558}, "tr": {"x": 225, "y": 558}, "br": {"x": 225, "y": 566}, "bl": {"x": 184, "y": 566}}}, {"text": "PAYMENT", "confidence": 0, "boundingBox": {"tl": {"x": 229, "y": 558}, "tr": {"x": 269, "y": 558}, "br": {"x": 269, "y": 565}, "bl": {"x": 229, "y": 565}}}, {"text": "METHODS", "confidence": 0, "boundingBox": {"tl": {"x": 272, "y": 558}, "tr": {"x": 315, "y": 558}, "br": {"x": 315, "y": 565}, "bl": {"x": 272, "y": 565}}}, {"text": "PAYPAL", "confidence": 0, "boundingBox": {"tl": {"x": 204, "y": 575}, "tr": {"x": 235, "y": 575}, "br": {"x": 235, "y": 584}, "bl": {"x": 204, "y": 584}}}, {"text": ",", "confidence": 0, "boundingBox": {"tl": {"x": 234, "y": 575}, "tr": {"x": 237, "y": 575}, "br": {"x": 237, "y": 583}, "bl": {"x": 234, "y": 583}}}, {"text": "BANK", "confidence": 0, "boundingBox": {"tl": {"x": 240, "y": 575}, "tr": {"x": 264, "y": 575}, "br": {"x": 264, "y": 583}, "bl": {"x": 240, "y": 583}}}, {"text": "DRAFT", "confidence": 0, "boundingBox": {"tl": {"x": 267, "y": 575}, "tr": {"x": 294, "y": 575}, "br": {"x": 294, "y": 583}, "bl": {"x": 267, "y": 583}}}, {"text": "WE", "confidence": 0, "boundingBox": {"tl": {"x": 162, "y": 603}, "tr": {"x": 177, "y": 603}, "br": {"x": 177, "y": 610}, "bl": {"x": 162, "y": 610}}}, {"text": "TRULY", "confidence": 0, "boundingBox": {"tl": {"x": 180, "y": 603}, "tr": {"x": 207, "y": 603}, "br": {"x": 207, "y": 610}, "bl": {"x": 180, "y": 610}}}, {"text": "APPRECIATE", "confidence": 0, "boundingBox": {"tl": {"x": 209, "y": 603}, "tr": {"x": 260, "y": 603}, "br": {"x": 260, "y": 610}, "bl": {"x": 209, "y": 610}}}, {"text": "YOUR", "confidence": 0, "boundingBox": {"tl": {"x": 264, "y": 603}, "tr": {"x": 289, "y": 603}, "br": {"x": 289, "y": 610}, "bl": {"x": 264, "y": 610}}}, {"text": "BUSINESS", "confidence": 0, "boundingBox": {"tl": {"x": 292, "y": 603}, "tr": {"x": 333, "y": 603}, "br": {"x": 333, "y": 610}, "bl": {"x": 292, "y": 610}}}, {"text": "!", "confidence": 0, "boundingBox": {"tl": {"x": 333, "y": 603}, "tr": {"x": 336, "y": 603}, "br": {"x": 336, "y": 610}, "bl": {"x": 333, "y": 610}}}, {"text": "AMOUNT", "confidence": 0, "boundingBox": {"tl": {"x": 385, "y": 260}, "tr": {"x": 423, "y": 260}, "br": {"x": 423, "y": 266}, "bl": {"x": 385, "y": 266}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 383, "y": 292}, "tr": {"x": 388, "y": 292}, "br": {"x": 388, "y": 300}, "bl": {"x": 383, "y": 300}}}, {"text": "1599.00", "confidence": 0, "boundingBox": {"tl": {"x": 389, "y": 292}, "tr": {"x": 425, "y": 292}, "br": {"x": 425, "y": 300}, "bl": {"x": 389, "y": 300}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 385, "y": 327}, "tr": {"x": 390, "y": 327}, "br": {"x": 390, "y": 334}, "bl": {"x": 385, "y": 334}}}, {"text": "199.00", "confidence": 0, "boundingBox": {"tl": {"x": 390, "y": 326}, "tr": {"x": 421, "y": 326}, "br": {"x": 421, "y": 333}, "bl": {"x": 390, "y": 333}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 386, "y": 361}, "tr": {"x": 391, "y": 361}, "br": {"x": 391, "y": 368}, "bl": {"x": 386, "y": 368}}}, {"text": "499.00", "confidence": 0, "boundingBox": {"tl": {"x": 391, "y": 361}, "tr": {"x": 422, "y": 361}, "br": {"x": 422, "y": 368}, "bl": {"x": 391, "y": 368}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 386, "y": 396}, "tr": {"x": 391, "y": 396}, "br": {"x": 391, "y": 403}, "bl": {"x": 386, "y": 403}}}, {"text": "250.00", "confidence": 0, "boundingBox": {"tl": {"x": 391, "y": 396}, "tr": {"x": 422, "y": 396}, "br": {"x": 422, "y": 403}, "bl": {"x": 391, "y": 403}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 384, "y": 427}, "tr": {"x": 388, "y": 427}, "br": {"x": 388, "y": 433}, "bl": {"x": 384, "y": 433}}}, {"text": "2547.00", "confidence": 0, "boundingBox": {"tl": {"x": 389, "y": 426}, "tr": {"x": 423, "y": 426}, "br": {"x": 423, "y": 432}, "bl": {"x": 389, "y": 432}}}, {"text": "-", "confidence": 0, "boundingBox": {"tl": {"x": 387, "y": 451}, "tr": {"x": 391, "y": 451}, "br": {"x": 391, "y": 458}, "bl": {"x": 387, "y": 458}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 390, "y": 450}, "tr": {"x": 395, "y": 450}, "br": {"x": 395, "y": 457}, "bl": {"x": 390, "y": 457}}}, {"text": "47.00", "confidence": 0, "boundingBox": {"tl": {"x": 395, "y": 450}, "tr": {"x": 419, "y": 450}, "br": {"x": 419, "y": 457}, "bl": {"x": 395, "y": 457}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 379, "y": 477}, "tr": {"x": 385, "y": 477}, "br": {"x": 385, "y": 486}, "bl": {"x": 379, "y": 486}}}, {"text": "2500.00", "confidence": 0, "boundingBox": {"tl": {"x": 385, "y": 477}, "tr": {"x": 428, "y": 477}, "br": {"x": 428, "y": 486}, "bl": {"x": 385, "y": 486}}}]} | {"type": "object", "properties": {"success": {"type": "boolean"}, "text": {"type": "string"}, "locale": {"type": "string"}, "dimensions": {"type": "object", "properties": {"height": {"type": "integer"}, "width": {"type": "integer"}, "type": {"type": "string"}}}, "ocr": {"type": "array", "items": {"type": "object", "properties": {"text": {"type": "string"}, "confidence": {"type": "integer"}, "boundingBox": {"type": "object", "properties": {"tl": {"type": "object", "properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}}, "tr": {"type": "object", "properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}}, "br": {"type": "object", "properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}}, "bl": {"type": "object", "properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}}}}}}}}} |
a1ca7aa2-2a91-4573-8ceb-51f3a9bdd0d0/ff38a267-3cd3-40db-8704-16995b73442b/0/0 | OCR Text Detection | Extract text, boundingBoxes, score, locale and dimensions from image using (google vision text detection) | 4.9 | GET Text Detection | the important thing is that you should use image as a query name
Example (url?image=“image_url”) | 200 | New Example | {"success": true, "text": "SMASHING DOCS\nYOUR COMPANY\nSTREET ADDRESS\nCITY, ZIP CODE\nPHONE\nDESCRIPTION\nWEB DESIGN & DEVELOPMENT\nLOGO DESIGN\nSTATIONERY DESIGN\nCONTENT WRITING\nINVOICE NO. #000188\nDATE: NOVEMBER 18TH, 2017\nTO\nCLIENT COMPANY\nSTREET ADDRESS\nCITY, ZIP CODE\nPHONE\nSUB TOTAL\nDISCOUNT\nTOTAL\nACCEPTED PAYMENT METHODS\nPAYPAL, BANK DRAFT\nWE TRULY APPRECIATE YOUR BUSINESS!\nAMOUNT\n$1599.00\n$199.00\n$499.00\n$250.00\n$2547.00\n-$47.00\n$2500.00", "locale": "en", "dimensions": {"height": 643, "width": 500, "type": "jpg"}, "ocr": [{"text": "SMASHING", "confidence": 0, "boundingBox": {"tl": {"x": 81, "y": 82}, "tr": {"x": 149, "y": 82}, "br": {"x": 149, "y": 92}, "bl": {"x": 81, "y": 92}}}, {"text": "DOCS", "confidence": 0, "boundingBox": {"tl": {"x": 152, "y": 82}, "tr": {"x": 187, "y": 82}, "br": {"x": 187, "y": 92}, "bl": {"x": 152, "y": 92}}}, {"text": "YOUR", "confidence": 0, "boundingBox": {"tl": {"x": 55, "y": 117}, "tr": {"x": 94, "y": 117}, "br": {"x": 94, "y": 129}, "bl": {"x": 55, "y": 129}}}, {"text": "COMPANY", "confidence": 0, "boundingBox": {"tl": {"x": 97, "y": 117}, "tr": {"x": 167, "y": 118}, "br": {"x": 167, "y": 130}, "bl": {"x": 97, "y": 129}}}, {"text": "STREET", "confidence": 0, "boundingBox": {"tl": {"x": 56, "y": 135}, "tr": {"x": 88, "y": 135}, "br": {"x": 88, "y": 145}, "bl": {"x": 56, "y": 145}}}, {"text": "ADDRESS", "confidence": 0, "boundingBox": {"tl": {"x": 89, "y": 135}, "tr": {"x": 131, "y": 135}, "br": {"x": 131, "y": 144}, "bl": {"x": 89, "y": 144}}}, {"text": "CITY", "confidence": 0, "boundingBox": {"tl": {"x": 56, "y": 152}, "tr": {"x": 75, "y": 152}, "br": {"x": 75, "y": 161}, "bl": {"x": 56, "y": 161}}}, {"text": ",", "confidence": 0, "boundingBox": {"tl": {"x": 74, "y": 152}, "tr": {"x": 77, "y": 152}, "br": {"x": 77, "y": 160}, "bl": {"x": 74, "y": 160}}}, {"text": "ZIP", "confidence": 0, "boundingBox": {"tl": {"x": 79, "y": 152}, "tr": {"x": 92, "y": 152}, "br": {"x": 92, "y": 160}, "bl": {"x": 79, "y": 160}}}, {"text": "CODE", "confidence": 0, "boundingBox": {"tl": {"x": 96, "y": 152}, "tr": {"x": 121, "y": 152}, "br": {"x": 121, "y": 160}, "bl": {"x": 96, "y": 160}}}, {"text": "PHONE", "confidence": 0, "boundingBox": {"tl": {"x": 56, "y": 168}, "tr": {"x": 88, "y": 168}, "br": {"x": 88, "y": 176}, "bl": {"x": 56, "y": 176}}}, {"text": "DESCRIPTION", "confidence": 0, "boundingBox": {"tl": {"x": 69, "y": 260}, "tr": {"x": 124, "y": 260}, "br": {"x": 124, "y": 267}, "bl": {"x": 69, "y": 267}}}, {"text": "WEB", "confidence": 0, "boundingBox": {"tl": {"x": 69, "y": 291}, "tr": {"x": 91, "y": 291}, "br": {"x": 91, "y": 300}, "bl": {"x": 69, "y": 300}}}, {"text": "DESIGN", "confidence": 0, "boundingBox": {"tl": {"x": 94, "y": 291}, "tr": {"x": 128, "y": 291}, "br": {"x": 128, "y": 300}, "bl": {"x": 94, "y": 300}}}, {"text": "&", "confidence": 0, "boundingBox": {"tl": {"x": 133, "y": 291}, "tr": {"x": 140, "y": 291}, "br": {"x": 140, "y": 300}, "bl": {"x": 133, "y": 300}}}, {"text": "DEVELOPMENT", "confidence": 0, "boundingBox": {"tl": {"x": 143, "y": 291}, "tr": {"x": 214, "y": 291}, "br": {"x": 214, "y": 300}, "bl": {"x": 143, "y": 300}}}, {"text": "LOGO", "confidence": 0, "boundingBox": {"tl": {"x": 69, "y": 325}, "tr": {"x": 97, "y": 325}, "br": {"x": 97, "y": 334}, "bl": {"x": 69, "y": 334}}}, {"text": "DESIGN", "confidence": 0, "boundingBox": {"tl": {"x": 100, "y": 325}, "tr": {"x": 135, "y": 325}, "br": {"x": 135, "y": 333}, "bl": {"x": 100, "y": 333}}}, {"text": "STATIONERY", "confidence": 0, "boundingBox": {"tl": {"x": 69, "y": 360}, "tr": {"x": 126, "y": 360}, "br": {"x": 126, "y": 368}, "bl": {"x": 69, "y": 368}}}, {"text": "DESIGN", "confidence": 0, "boundingBox": {"tl": {"x": 130, "y": 360}, "tr": {"x": 165, "y": 360}, "br": {"x": 165, "y": 368}, "bl": {"x": 130, "y": 368}}}, {"text": "CONTENT", "confidence": 0, "boundingBox": {"tl": {"x": 70, "y": 395}, "tr": {"x": 115, "y": 395}, "br": {"x": 115, "y": 403}, "bl": {"x": 70, "y": 403}}}, {"text": "WRITING", "confidence": 0, "boundingBox": {"tl": {"x": 118, "y": 395}, "tr": {"x": 158, "y": 395}, "br": {"x": 158, "y": 403}, "bl": {"x": 118, "y": 403}}}, {"text": "INVOICE", "confidence": 0, "boundingBox": {"tl": {"x": 274, "y": 54}, "tr": {"x": 338, "y": 54}, "br": {"x": 338, "y": 66}, "bl": {"x": 274, "y": 66}}}, {"text": "NO", "confidence": 0, "boundingBox": {"tl": {"x": 343, "y": 54}, "tr": {"x": 367, "y": 54}, "br": {"x": 367, "y": 66}, "bl": {"x": 343, "y": 66}}}, {"text": ".", "confidence": 0, "boundingBox": {"tl": {"x": 369, "y": 54}, "tr": {"x": 373, "y": 54}, "br": {"x": 373, "y": 66}, "bl": {"x": 369, "y": 66}}}, {"text": "#", "confidence": 0, "boundingBox": {"tl": {"x": 378, "y": 54}, "tr": {"x": 388, "y": 54}, "br": {"x": 388, "y": 66}, "bl": {"x": 378, "y": 66}}}, {"text": "000188", "confidence": 0, "boundingBox": {"tl": {"x": 388, "y": 54}, "tr": {"x": 442, "y": 54}, "br": {"x": 442, "y": 66}, "bl": {"x": 388, "y": 66}}}, {"text": "DATE", "confidence": 0, "boundingBox": {"tl": {"x": 275, "y": 98}, "tr": {"x": 302, "y": 98}, "br": {"x": 302, "y": 109}, "bl": {"x": 275, "y": 109}}}, {"text": ":", "confidence": 0, "boundingBox": {"tl": {"x": 301, "y": 98}, "tr": {"x": 305, "y": 98}, "br": {"x": 305, "y": 109}, "bl": {"x": 301, "y": 109}}}, {"text": "NOVEMBER", "confidence": 0, "boundingBox": {"tl": {"x": 309, "y": 98}, "tr": {"x": 370, "y": 98}, "br": {"x": 370, "y": 109}, "bl": {"x": 309, "y": 109}}}, {"text": "18TH", "confidence": 0, "boundingBox": {"tl": {"x": 373, "y": 98}, "tr": {"x": 393, "y": 98}, "br": {"x": 393, "y": 109}, "bl": {"x": 373, "y": 109}}}, {"text": ",", "confidence": 0, "boundingBox": {"tl": {"x": 394, "y": 98}, "tr": {"x": 398, "y": 98}, "br": {"x": 398, "y": 109}, "bl": {"x": 394, "y": 109}}}, {"text": "2017", "confidence": 0, "boundingBox": {"tl": {"x": 399, "y": 98}, "tr": {"x": 425, "y": 98}, "br": {"x": 425, "y": 109}, "bl": {"x": 399, "y": 109}}}, {"text": "TO", "confidence": 0, "boundingBox": {"tl": {"x": 273, "y": 132}, "tr": {"x": 288, "y": 132}, "br": {"x": 288, "y": 139}, "bl": {"x": 273, "y": 139}}}, {"text": "CLIENT", "confidence": 0, "boundingBox": {"tl": {"x": 275, "y": 160}, "tr": {"x": 321, "y": 160}, "br": {"x": 321, "y": 172}, "bl": {"x": 275, "y": 172}}}, {"text": "COMPANY", "confidence": 0, "boundingBox": {"tl": {"x": 325, "y": 160}, "tr": {"x": 394, "y": 160}, "br": {"x": 394, "y": 171}, "bl": {"x": 325, "y": 171}}}, {"text": "STREET", "confidence": 0, "boundingBox": {"tl": {"x": 274, "y": 179}, "tr": {"x": 306, "y": 179}, "br": {"x": 306, "y": 188}, "bl": {"x": 274, "y": 188}}}, {"text": "ADDRESS", "confidence": 0, "boundingBox": {"tl": {"x": 308, "y": 179}, "tr": {"x": 350, "y": 179}, "br": {"x": 350, "y": 187}, "bl": {"x": 308, "y": 187}}}, {"text": "CITY", "confidence": 0, "boundingBox": {"tl": {"x": 274, "y": 195}, "tr": {"x": 293, "y": 195}, "br": {"x": 293, "y": 204}, "bl": {"x": 274, "y": 204}}}, {"text": ",", "confidence": 0, "boundingBox": {"tl": {"x": 292, "y": 195}, "tr": {"x": 295, "y": 195}, "br": {"x": 295, "y": 203}, "bl": {"x": 292, "y": 203}}}, {"text": "ZIP", "confidence": 0, "boundingBox": {"tl": {"x": 297, "y": 195}, "tr": {"x": 311, "y": 195}, "br": {"x": 311, "y": 203}, "bl": {"x": 297, "y": 203}}}, {"text": "CODE", "confidence": 0, "boundingBox": {"tl": {"x": 314, "y": 195}, "tr": {"x": 338, "y": 195}, "br": {"x": 338, "y": 203}, "bl": {"x": 314, "y": 203}}}, {"text": "PHONE", "confidence": 0, "boundingBox": {"tl": {"x": 274, "y": 211}, "tr": {"x": 308, "y": 211}, "br": {"x": 308, "y": 220}, "bl": {"x": 274, "y": 220}}}, {"text": "SUB", "confidence": 0, "boundingBox": {"tl": {"x": 304, "y": 427}, "tr": {"x": 321, "y": 427}, "br": {"x": 321, "y": 434}, "bl": {"x": 304, "y": 434}}}, {"text": "TOTAL", "confidence": 0, "boundingBox": {"tl": {"x": 324, "y": 427}, "tr": {"x": 351, "y": 427}, "br": {"x": 351, "y": 434}, "bl": {"x": 324, "y": 434}}}, {"text": "DISCOUNT", "confidence": 0, "boundingBox": {"tl": {"x": 306, "y": 451}, "tr": {"x": 352, "y": 451}, "br": {"x": 352, "y": 457}, "bl": {"x": 306, "y": 457}}}, {"text": "TOTAL", "confidence": 0, "boundingBox": {"tl": {"x": 315, "y": 476}, "tr": {"x": 351, "y": 477}, "br": {"x": 351, "y": 487}, "bl": {"x": 315, "y": 486}}}, {"text": "ACCEPTED", "confidence": 0, "boundingBox": {"tl": {"x": 184, "y": 558}, "tr": {"x": 225, "y": 558}, "br": {"x": 225, "y": 566}, "bl": {"x": 184, "y": 566}}}, {"text": "PAYMENT", "confidence": 0, "boundingBox": {"tl": {"x": 229, "y": 558}, "tr": {"x": 269, "y": 558}, "br": {"x": 269, "y": 565}, "bl": {"x": 229, "y": 565}}}, {"text": "METHODS", "confidence": 0, "boundingBox": {"tl": {"x": 272, "y": 558}, "tr": {"x": 315, "y": 558}, "br": {"x": 315, "y": 565}, "bl": {"x": 272, "y": 565}}}, {"text": "PAYPAL", "confidence": 0, "boundingBox": {"tl": {"x": 204, "y": 575}, "tr": {"x": 235, "y": 575}, "br": {"x": 235, "y": 584}, "bl": {"x": 204, "y": 584}}}, {"text": ",", "confidence": 0, "boundingBox": {"tl": {"x": 234, "y": 575}, "tr": {"x": 237, "y": 575}, "br": {"x": 237, "y": 583}, "bl": {"x": 234, "y": 583}}}, {"text": "BANK", "confidence": 0, "boundingBox": {"tl": {"x": 240, "y": 575}, "tr": {"x": 264, "y": 575}, "br": {"x": 264, "y": 583}, "bl": {"x": 240, "y": 583}}}, {"text": "DRAFT", "confidence": 0, "boundingBox": {"tl": {"x": 267, "y": 575}, "tr": {"x": 294, "y": 575}, "br": {"x": 294, "y": 583}, "bl": {"x": 267, "y": 583}}}, {"text": "WE", "confidence": 0, "boundingBox": {"tl": {"x": 162, "y": 603}, "tr": {"x": 177, "y": 603}, "br": {"x": 177, "y": 610}, "bl": {"x": 162, "y": 610}}}, {"text": "TRULY", "confidence": 0, "boundingBox": {"tl": {"x": 180, "y": 603}, "tr": {"x": 207, "y": 603}, "br": {"x": 207, "y": 610}, "bl": {"x": 180, "y": 610}}}, {"text": "APPRECIATE", "confidence": 0, "boundingBox": {"tl": {"x": 209, "y": 603}, "tr": {"x": 260, "y": 603}, "br": {"x": 260, "y": 610}, "bl": {"x": 209, "y": 610}}}, {"text": "YOUR", "confidence": 0, "boundingBox": {"tl": {"x": 264, "y": 603}, "tr": {"x": 289, "y": 603}, "br": {"x": 289, "y": 610}, "bl": {"x": 264, "y": 610}}}, {"text": "BUSINESS", "confidence": 0, "boundingBox": {"tl": {"x": 292, "y": 603}, "tr": {"x": 333, "y": 603}, "br": {"x": 333, "y": 610}, "bl": {"x": 292, "y": 610}}}, {"text": "!", "confidence": 0, "boundingBox": {"tl": {"x": 333, "y": 603}, "tr": {"x": 336, "y": 603}, "br": {"x": 336, "y": 610}, "bl": {"x": 333, "y": 610}}}, {"text": "AMOUNT", "confidence": 0, "boundingBox": {"tl": {"x": 385, "y": 260}, "tr": {"x": 423, "y": 260}, "br": {"x": 423, "y": 266}, "bl": {"x": 385, "y": 266}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 383, "y": 292}, "tr": {"x": 388, "y": 292}, "br": {"x": 388, "y": 300}, "bl": {"x": 383, "y": 300}}}, {"text": "1599.00", "confidence": 0, "boundingBox": {"tl": {"x": 389, "y": 292}, "tr": {"x": 425, "y": 292}, "br": {"x": 425, "y": 300}, "bl": {"x": 389, "y": 300}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 385, "y": 327}, "tr": {"x": 390, "y": 327}, "br": {"x": 390, "y": 334}, "bl": {"x": 385, "y": 334}}}, {"text": "199.00", "confidence": 0, "boundingBox": {"tl": {"x": 390, "y": 326}, "tr": {"x": 421, "y": 326}, "br": {"x": 421, "y": 333}, "bl": {"x": 390, "y": 333}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 386, "y": 361}, "tr": {"x": 391, "y": 361}, "br": {"x": 391, "y": 368}, "bl": {"x": 386, "y": 368}}}, {"text": "499.00", "confidence": 0, "boundingBox": {"tl": {"x": 391, "y": 361}, "tr": {"x": 422, "y": 361}, "br": {"x": 422, "y": 368}, "bl": {"x": 391, "y": 368}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 386, "y": 396}, "tr": {"x": 391, "y": 396}, "br": {"x": 391, "y": 403}, "bl": {"x": 386, "y": 403}}}, {"text": "250.00", "confidence": 0, "boundingBox": {"tl": {"x": 391, "y": 396}, "tr": {"x": 422, "y": 396}, "br": {"x": 422, "y": 403}, "bl": {"x": 391, "y": 403}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 384, "y": 427}, "tr": {"x": 388, "y": 427}, "br": {"x": 388, "y": 433}, "bl": {"x": 384, "y": 433}}}, {"text": "2547.00", "confidence": 0, "boundingBox": {"tl": {"x": 389, "y": 426}, "tr": {"x": 423, "y": 426}, "br": {"x": 423, "y": 432}, "bl": {"x": 389, "y": 432}}}, {"text": "-", "confidence": 0, "boundingBox": {"tl": {"x": 387, "y": 451}, "tr": {"x": 391, "y": 451}, "br": {"x": 391, "y": 458}, "bl": {"x": 387, "y": 458}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 390, "y": 450}, "tr": {"x": 395, "y": 450}, "br": {"x": 395, "y": 457}, "bl": {"x": 390, "y": 457}}}, {"text": "47.00", "confidence": 0, "boundingBox": {"tl": {"x": 395, "y": 450}, "tr": {"x": 419, "y": 450}, "br": {"x": 419, "y": 457}, "bl": {"x": 395, "y": 457}}}, {"text": "$", "confidence": 0, "boundingBox": {"tl": {"x": 379, "y": 477}, "tr": {"x": 385, "y": 477}, "br": {"x": 385, "y": 486}, "bl": {"x": 379, "y": 486}}}, {"text": "2500.00", "confidence": 0, "boundingBox": {"tl": {"x": 385, "y": 477}, "tr": {"x": 428, "y": 477}, "br": {"x": 428, "y": 486}, "bl": {"x": 385, "y": 486}}}]} | {"type": "object", "properties": {"success": {"type": "boolean"}, "text": {"type": "string"}, "locale": {"type": "string"}, "dimensions": {"type": "object", "properties": {"height": {"type": "integer"}, "width": {"type": "integer"}, "type": {"type": "string"}}}, "ocr": {"type": "array", "items": {"type": "object", "properties": {"text": {"type": "string"}, "confidence": {"type": "integer"}, "boundingBox": {"type": "object", "properties": {"tl": {"type": "object", "properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}}, "tr": {"type": "object", "properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}}, "br": {"type": "object", "properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}}, "bl": {"type": "object", "properties": {"x": {"type": "integer"}, "y": {"type": "integer"}}}}}}}}}} |
01528f5a-faa9-4903-8063-e5894a6a236d/efebd986-5097-4cf0-9a72-9bb573bb6bbb/0/0 | midjourney api | mj easily-integrated Midjourney API solutions for your applications! | 8.6 | imageChange | Slight changes based on an image generated | 200 | New Example | {"task_id": "f4709439-3948-44fe-9da9-bcc23de1bfe6", "success": true, "message": ""} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"task_id": {"type": "string"}, "success": {"type": "boolean"}, "message": {"type": "string"}}, "required": ["message", "success", "task_id"]} |
01528f5a-faa9-4903-8063-e5894a6a236d/fae3a55b-71bd-45a4-8fdc-1293599d2c35/0/0 | midjourney api | mj easily-integrated Midjourney API solutions for your applications! | 8.6 | createImages | Generates up to 4 images from a text prompt. | 200 | New Example | {"task_id": "f4709439-3948-44fe-9da9-bcc23de1bfe6", "success": true, "message": ""} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"task_id": {"type": "string"}, "success": {"type": "boolean"}, "message": {"type": "string"}}, "required": ["message", "success", "task_id"]} |
01528f5a-faa9-4903-8063-e5894a6a236d/7b8a5b25-cfed-4d10-a8bd-53c7a303e634/0/0 | midjourney api | mj easily-integrated Midjourney API solutions for your applications! | 8.6 | fileChange | Changes based on uploaded images | 200 | New Example | {"key1": "value", "key2": "value"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"key1": {"type": "string"}, "key2": {"type": "string"}}, "required": ["key1", "key2"]} |
01528f5a-faa9-4903-8063-e5894a6a236d/4a2826c4-0865-4bae-ab0b-7d9f06845f02/0/0 | midjourney api | mj easily-integrated Midjourney API solutions for your applications! | 8.6 | fileChangeFast | Changes based on uploaded images | 200 | New Example | {"task_id": "f4709439-3948-44fe-9da9-bcc23de1bfe6", "success": true, "message": ""} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"task_id": {"type": "string"}, "success": {"type": "boolean"}, "message": {"type": "string"}}, "required": ["message", "success", "task_id"]} |
01528f5a-faa9-4903-8063-e5894a6a236d/48daa0e2-75ee-4973-bc30-7d8a3b78f645/0/0 | midjourney api | mj easily-integrated Midjourney API solutions for your applications! | 8.6 | scalingFast | Scale the image canvas to its original size | 200 | New Example | {"task_id": "f4709439-3948-44fe-9da9-bcc23de1bfe6", "success": true, "message": ""} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"task_id": {"type": "string"}, "success": {"type": "boolean"}, "message": {"type": "string"}}, "required": ["message", "success", "task_id"]} |
01528f5a-faa9-4903-8063-e5894a6a236d/691c815c-eaa4-416f-9804-42541395a6f9/0/0 | midjourney api | mj easily-integrated Midjourney API solutions for your applications! | 8.6 | imageChangeFast | Slight changes based on one of the pictures | 200 | New Example | {"task_id": "f4709439-3948-44fe-9da9-bcc23de1bfe6", "success": true, "message": ""} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"task_id": {"type": "string"}, "success": {"type": "boolean"}, "message": {"type": "string"}}, "required": ["message", "success", "task_id"]} |
01528f5a-faa9-4903-8063-e5894a6a236d/6200a78c-6563-4d12-a30d-2486e03a083e/0/0 | midjourney api | mj easily-integrated Midjourney API solutions for your applications! | 8.6 | createImagesFast | Based on the description to generate 4 pictures | 200 | New Example | {"task_id": "675859ff-be8d-4dac-a147-62aeced32f5f", "status": "success", "message": ""} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"task_id": {"type": "string"}, "status": {"type": "string"}, "message": {"type": "string"}}, "required": ["message", "status", "task_id"]} |
01528f5a-faa9-4903-8063-e5894a6a236d/1ae10aca-59db-40ea-95a7-3a5680f5e1c0/0/0 | midjourney api | mj easily-integrated Midjourney API solutions for your applications! | 8.6 | upscaleFast | Upscale one of the four generated images to a higher resolution | 200 | New Example | {"task_id": "f4709439-3948-44fe-9da9-bcc23de1bfe6", "success": true, "message": ""} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"task_id": {"type": "string"}, "success": {"type": "boolean"}, "message": {"type": "string"}}, "required": ["message", "success", "task_id"]} |
01528f5a-faa9-4903-8063-e5894a6a236d/84fe06da-388d-4015-a775-d5df55ffb2f0/0/0 | midjourney api | mj easily-integrated Midjourney API solutions for your applications! | 8.6 | fetchResultFast | According to the task id for structure | 200 | New Example | {"task_id": "85b50975-86a7-46ca-b6c9-b85c33d59753", "status": "finished", "process_time": 36.548707972, "meta": {"account_id": "3603a1c2-11a8-4227-b5bc-8b9fb35fe58a", "task_type": "imagine", "origin_task_id": "", "bot_hash": "12fe2a39feaeae4829319a015bf6e12433d86f07df8078c1f84fcdb0dbccf6f3", "created_at": 1694166483, "started_at": 1694166483, "ended_at": 1694166520, "process_mode": "fast", "frozen_credit": 15, "task_request": {"aspect_ratio": "1:1", "process_mode": "fast", "prompt": "A cat in armor"}, "task_param": {"prompt": "A cat in armor", "index": "", "zoom_ratio": "2", "aspect_ratio": "1:1", "direction": ""}}, "task_result": {"discord_image_url": "https://cdn.discordapp.com/attachments/1148549525271760906/1149642434842730587/jhon0232_A_cat_in_armor_0938e0c6-852b-4fac-9231-33468e1cbd9e.png", "image_url": "https://img.midjourneyapi.xyz/mj/jhon0232_A_cat_in_armor_0938e0c6-852b-4fac-9231-33468e1cbd9e.png", "image_id": "0938e0c6-852b-4fac-9231-33468e1cbd9e", "result_message_id": "1149642435434119250", "credit": 15, "message": "", "error_messages": null, "need_retry": false}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"task_id": {"type": "string"}, "status": {"type": "string"}, "process_time": {"type": "number"}, "meta": {"type": "object", "properties": {"account_id": {"type": "string"}, "task_type": {"type": "string"}, "origin_task_id": {"type": "string"}, "bot_hash": {"type": "string"}, "created_at": {"type": "integer"}, "started_at": {"type": "integer"}, "ended_at": {"type": "integer"}, "process_mode": {"type": "string"}, "frozen_credit": {"type": "integer"}, "task_request": {"type": "object", "properties": {"aspect_ratio": {"type": "string"}, "process_mode": {"type": "string"}, "prompt": {"type": "string"}}, "required": ["aspect_ratio", "process_mode", "prompt"]}, "task_param": {"type": "object", "properties": {"prompt": {"type": "string"}, "index": {"type": "string"}, "zoom_ratio": {"type": "string"}, "aspect_ratio": {"type": "string"}, "direction": {"type": "string"}}, "required": ["aspect_ratio", "direction", "index", "prompt", "zoom_ratio"]}}, "required": ["account_id", "bot_hash", "created_at", "ended_at", "frozen_credit", "origin_task_id", "process_mode", "started_at", "task_param", "task_request", "task_type"]}, "task_result": {"type": "object", "properties": {"discord_image_url": {"type": "string"}, "image_url": {"type": "string"}, "image_id": {"type": "string"}, "result_message_id": {"type": "string"}, "credit": {"type": "integer"}, "message": {"type": "string"}, "error_messages": {"type": "null"}, "need_retry": {"type": "boolean"}}, "required": ["credit", "discord_image_url", "error_messages", "image_id", "image_url", "message", "need_retry", "result_message_id"]}}, "required": ["meta", "process_time", "status", "task_id", "task_result"]} |
11ead771-5871-4f45-9725-c3cad418cdb5/7c5808b5-13ed-4ab9-9080-40d7acb2b155/0/0 | Web Detection | Extract list of entities, full matches urls, partial matches urls, visual matches urls, pages, locale and dimensions from image using (google vision web detection) | 8.6 | GET Web Detection | the important thing is that you should use image as a query name
Example (url?image=“image_url”) | 200 | New Example | {"entities": [{"score": 1.0880999565124512, "text": "Barbie"}, {"score": 0.8342400193214417, "text": "Barbie Extra"}, {"score": 0.7197999954223633, "text": "Doll"}, {"score": 0.6668199896812439, "text": "Mattel Barbie Fashionistas"}, {"score": 0.6431999802589417, "text": "Amazon.com"}, {"score": 0.6007000207901001, "text": "Mattel"}, {"score": 0.5788999795913696, "text": "Fashion"}, {"score": 0.5745000243186951, "text": "Clothing"}, {"score": 0.5318999886512756, "text": "Fashion doll"}, {"score": 0.527899980545044, "text": "Barbie Extra Fluffy Pink Jacket Doll"}], "fullMatches": [{"url": "https://n.nordstrommedia.com/id/sr3/1742da2a-9e4d-427b-bde8-3cda5ae2ad48.jpeg", "score": 0}, {"url": "https://i.pinimg.com/originals/ef/e9/56/efe956981988b9eee60addc7eaeca47a.jpg", "score": 0}, {"url": "https://www.toychamp.nl/media/image/01588552/01588552_002.jpg", "score": 0}, {"url": "https://cdn.dsmcdn.com/ty257/product/media/images/20211127/19/171842/323938681/1/1_org_zoom.jpg", "score": 0}, {"url": "https://static.ticimax.cloud/38902/uploads/urunresimleri/buyuk/barbie-extra-teddy-bear-bulldog-1-3a9b.jpg", "score": 0}, {"url": "https://www.mall.hr/i/62571796", "score": 0}, {"url": "https://pisces.bbystatic.com/image2/BestBuy_US/images/products/6476/6476934ld.jpg", "score": 0}, {"url": "https://img.testbericht.de/puppen/6489548/XXL1_barbie-extra-doll-6-in-teddy-bear-jacket-and-shorts-with-pet-gxf08.jpg", "score": 0}, {"url": "https://i5.walmartimages.com/asr/622f745b-80fb-42e8-8929-b6dffb0951d0.5a39c5d87f6335336f751351a14a79a1.jpeg", "score": 0}, {"url": "https://content2.rozetka.com.ua/goods/images/original/226179856.jpg", "score": 0}], "partialMatches": [{"url": "https://avatars.mds.yandex.net/get-mpic/5215925/img_id4862938180215999126.jpeg/300x400", "score": 0}, {"url": "https://www.mimovrste.com/i/62571796/550/550", "score": 0}, {"url": "https://www.picclickimg.com/vboAAOSwbDtjtEVI/Barbie-bambola-extra-con-capelli-pervinca.webp", "score": 0}, {"url": "https://cdn.webshopapp.com/shops/97304/files/411830237/barbie-barbie-extra-doll-assortiment-1-exemplaar.jpg", "score": 0}, {"url": "https://bin.smyk.com/media/product/760/2/barbie-extra-moda-lalka-ze-zwierzatkiem-6-6958113.jpg", "score": 0}, {"url": "https://content1.rozetka.com.ua/goods/images/base_action/253640423.jpg", "score": 0}, {"url": "https://ecsmedia.pl/c/barbie-lalka-extra-moda-rozowy-komplet-fioletowe-wlosy-b-iext125169011.jpg", "score": 0}, {"url": "https://i.pinimg.com/170x/25/24/cf/2524cf809054f5b4d22353bf12ccda3d.jpg", "score": 0}, {"url": "https://a.allegroimg.com/s400/110747/f579a4a547e6b49114fb9e7d8675/BARBIE-EXTRA-MODA-LALKA-I-ZWIERZATKO-GXF08", "score": 0}, {"url": "https://http2.mlstatic.com/D_NQ_NP_871223-MLA46812604902_072021-O.jpg", "score": 0}], "visualMatches": [{"url": "https://i5.walmartimages.ca/images/Enlarge/776/624/6000204776624.jpg", "score": 0}, {"url": "https://m2.bargainmax.co.uk/media/catalog/product/cache/98f2d9312fcbfb88690d91bfd6521157/t/u/tumbnail_011d6279-4b28-4c12-a8d3-394e416059e1.jpg", "score": 0}, {"url": "https://www.thetoyshop.com/medias/549562-1-1200Wx1200H?context=bWFzdGVyfHJvb3R8Njk5MDd8aW1hZ2UvanBlZ3xoZDkvaDY1LzkzMDk3ODcyNTg5MTAuanBnfDY3NGY2ZWIyZmViOTQzNmVlZDc0OWVkODg3NDBkNTI1YzJiNzdiOTY3NWYyZThmYWNhODJmODU1NjZjMmJkZDg", "score": 0}, {"url": "https://cdn.akakce.com/z/barbie/barbie-extra-bebek-grn27.jpg", "score": 0}, {"url": "https://media.very.ie/i/littlewoodsireland/U4AVA_SQ1_0000000099_N_A_SLf?$300x400_retinamobilex2$", "score": 0}, {"url": "https://target.scene7.com/is/image/Target/GUEST_8d0d2c13-3338-4d4b-98d2-35e59e4e4969?wid=488&hei=488&fmt=pjpeg", "score": 0}, {"url": "https://i5.walmartimages.ca/images/Enlarge/858/023/6000204858023.jpg", "score": 0}, {"url": "https://target.scene7.com/is/image/Target/GUEST_ffbfda6e-547d-4d1b-92e5-b824ea004588?wid=488&hei=488&fmt=pjpeg", "score": 0}, {"url": "https://www.youloveit.com/uploads/posts/2023-05/1682923797_youloveit_com_barbie_extra_fly_beach_doll1.jpg", "score": 0}, {"url": "https://media.entertainmentearth.com/assets/images/0b5fc4350d3f4a7d8b3a38b250c2e26bxl.jpg", "score": 0}], "pages": [{"fullMatches": [{"url": "https://m.media-amazon.com/images/I/71bRgpU6CBL._AC_UF894,1000_QL80_.jpg", "score": 0}], "partialMatches": [], "url": "https://www.amazon.com/Barbie-Matching-Periwinkle-Accessories-Multiple/dp/B08TZYNGW8", "score": 0, "title": "<b>Barbie Extra</b> Doll & Accessories with Long Periwinkle Hair, Teddy ..."}, {"fullMatches": [{"url": "https://preview.redd.it/7u1xws6a3th91.jpg?width=640&crop=smart&auto=webp&s=ca94f7ba84d69719230b390241a4346c85f5ba10", "score": 0}], "partialMatches": [], "url": "https://www.reddit.com/r/Barbie/comments/woqckn/do_you_have_a_favorite_barbie_extra_doll_a_least/", "score": 0, "title": "do you have a favorite <b>Barbie extra</b> doll? a least favorite? - Reddit"}, {"fullMatches": [{"url": "https://www.rushed.shop/wp-content/uploads/2013/06/5.3_BARBIE-EXTRA.jpg", "score": 0}], "partialMatches": [], "url": "https://horiconphoenix.com/?items=barbie+extra+4+and+5_MTJfNjVfNjY2MDIxOV8zOQ==", "score": 0, "title": "<b>Barbie Extra</b> 4 And 5 Online, SAVE 53% - horiconphoenix.com"}, {"fullMatches": [{"url": "https://m.media-amazon.com/images/I/71bRgpU6CBL._AC_SL1500_.jpg", "score": 0}], "partialMatches": [], "url": "https://horiconphoenix.com/?items=barbie+extra+n%C3%BAmero+8_MTJfNjVfNjY2MDE2NV8zNw==", "score": 0, "title": "<b>Barbie Extra</b> N\u00famero 8 Austria, SAVE 45% - horiconphoenix.com"}, {"fullMatches": [{"url": "https://www.homefurniturelife.com/wp-content/uploads/2021/11/71bRgpU6CBL._AC_SL1500_.jpg", "score": 0}], "partialMatches": [], "url": "http://maheshwarisevasadan.com/modt.aspx?iid=379015330&cid=94", "score": 0, "title": "<b>barbie extra</b> dolls"}, {"fullMatches": [{"url": "https://www.homefurniturelife.com/wp-content/uploads/2021/11/71bRgpU6CBL._AC_SL1500_.jpg", "score": 0}], "partialMatches": [], "url": "http://maheshwarisevasadan.com/modt.aspx?iid=379015335&cid=94", "score": 0, "title": "<b>barbie extra</b> dolls"}, {"fullMatches": [], "partialMatches": [{"url": "https://m.media-amazon.com/images/I/71bRgpU6CBL._AC_CR0%2C0%2C0%2C0_SX352_SY330_.jpg", "score": 0}], "url": "https://www.amazon.co.uk/stores/Barbie/Extra/page/29755ABE-C61B-4036-8840-48893AB8DE87", "score": 0, "title": "Barbie: Extra - Amazon.co.uk"}, {"fullMatches": [{"url": "https://www.homefurniturelife.com/wp-content/uploads/2021/11/71bRgpU6CBL._AC_SL1500_.jpg", "score": 0}], "partialMatches": [], "url": "http://maheshwarisevasadan.com/modt.aspx?iid=375856179&cid=94", "score": 0, "title": "<b>barbie extra</b> toys"}, {"fullMatches": [{"url": "https://www.homefurniturelife.com/wp-content/uploads/2021/11/71bRgpU6CBL._AC_SL1500_.jpg", "score": 0}], "partialMatches": [], "url": "http://maheshwarisevasadan.com/modt.aspx?iid=375856173&cid=94", "score": 0, "title": "<b>barbie extra</b> toys"}, {"fullMatches": [{"url": "https://m.media-amazon.com/images/I/71bRgpU6CBL._AC_SL1500_.jpg", "score": 0}], "partialMatches": [], "url": "https://kupeliler.com.tr/helo.aspx?iid=194303716-barbie+extra+fluffy&cid=56", "score": 0, "title": "<b>barbie extra</b> fluffy Online Shopping"}], "success": true, "dimensions": {"height": 1520, "width": 821, "type": "jpg"}} | {"type": "object", "properties": {"entities": {"type": "array", "items": {"type": "object", "properties": {"score": {"type": "number"}, "text": {"type": "string"}}}}, "fullMatches": {"type": "array", "items": {"type": "object", "properties": {"url": {"type": "string"}, "score": {"type": "integer"}}}}, "partialMatches": {"type": "array", "items": {"type": "object", "properties": {"url": {"type": "string"}, "score": {"type": "integer"}}}}, "visualMatches": {"type": "array", "items": {"type": "object", "properties": {"url": {"type": "string"}, "score": {"type": "integer"}}}}, "pages": {"type": "array", "items": {"type": "object", "properties": {"fullMatches": {"type": "array"}, "partialMatches": {"type": "array"}, "url": {"type": "string"}, "score": {"type": "integer"}, "title": {"type": "string"}}}}, "success": {"type": "boolean"}, "dimensions": {"type": "object", "properties": {"height": {"type": "integer"}, "width": {"type": "integer"}, "type": {"type": "string"}}}}} |
11ead771-5871-4f45-9725-c3cad418cdb5/d1b483b7-6a09-4227-9233-92bbcce4628d/0/0 | Web Detection | Extract list of entities, full matches urls, partial matches urls, visual matches urls, pages, locale and dimensions from image using (google vision web detection) | 8.6 | POST Web Detection | the important thing is that you should use image as a form_data key
Example const data = new FormData(); data.append(‘image’, ‘image_path’); | 200 | New Example | {"entities": [{"score": 1.0880999565124512, "text": "Barbie"}, {"score": 0.8342400193214417, "text": "Barbie Extra"}, {"score": 0.7197999954223633, "text": "Doll"}, {"score": 0.6668199896812439, "text": "Mattel Barbie Fashionistas"}, {"score": 0.6431999802589417, "text": "Amazon.com"}, {"score": 0.6007000207901001, "text": "Mattel"}, {"score": 0.5788999795913696, "text": "Fashion"}, {"score": 0.5745000243186951, "text": "Clothing"}, {"score": 0.5318999886512756, "text": "Fashion doll"}, {"score": 0.527899980545044, "text": "Barbie Extra Fluffy Pink Jacket Doll"}], "fullMatches": [{"url": "https://n.nordstrommedia.com/id/sr3/1742da2a-9e4d-427b-bde8-3cda5ae2ad48.jpeg", "score": 0}, {"url": "https://i.pinimg.com/originals/ef/e9/56/efe956981988b9eee60addc7eaeca47a.jpg", "score": 0}, {"url": "https://www.toychamp.nl/media/image/01588552/01588552_002.jpg", "score": 0}, {"url": "https://cdn.dsmcdn.com/ty257/product/media/images/20211127/19/171842/323938681/1/1_org_zoom.jpg", "score": 0}, {"url": "https://static.ticimax.cloud/38902/uploads/urunresimleri/buyuk/barbie-extra-teddy-bear-bulldog-1-3a9b.jpg", "score": 0}, {"url": "https://www.mall.hr/i/62571796", "score": 0}, {"url": "https://pisces.bbystatic.com/image2/BestBuy_US/images/products/6476/6476934ld.jpg", "score": 0}, {"url": "https://img.testbericht.de/puppen/6489548/XXL1_barbie-extra-doll-6-in-teddy-bear-jacket-and-shorts-with-pet-gxf08.jpg", "score": 0}, {"url": "https://i5.walmartimages.com/asr/622f745b-80fb-42e8-8929-b6dffb0951d0.5a39c5d87f6335336f751351a14a79a1.jpeg", "score": 0}, {"url": "https://content2.rozetka.com.ua/goods/images/original/226179856.jpg", "score": 0}], "partialMatches": [{"url": "https://avatars.mds.yandex.net/get-mpic/5215925/img_id4862938180215999126.jpeg/300x400", "score": 0}, {"url": "https://www.mimovrste.com/i/62571796/550/550", "score": 0}, {"url": "https://www.picclickimg.com/vboAAOSwbDtjtEVI/Barbie-bambola-extra-con-capelli-pervinca.webp", "score": 0}, {"url": "https://cdn.webshopapp.com/shops/97304/files/411830237/barbie-barbie-extra-doll-assortiment-1-exemplaar.jpg", "score": 0}, {"url": "https://bin.smyk.com/media/product/760/2/barbie-extra-moda-lalka-ze-zwierzatkiem-6-6958113.jpg", "score": 0}, {"url": "https://content1.rozetka.com.ua/goods/images/base_action/253640423.jpg", "score": 0}, {"url": "https://ecsmedia.pl/c/barbie-lalka-extra-moda-rozowy-komplet-fioletowe-wlosy-b-iext125169011.jpg", "score": 0}, {"url": "https://i.pinimg.com/170x/25/24/cf/2524cf809054f5b4d22353bf12ccda3d.jpg", "score": 0}, {"url": "https://a.allegroimg.com/s400/110747/f579a4a547e6b49114fb9e7d8675/BARBIE-EXTRA-MODA-LALKA-I-ZWIERZATKO-GXF08", "score": 0}, {"url": "https://http2.mlstatic.com/D_NQ_NP_871223-MLA46812604902_072021-O.jpg", "score": 0}], "visualMatches": [{"url": "https://i5.walmartimages.ca/images/Enlarge/776/624/6000204776624.jpg", "score": 0}, {"url": "https://m2.bargainmax.co.uk/media/catalog/product/cache/98f2d9312fcbfb88690d91bfd6521157/t/u/tumbnail_011d6279-4b28-4c12-a8d3-394e416059e1.jpg", "score": 0}, {"url": "https://www.thetoyshop.com/medias/549562-1-1200Wx1200H?context=bWFzdGVyfHJvb3R8Njk5MDd8aW1hZ2UvanBlZ3xoZDkvaDY1LzkzMDk3ODcyNTg5MTAuanBnfDY3NGY2ZWIyZmViOTQzNmVlZDc0OWVkODg3NDBkNTI1YzJiNzdiOTY3NWYyZThmYWNhODJmODU1NjZjMmJkZDg", "score": 0}, {"url": "https://cdn.akakce.com/z/barbie/barbie-extra-bebek-grn27.jpg", "score": 0}, {"url": "https://media.very.ie/i/littlewoodsireland/U4AVA_SQ1_0000000099_N_A_SLf?$300x400_retinamobilex2$", "score": 0}, {"url": "https://target.scene7.com/is/image/Target/GUEST_8d0d2c13-3338-4d4b-98d2-35e59e4e4969?wid=488&hei=488&fmt=pjpeg", "score": 0}, {"url": "https://i5.walmartimages.ca/images/Enlarge/858/023/6000204858023.jpg", "score": 0}, {"url": "https://target.scene7.com/is/image/Target/GUEST_ffbfda6e-547d-4d1b-92e5-b824ea004588?wid=488&hei=488&fmt=pjpeg", "score": 0}, {"url": "https://www.youloveit.com/uploads/posts/2023-05/1682923797_youloveit_com_barbie_extra_fly_beach_doll1.jpg", "score": 0}, {"url": "https://media.entertainmentearth.com/assets/images/0b5fc4350d3f4a7d8b3a38b250c2e26bxl.jpg", "score": 0}], "pages": [{"fullMatches": [{"url": "https://m.media-amazon.com/images/I/71bRgpU6CBL._AC_UF894,1000_QL80_.jpg", "score": 0}], "partialMatches": [], "url": "https://www.amazon.com/Barbie-Matching-Periwinkle-Accessories-Multiple/dp/B08TZYNGW8", "score": 0, "title": "<b>Barbie Extra</b> Doll & Accessories with Long Periwinkle Hair, Teddy ..."}, {"fullMatches": [{"url": "https://preview.redd.it/7u1xws6a3th91.jpg?width=640&crop=smart&auto=webp&s=ca94f7ba84d69719230b390241a4346c85f5ba10", "score": 0}], "partialMatches": [], "url": "https://www.reddit.com/r/Barbie/comments/woqckn/do_you_have_a_favorite_barbie_extra_doll_a_least/", "score": 0, "title": "do you have a favorite <b>Barbie extra</b> doll? 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a79a62c4-561a-407b-9510-a8e99a9d36bb/cb2caf74-cdef-4ac9-9590-ca7b7828d616/0/0 | Pose Recognition | A single pose or multiple poses can be estimated from an image. | 0.1 | Get Single Pose Recognition | Single pose estimation is the simpler and faster of algorithms when there is only one person in the image. The disadvantage is that if there are multiple persons in an image, keypoints from both persons will likely be estimated as being part of the same single pose. | 200 | Response | {"file": "pose.jpg", "keypoints": [{"part": "nose", "position": {"x": 486.51752160217046, "y": 93.77300257256059}, "score": 0.9976345896720886}, {"part": "leftEye", "position": {"x": 497.4696902263953, "y": 85.49076211684408}, "score": 0.9912968873977661}, {"part": "rightEye", "position": {"x": 468.5024406937773, "y": 85.38824116302371}, "score": 0.9938976764678955}, {"part": "leftEar", "position": {"x": 516.6078429463308, "y": 103.56487804071449}, "score": 0.7203822731971741}, {"part": "rightEar", "position": {"x": 442.9118572784305, "y": 98.53178361232179}, "score": 0.8577080965042114}, {"part": "leftShoulder", "position": {"x": 534.1397735951939, "y": 182.39157730874385}, "score": 0.9948378205299377}, {"part": "rightShoulder", "position": {"x": 414.45513298539333, "y": 195.72682975145628}, "score": 0.993786633014679}, {"part": "leftElbow", "position": {"x": 670.1310842788637, "y": 256.9110137405099}, "score": 0.9971317648887634}, {"part": "rightElbow", "position": {"x": 287.6360312139014, "y": 283.1791168984736}, "score": 0.9949814081192017}, {"part": "leftWrist", "position": {"x": 579.7123902317151, "y": 340.3300232052339}, "score": 0.9840540289878845}, {"part": "rightWrist", "position": {"x": 390.4524379388831, "y": 336.1793492536136}, "score": 0.988020122051239}, {"part": "leftHip", "position": {"x": 532.9367813525961, "y": 427.9439275125585}, "score": 0.9970468282699585}, {"part": "rightHip", "position": {"x": 437.578787005829, "y": 425.3424094233531}, "score": 0.9934108853340149}, {"part": "leftKnee", "position": {"x": 568.7672537124574, "y": 635.7370282481153}, "score": 0.9777266383171082}, {"part": "rightKnee", "position": {"x": 420.1938329318154, "y": 631.3680153932089}, "score": 0.9534801244735718}, {"part": "leftAnkle", "position": {"x": 587.0069713073018, "y": 733.9135518946072}, "score": 0.38912293314933777}, {"part": "rightAnkle", "position": {"x": 407.5033112165993, "y": 834.7524916355711}, "score": 0.7433780431747437}], "score": 0.9157586325617397} | {"properties": {"keypoints": {"items": {"properties": {"part": {"type": "string"}, "position": {"properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "type": "object"}, "score": {"type": "number"}}, "type": "object"}, "type": "array"}, "score": {"type": "number"}}, "type": "object"} |
a79a62c4-561a-407b-9510-a8e99a9d36bb/45c92e90-0c3e-4a98-b8ea-5f1545a11640/1/0 | Pose Recognition | A single pose or multiple poses can be estimated from an image. | 0.1 | Get Multiple Pose Recognition | Multiple Pose estimation can decode multiple poses in an image. It is more complex and slightly slower than the single person algorithm, but has the advantage that if multiple people appear in an image, their detected keypoints are less likely to be associated with the wrong pose. Even if the usecase is to detect a single person’s pose, this algorithm may be more desirable in that the accidental effect of two poses being joined together won’t occur when multiple people appear in the image. | 200 | Response | {"file": "multi pose.jpg", "poses": [{"keypoints": [{"part": "nose", "position": {"x": 274.1086200981289, "y": 13.6417678441519}, "score": 0.5411428213119507}, {"part": "leftEye", "position": {"x": 275.86248922904645, "y": 10.585654810244932}, "score": 0.4908600449562073}, {"part": "rightEye", "position": {"x": 273.3769062428159, "y": 10.463693999594753}, "score": 0.35529935359954834}, {"part": "leftEar", "position": {"x": 283.47468886393983, "y": 11.827814262664745}, "score": 0.535990297794342}, {"part": "rightEar", "position": {"x": 277.18498539831853, "y": 11.266981315520027}, "score": 0.17906175553798676}, {"part": "leftShoulder", "position": {"x": 284.2742651816936, "y": 24.436683486871686}, "score": 0.9660024046897888}, {"part": "rightShoulder", 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"score": 0.14400769770145416}, {"part": "leftAnkle", "position": {"x": 133.16290527002357, "y": 134.96391442592042}, "score": 0.2490415722131729}, {"part": "rightAnkle", "position": {"x": 88.692387718171, "y": 136.25211913576388}, "score": 0.21963369846343994}], "score": 0.5144229618942037}, {"keypoints": [{"part": "nose", "position": {"x": 29.149088135953082, "y": 20.845761028245263}, "score": 0.6671526432037354}, {"part": "leftEye", "position": {"x": 27.949106646882885, "y": 16.356043153235888}, "score": 0.5952476859092712}, {"part": "rightEye", "position": {"x": 21.12120134821198, "y": 16.476221871283272}, "score": 0.13174772262573242}, {"part": "leftEar", "position": {"x": 33.555325088797844, "y": 13.723334462726172}, "score": 0.6881288290023804}, {"part": "rightEar", "position": {"x": 29.30166248217631, "y": 15.622944093399937}, "score": 0.09066946059465408}, {"part": "leftShoulder", "position": {"x": 40.712996122902005, "y": 24.872572670650854}, "score": 0.8639896512031555}, {"part": "rightShoulder", "position": {"x": 38.24405396494884, "y": 26.239396649112038}, "score": 0.8818572759628296}, {"part": "leftElbow", "position": {"x": 38.232191017165725, "y": 44.46903955611738}, "score": 0.6713191866874695}, {"part": "rightElbow", "position": {"x": 33.45262772378291, "y": 43.12469753309911}, "score": 0.7621248364448547}, {"part": "leftWrist", "position": {"x": 20.815894993362726, "y": 69.25407073006093}, "score": 0.46827200055122375}, {"part": "rightWrist", "position": {"x": 19.231534236136113, "y": 67.16178678724089}, "score": 0.7852033972740173}, {"part": "leftHip", "position": {"x": 50.66296599718384, "y": 57.80401322442735}, "score": 0.6819939017295837}, {"part": "rightHip", "position": {"x": 45.30126649581969, "y": 58.54543523194725}, "score": 0.7738933563232422}, {"part": "leftKnee", "position": {"x": 42.58784839615284, "y": 95.18176141879903}, "score": 0.5502144694328308}, {"part": "rightKnee", "position": {"x": 40.17881697717808, "y": 95.1411580779673}, "score": 0.5322571396827698}, {"part": "leftAnkle", "position": {"x": 61.164681920745494, "y": 137.46573173767862}, "score": 0.1598166525363922}, {"part": "rightAnkle", "position": {"x": 63.275433328828925, "y": 138.1399968895003}, "score": 0.17692327499389648}], "score": 0.557694793185767}]} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"file": {"type": "string"}, "poses": {"type": "array", "items": {"type": "object", "properties": {"keypoints": {"type": "array", "items": {"type": "object", "properties": {"part": {"type": "string"}, "position": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}, "score": {"type": "number"}}, "required": ["part", "position", "score"]}}, "score": {"type": "number"}}, "required": ["keypoints", "score"]}}}, "required": ["file", "poses"]} |
9d130140-a3d8-442b-9ff2-c3791b6b78ea/88d9e18d-71bc-4f4c-a237-d6c7eba51fbc/0/0 | ReciclAPI - Garbage Detection | Use this API to identify types of garbage. The images can be classified among 10 different types: | 7.9 | Classify Garbage Image | Use this endpoint to identify a submitted image.
The returned result will be a set of the three most probable classes.
Images can be submitted under a URL or a base64 encoding format. | 200 | Response | [{"class": "plastic", "confidence": 100}, {"class": "glass", "confidence": 8.849078632535164e-20}, {"class": "metal", "confidence": 7.507595041004632e-20}, {"class": "paper", "confidence": 5.594982086328094e-21}, {"class": "biological", "confidence": 2.788582085055467e-25}, {"class": "clothes", "confidence": 1.9268320018249382e-26}, {"class": "trash", "confidence": 1.3884486377449375e-26}, {"class": "shoes", "confidence": 7.249125079035103e-28}, {"class": "cardboard", "confidence": 3.307523719739195e-31}, {"class": "battery", "confidence": 8.262202825945937e-34}] | {"$schema": "http://json-schema.org/schema#", "type": "array", "items": {"type": "object", "properties": {"class": {"type": "string"}, "confidence": {"type": "number"}}, "required": ["class", "confidence"]}} |
b0517575-3465-46d2-beae-597a0acd7f0f/56abb96b-edfc-46d2-944e-854db72d06d0/0/0 | NSFW images detection and classification | Adult content detection technology identifies explicit content in images that are not safe for work or other public places. Our AI model detects exposed parts of a body and classifies if they are considered NSFW content. | 9.8 | NSFW content classification from image (url) |
For more details contact us | 200 | Response | {"unsafe": false, "objects": [{"box": [156, 239, 473, 589], "score": 0.8797686696052551, "label": "FACE_F"}]} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"unsafe": {"type": "boolean"}, "objects": {"type": "array", "items": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "integer"}}, "score": {"type": "number"}, "label": {"type": "string"}}, "required": ["box", "label", "score"]}}}, "required": ["objects", "unsafe"]} |
b0517575-3465-46d2-beae-597a0acd7f0f/f842fc3e-5168-407b-adbe-f190db2e72b4/0/0 | NSFW images detection and classification | Adult content detection technology identifies explicit content in images that are not safe for work or other public places. Our AI model detects exposed parts of a body and classifies if they are considered NSFW content. | 9.8 | NSFW content classification from attached image file |
For more details contact us | 200 | New Example | {"unsafe": false, "objects": [{"box": [300, 220, 427, 348], "score": 0.9650670289993286, "label": "FACE_F"}, {"box": [264, 599, 405, 778], "score": 0.741856575012207, "label": "EXPOSED_BELLY"}, {"box": [291, 827, 354, 889], "score": 0.6728091835975647, "label": "COVERED_GENITALIA_F"}]} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"unsafe": {"type": "boolean"}, "objects": {"type": "array", "items": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "integer"}}, "score": {"type": "number"}, "label": {"type": "string"}}, "required": ["box", "label", "score"]}}}, "required": ["objects", "unsafe"]} |
4ff73d61-1022-421d-bb22-f5cdc76bb2dd/3cc8c44b-919d-4a03-9eb4-97ff14015eb6/0/0 | Liquor Recognition | Recognize labels on liquor bottles quickly, with over 25,000 different labels to choose from. The results include information on the liquor brand and the kind of spirit. | null | Analyze Image | Performs image analysis | 400 | New Example | {"status": 400, "error": "Missing image or url field"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"status": {"type": "integer"}, "error": {"type": "string"}}, "required": ["error", "status"]} |
4ff73d61-1022-421d-bb22-f5cdc76bb2dd/3cc8c44b-919d-4a03-9eb4-97ff14015eb6/1/0 | Liquor Recognition | Recognize labels on liquor bottles quickly, with over 25,000 different labels to choose from. The results include information on the liquor brand and the kind of spirit. | null | Analyze Image | Performs image analysis | 200 | New Example | {"results": [{"bounds": [{"x": 0.35559746623039246, "y": 0.023603560402989388}, {"x": 0.7541176676750183, "y": 0.023603560402989388}, {"x": 0.7541176676750183, "y": 0.8358347415924072}, {"x": 0.35559746623039246, "y": 0.8358347415924072}], "entities": [{"id": "97cca5c9-621e-4554-bbc2-84adc613c10f", "name": "Chivas Regal", "cost": 1, "abv": "40", "type": "Scotch", "score": 0.8}, {"id": "00390915-941c-4981-9aaf-d74af436314f", "name": "Chivas Regal The Icon", "cost": 5, "abv": "40", "type": "Scotch", "score": 0.77}, {"id": "2a2569d6-5ad7-4da9-9a4f-91ea56ca1f09", "name": "Chivas Regal 25 Yr", "cost": 5, "abv": "40", "type": "Scotch", "score": 0.65}]}]} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"results": {"type": "array", "items": {"type": "object", "properties": {"bounds": {"type": "array", "items": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}}, "required": ["x", "y"]}}, "entities": {"type": "array", "items": {"type": "object", "properties": {"id": {"type": "string"}, "name": {"type": "string"}, "cost": {"type": "integer"}, "abv": {"type": "string"}, "type": {"type": "string"}, "score": {"type": "number"}}, "required": ["abv", "cost", "id", "name", "score", "type"]}}}, "required": ["bounds", "entities"]}}}, "required": ["results"]} |
723ad98a-b260-4a24-824e-127be6f49d45/fd31ce36-e6f8-4c1d-9228-c4bb75feb4c1/0/0 | Face Recognition AI | AI Face. Intelligent image analysis, face recognition, face detection and face perception. Restore real face information, facial feature, age, expression, sex etc. Cooperation with Center for Visual Studies of China. | null | AI Face | Face recognition (many person image), face detection and face perception. Restore real face information, facial feature, age, expression, sex etc. | 200 | Response | {"FACE_LIST_ENTITY": [{"AGE": 27, "BEAUTY": 71.37, "EMOTION_SCORE": 0.99, "EMOTION_TYPE": "NEUTRAL", "FACE_TYPE": "\u9a6c\u8138", "GENDER": "MALE", "HEIGHT": 70, "ID": 0, "LEFT": 346.33, "TOP": 78.99, "WIDTH": 78}, {"AGE": 28, "BEAUTY": 66.04, "EMOTION_SCORE": 0.93, "EMOTION_TYPE": "NEUTRAL", "FACE_TYPE": "\u9a6c\u8138", "GENDER": "MALE", "HEIGHT": 43, "ID": 1, "LEFT": 45.73, "TOP": 114.3, "WIDTH": 51}, {"AGE": 29, "BEAUTY": 71.77, "EMOTION_SCORE": 0.99, "EMOTION_TYPE": "NEUTRAL", "FACE_TYPE": "\u9a6c\u8138", "GENDER": "MALE", "HEIGHT": 42, "ID": 2, "LEFT": 456.17, "TOP": 118.35, "WIDTH": 47}, {"AGE": 32, "BEAUTY": 67.12, "EMOTION_SCORE": 0.84, "EMOTION_TYPE": "NEUTRAL", "FACE_TYPE": "\u9a6c\u8138", "GENDER": "FEMALE", "HEIGHT": 43, "ID": 3, "LEFT": 234.59, "TOP": 154.03, "WIDTH": 43}, {"AGE": 28, "BEAUTY": 73.48, "EMOTION_SCORE": 0.93, "EMOTION_TYPE": "NEUTRAL", "FACE_TYPE": "\u9a6c\u8138", "GENDER": "FEMALE", "HEIGHT": 42, "ID": 4, "LEFT": 141.15, "TOP": 142.88, "WIDTH": 44}, {"AGE": 22, "BEAUTY": 62.26, "EMOTION_SCORE": 0.63, "EMOTION_TYPE": "NEUTRAL", "FACE_TYPE": "\u9a6c\u8138", "GENDER": "MALE", "HEIGHT": 38, "ID": 5, "LEFT": 638.57, "TOP": 134.72, "WIDTH": 41}, {"AGE": 27, "BEAUTY": 61.64, "EMOTION_SCORE": 0.99, "EMOTION_TYPE": "NEUTRAL", "FACE_TYPE": "\u9a6c\u8138", "GENDER": "FEMALE", "HEIGHT": 38, "ID": 6, "LEFT": 576.53, "TOP": 168.5, "WIDTH": 39}], "FACE_STATICS_BASE": {"AVG_AGE": 27.571428571428573, "COUNT_FEMALE": 3, "COUNT_MALE": 4, "MAX_AGE": 3, "MAX_ANGRY": "", "MAX_BEAUTY": 4, "MAX_DISGUST": "", "MAX_FEAR": "", "MAX_HAPPY": "", "MAX_NEUTRAL": 0, "MAX_SAD": "", "MAX_SURPRISE": "", "MIN_AGE": 5, "MIN_BEAUTY": 6, "TOTAL_AGE": 193}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"FACE_LIST_ENTITY": {"type": "array", "items": {"type": "object", "properties": {"AGE": {"type": "integer"}, "BEAUTY": {"type": "number"}, "EMOTION_SCORE": {"type": "number"}, "EMOTION_TYPE": {"type": "string"}, "FACE_TYPE": {"type": "string"}, "GENDER": {"type": "string"}, "HEIGHT": {"type": "integer"}, "ID": {"type": "integer"}, "LEFT": {"type": "number"}, "TOP": {"type": "number"}, "WIDTH": {"type": "integer"}}, "required": ["AGE", "BEAUTY", "EMOTION_SCORE", "EMOTION_TYPE", "FACE_TYPE", "GENDER", "HEIGHT", "ID", "LEFT", "TOP", "WIDTH"]}}, "FACE_STATICS_BASE": {"type": "object", "properties": {"AVG_AGE": {"type": "number"}, "COUNT_FEMALE": {"type": "integer"}, "COUNT_MALE": {"type": "integer"}, "MAX_AGE": {"type": "integer"}, "MAX_ANGRY": {"type": "string"}, "MAX_BEAUTY": {"type": "integer"}, "MAX_DISGUST": {"type": "string"}, "MAX_FEAR": {"type": "string"}, "MAX_HAPPY": {"type": "string"}, "MAX_NEUTRAL": {"type": "integer"}, "MAX_SAD": {"type": "string"}, "MAX_SURPRISE": {"type": "string"}, "MIN_AGE": {"type": "integer"}, "MIN_BEAUTY": {"type": "integer"}, "TOTAL_AGE": {"type": "integer"}}, "required": ["AVG_AGE", "COUNT_FEMALE", "COUNT_MALE", "MAX_AGE", "MAX_ANGRY", "MAX_BEAUTY", "MAX_DISGUST", "MAX_FEAR", "MAX_HAPPY", "MAX_NEUTRAL", "MAX_SAD", "MAX_SURPRISE", "MIN_AGE", "MIN_BEAUTY", "TOTAL_AGE"]}}, "required": ["FACE_LIST_ENTITY", "FACE_STATICS_BASE"]} |
1bb1c67c-1cf9-425e-9c2e-278fd6dda6c5/a92520df-31fc-430d-b50a-cfe031ac970b/1/0 | Background Removal | This API provides image foreground segmentation with consequent background removal. [.
Usually such images have extensions: .jpg, .jpeg, .png.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
The size of image file must be less than 16Mb. | 200 | Unsupported media type | {"results": [{"status": {"code": "failure", "message": "Unsupported media type. Expected one of ['image/jpeg', 'image/png']. Got 'text/plain'."}, "name": "file.txt", "md5": "d41d8cd98f00b204e9800998ecf8427e", "entities": []}]} | {"type": "object", "properties": {"results": {"type": "array", "items": {"type": "object", "properties": {"status": {"type": "object", "properties": {"code": {"type": "string"}, "message": {"type": "string"}}}, "name": {"type": "string"}, "md5": {"type": "string"}, "entities": {"type": "array", "items": {"type": "object", "properties": {"kind": {"type": "string"}, "name": {"type": "string"}, "image": {"type": "string"}}}}}}}}} |
1bb1c67c-1cf9-425e-9c2e-278fd6dda6c5/a92520df-31fc-430d-b50a-cfe031ac970b/1/1 | Background Removal | This API provides image foreground segmentation with consequent background removal. [.
Usually such images have extensions: .jpg, .jpeg, .png.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
The size of image file must be less than 16Mb. | 200 | Successful background removal | {"results": [{"status": {"code": "ok", "message": "Success"}, "name": "img.jpg", "md5": "f2d13d0242b98aae82bc7a6dc76e1ea9", "width": 1024, "height": 768, "entities": [{"kind": "image", "name": "general-fg-image", "image": "iVBORw0KGgoAAAA...YII="}]}]} | {"type": "object", "properties": {"results": {"type": "array", "items": {"type": "object", "properties": {"status": {"type": "object", "properties": {"code": {"type": "string"}, "message": {"type": "string"}}}, "name": {"type": "string"}, "md5": {"type": "string"}, "entities": {"type": "array", "items": {"type": "object", "properties": {"kind": {"type": "string"}, "name": {"type": "string"}, "image": {"type": "string"}}}}}}}}} |
1bb1c67c-1cf9-425e-9c2e-278fd6dda6c5/a92520df-31fc-430d-b50a-cfe031ac970b/2/0 | Background Removal | This API provides image foreground segmentation with consequent background removal. [.
Usually such images have extensions: .jpg, .jpeg, .png.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
The size of image file must be less than 16Mb. | 422 | Missing image/url | {"detail": "Missing image or url field."} | {"type": "object", "properties": {"detail": {"type": "string"}}} |
1bb1c67c-1cf9-425e-9c2e-278fd6dda6c5/e805f9a8-780a-4d23-82c1-74eb56c74fec/0/0 | Background Removal | This API provides image foreground segmentation with consequent background removal. [![Telegram](https://img.shields.io/badge/-Tel... | 9.7 | Get list of available modes | The service provides three options for background removal results:
* Mask of foreground.
* Image with foreground object.
* Image with foreground object with shadow. | 200 | List of modes | ["fg-image", "fg-image-shadow", "fg-mask"] | {"type": "array", "items": {"type": "string"}} |
d2b1ccc5-9ef3-44fc-93f6-c7b6c375f829/b6af6bcc-a55b-4175-b4a9-c3c2c19becab/0/0 | Face Filters | Based on AI algorithm to simulate realistic makeup effect, it further enhances the face beautification effect by adding lipstick, highlighter, whole makeup and other makeup materials. Users can choose different beauty types and match with personalized beauty materials to complete the makeup look. | null | Face Filters | Face Filters | 200 | Success | {"request_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "log_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "error_code": 0, "data": {"image_url": "Temporary address"}} | {"request_id": {"type": "string", "required": false, "description": "Request ID."}, "log_id": {"type": "string", "required": false, "description": "Log ID."}, "error_code": {"type": "integer", "required": false, "description": "Error Code."}, "error_code_str": {"type": "string", "required": false, "description": "Error Code."}, "error_msg": {"type": "string", "required": false, "description": "Error Message."}, "data": {"type": "Object", "required": false, "description": "The content of the result data returned.", "properties": {"image_url": {"type": "String", "required": false, "description": "Resulting image URL address. **Note**: The URL address is a temporary address, valid for 1 day, after which it will not be accessible."}}}} |
d2b1ccc5-9ef3-44fc-93f6-c7b6c375f829/b6af6bcc-a55b-4175-b4a9-c3c2c19becab/0/1 | Face Filters | Based on AI algorithm to simulate realistic makeup effect, it further enhances the face beautification effect by adding lipstick, highlighter, whole makeup and other makeup materials. Users can choose different beauty types and match with personalized beauty materials to complete the makeup look. | null | Face Filters | Face Filters | 200 | Error | {"request_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "log_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "error_code": 400, "error_code_str": "ERROR_PARAMETERS", "error_msg": "image cannot be empty"} | {"request_id": {"type": "string", "required": false, "description": "Request ID."}, "log_id": {"type": "string", "required": false, "description": "Log ID."}, "error_code": {"type": "integer", "required": false, "description": "Error Code."}, "error_code_str": {"type": "string", "required": false, "description": "Error Code."}, "error_msg": {"type": "string", "required": false, "description": "Error Message."}, "data": {"type": "Object", "required": false, "description": "The content of the result data returned.", "properties": {"image_url": {"type": "String", "required": false, "description": "Resulting image URL address. **Note**: The URL address is a temporary address, valid for 1 day, after which it will not be accessible."}}}} |
d2b1ccc5-9ef3-44fc-93f6-c7b6c375f829/b6af6bcc-a55b-4175-b4a9-c3c2c19becab/1/0 | Face Filters | Based on AI algorithm to simulate realistic makeup effect, it further enhances the face beautification effect by adding lipstick, highlighter, whole makeup and other makeup materials. Users can choose different beauty types and match with personalized beauty materials to complete the makeup look. | null | Face Filters | Face Filters | 401 | Example | {"message": "Invalid API key in request"} | {"message": {"type": "String", "required": true, "example": "Invalid API key in request", "description": "Error Message."}} |
e1940b5f-4a6e-4483-a4a6-fd1bca9f690e/309542c5-c03f-461b-84fd-25d7ca868481/0/0 | Bring photos to life | Based on face driving algorithm, it can turn the face in the image into dynamic effect. It can drive the five senses in the image to do any action, make the face vivid, turn static into dynamic, and make the user's photo full of dynamic feeling. The difference with expression editing is that this ability supports any face to generate all kinds of dynamic and complex movements, and can make long time dynamic expressions. It can be used for online entertainment video generation, expression pack... | 6.2 | Bring photos to life | Bring photos to life | 401 | Example | {"message": "Invalid API key in request"} | {"message": {"type": "String", "required": true, "example": "Invalid API key in request", "description": "Error Message."}} |
e1940b5f-4a6e-4483-a4a6-fd1bca9f690e/309542c5-c03f-461b-84fd-25d7ca868481/1/0 | Bring photos to life | Based on face driving algorithm, it can turn the face in the image into dynamic effect. It can drive the five senses in the image to do any action, make the face vivid, turn static into dynamic, and make the user's photo full of dynamic feeling. The difference with expression editing is that this ability supports any face to generate all kinds of dynamic and complex movements, and can make long time dynamic expressions. It can be used for online entertainment video generation, expression pack... | 6.2 | Bring photos to life | Bring photos to life | 200 | Success | {"request_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "log_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "error_code": 0, "data": {"video": ""}} | {"request_id": {"type": "string", "required": false, "description": "Request ID."}, "log_id": {"type": "string", "required": false, "description": "Log ID."}, "error_code": {"type": "integer", "required": false, "description": "Error Code."}, "error_code_str": {"type": "string", "required": false, "description": "Error Code."}, "error_msg": {"type": "string", "required": false, "description": "Error Message."}, "data": {"type": "Object", "required": false, "description": "The content of the result data returned.", "properties": {"video": {"type": "String", "required": false, "description": "The result video, returning the Base64 encoding of the video."}}}} |
e1940b5f-4a6e-4483-a4a6-fd1bca9f690e/309542c5-c03f-461b-84fd-25d7ca868481/1/1 | Bring photos to life | Based on face driving algorithm, it can turn the face in the image into dynamic effect. It can drive the five senses in the image to do any action, make the face vivid, turn static into dynamic, and make the user's photo full of dynamic feeling. The difference with expression editing is that this ability supports any face to generate all kinds of dynamic and complex movements, and can make long time dynamic expressions. It can be used for online entertainment video generation, expression pack... | 6.2 | Bring photos to life | Bring photos to life | 200 | Error | {"request_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "log_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "error_code": 400, "error_code_str": "ERROR_PARAMETERS", "error_msg": "image cannot be empty"} | {"request_id": {"type": "string", "required": false, "description": "Request ID."}, "log_id": {"type": "string", "required": false, "description": "Log ID."}, "error_code": {"type": "integer", "required": false, "description": "Error Code."}, "error_code_str": {"type": "string", "required": false, "description": "Error Code."}, "error_msg": {"type": "string", "required": false, "description": "Error Message."}, "data": {"type": "Object", "required": false, "description": "The content of the result data returned.", "properties": {"video": {"type": "String", "required": false, "description": "The result video, returning the Base64 encoding of the video."}}}} |
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