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755be0b6-2462-4e2c-8641-c12c848a4cd8/5bc844b8-6e3d-4668-8480-dd5ad35fb6dd/0/0 | FaceAnalysis | Use our API for face detection, facial emotions, age and gender. | 8.3 | Emotions | 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"}}}}}}}} |
755be0b6-2462-4e2c-8641-c12c848a4cd8/3ad68d2c-eceb-4d9c-accc-a314794ee8b6/0/0 | FaceAnalysis | Use our API for face detection, facial emotions, age and gender. | 8.3 | BMI | Endpoint for BMI estimation based on photo of face.
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}, "bmi": 22.93121337890625}, {"crop": {"x1": 0.11643223464488983, "y1": 0.5169875025749207, "x2": 0.18099753558635712, "y2": 0.39529597759246826, "score": 0.998594343662262}, "bmi": 26.735450744628906}, {"crop": {"x1": 0.8563294410705566, "y1": 0.24784332513809204, "x2": 0.917901873588562, "y2": 0.12237813323736191, "score": 0.9980091452598572}, "bmi": 26.188823699951172}, {"crop": {"x1": 0.1482899785041809, "y1": 0.3003842234611511, "x2": 0.20176897943019867, "y2": 0.20043990015983582, "score": 0.9973342418670654}, "bmi": 29.62228775024414}, {"crop": {"x1": 0.598613440990448, "y1": 0.3124071955680847, "x2": 0.6528789401054382, "y2": 0.20392273366451263, "score": 0.9954931735992432}, "bmi": 25.00752830505371}, {"crop": {"x1": 0.7280519008636475, "y1": 0.5563205480575562, "x2": 0.7874197363853455, "y2": 0.4102036952972412, "score": 0.7963487505912781}, "bmi": 23.535701751708984}]} | {"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"}}}, "bmi": {"type": "number"}}}}}} |
755be0b6-2462-4e2c-8641-c12c848a4cd8/18812cec-50ff-4ae3-85b2-e581fa08ea74/0/0 | FaceAnalysis | Use our API for face detection, facial emotions, age and gender. | 8.3 | 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"}}}}}}}} |
755be0b6-2462-4e2c-8641-c12c848a4cd8/613681ac-2b20-457d-8f23-edbad8eb1089/0/0 | FaceAnalysis | Use our API for face detection, facial emotions, age and gender. | 8.3 | Face Detection by URL | Endpoint for face detection. Send us link to image, we return position of detected faces.
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}}, {"crop": {"x1": 0.11643223464488983, "y1": 0.5169875025749207, "x2": 0.18099753558635712, "y2": 0.39529597759246826, "score": 0.998594343662262}}, {"crop": {"x1": 0.8563294410705566, "y1": 0.24784332513809204, "x2": 0.917901873588562, "y2": 0.12237813323736191, "score": 0.9980091452598572}}, {"crop": {"x1": 0.1482899785041809, "y1": 0.3003842234611511, "x2": 0.20176897943019867, "y2": 0.20043990015983582, "score": 0.9973342418670654}}, {"crop": {"x1": 0.598613440990448, "y1": 0.3124071955680847, "x2": 0.6528789401054382, "y2": 0.20392271876335144, "score": 0.9954931735992432}}, {"crop": {"x1": 0.7280519604682922, "y1": 0.5563205480575562, "x2": 0.7874197959899902, "y2": 0.4102036952972412, "score": 0.7963487505912781}}]} | {"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"}}}}}}}} |
76af4359-80ed-4b52-8b9f-f7f5895c4194/f4553df8-5bdf-4c86-9f76-d69fe38bf360/0/0 | Photo to 3d slow-motion | Based on portrait segmentation technology, it realizes 2D to 3D conversion of photos to achieve the effect of 3D naked eye slow motion. The selfie image and the background image using Hitchcock technology senseless fusion, open the scene stretching video shooting new way to play, can be used for personal social entertainment, film and television drama effect simulation shooting, etc. | 7.5 | Photo to 3d slow-motion | Photo to 3d slow-motion | 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."}}}} |
76af4359-80ed-4b52-8b9f-f7f5895c4194/f4553df8-5bdf-4c86-9f76-d69fe38bf360/0/1 | Photo to 3d slow-motion | Based on portrait segmentation technology, it realizes 2D to 3D conversion of photos to achieve the effect of 3D naked eye slow motion. The selfie image and the background image using Hitchcock technology senseless fusion, open the scene stretching video shooting new way to play, can be used for personal social entertainment, film and television drama effect simulation shooting, etc. | 7.5 | Photo to 3d slow-motion | Photo to 3d slow-motion | 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."}}}} |
76af4359-80ed-4b52-8b9f-f7f5895c4194/f4553df8-5bdf-4c86-9f76-d69fe38bf360/1/0 | Photo to 3d slow-motion | Based on portrait segmentation technology, it realizes 2D to 3D conversion of photos to achieve the effect of 3D naked eye slow motion. The selfie image and the background image using Hitchcock technology senseless fusion, open the scene stretching video shooting new way to play, can be used for personal social entertainment, film and television drama effect simulation shooting, etc. | 7.5 | Photo to 3d slow-motion | Photo to 3d slow-motion | 401 | Example | {"message": "Invalid API key in request"} | {"message": {"type": "String", "required": true, "example": "Invalid API key in request", "description": "Error Message."}} |
2620bf8e-5c1e-407d-9d6b-c29a4d1c3664/c7d481a7-f86a-45f0-be7f-14f4d5383691/0/0 | OCR | This API processes images and performs Optical Character Recognition. [](https://t.me/... | 9.7 | Analyse image and return results | Performs actual image analysis and responds with results.
Image must be a regular JPEG or PNG image (with or without transparency) or PDF file.
Usually such images have extensions: .jpg, .jpeg, .png, .pdf. In case of PDF
each page will be converted to PNG image and processed separately.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
application/pdf
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"}}} |
2620bf8e-5c1e-407d-9d6b-c29a4d1c3664/c7d481a7-f86a-45f0-be7f-14f4d5383691/2/0 | OCR | This API processes images and performs Optical Character Recognition. [](https://t.me/... | 9.7 | Analyse image and return results | Performs actual image analysis and responds with results.
Image must be a regular JPEG or PNG image (with or without transparency) or PDF file.
Usually such images have extensions: .jpg, .jpeg, .png, .pdf. In case of PDF
each page will be converted to PNG image and processed separately.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
application/pdf
The size of image file must be less than 16Mb. | 200 | Successul recognition | {"results": [{"status": {"code": "ok", "message": "Success"}, "name": "s.png", "md5": "7009ed0064efa278ed529d382e968dcb", "width": 1024, "height": 768, "entities": [{"kind": "objects", "name": "text", "objects": [{"box": [0.024024024024024024, 0.14107883817427386, 0.8678678678678678, 0.7344398340248963], "entities": [{"kind": "text", "name": "text", "text": "EAST\nNORTH\nINTERSTATE\nBUSINESS\n40 85\n"}]}]}]}]} | {"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"}, "objects": {"type": "array", "items": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "number"}}, "entities": {"type": "array", "items": {"type": "object", "properties": {"kind": {"type": "string"}, "name": {"type": "string"}, "text": {"type": "string"}}}}}}}}}}}}}}} |
2620bf8e-5c1e-407d-9d6b-c29a4d1c3664/661d1cfe-08cf-4dfc-9ad3-b439fba66b86/0/0 | OCR | This API processes images and performs Optical Character Recognition. [](https://t.me/... | 9.7 | Get list of algorithms | Service provides alternative algorithms that may be used for OCR.
The idea behind multiple algorithms is to let client try different algorithms to get the best one that matches client's use case. | 200 | Get list of algorithms | ["simple-text", "simple-words"] | {"type": "array", "items": {"type": "string"}} |
b14ed285-1930-4bd0-b31b-f69fc7da4a82/c025e3f5-fdbc-471a-abfd-4cadd7ac5ea1/0/0 | SwiftScan OCR API | Fast, Accurate, and Cost-effective OCR Solution | 7.6 | Large images | The processLargeImage endpoint is designed to handle OCR processing for large images exceeding 0.5 Megapixels. Equipped to manage high-resolution images, this endpoint ensures accurate text extraction even from intricate, detailed visuals. Utilize this endpoint for images with higher resolution and larger dimensions to ensure precise OCR outcomes. | 200 | Basic response | {"text": "Payment not ready\nPlease proceed to the V.A.T. section and input your ID\nID:\nENTER"} | {"text": "text if output parameter is missing or 'simple'", "boxes": [{"center": {"x": 0, "y": 0}, "size": {"width": 0, "height": 0}, "text": "text if output parameter is 'advanced'"}]} |
b14ed285-1930-4bd0-b31b-f69fc7da4a82/c025e3f5-fdbc-471a-abfd-4cadd7ac5ea1/0/1 | SwiftScan OCR API | Fast, Accurate, and Cost-effective OCR Solution | 7.6 | Large images | The processLargeImage endpoint is designed to handle OCR processing for large images exceeding 0.5 Megapixels. Equipped to manage high-resolution images, this endpoint ensures accurate text extraction even from intricate, detailed visuals. Utilize this endpoint for images with higher resolution and larger dimensions to ensure precise OCR outcomes. | 200 | Advanced example | {"boxes": [{"center": {"x": 10, "y": 32}, "size": {"width": 68, "height": 28}, "text": "Payment not ready"}, {"center": {"x": 11, "y": 52}, "size": {"width": 166, "height": 30}, "text": "Please proceed to the V.A.T. section and input your ID"}, {"center": {"x": 34, "y": 77}, "size": {"width": 14, "height": 23}, "text": "ID:"}, {"center": {"x": 63, "y": 96}, "size": {"width": 42, "height": 31}, "text": "ENTER"}]} | {"text": "text if output parameter is missing or 'simple'", "boxes": [{"center": {"x": 0, "y": 0}, "size": {"width": 0, "height": 0}, "text": "text if output parameter is 'advanced'"}]} |
b14ed285-1930-4bd0-b31b-f69fc7da4a82/53f92579-ad01-439e-9e63-a5c8e1c0dd82/0/0 | SwiftScan OCR API | Fast, Accurate, and Cost-effective OCR Solution | 7.6 | Small images | The processSmallImage endpoint is optimized for processing images of up to 0.5 Megapixels. Ideal for smaller images, this endpoint provides a cost-effective solution for accurate and fast OCR processing while keeping your expenses low. Utilize this endpoint for images with dimensions not exceeding 0.5 Megapixels to benefit from reduced pricing compared to processing larger images. | 200 | Basic response | {"text": "Payment not ready\nPlease proceed to the V.A.T. section and input your ID\nID:\nENTER"} | {"text": "text if output parameter is missing or 'simple'", "boxes": [{"center": {"x": 0, "y": 0}, "size": {"width": 0, "height": 0}, "text": "text if output parameter is 'advanced'"}]} |
b14ed285-1930-4bd0-b31b-f69fc7da4a82/53f92579-ad01-439e-9e63-a5c8e1c0dd82/0/1 | SwiftScan OCR API | Fast, Accurate, and Cost-effective OCR Solution | 7.6 | Small images | The processSmallImage endpoint is optimized for processing images of up to 0.5 Megapixels. Ideal for smaller images, this endpoint provides a cost-effective solution for accurate and fast OCR processing while keeping your expenses low. Utilize this endpoint for images with dimensions not exceeding 0.5 Megapixels to benefit from reduced pricing compared to processing larger images. | 200 | Advanced response | {"boxes": [{"center": {"x": 10, "y": 32}, "size": {"width": 68, "height": 28}, "text": "Payment not ready"}, {"center": {"x": 11, "y": 52}, "size": {"width": 166, "height": 30}, "text": "Please proceed to the V.A.T. section and input your ID"}, {"center": {"x": 34, "y": 77}, "size": {"width": 14, "height": 23}, "text": "ID:"}, {"center": {"x": 63, "y": 96}, "size": {"width": 42, "height": 31}, "text": "ENTER"}]} | {"text": "text if output parameter is missing or 'simple'", "boxes": [{"center": {"x": 0, "y": 0}, "size": {"width": 0, "height": 0}, "text": "text if output parameter is 'advanced'"}]} |
29964d00-895c-4bf3-8075-436022f75d23/58b0623f-9d0b-4cd4-8a55-1d9a946a9e2c/0/0 | Passport MRZ Extract | Scan MRZ from Passports documents and send the result back as JSON string. | null | Send Image with URL | GET Passport MRZ by sending document image as a image_url | 200 | Success | {"request": {"image_url": "https://raw.githubusercontent.com/Arg0s1080/mrz/master/docs/images/passports/ICAO_Example.png"}, "passport_mrz": "P<UTOERIKSSON<<ANNA<MARIA<<<<<<<<<<<<<<<<<<< L898902C36UT07408122F1204159ZE184226B<<<<<10", "success": true, "message": "Passport MRZ information has extracted completely!"} | {"type": "object", "properties": {"request": {"type": "object", "properties": {"image_url": {"type": "string"}}}, "passport_mrz": {"type": "string"}, "success": {"type": "boolean"}, "message": {"type": "string"}}} |
29964d00-895c-4bf3-8075-436022f75d23/58b0623f-9d0b-4cd4-8a55-1d9a946a9e2c/1/0 | Passport MRZ Extract | Scan MRZ from Passports documents and send the result back as JSON string. | null | Send Image with URL | GET Passport MRZ by sending document image as a image_url | 400 | Error | {"request": {"image_url": "https://raw.githubusercontent.com/Arg0s1080/mrz/master/docs/images/passports/ICAO_Example.pn"}, "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"}}} |
f43ec601-a597-4d92-a794-d333fa9d74af/e68fc709-a061-47ad-a161-98836fcc1ad4/0/0 | Indonesia ID Card OCR | Extraction of all text, including head portrait, from Indonesian identity cards | 6.5 | Indonesia 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": "11/22/2023 01:23:42 AM", "result": {"rotated_image_height": 342, "image_angle": 0, "rotated_image_width": 518, "details": {"kel_desa": {"value": "PEGADUNGAN", "position": {"bottom": 193, "left": 143, "right": 228, "top": 182}}, "agama": {"value": "ISLAM", "position": {"bottom": 221, "left": 144, "right": 179, "top": 212}}, "head_portrait": {"value": 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", 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PASTI CEPAT A7/66", "position": {"bottom": 163, "left": 145, "right": 271, "top": 154}}, "kecamatan": {"value": "KALIDERES", "position": {"bottom": 206, "left": 143, "right": 211, "top": 197}}, "gol_darah": {"value": "B", "position": {"bottom": 148, "left": 323, "right": 331, "top": 138}}, "provinsi": {"value": "DKI JAKARTA", "position": {"bottom": 36, "left": 153, "right": 357, "top": 20}}, "city": {"value": "JAKARTA BARAT", "position": {"bottom": 57, "left": 185, "right": 327, "top": 44}}, "pekerjaan": {"value": "PEGAWAI SWASTA", "position": {"bottom": 251, "left": 144, "right": 250, "top": 241}}, "kewarganegaraan": {"value": "WNI", "position": {"bottom": 265, "left": 144, "right": 168, "top": 256}}, "nik": {"value": "3171234567890123", "position": {"bottom": 97, "left": 135, "right": 306, "top": 84}}, "issue_date": {"value": "02-12-2012", "position": {"bottom": 274, "left": 392, "right": 449, "top": 264}}, "status_perkawinan": {"value": "KAWIN", "position": {"bottom": 236, "left": 144, "right": 182, "top": 226}}}, "category": {"kel_desa": "one_to_one", "agama": "one_to_one", "head_portrait": "one_to_one", "nama": "one_to_one", "berlaku_hingga": "one_to_one", "tempat_tgl_lahir": "one_to_one", "rt_rw": "one_to_one", "kecamatan": "one_to_one", "alamat": "one_to_one", "nik": "one_to_one", "gol_darah": "one_to_one", "provinsi": "one_to_one", "city": "one_to_one", "pekerjaan": "one_to_one", "kewarganegaraan": "one_to_one", "jenis_kelamin": "one_to_one", "issue_date": "one_to_one", "status_perkawinan": "one_to_one"}, "type": "indonesia_id_card"}} | {"$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"}, "details": {"type": "object", "properties": {"kel_desa": {"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"]}, "agama": {"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"]}, "berlaku_hingga": {"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"]}, "nama": {"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"]}, "tempat_tgl_lahir": {"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"]}, "jenis_kelamin": {"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"]}, "rt_rw": {"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"]}, "alamat": {"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"]}, "kecamatan": {"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"]}, "gol_darah": {"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"]}, "provinsi": {"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"]}, "city": {"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"]}, "pekerjaan": {"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"]}, "kewarganegaraan": {"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"]}, "nik": {"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"]}, "status_perkawinan": {"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": ["agama", "alamat", "berlaku_hingga", "city", "gol_darah", "head_portrait", "issue_date", "jenis_kelamin", "kecamatan", "kel_desa", "kewarganegaraan", "nama", "nik", "pekerjaan", "provinsi", "rt_rw", "status_perkawinan", "tempat_tgl_lahir"]}, "category": {"type": "object", "properties": {"kel_desa": {"type": "string"}, "agama": {"type": "string"}, "head_portrait": {"type": "string"}, "nama": {"type": "string"}, "berlaku_hingga": {"type": "string"}, "tempat_tgl_lahir": {"type": "string"}, "rt_rw": {"type": "string"}, "kecamatan": {"type": "string"}, "alamat": {"type": "string"}, "nik": {"type": "string"}, "gol_darah": {"type": "string"}, "provinsi": {"type": "string"}, "city": {"type": "string"}, "pekerjaan": {"type": "string"}, "kewarganegaraan": {"type": "string"}, "jenis_kelamin": {"type": "string"}, "issue_date": {"type": "string"}, "status_perkawinan": {"type": "string"}}, "required": ["agama", "alamat", "berlaku_hingga", "city", "gol_darah", "head_portrait", "issue_date", "jenis_kelamin", "kecamatan", "kel_desa", "kewarganegaraan", "nama", "nik", "pekerjaan", "provinsi", "rt_rw", "status_perkawinan", "tempat_tgl_lahir"]}, "type": {"type": "string"}}, "required": ["category", "details", "image_angle", "rotated_image_height", "rotated_image_width", "type"]}}, "required": ["code", "date", "result", "status"]} |
4beb9978-2aa6-4c87-a135-0ea3bb8171d3/0c286197-6a74-4d4f-916b-7e70b5482806/0/0 | Facial Aesthetics | Based on AI algorithm to adjust the five facial parts in detail, the adjustment content is: face, eyes, nose, mouth, other, etc. | null | Facial Aesthetics | Facial Aesthetics | 401 | Example | {"message": "Invalid API key in request"} | {"message": {"type": "String", "required": true, "example": "Invalid API key in request", "description": "Error Message."}} |
4beb9978-2aa6-4c87-a135-0ea3bb8171d3/0c286197-6a74-4d4f-916b-7e70b5482806/1/0 | Facial Aesthetics | Based on AI algorithm to adjust the five facial parts in detail, the adjustment content is: face, eyes, nose, mouth, other, etc. | null | Facial Aesthetics | Facial Aesthetics | 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."}}}} |
4beb9978-2aa6-4c87-a135-0ea3bb8171d3/0c286197-6a74-4d4f-916b-7e70b5482806/1/1 | Facial Aesthetics | Based on AI algorithm to adjust the five facial parts in detail, the adjustment content is: face, eyes, nose, mouth, other, etc. | null | Facial Aesthetics | Facial Aesthetics | 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."}}}} |
1dcc53f8-a143-4532-a214-661972676ec4/2f014a23-ca67-474e-ba70-ac1e0ca71096/0/0 | Vehicle Make and Model Recognition | Provides you with the ability to detect the make, model, generation, color and angle of a car from its image. Our API is powered by computer vision and deep learning techniques to correctly identify cars under different lighting and weather conditions.
Currently, we are able to detect more than 3,000 different car models. This number is growing every month as we add new cars to our database. | 9.4 | Vehicle Make and Model Recognition | Support JPEG, PNG, BMP, GIF, and TIFF. | 200 | Response | {"bbox": {"br_x": 0.9667, "br_y": 0.7576, "tl_x": 0.0494, "tl_y": 0.2398}, "confidence": "75.71", "date": "05/12/2023 09:40:24 PM", "service": "vmmr", "status": "SUCCESS", "vehicle": {"angle": "Front Right", "color": "Silver", "generation": "IV (W205) facelift (2018-)", "make": "Mercedes-Benz", "model": "C-klasse AMG", "years": "2018-"}, "version": "2.0"} | {"type": "object", "properties": {"service": {"type": "string"}, "version": {"type": "string"}, "date": {"type": "string"}, "status": {"type": "string"}, "vehicle": {"type": "array", "items": {"type": "object", "properties": {"make": {"type": "string"}, "model": {"type": "string"}, "generation": {"type": "string"}, "body_style": {"type": "string"}, "doors": {"type": "string"}, "confidence": {"type": "number"}}}}, "left": {"type": "integer"}, "top": {"type": "integer"}, "width": {"type": "integer"}, "height": {"type": "integer"}}} |
b0d0b987-8c85-40b6-90cc-27288646d2fc/986637c7-41bb-4e3b-ae64-f57788b88d10/0/0 | Easy OCR | OCR made easy | 8.1 | Extract data from file url | Supported format: application/pdf, image/gif, image/tiff, image/tiff, image/jpeg, image/jpeg, image/png, image/bmp, image/webp | 201 | New Example | {"data": "Extracted data"} | {"type": "object", "properties": {"data": {"type": "string"}}} |
33d6ee73-c258-4107-9455-cf32e0bd569e/627d03c9-4024-4a09-b056-fe564dc25504/0/0 | General Classification | This Image Labelling API is a ready-to-use solution for image classification. As an output, it gives you the probability of a certain class(es) to be present in a corresponding input image. [](https://api4.ai/apis/image-labelling?utm_source=general_cls_rapidapi&u... | 8.7 | Analyse image and return results | Performs actual image analysis and responds with results.
Image requirements
Image must be a regular JPEG or PNG image (with or without transparency) or PDF file.
Usually such images have extensions: .jpg, .jpeg, .png, .pdf. In case of PDF
each page will be converted to PNG image and processed separately.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
application/pdf
The size of image file must be less than 16Mb. | 200 | Successful classification | {"results": [{"status": {"code": "ok", "message": "Success"}, "name": "image.jpg", "md5": "6ea449c4645b8811eef1342040725687", "width": 1024, "height": 768, "entities": [{"kind": "classes", "name": "general-image-classes", "classes": {"tench": 5.591508625002461e-07, "goldfish": 4.238243946019793e-06, "great white shark": 9.770637916517444e-06}}]}]} | {"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"}, "classes": {"type": "object"}}}}}}}}} |
33d6ee73-c258-4107-9455-cf32e0bd569e/627d03c9-4024-4a09-b056-fe564dc25504/0/1 | General Classification | This Image Labelling API is a ready-to-use solution for image classification. As an output, it gives you the probability of a certain class(es) to be present in a corresponding input image. [](https://api4.ai/apis/image-labelling?utm_source=general_cls_rapidapi&u... | 8.7 | Analyse image and return results | Performs actual image analysis and responds with results.
Image requirements
Image must be a regular JPEG or PNG image (with or without transparency) or PDF file.
Usually such images have extensions: .jpg, .jpeg, .png, .pdf. In case of PDF
each page will be converted to PNG image and processed separately.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
application/pdf
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"}, "classes": {"type": "object"}}}}}}}}} |
33d6ee73-c258-4107-9455-cf32e0bd569e/627d03c9-4024-4a09-b056-fe564dc25504/1/0 | General Classification | This Image Labelling API is a ready-to-use solution for image classification. As an output, it gives you the probability of a certain class(es) to be present in a corresponding input image. [](https://api4.ai/apis/image-labelling?utm_source=general_cls_rapidapi&u... | 8.7 | Analyse image and return results | Performs actual image analysis and responds with results.
Image requirements
Image must be a regular JPEG or PNG image (with or without transparency) or PDF file.
Usually such images have extensions: .jpg, .jpeg, .png, .pdf. In case of PDF
each page will be converted to PNG image and processed separately.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
application/pdf
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"}}} |
33d6ee73-c258-4107-9455-cf32e0bd569e/e0f3dbfa-083e-40c0-a65b-569a86a6b656/0/0 | General Classification | This Image Labelling API is a ready-to-use solution for image classification. As an output, it gives you the probability of a certain class(es) to be present in a corresponding input image. [](https://api4.ai/apis/image-labelling?utm_source=general_cls_rapidapi&u... | 8.7 | Get list of algorithms | Service provides alternative algorithms that may be used for image classification.
The idea behind multiple algorithms is to let client try different algorithms to get the best one that matches client's use case. | 200 | List of algorithms | ["algo1", "algo2", "algo3"] | {"type": "array", "items": {"type": "string"}} |
8ae6d3f7-8690-4345-a291-0edd4f1087e5/9513f85f-dcc0-4e9c-9bac-c5a4d097c302/0/0 | Face Beauty | Based on AI algorithm to optimize the beauty of the face in the image, it supports a variety of effects such as peeling, removing dark circles, lines, and whitening. | 7.3 | Face Beauty | Face Beauty | 401 | Example | {"message": "Invalid API key in request"} | {"message": {"type": "String", "required": true, "example": "Invalid API key in request", "description": "Error Message."}} |
8ae6d3f7-8690-4345-a291-0edd4f1087e5/9513f85f-dcc0-4e9c-9bac-c5a4d097c302/1/0 | Face Beauty | Based on AI algorithm to optimize the beauty of the face in the image, it supports a variety of effects such as peeling, removing dark circles, lines, and whitening. | 7.3 | Face Beauty | Face Beauty | 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."}}}} |
8ae6d3f7-8690-4345-a291-0edd4f1087e5/9513f85f-dcc0-4e9c-9bac-c5a4d097c302/1/1 | Face Beauty | Based on AI algorithm to optimize the beauty of the face in the image, it supports a variety of effects such as peeling, removing dark circles, lines, and whitening. | 7.3 | Face Beauty | Face Beauty | 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."}}}} |
d34288c1-f8cb-4f4f-9211-8cd754a33cdf/a5ed147f-33a0-42a0-8d11-9d666999758c/1/0 | VRT Visual Recognition Tool | VRT makes it easy to add image analysis to your applications. You provide an image to the API and the service detects objects, people, faces; extracts text (lines and words with geometry), scenes and activities. It provides content moderation API to detect any inappropriate, unwanted, or offensive content. It can also detect celebrities (with their emotions), labels, personal protective equipment (PPE) and provide highly accurate facial analysis (bounding box, landmarks, age range, gender, ... | 8.8 | Detect Labels | Detects instances of real-world entities within an image provided as input. This includes objects like flower, tree, and table; events like wedding, graduation, and birthday party; and concepts like landscape, evening, and nature. | 200 | Response | {"Labels": [{"Name": "Person", "Confidence": 99.62307739257812, "Instances": [{"BoundingBox": {"Width": 0.18311606347560883, "Height": 0.8807265162467957, "Left": 0.1469680219888687, "Top": 0.07128725200891495}, "Confidence": 99.62307739257812}, {"BoundingBox": {"Width": 0.1159493550658226, "Height": 0.5291457772254944, "Left": 0.3030718266963959, "Top": 0.3326877951622009}, "Confidence": 98.88069152832031}], "Parents": []}, {"Name": "Human", "Confidence": 99.62307739257812, "Instances": [], "Parents": []}, {"Name": "People", "Confidence": 98.99781799316406, "Instances": [], "Parents": [{"Name": "Person"}]}, {"Name": "Family", "Confidence": 98.7975082397461, "Instances": [], "Parents": [{"Name": "People"}, {"Name": "Person"}]}, {"Name": "Tree", "Confidence": 76.4510726928711, "Instances": [], "Parents": [{"Name": "Plant"}]}, {"Name": "Plant", "Confidence": 76.4510726928711, "Instances": [], "Parents": []}, {"Name": "Clothing", "Confidence": 71.42993927001953, "Instances": [], "Parents": []}, {"Name": "Apparel", "Confidence": 71.42993927001953, "Instances": [], "Parents": []}, {"Name": "Girl", "Confidence": 65.82240295410156, "Instances": [], "Parents": [{"Name": "Female"}, {"Name": "Person"}]}, {"Name": "Female", "Confidence": 65.82240295410156, "Instances": [], "Parents": [{"Name": "Person"}]}, {"Name": "Kid", "Confidence": 60.11241149902344, "Instances": [], "Parents": [{"Name": "Person"}]}, {"Name": "Child", "Confidence": 60.11241149902344, "Instances": [], "Parents": [{"Name": "Person"}]}, {"Name": "Hand", "Confidence": 52.46139907836914, "Instances": [], "Parents": []}, {"Name": "Teen", "Confidence": 51.2952880859375, "Instances": [], "Parents": [{"Name": "Person"}]}], "LabelModelVersion": "2.0"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"Labels": {"type": "array", "items": {"type": "object", "properties": {"Name": {"type": "string"}, "Confidence": {"type": "number"}, "Instances": {"type": "array", "items": {"type": "object", "properties": {"BoundingBox": {"type": "object", "properties": {"Width": {"type": "number"}, "Height": {"type": "number"}, "Left": {"type": "number"}, "Top": {"type": "number"}}, "required": ["Height", "Left", "Top", "Width"]}, "Confidence": {"type": "number"}}, "required": ["BoundingBox", "Confidence"]}}, "Parents": {"type": "array", "items": {"type": "object", "properties": {"Name": {"type": "string"}}, "required": ["Name"]}}}, "required": ["Confidence", "Instances", "Name", "Parents"]}}, "LabelModelVersion": {"type": "string"}}, "required": ["LabelModelVersion", "Labels"]} |
d34288c1-f8cb-4f4f-9211-8cd754a33cdf/3ab416bd-e544-411a-b37c-85d2d33b2031/1/0 | VRT Visual Recognition Tool | VRT makes it easy to add image analysis to your applications. You provide an image to the API and the service detects objects, people, faces; extracts text (lines and words with geometry), scenes and activities. It provides content moderation API to detect any inappropriate, unwanted, or offensive content. It can also detect celebrities (with their emotions), labels, personal protective equipment (PPE) and provide highly accurate facial analysis (bounding box, landmarks, age range, gender, ... | 8.8 | Recognize Celebrities | Returns a list of celebrities recognized in the input image along with information on bounding box, landmarks, emotions, smile, confidence. Also returns links to additional information such as wiki or imdb and a list of unrecognized faces | 200 | Response | {"CelebrityFaces": [{"Urls": ["www.wikidata.org/wiki/Q40523", "www.imdb.com/name/nm0000210"], "Name": "Julia Roberts", "Id": "bz8qt4A", "Face": {"BoundingBox": {"Width": 0.2779480814933777, "Height": 0.6511512994766235, "Left": 0.31650862097740173, "Top": 0.11398237943649292}, "Confidence": 99.9979248046875, "Landmarks": [{"Type": "mouthRight", "X": 0.4727935791015625, "Y": 0.5565089583396912}, {"Type": "mouthLeft", "X": 0.36740973591804504, "Y": 0.5408243536949158}, {"Type": "eyeRight", "X": 0.4927210509777069, "Y": 0.3306288719177246}, {"Type": "eyeLeft", "X": 0.3668394386768341, "Y": 0.31195327639579773}, {"Type": "nose", "X": 0.42120352387428284, "Y": 0.43851035833358765}], "Pose": {"Roll": 4.497915267944336, "Yaw": -1.7278428077697754, "Pitch": 4.092052459716797}, "Quality": {"Brightness": 76.88951873779297, "Sharpness": 92.22801208496094}, "Emotions": [{"Type": "HAPPY", "Confidence": 99.45742797851562}, {"Type": "SURPRISED", "Confidence": 0.24471315741539001}, {"Type": "CONFUSED", "Confidence": 0.08488202095031738}, {"Type": "ANGRY", "Confidence": 0.05532999709248543}, {"Type": "DISGUSTED", "Confidence": 0.053107015788555145}, {"Type": "CALM", "Confidence": 0.0517413429915905}, {"Type": "SAD", "Confidence": 0.0402027890086174}, {"Type": "FEAR", "Confidence": 0.01260911114513874}], "Smile": {"Value": true, "Confidence": 99.87355041503906}}, "MatchConfidence": 99.96967315673828, "KnownGender": {"Type": "Female"}}], "UnrecognizedFaces": [{"BoundingBox": {"Width": 0.10472958534955978, "Height": 0.3193255364894867, "Left": 0.09103084355592728, "Top": 0.4002256691455841}, "Confidence": 99.98916625976562, "Landmarks": [{"Type": "mouthRight", "X": 0.13677579164505005, "Y": 0.5324243307113647}, {"Type": "mouthLeft", "X": 0.13312111794948578, "Y": 0.5438924431800842}, {"Type": "eyeRight", "X": 0.13369591534137726, "Y": 0.5120616555213928}, {"Type": "eyeLeft", "X": 0.12895667552947998, "Y": 0.5260438919067383}, {"Type": "nose", "X": 0.1263064593076706, "Y": 0.5063422918319702}], "Pose": {"Roll": 1.3458002805709839, "Yaw": -16.28550910949707, "Pitch": 5.087915420532227}, "Quality": {"Brightness": 83.0241470336914, "Sharpness": 53.330047607421875}, "Emotions": [{"Type": "ANGRY", "Confidence": 31.812482833862305}, {"Type": "FEAR", "Confidence": 23.902759552001953}, {"Type": "CALM", "Confidence": 22.01038360595703}, {"Type": "SAD", "Confidence": 11.301458358764648}, {"Type": "SURPRISED", "Confidence": 4.149228572845459}, {"Type": "CONFUSED", "Confidence": 3.3912312984466553}, {"Type": "HAPPY", "Confidence": 2.311668634414673}, {"Type": "DISGUSTED", "Confidence": 1.12078058719635}], "Smile": {"Value": false, "Confidence": 91.09114074707031}}]} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"CelebrityFaces": {"type": "array", "items": {"type": "object", "properties": {"Urls": {"type": "array", "items": {"type": "string"}}, "Name": {"type": "string"}, "Id": {"type": "string"}, "Face": {"type": "object", "properties": {"BoundingBox": {"type": "object", "properties": {"Width": {"type": "number"}, "Height": {"type": "number"}, "Left": {"type": "number"}, "Top": {"type": "number"}}, "required": ["Height", "Left", "Top", "Width"]}, "Confidence": {"type": "number"}, "Landmarks": {"type": "array", "items": {"type": "object", "properties": {"Type": {"type": "string"}, "X": {"type": "number"}, "Y": {"type": "number"}}, "required": ["Type", "X", "Y"]}}, "Pose": {"type": "object", "properties": {"Roll": {"type": "number"}, "Yaw": {"type": "number"}, "Pitch": {"type": "number"}}, "required": ["Pitch", "Roll", "Yaw"]}, "Quality": {"type": "object", "properties": {"Brightness": {"type": "number"}, "Sharpness": {"type": "number"}}, "required": ["Brightness", "Sharpness"]}, "Emotions": {"type": "array", "items": {"type": "object", "properties": {"Type": {"type": "string"}, "Confidence": {"type": "number"}}, "required": ["Confidence", "Type"]}}, "Smile": {"type": "object", "properties": {"Value": {"type": "boolean"}, "Confidence": {"type": "number"}}, "required": ["Confidence", "Value"]}}, "required": ["BoundingBox", "Confidence", "Emotions", "Landmarks", "Pose", "Quality", "Smile"]}, "MatchConfidence": {"type": "number"}, "KnownGender": {"type": "object", "properties": {"Type": {"type": "string"}}, "required": ["Type"]}}, "required": ["Face", "Id", "KnownGender", "MatchConfidence", "Name", "Urls"]}}, "UnrecognizedFaces": {"type": "array", "items": {"type": "object", "properties": {"BoundingBox": {"type": "object", "properties": {"Width": {"type": "number"}, "Height": {"type": "number"}, "Left": {"type": "number"}, "Top": {"type": "number"}}, "required": ["Height", "Left", "Top", "Width"]}, "Confidence": {"type": "number"}, "Landmarks": {"type": "array", "items": {"type": "object", "properties": {"Type": {"type": "string"}, "X": {"type": "number"}, "Y": {"type": "number"}}, "required": ["Type", "X", "Y"]}}, "Pose": {"type": "object", "properties": {"Roll": {"type": "number"}, "Yaw": {"type": "number"}, "Pitch": {"type": "number"}}, "required": ["Pitch", "Roll", "Yaw"]}, "Quality": {"type": "object", "properties": {"Brightness": {"type": "number"}, "Sharpness": {"type": "number"}}, "required": ["Brightness", "Sharpness"]}, "Emotions": {"type": "array", "items": {"type": "object", "properties": {"Type": {"type": "string"}, "Confidence": {"type": "number"}}, "required": ["Confidence", "Type"]}}, "Smile": {"type": "object", "properties": {"Value": {"type": "boolean"}, "Confidence": {"type": "number"}}, "required": ["Confidence", "Value"]}}, "required": ["BoundingBox", "Confidence", "Emotions", "Landmarks", "Pose", "Quality", "Smile"]}}}, "required": ["CelebrityFaces", "UnrecognizedFaces"]} |
d34288c1-f8cb-4f4f-9211-8cd754a33cdf/ca3c8231-c104-4c95-be39-33ad630a5770/1/0 | VRT Visual Recognition Tool | VRT makes it easy to add image analysis to your applications. You provide an image to the API and the service detects objects, people, faces; extracts text (lines and words with geometry), scenes and activities. It provides content moderation API to detect any inappropriate, unwanted, or offensive content. It can also detect celebrities (with their emotions), labels, personal protective equipment (PPE) and provide highly accurate facial analysis (bounding box, landmarks, age range, gender, ... | 8.8 | Detect Protective Equipment | Provides information about bounding boxes and confidence for persons and PE detected, scores for the body parts detected, and boolean values and confidence for whether the PE covers the corresponding body part. PE detected are: Face cover, Hand cover, Head cover. | 200 | Response | {"ProtectiveEquipmentModelVersion": "1.0", "Persons": [{"BodyParts": [{"Name": "FACE", "Confidence": 99.78857421875, "EquipmentDetections": []}, {"Name": "LEFT_HAND", "Confidence": 79.66788482666016, "EquipmentDetections": []}, {"Name": "RIGHT_HAND", "Confidence": 99.2331771850586, "EquipmentDetections": []}, {"Name": "HEAD", "Confidence": 99.98741912841797, "EquipmentDetections": [{"BoundingBox": {"Width": 0.1869625598192215, "Height": 0.2113569974899292, "Left": 0.41805994510650635, "Top": 0.026423964649438858}, "Confidence": 98.12809753417969, "Type": "HEAD_COVER", "CoversBodyPart": {"Confidence": 99.9966049194336, "Value": true}}]}], "BoundingBox": {"Width": 0.5520833134651184, "Height": 0.9356725215911865, "Left": 0.15625, "Top": 0.025341130793094635}, "Confidence": 99.96870422363281, "Id": 0}, {"BodyParts": [{"Name": "FACE", "Confidence": 99.9054183959961, "EquipmentDetections": []}, {"Name": "LEFT_HAND", "Confidence": 95.06452178955078, "EquipmentDetections": []}, {"Name": "HEAD", "Confidence": 99.99109649658203, "EquipmentDetections": [{"BoundingBox": {"Width": 0.12467150390148163, "Height": 0.19834937155246735, "Left": 0.4779881238937378, "Top": 0.025118909776210785}, "Confidence": 91.79896545410156, "Type": "HEAD_COVER", "CoversBodyPart": {"Confidence": 100, "Value": false}}]}], "BoundingBox": {"Width": 0.4817708432674408, "Height": 0.9376218318939209, "Left": 0.5130208134651184, "Top": 0.03313840180635452}, "Confidence": 99.92096710205078, "Id": 1}, {"BodyParts": [{"Name": "FACE", "Confidence": 99.19373321533203, "EquipmentDetections": []}, {"Name": "HEAD", "Confidence": 99.6342544555664, "EquipmentDetections": [{"BoundingBox": {"Width": 0.2694740891456604, "Height": 0.224367156624794, "Left": 0.0020209301728755236, "Top": 0}, "Confidence": 92.76956176757812, "Type": "HEAD_COVER", "CoversBodyPart": {"Confidence": 97.8458251953125, "Value": true}}]}], "BoundingBox": {"Width": 0.33984375, "Height": 0.9902533888816833, "Left": 0, "Top": 0}, "Confidence": 99.07548522949219, "Id": 2}], "Summary": {"PersonsWithRequiredEquipment": [0, 2], "PersonsWithoutRequiredEquipment": [1], "PersonsIndeterminate": []}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"ProtectiveEquipmentModelVersion": {"type": "string"}, "Persons": {"type": "array", "items": {"type": "object", "properties": {"BodyParts": {"type": "array", "items": {"type": "object", "properties": {"Name": {"type": "string"}, "Confidence": {"type": "number"}, "EquipmentDetections": {"type": "array", "items": {"type": "object", "properties": {"BoundingBox": {"type": "object", "properties": {"Width": {"type": "number"}, "Height": {"type": "number"}, "Left": {"type": "number"}, "Top": {"type": "number"}}, "required": ["Height", "Left", "Top", "Width"]}, "Confidence": {"type": "number"}, "Type": {"type": "string"}, "CoversBodyPart": {"type": "object", "properties": {"Confidence": {"type": "number"}, "Value": {"type": "boolean"}}, "required": ["Confidence", "Value"]}}, "required": ["BoundingBox", "Confidence", "CoversBodyPart", "Type"]}}}, "required": ["Confidence", "EquipmentDetections", "Name"]}}, "BoundingBox": {"type": "object", "properties": {"Width": {"type": "number"}, "Height": {"type": "number"}, "Left": {"type": "number"}, "Top": {"type": "number"}}, "required": ["Height", "Left", "Top", "Width"]}, "Confidence": {"type": "number"}, "Id": {"type": "integer"}}, "required": ["BodyParts", "BoundingBox", "Confidence", "Id"]}}, "Summary": {"type": "object", "properties": {"PersonsWithRequiredEquipment": {"type": "array", "items": {"type": "integer"}}, "PersonsWithoutRequiredEquipment": {"type": "array", "items": {"type": "integer"}}, "PersonsIndeterminate": {"type": "array"}}, "required": ["PersonsIndeterminate", "PersonsWithRequiredEquipment", "PersonsWithoutRequiredEquipment"]}}, "required": ["Persons", "ProtectiveEquipmentModelVersion", "Summary"]} |
d34288c1-f8cb-4f4f-9211-8cd754a33cdf/5ac88beb-4202-4290-9c80-668f00535e59/0/0 | VRT Visual Recognition Tool | VRT makes it easy to add image analysis to your applications. You provide an image to the API and the service detects objects, people, faces; extracts text (lines and words with geometry), scenes and activities. It provides content moderation API to detect any inappropriate, unwanted, or offensive content. It can also detect celebrities (with their emotions), labels, personal protective equipment (PPE) and provide highly accurate facial analysis (bounding box, landmarks, age range, gender, ... | 8.8 | Detect Faces | For each face detected, returns face details. These details include a bounding box of the face, a confidence value (that the bounding box contains a face), and a fixed set of attributes such as facial landmarks, age range, beard, sunglasses, emotions and so on. | 200 | Response | {"FaceDetails": [{"BoundingBox": {"Width": 0.24642810225486755, "Height": 0.4775759279727936, "Left": 0.4001295268535614, "Top": 0.14027173817157745}, "AgeRange": {"Low": 18, "High": 26}, "Smile": {"Value": false, "Confidence": 98.28479766845703}, "Eyeglasses": {"Value": false, "Confidence": 99.9305419921875}, "Sunglasses": {"Value": false, "Confidence": 99.97050476074219}, "Gender": {"Value": "Female", "Confidence": 99.98662567138672}, "Beard": {"Value": false, "Confidence": 99.12319946289062}, "Mustache": {"Value": false, "Confidence": 99.57901000976562}, "EyesOpen": {"Value": true, "Confidence": 90.15392303466797}, "MouthOpen": {"Value": false, "Confidence": 62.122596740722656}, "Emotions": [{"Type": "CALM", "Confidence": 98.21260070800781}, {"Type": "SAD", "Confidence": 0.6913503408432007}, {"Type": "DISGUSTED", "Confidence": 0.22906465828418732}, {"Type": "CONFUSED", "Confidence": 0.21956491470336914}, {"Type": "SURPRISED", "Confidence": 0.17961537837982178}, {"Type": "ANGRY", "Confidence": 0.17323502898216248}, {"Type": "HAPPY", "Confidence": 0.14812010526657104}, {"Type": "FEAR", "Confidence": 0.1464502513408661}], "Landmarks": [{"Type": "eyeLeft", "X": 0.477579802274704, "Y": 0.34069836139678955}, {"Type": "eyeRight", "X": 0.5869889855384827, "Y": 0.3350389897823334}, {"Type": "mouthLeft", "X": 0.4954322576522827, "Y": 0.5088604688644409}, {"Type": "mouthRight", "X": 0.5863114595413208, "Y": 0.5037152767181396}, {"Type": "nose", "X": 0.5494407415390015, "Y": 0.42135703563690186}, {"Type": "leftEyeBrowLeft", "X": 0.43004873394966125, "Y": 0.30618032813072205}, {"Type": "leftEyeBrowRight", "X": 0.5021782517433167, "Y": 0.2877100110054016}, {"Type": "leftEyeBrowUp", "X": 0.46749603748321533, "Y": 0.2817084491252899}, {"Type": "rightEyeBrowLeft", "X": 0.565251886844635, "Y": 0.2844911515712738}, {"Type": "rightEyeBrowRight", "X": 0.6211010217666626, "Y": 0.29609957337379456}, {"Type": "rightEyeBrowUp", "X": 0.5945325493812561, "Y": 0.275147944688797}, {"Type": "leftEyeLeft", "X": 0.4561775326728821, "Y": 0.3418004512786865}, {"Type": "leftEyeRight", "X": 0.4991178810596466, "Y": 0.34104686975479126}, {"Type": "leftEyeUp", "X": 0.47734665870666504, "Y": 0.33195891976356506}, {"Type": "leftEyeDown", "X": 0.4783203601837158, "Y": 0.3480178415775299}, {"Type": "rightEyeLeft", "X": 0.5652621388435364, "Y": 0.3376682996749878}, {"Type": "rightEyeRight", "X": 0.6047468781471252, "Y": 0.3338966965675354}, {"Type": "rightEyeUp", "X": 0.5873703360557556, "Y": 0.32622092962265015}, {"Type": "rightEyeDown", "X": 0.5868167877197266, "Y": 0.3422333896160126}, {"Type": "noseLeft", "X": 0.5213481187820435, "Y": 0.44388747215270996}, {"Type": "noseRight", "X": 0.562177300453186, "Y": 0.4415413737297058}, {"Type": "mouthUp", "X": 0.5448420643806458, "Y": 0.4818902015686035}, {"Type": "mouthDown", "X": 0.5454527139663696, "Y": 0.5328224897384644}, {"Type": "leftPupil", "X": 0.477579802274704, "Y": 0.34069836139678955}, {"Type": "rightPupil", "X": 0.5869889855384827, "Y": 0.3350389897823334}, {"Type": "upperJawlineLeft", "X": 0.39226409792900085, "Y": 0.3560470640659332}, {"Type": "midJawlineLeft", "X": 0.4253585934638977, "Y": 0.5335986614227295}, {"Type": "chinBottom", "X": 0.5438104271888733, "Y": 0.6211833953857422}, {"Type": "midJawlineRight", "X": 0.6212052702903748, "Y": 0.522617518901825}, {"Type": "upperJawlineRight", "X": 0.6328608393669128, "Y": 0.3432815372943878}], "Pose": {"Roll": -1.4004771709442139, "Yaw": 5.036016464233398, "Pitch": 6.474949359893799}, "Quality": {"Brightness": 70.5471420288086, "Sharpness": 89.85481262207031}, "Confidence": 99.99906158447266}]} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"FaceDetails": {"type": "array", "items": {"type": "object", "properties": {"BoundingBox": {"type": "object", "properties": {"Width": {"type": "number"}, "Height": {"type": "number"}, "Left": {"type": "number"}, "Top": {"type": "number"}}, "required": ["Height", "Left", "Top", "Width"]}, "AgeRange": {"type": "object", "properties": {"Low": {"type": "integer"}, "High": {"type": "integer"}}, "required": ["High", "Low"]}, "Smile": {"type": "object", "properties": {"Value": {"type": "boolean"}, "Confidence": {"type": "number"}}, "required": ["Confidence", "Value"]}, "Eyeglasses": {"type": "object", "properties": {"Value": {"type": "boolean"}, "Confidence": {"type": "number"}}, "required": ["Confidence", "Value"]}, "Sunglasses": {"type": "object", "properties": {"Value": {"type": "boolean"}, "Confidence": {"type": "number"}}, "required": ["Confidence", "Value"]}, "Gender": {"type": "object", "properties": {"Value": {"type": "string"}, "Confidence": {"type": "number"}}, "required": ["Confidence", "Value"]}, "Beard": {"type": "object", "properties": {"Value": {"type": "boolean"}, "Confidence": {"type": "number"}}, "required": ["Confidence", "Value"]}, "Mustache": {"type": "object", "properties": {"Value": {"type": "boolean"}, "Confidence": {"type": "number"}}, "required": ["Confidence", "Value"]}, "EyesOpen": {"type": "object", "properties": {"Value": {"type": "boolean"}, "Confidence": {"type": "number"}}, "required": ["Confidence", "Value"]}, "MouthOpen": {"type": "object", "properties": {"Value": {"type": "boolean"}, "Confidence": {"type": "number"}}, "required": ["Confidence", "Value"]}, "Emotions": {"type": "array", "items": {"type": "object", "properties": {"Type": {"type": "string"}, "Confidence": {"type": "number"}}, "required": ["Confidence", "Type"]}}, "Landmarks": {"type": "array", "items": {"type": "object", "properties": {"Type": {"type": "string"}, "X": {"type": "number"}, "Y": {"type": "number"}}, "required": ["Type", "X", "Y"]}}, "Pose": {"type": "object", "properties": {"Roll": {"type": "number"}, "Yaw": {"type": "number"}, "Pitch": {"type": "number"}}, "required": ["Pitch", "Roll", "Yaw"]}, "Quality": {"type": "object", "properties": {"Brightness": {"type": "number"}, "Sharpness": {"type": "number"}}, "required": ["Brightness", "Sharpness"]}, "Confidence": {"type": "number"}}, "required": ["AgeRange", "Beard", "BoundingBox", "Confidence", "Emotions", "Eyeglasses", "EyesOpen", "Gender", "Landmarks", "MouthOpen", "Mustache", "Pose", "Quality", "Smile", "Sunglasses"]}}}, "required": ["FaceDetails"]} |
d34288c1-f8cb-4f4f-9211-8cd754a33cdf/7335ba4a-573d-4f58-98a5-558d4ac2eafe/1/0 | VRT Visual Recognition Tool | VRT makes it easy to add image analysis to your applications. You provide an image to the API and the service detects objects, people, faces; extracts text (lines and words with geometry), scenes and activities. It provides content moderation API to detect any inappropriate, unwanted, or offensive content. It can also detect celebrities (with their emotions), labels, personal protective equipment (PPE) and provide highly accurate facial analysis (bounding box, landmarks, age range, gender, ... | 8.8 | Detect Moderation Labels | Detects unsafe content in a specified JPEG or PNG format image. | 200 | Response | {"ModerationLabels": [{"Confidence": 91.76309967041016, "Name": "Suggestive", "ParentName": ""}, {"Confidence": 91.76309967041016, "Name": "Female Swimwear Or Underwear", "ParentName": "Suggestive"}, {"Confidence": 90.65640258789062, "Name": "Revealing Clothes", "ParentName": "Suggestive"}], "ModerationModelVersion": "5.0"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"ModerationLabels": {"type": "array", "items": {"type": "object", "properties": {"Confidence": {"type": "number"}, "Name": {"type": "string"}, "ParentName": {"type": "string"}}, "required": ["Confidence", "Name", "ParentName"]}}, "ModerationModelVersion": {"type": "string"}}, "required": ["ModerationLabels", "ModerationModelVersion"]} |
d34288c1-f8cb-4f4f-9211-8cd754a33cdf/d444c84f-3e9f-4f0a-9b79-042255f5a873/1/0 | VRT Visual Recognition Tool | VRT makes it easy to add image analysis to your applications. You provide an image to the API and the service detects objects, people, faces; extracts text (lines and words with geometry), scenes and activities. It provides content moderation API to detect any inappropriate, unwanted, or offensive content. It can also detect celebrities (with their emotions), labels, personal protective equipment (PPE) and provide highly accurate facial analysis (bounding box, landmarks, age range, gender, ... | 8.8 | Detect Text | Detects text in the input image and converts it into machine-readable text. The response contains information about words or lines of text recognized in the image, the location of the detected text, and the accuracy of the geometry points around the detected text | 200 | Response | {"TextDetections": [{"DetectedText": "No Parking", "Type": "LINE", "Id": 0, "Confidence": 100, "Geometry": {"BoundingBox": {"Width": 0.49590712785720825, "Height": 0.1089436337351799, "Left": 0.2546355128288269, "Top": 0.23762275278568268}, "Polygon": [{"X": 0.25597691535949707, "Y": 0.23762275278568268}, {"X": 0.7505426406860352, "Y": 0.24409697949886322}, {"X": 0.749201238155365, "Y": 0.34656640887260437}, {"X": 0.2546355128288269, "Y": 0.34009218215942383}]}}, {"DetectedText": "No", "Type": "WORD", "Id": 1, "ParentId": 0, "Confidence": 100, "Geometry": {"BoundingBox": {"Width": 0.12398234009742737, "Height": 0.08233701437711716, "Left": 0.25490808486938477, "Top": 0.23836033046245575}, "Polygon": [{"X": 0.25617876648902893, "Y": 0.23836033046245575}, {"X": 0.37889042496681213, "Y": 0.239787295460701}, {"X": 0.37761977314949036, "Y": 0.3206973373889923}, {"X": 0.25490808486938477, "Y": 0.31927037239074707}]}}, {"DetectedText": "Parking", "Type": "WORD", "Id": 2, "ParentId": 0, "Confidence": 100, "Geometry": {"BoundingBox": {"Width": 0.3234441876411438, "Height": 0.10498371720314026, "Left": 0.42709845304489136, "Top": 0.24073155224323273}, "Polygon": [{"X": 0.42853277921676636, "Y": 0.24073155224323273}, {"X": 0.7505426406860352, "Y": 0.24409697949886322}, {"X": 0.7491083145141602, "Y": 0.3457152545452118}, {"X": 0.42709845304489136, "Y": 0.3423498272895813}]}}], "TextModelVersion": "3.0"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"TextDetections": {"type": "array", "items": {"type": "object", "properties": {"DetectedText": {"type": "string"}, "Type": {"type": "string"}, "Id": {"type": "integer"}, "Confidence": {"type": "integer"}, "Geometry": {"type": "object", "properties": {"BoundingBox": {"type": "object", "properties": {"Width": {"type": "number"}, "Height": {"type": "number"}, "Left": {"type": "number"}, "Top": {"type": "number"}}, "required": ["Height", "Left", "Top", "Width"]}, "Polygon": {"type": "array", "items": {"type": "object", "properties": {"X": {"type": "number"}, "Y": {"type": "number"}}, "required": ["X", "Y"]}}}, "required": ["BoundingBox", "Polygon"]}, "ParentId": {"type": "integer"}}, "required": ["Confidence", "DetectedText", "Geometry", "Id", "Type"]}}, "TextModelVersion": {"type": "string"}}, "required": ["TextDetections", "TextModelVersion"]} |
cdebd701-5080-4e1d-af34-9abe6a5f7733/c321b5c3-1626-40f9-8e6f-db345c7f6189/0/0 | Skin analyze Advanced | Analysis of skin condition, such as skin color, skin texture, double eyelids, eye bags, dark circles, wrinkles, acne, spots, etc. | 5.7 | Skin analyze Advanced | Skin analyze Advanced | 200 | Success | {"request_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "log_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "error_code": 0, "face_rectangle": {"top": 0, "left": 0, "width": 0, "height": 0}, "result": {"skin_color": {"value": 0, "confidence": 0.89}, "skin_age": {"value": 9}, "left_eyelids": {"value": 0, "confidence": 0.89}, "right_eyelids": {"value": 0, "confidence": 0.89}, "eye_pouch": {"value": 0, "confidence": 0.89}, "dark_circle": {"value": 0, "confidence": 0.89}, "forehead_wrinkle": {"value": 0, "confidence": 0.89}, "crows_feet": {"value": 0, "confidence": 0.89}, "eye_finelines": {"value": 0, "confidence": 0.89}, "glabella_wrinkle": {"value": 0, "confidence": 0.89}, "nasolabial_fold": {"value": 0, "confidence": 0.89}, "skin_type": {"skin_type": 0, "details": {"0": {"value": 1, "confidence": 0.89}, "1": {"value": 1, "confidence": 0.89}, "2": {"value": 0, "confidence": 0.01}, "3": {"value": 0, "confidence": 0.01}}}, "pores_forehead": {"value": 0, "confidence": 1}, "pores_left_cheek": {"value": 0, "confidence": 1}, "pores_right_cheek": {"value": 0, "confidence": 1}, "pores_jaw": {"value": 0, "confidence": 1}, "blackhead": {"value": 0, "confidence": 1}, "acne": {"rectangle": [{"width": 3, "top": 17, "height": 1, "left": 35}, {"width": 4, "top": 20, "height": 1, "left": 35}]}, "closed_comedones": {"rectangle": [{"width": 3, "top": 17, "height": 1, "left": 35}, {"width": 4, "top": 20, "height": 1, "left": 35}]}, "mole": {"rectangle": [{"width": 3, "top": 17, "height": 1, "left": 35}, {"width": 4, "top": 20, "height": 1, "left": 35}]}, "skin_spot": {"rectangle": [{"width": 3, "top": 17, "height": 1, "left": 35}, {"width": 4, "top": 20, "height": 1, "left": 35}]}}} | {"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."}} |
cdebd701-5080-4e1d-af34-9abe6a5f7733/c321b5c3-1626-40f9-8e6f-db345c7f6189/0/1 | Skin analyze Advanced | Analysis of skin condition, such as skin color, skin texture, double eyelids, eye bags, dark circles, wrinkles, acne, spots, etc. | 5.7 | Skin analyze Advanced | Skin analyze Advanced | 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."}} |
cdebd701-5080-4e1d-af34-9abe6a5f7733/c321b5c3-1626-40f9-8e6f-db345c7f6189/1/0 | Skin analyze Advanced | Analysis of skin condition, such as skin color, skin texture, double eyelids, eye bags, dark circles, wrinkles, acne, spots, etc. | 5.7 | Skin analyze Advanced | Skin analyze Advanced | 401 | Example | {"message": "Invalid API key in request"} | {"type": "object", "properties": {"message": {"type": "string", "required": true, "example": "Invalid API key in request", "description": "Error Message."}}} |
40dcaef7-d9ba-4305-8a60-4fadd2dc5fb3/25466cd2-c8f2-4825-9d2a-665b5cde187c/0/0 | Apparel Classification | Clothing classification to classify apparel and recognize accessories such as hats, handbags, gloves, pants, and other types. | 6.4 | Analyze Image | Classify the apparel in the image. | 200 | Response | [{"id": "ai_JxJjVSBZ", "name": "crewneck", "value": 0.8680347, "app_id": "main"}, {"id": "ai_49LhcN8W", "name": "mini dress", "value": 0.8212199, "app_id": "main"}, {"id": "ai_0LhjCzXX", "name": "top", "value": 0.79237443, "app_id": "main"}, {"id": "ai_nPm2pndl", "name": "long-sleeve", "value": 0.75208014, "app_id": "main"}, {"id": "ai_9TVx60XR", "name": "sleeveless", "value": 0.70368457, "app_id": "main"}, {"id": "ai_SwHTDbfx", "name": "midi dress", "value": 0.69866574, "app_id": "main"}, {"id": "ai_LdnJskHR", "name": "hoodie", "value": 0.6568494, "app_id": "main"}, {"id": "ai_wr0RRFsz", "name": "leather", "value": 0.5954651, "app_id": "main"}, {"id": "ai_gh5dKpSW", "name": "pants", "value": 0.5950879, "app_id": "main"}, {"id": "ai_Wn0HQcZg", "name": "chiffon", "value": 0.5863132, "app_id": "main"}, {"id": "ai_zXgMs4Lf", "name": "colorblock", "value": 0.5643139, "app_id": "main"}, {"id": "ai_VqJGTDXg", "name": "maxi dress", "value": 0.4942478, "app_id": "main"}, {"id": "ai_JXB7VH47", "name": "t-shirt", "value": 0.4675879, "app_id": "main"}, {"id": "ai_0llr5l2h", "name": "knit", "value": 0.3463685, "app_id": "main"}, {"id": "ai_JsDt84F9", "name": "floral", "value": 0.33969122, "app_id": "main"}, {"id": "ai_v1qj79B7", "name": "turtleneck", "value": 0.33672437, "app_id": "main"}, {"id": "ai_hqsFFXGR", "name": "graphic", "value": 0.32474455, "app_id": "main"}, {"id": "ai_TWGtXqlf", "name": "shoulder bag", "value": 0.24725997, "app_id": "main"}, {"id": "ai_d97zPsNG", "name": "v-neck", "value": 0.2099538, "app_id": "main"}, {"id": "ai_wvKpkZFb", "name": "spaghetti strap", "value": 0.20479181, "app_id": "main"}] | {"$schema": "http://json-schema.org/schema#", "type": "array", "items": {"type": "object", "properties": {"id": {"type": "string"}, "name": {"type": "string"}, "value": {"type": "number"}, "app_id": {"type": "string"}}, "required": ["app_id", "id", "name", "value"]}} |
230ac25d-c259-4fb3-b9c6-8bad9fe498a6/f130adef-af7b-4f07-a7fa-9c53c8175cca/0/0 | ComputerVisionAPIs | Face Recognition / Liveness Detection, License Plate / Credit Card / ID Card / Passport / Bank Check Document Recognition (Check Tutorials for Usage) | 0.2 | Face_Liveness | Face Liveness Detection (Phone Selfie)
Sample Images: https://ibb.co/album/x33rzs | 200 | Genuine | {"data": {"box": [166, 456, 423, 429], "message": "Genuine", "status": "genuine"}, "status": "ok"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"data": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "integer"}}, "message": {"type": "string"}, "status": {"type": "string"}}, "required": ["box", "message", "status"]}, "status": {"type": "string"}}, "required": ["data", "status"]} |
230ac25d-c259-4fb3-b9c6-8bad9fe498a6/f130adef-af7b-4f07-a7fa-9c53c8175cca/0/1 | ComputerVisionAPIs | Face Recognition / Liveness Detection, License Plate / Credit Card / ID Card / Passport / Bank Check Document Recognition (Check Tutorials for Usage) | 0.2 | Face_Liveness | Face Liveness Detection (Phone Selfie)
Sample Images: https://ibb.co/album/x33rzs | 200 | Spoof | {"data": {"box": [235, 400, 228, 228], "message": "Spoof (Please move closer)", "status": "spoof"}, "status": "ok"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"data": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "integer"}}, "message": {"type": "string"}, "status": {"type": "string"}}, "required": ["box", "message", "status"]}, "status": {"type": "string"}}, "required": ["data", "status"]} |
230ac25d-c259-4fb3-b9c6-8bad9fe498a6/4eb93869-40a8-475b-b308-4d34e62009f1/0/0 | ComputerVisionAPIs | Face Recognition / Liveness Detection, License Plate / Credit Card / ID Card / Passport / Bank Check Document Recognition (Check Tutorials for Usage) | 0.2 | Face_FeatureVec | Face Feature Vector
Sample Images: https://ibb.co/album/x33rzs | 200 | Example_1 | {"data": {"box": [168, 351, 525, 915], "feature": [-0.025944426655769348, -0.03811771050095558, 0.03270559012889862, -0.06142697483301163, 0.033153608441352844, -0.0658990740776062, -0.0063834344036877155, 0.05897287651896477, -0.009651909582316875, 0.08713692426681519, -0.03063877299427986, 0.0451023206114769, 0.06619363278150558, -0.03961910679936409, 0.02553546614944935, -0.042586617171764374, -0.055828820914030075, -0.02360440604388714, 0.05741657689213753, 0.018780913203954697, 0.07769914716482162, 0.02165822871029377, 0.0006759734824299812, 0.1581493467092514, -0.0641438290476799, 0.09826409071683884, 0.10017958283424377, 0.1586667001247406, -0.03787348046898842, 0.046933941543102264, -0.04074961319565773, 0.05285557731986046, -0.014075438492000103, 0.0016337543493136764, -0.03752332180738449, -0.04561208188533783, 0.037800129503011703, 0.04907815158367157, 0.020134015008807182, 0.017415449023246765, 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-0.0030445391312241554, 0.08048226684331894, -0.03761448338627815, -0.0036841994151473045, -0.030444882810115814, 0.00038799754111096263, -0.02203592099249363, -0.017907632514834404, 0.1646309345960617, 0.10337021946907043, 0.01188749447464943, 0.06821253895759583, 0.057685572654008865, -0.0806773230433464, -0.038736242800951004, -0.04175954312086105, 0.06165926903486252, 0.016763154417276382, 0.08753245323896408, 0.10987704247236252, 0.0820934921503067, 0.04444145783782005, -0.011657548137009144, 0.21020333468914032, 0.0315619520843029, 0.0026594167575240135, -0.0033833549823611975, 0.08098971843719482, 0.05971522629261017, 0.02572927437722683, 0.05929170548915863, 0.1656261384487152, -0.042135730385780334, -0.028084682300686836, 0.02924465760588646, -0.016747599467635155], "status": "ok"}, "status": "ok"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"data": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "integer"}}, "feature": {"type": "array", "items": {"type": "number"}}, "status": {"type": "string"}}, "required": ["box", "feature", "status"]}, "status": {"type": "string"}}, "required": ["data", "status"]} |
230ac25d-c259-4fb3-b9c6-8bad9fe498a6/142cbd36-2aef-44e3-9f00-a80dfa78d3e9/0/0 | ComputerVisionAPIs | Face Recognition / Liveness Detection, License Plate / Credit Card / ID Card / Passport / Bank Check Document Recognition (Check Tutorials for Usage) | 0.2 | Ocr_CreditCard | Supports all cards(credit, debit, travel, prepaid, corporate…) from Visa, MasterCard, American Express, RuPay, Discover…
Sample Images: https://ibb.co/album/x33rzs | 200 | Example_1 | {"data": [{"box": [361, 107, 432, 306], "info": "Card #1", "is_valid": true, "values": [{"name": "number", "valid": true, "value": "5391232061279498"}, {"name": "valid_thru", "value": "05/19"}, {"name": "holder_name", "value": "REVOLUT"}]}], "status": "ok"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"data": {"type": "array", "items": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "integer"}}, "info": {"type": "string"}, "is_valid": {"type": "boolean"}, "values": {"type": "array", "items": {"type": "object", "properties": {"name": {"type": "string"}, "valid": {"type": "boolean"}, "value": {"type": "string"}}, "required": ["name", "value"]}}}, "required": ["box", "info", "is_valid", "values"]}}, "status": {"type": "string"}}, "required": ["data", "status"]} |
230ac25d-c259-4fb3-b9c6-8bad9fe498a6/48713ba2-3719-4744-80e9-8ade4f850594/0/0 | ComputerVisionAPIs | Face Recognition / Liveness Detection, License Plate / Credit Card / ID Card / Passport / Bank Check Document Recognition (Check Tutorials for Usage) | 0.2 | Ocr_ALPR | License Plate Recognition
Sample Images: https://ibb.co/album/x33rzs | 200 | Example_1 | {"data": [{"box": [1477, 97, 85, 36], "color": "silver", "country": "New Zealand", "manufacturer": "ford", "model": "focus", "type": "coupe", "value": "H553LLP"}, {"box": [1075, 58, 82, 32], "color": "black", "country": "New Zealand", "manufacturer": "volvo", "model": "xc90", "type": "suv", "value": "KT5XXA"}, {"box": [2885, 395, 100, 41], "color": "black", "country": "United Kingdom of Great Britain and Northern Ireland", "manufacturer": "mercedes benz", "model": "e", "type": "sedan", "value": "LA07NBA"}, {"box": [3449, 488, 109, 48], "color": "white", "country": "United Kingdom of Great Britain and Northern Ireland", "manufacturer": "ford", "model": "transit", "type": "campervan", "value": "YBI9XPR"}, {"box": [243, 1144, 186, 85], "color": "red", "country": "United Kingdom of Great Britain and Northern Ireland", "manufacturer": "nissan", "model": "juke", "type": "hatchback", "value": "PJ66HFW"}, {"box": [3181, 1248, 270, 88], "color": "black", "country": "Armenia", "manufacturer": "ford", "model": "fiesta", "type": "sedan", "value": "LDI2GFU"}, {"box": [1355, 1566, 224, 91], "color": "white", "country": "United Kingdom of Great Britain and Northern Ireland", "manufacturer": "volkswagen", "model": "golf", "type": "hatchback", "value": "PK07RXV"}], "status": "ok"} | {"type": "object"} |
230ac25d-c259-4fb3-b9c6-8bad9fe498a6/2c656f6a-cc69-42a5-947a-4598afaec8a2/0/0 | ComputerVisionAPIs | Face Recognition / Liveness Detection, License Plate / Credit Card / ID Card / Passport / Bank Check Document Recognition (Check Tutorials for Usage) | 0.2 | Ocr_IDCard | Suppport All Kinds of Local Identity Cards, Passports, Driver Licenses, Visas, Resident Cards… by recognizing MRZ (Machine Readable Zone).
Sample Images: https://ibb.co/album/x33rzs | 200 | Example_1 | {"data": [{"box": [52, 601, 1154, 95], "value": ["P<AUSCITIZEN<<JANE<<<<<<<<<<<<<<<<<<<<<<<<<<", "PA09404433AUS8406077F1903212<17332717P<<<<68"]}], "status": "ok"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"data": {"type": "array", "items": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "integer"}}, "value": {"type": "array", "items": {"type": "string"}}}, "required": ["box", "value"]}}, "status": {"type": "string"}}, "required": ["data", "status"]} |
230ac25d-c259-4fb3-b9c6-8bad9fe498a6/5f284f3f-4ca4-4983-94f7-42da11a93586/0/0 | ComputerVisionAPIs | Face Recognition / Liveness Detection, License Plate / Credit Card / ID Card / Passport / Bank Check Document Recognition (Check Tutorials for Usage) | 0.2 | Ocr_BankCheck | Supports E-13B or CMC-7 format documents by recognizing MICR (Magnetic ink character recognition)
Sample Images: https://ibb.co/album/x33rzs | 200 | Example_1 | {"data": [{"box": [0, 471, 1279, 80], "info": "CMC-7", "value": "H10443928H0183012765I700600004022F"}], "status": "ok"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"data": {"type": "array", "items": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "integer"}}, "info": {"type": "string"}, "value": {"type": "string"}}, "required": ["box", "info", "value"]}}, "status": {"type": "string"}}, "required": ["data", "status"]} |
230ac25d-c259-4fb3-b9c6-8bad9fe498a6/e8dd3b48-3d34-40d5-ba33-2d9bf2e97309/0/0 | ComputerVisionAPIs | Face Recognition / Liveness Detection, License Plate / Credit Card / ID Card / Passport / Bank Check Document Recognition (Check Tutorials for Usage) | 0.2 | Face_Compare | Face Compare
Sample Images: https://ibb.co/album/x33rzs | 200 | Example_1 | {"data": {"box": [235, 400, 228, 228], "message": "Spoof (Please move closer)", "status": "spoof"}, "status": "ok"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"data": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "integer"}}, "message": {"type": "string"}, "status": {"type": "string"}}, "required": ["box", "message", "status"]}, "status": {"type": "string"}}, "required": ["data", "status"]} |
3d418969-546f-4b0b-99dc-b0089890fb8b/58c124a1-505e-4901-998c-b8e197c281a9/0/0 | Moderately | Detect nudity, inappropriate, unwanted, or offensive content in images. | null | Image | Send in an image URL and get a list of inappropriate, unwanted, or offensive labels detected for the uploaded image.
If no inappropriate content is detected in the image, the response is an empty list.
Works for images up of size up to 5MB
Properties scanned for:
- Explicit Nudity
- Suggestive (i.e. partial nudity)
- Violence
- Visually Disturbing
- Rude Gestures
- Drugs
- Tobacco
- Alcohol
- Gambling
- Hate Symbols | 500 | Internal server error | {"message": "Internal server error"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"message": {"type": "string"}}, "required": ["message"]} |
3d418969-546f-4b0b-99dc-b0089890fb8b/58c124a1-505e-4901-998c-b8e197c281a9/1/0 | Moderately | Detect nudity, inappropriate, unwanted, or offensive content in images. | null | Image | Send in an image URL and get a list of inappropriate, unwanted, or offensive labels detected for the uploaded image.
If no inappropriate content is detected in the image, the response is an empty list.
Works for images up of size up to 5MB
Properties scanned for:
- Explicit Nudity
- Suggestive (i.e. partial nudity)
- Violence
- Visually Disturbing
- Rude Gestures
- Drugs
- Tobacco
- Alcohol
- Gambling
- Hate Symbols | 403 | Forbidden | {"message": "Forbidden"} | {"type": "object", "properties": {"message": {"type": "string"}}} |
3d418969-546f-4b0b-99dc-b0089890fb8b/58c124a1-505e-4901-998c-b8e197c281a9/2/0 | Moderately | Detect nudity, inappropriate, unwanted, or offensive content in images. | null | Image | Send in an image URL and get a list of inappropriate, unwanted, or offensive labels detected for the uploaded image.
If no inappropriate content is detected in the image, the response is an empty list.
Works for images up of size up to 5MB
Properties scanned for:
- Explicit Nudity
- Suggestive (i.e. partial nudity)
- Violence
- Visually Disturbing
- Rude Gestures
- Drugs
- Tobacco
- Alcohol
- Gambling
- Hate Symbols | 404 | Image not found | {"message": "Image not found"} | {"type": "object", "properties": {"error": {"type": "string"}}} |
3d418969-546f-4b0b-99dc-b0089890fb8b/58c124a1-505e-4901-998c-b8e197c281a9/3/0 | Moderately | Detect nudity, inappropriate, unwanted, or offensive content in images. | null | Image | Send in an image URL and get a list of inappropriate, unwanted, or offensive labels detected for the uploaded image.
If no inappropriate content is detected in the image, the response is an empty list.
Works for images up of size up to 5MB
Properties scanned for:
- Explicit Nudity
- Suggestive (i.e. partial nudity)
- Violence
- Visually Disturbing
- Rude Gestures
- Drugs
- Tobacco
- Alcohol
- Gambling
- Hate Symbols | 400 | Invalid image format | {"message": "Invalid image format. Allowed formats are JPEG and PNG"} | {"type": "object", "properties": {"message": {"type": "string"}}} |
3d418969-546f-4b0b-99dc-b0089890fb8b/58c124a1-505e-4901-998c-b8e197c281a9/3/1 | Moderately | Detect nudity, inappropriate, unwanted, or offensive content in images. | null | Image | Send in an image URL and get a list of inappropriate, unwanted, or offensive labels detected for the uploaded image.
If no inappropriate content is detected in the image, the response is an empty list.
Works for images up of size up to 5MB
Properties scanned for:
- Explicit Nudity
- Suggestive (i.e. partial nudity)
- Violence
- Visually Disturbing
- Rude Gestures
- Drugs
- Tobacco
- Alcohol
- Gambling
- Hate Symbols | 400 | Image too large | {"message": "Image is too large. Files up to 5MB are allowed"} | {"type": "object", "properties": {"message": {"type": "string"}}} |
3d418969-546f-4b0b-99dc-b0089890fb8b/58c124a1-505e-4901-998c-b8e197c281a9/3/2 | Moderately | Detect nudity, inappropriate, unwanted, or offensive content in images. | null | Image | Send in an image URL and get a list of inappropriate, unwanted, or offensive labels detected for the uploaded image.
If no inappropriate content is detected in the image, the response is an empty list.
Works for images up of size up to 5MB
Properties scanned for:
- Explicit Nudity
- Suggestive (i.e. partial nudity)
- Violence
- Visually Disturbing
- Rude Gestures
- Drugs
- Tobacco
- Alcohol
- Gambling
- Hate Symbols | 400 | No Image URL provided | {"message": "No image URL provided"} | {"type": "object", "properties": {"message": {"type": "string"}}} |
3d418969-546f-4b0b-99dc-b0089890fb8b/58c124a1-505e-4901-998c-b8e197c281a9/4/0 | Moderately | Detect nudity, inappropriate, unwanted, or offensive content in images. | null | Image | Send in an image URL and get a list of inappropriate, unwanted, or offensive labels detected for the uploaded image.
If no inappropriate content is detected in the image, the response is an empty list.
Works for images up of size up to 5MB
Properties scanned for:
- Explicit Nudity
- Suggestive (i.e. partial nudity)
- Violence
- Visually Disturbing
- Rude Gestures
- Drugs
- Tobacco
- Alcohol
- Gambling
- Hate Symbols | 200 | Nudity detected | [{"confidence": 99.93199920654297, "name": "Explicit Nudity"}] | {"type": "array", "items": {"type": "object", "properties": {"confidence": {"type": "number"}, "name": {"type": "string"}}}} |
de252d02-45fa-4cf2-a087-e74a1e9f523c/dda22417-b354-4024-843d-484dae5fe6ab/0/0 | Singapore Driving License OCR | Extract all the key fields from Singapore Driving License including Singapore Driving License Card Number, Name, Date of Birth, Date of Issue and Head Portrait. | 5.7 | Singapore 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 01:13:41 AM", "result": {"rotated_image_height": 365, "image_angle": 0, "rotated_image_width": 525, "type": "singapore_driving_license", "details": {"birthday": {"value": "18 Dec 1959", "position": {"bottom": 253, "left": 321, "right": 418, "top": 238}}, "barcode_number": {"value": "002002000F", "position": {"bottom": 337, "left": 209, "right": 298, "top": 326}}, "license_number": {"value": "S8109999E", "position": {"bottom": 120, "left": 349, "right": 520, "top": 97}}, "name": {"value": "NIRMAL GHOGH", "position": {"bottom": 170, "left": 258, "right": 402, "top": 154}}, "issue_date": {"value": "26 Nov 2013", "position": {"bottom": 276, "left": 319, "right": 418, "top": 260}}, "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": {"birthday": {"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"]}, "barcode_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"]}, "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": ["barcode_number", "birthday", "head_portrait", "issue_date", "license_number", "name"]}}, "required": ["details", "image_angle", "rotated_image_height", "rotated_image_width", "type"]}}, "required": ["code", "date", "result", "status"]} |
6356fa26-5656-4a50-8904-c9ca84024d54/def13478-7728-4b4b-ba04-03f27ae52811/0/0 | Credit Card OCR | Extracting text from credit cards. | 8.4 | Credit Card OCR | Credit Card OCR | 200 | Response | {"code": "200", "message": "SUCCESS", "data": {"errorCode": 0, "ocr": {"cardNumber": "4000 1234 5678 9010", "category": "Visa", "name": "A.MILLER", "validState": 0, "validThru": "12/20"}, "position": {"left": 26, "bottom": 279, "right": 484, "top": 1}, "score": 1}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"code": {"type": "string"}, "message": {"type": "string"}, "data": {"type": "object", "properties": {"errorCode": {"type": "integer"}, "ocr": {"type": "object", "properties": {"cardNumber": {"type": "string"}, "category": {"type": "string"}, "name": {"type": "string"}, "validState": {"type": "integer"}, "validThru": {"type": "string"}}, "required": ["cardNumber", "category", "name", "validState", "validThru"]}, "position": {"type": "object", "properties": {"left": {"type": "integer"}, "bottom": {"type": "integer"}, "right": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}, "score": {"type": "integer"}}, "required": ["errorCode", "ocr", "position", "score"]}}, "required": ["code", "data", "message"]} |
73b7675b-5053-4837-9f83-7dfc40d40527/8cc0c8f3-975a-468c-9868-826636d4e0b0/0/0 | Brand Recognition | This ready-to-use API provides high-accuracy brand detection and logo recognition. [ or PDF file.
Usually such images have extensions: .jpg, .jpeg, .png, .pdf. In case of PDF
each page will be converted to PNG image and processed separately.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
application/pdf
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"}}} |
73b7675b-5053-4837-9f83-7dfc40d40527/8cc0c8f3-975a-468c-9868-826636d4e0b0/1/0 | Brand Recognition | This ready-to-use API provides high-accuracy brand detection and logo recognition. [ or PDF file.
Usually such images have extensions: .jpg, .jpeg, .png, .pdf. In case of PDF
each page will be converted to PNG image and processed separately.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
application/pdf
The size of image file must be less than 16Mb. | 200 | Successful detection | {"results": [{"status": {"code": "ok", "message": "Success"}, "name": "image.jpg", "md5": "a447c0aa2b2b89aa6ffb488317c0ab1e", "width": 1024, "height": 768, "entities": [{"kind": "objects", "name": "logo-detector", "objects": [{"box": [0.5650924444198608, 0.1460374891757965, 0.15659332275390625, 0.1803402304649353], "entities": [{"kind": "classes", "name": "classes", "classes": {"McDonalds": 0.3759486973285675}}]}]}]}]} | {"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"}, "objects": {"type": "array", "items": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "number"}}, "entities": {"type": "array", "items": {"type": "object", "properties": {"kind": {"type": "string"}, "name": {"type": "string"}, "classes": {"type": "object"}}}}}}}}}}}}}}} |
73b7675b-5053-4837-9f83-7dfc40d40527/8cc0c8f3-975a-468c-9868-826636d4e0b0/1/1 | Brand Recognition | This ready-to-use API provides high-accuracy brand detection and logo recognition. [ or PDF file.
Usually such images have extensions: .jpg, .jpeg, .png, .pdf. In case of PDF
each page will be converted to PNG image and processed separately.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
application/pdf
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"}, "objects": {"type": "array", "items": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "number"}}, "entities": {"type": "array", "items": {"type": "object", "properties": {"kind": {"type": "string"}, "name": {"type": "string"}, "classes": {"type": "object"}}}}}}}}}}}}}}} |
9a2aa2a4-1352-49bf-b52d-e1569ddb0a1f/1efa3cb9-0246-4dd9-bf0e-88965a9e60bc/0/0 | Cartoon yourself | Using advanced adversarial generative network technology, we can break through the "next generation wall" with one click, retain user features in multiple dimensions to achieve the effect of a thousand faces, and with a variety of comic style image migration, we can generate highly cute comic faces with artistic beauty for users.Based on the stylized special effects solution-EffectGAN with small sample generation technology, the intelligent creation team creates a variety of special effects. ... | 9.1 | Cartoon yourself | Cartoon yourself | 200 | Success | {"request_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "log_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "error_code": 0, "data": {"image_url": "Resulting image URL 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": {"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."}}}} |
9a2aa2a4-1352-49bf-b52d-e1569ddb0a1f/1efa3cb9-0246-4dd9-bf0e-88965a9e60bc/0/1 | Cartoon yourself | Using advanced adversarial generative network technology, we can break through the "next generation wall" with one click, retain user features in multiple dimensions to achieve the effect of a thousand faces, and with a variety of comic style image migration, we can generate highly cute comic faces with artistic beauty for users.Based on the stylized special effects solution-EffectGAN with small sample generation technology, the intelligent creation team creates a variety of special effects. ... | 9.1 | Cartoon yourself | Cartoon yourself | 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": {"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."}}}} |
9a2aa2a4-1352-49bf-b52d-e1569ddb0a1f/1efa3cb9-0246-4dd9-bf0e-88965a9e60bc/1/0 | Cartoon yourself | Using advanced adversarial generative network technology, we can break through the "next generation wall" with one click, retain user features in multiple dimensions to achieve the effect of a thousand faces, and with a variety of comic style image migration, we can generate highly cute comic faces with artistic beauty for users.Based on the stylized special effects solution-EffectGAN with small sample generation technology, the intelligent creation team creates a variety of special effects. ... | 9.1 | Cartoon yourself | Cartoon yourself | 401 | Example | {"message": "Invalid API key in request"} | {"message": {"type": "String", "required": true, "example": "Invalid API key in request", "description": "Error Message."}} |
d9d164a7-8fdb-48e6-9f86-dcb76f9d2fe0/24451dbb-4d0b-4246-8987-960dd8d00c2e/0/0 | Face Verification | PresentID Face verification API(photo id matching) evaluates whether two faces belong to the same person or not. | 9.4 | Send image with Base64 | Face verification API(photo id matching) evaluates whether two faces belong to the same person or not.
Image size should not exceed 8 MB
Also, the images should not be larger than 5000 pixels and smaller than 50 pixels | 200 | New Example | {"statusCode": 200, "statusMessage": "OK", "hasError": false, "data": {"resultIndex": 3, "resultMessage": "The two faces belong to the different people.", "similarPercent": 0.5538576119399039}, "imageSpecs": [{"leftTop": {"isEmpty": false, "x": 452, "y": 107}, "rightTop": {"isEmpty": false, "x": 276, "y": 110}, "rightBottom": {"isEmpty": false, "x": 279, "y": 286}, "leftBottom": {"isEmpty": false, "x": 455, "y": 283}}, {"leftTop": {"isEmpty": false, "x": 953, "y": 172}, "rightTop": {"isEmpty": false, "x": 329, "y": 248}, "rightBottom": {"isEmpty": false, "x": 405, "y": 872}, "leftBottom": {"isEmpty": false, "x": 1029, "y": 796}}]} | {"type": "object", "properties": {"statusCode": {"type": "integer"}, "statusMessage": {"type": "string"}, "hasError": {"type": "boolean"}, "data": {"type": "object", "properties": {"resultIndex": {"type": "integer"}, "resultMessage": {"type": "string"}, "similarPercent": {"type": "number"}}}, "imageSpecs": {"type": "array", "items": {"type": "object", "properties": {"leftTop": {"type": "object", "properties": {"isEmpty": {"type": "boolean"}, "x": {"type": "integer"}, "y": {"type": "integer"}}}, "rightTop": {"type": "object", "properties": {"isEmpty": {"type": "boolean"}, "x": {"type": "integer"}, "y": {"type": "integer"}}}, "rightBottom": {"type": "object", "properties": {"isEmpty": {"type": "boolean"}, "x": {"type": "integer"}, "y": {"type": "integer"}}}, "leftBottom": {"type": "object", "properties": {"isEmpty": {"type": "boolean"}, "x": {"type": "integer"}, "y": {"type": "integer"}}}}}}}} |
d9d164a7-8fdb-48e6-9f86-dcb76f9d2fe0/097db741-a7b8-46d0-bec1-75f8e7d64502/0/0 | Face Verification | PresentID Face verification API(photo id matching) evaluates whether two faces belong to the same person or not. | 9.4 | Send image with image URL | Face verification API(photo id matching) evaluates whether two faces belong to the same person or not.
Image size should not exceed 8 MB
Also, the images should not be larger than 5000 pixels and smaller than 50 pixels | 200 | Response | {"statusCode": 200, "statusMessage": "OK", "hasError": false, "data": {"resultIndex": 3, "resultMessage": "The two faces belong to the different people.", "similarPercent": 0.5538576119399039}, "imageSpecs": [{"leftTop": {"isEmpty": false, "x": 452, "y": 107}, "rightTop": {"isEmpty": false, "x": 276, "y": 110}, "rightBottom": {"isEmpty": false, "x": 279, "y": 286}, "leftBottom": {"isEmpty": false, "x": 455, "y": 283}}, {"leftTop": {"isEmpty": false, "x": 953, "y": 172}, "rightTop": {"isEmpty": false, "x": 329, "y": 248}, "rightBottom": {"isEmpty": false, "x": 405, "y": 872}, "leftBottom": {"isEmpty": false, "x": 1029, "y": 796}}]} | {"type": "object", "properties": {"statusCode": {"type": "integer"}, "statusMessage": {"type": "string"}, "hasError": {"type": "boolean"}, "data": {"type": "object", "properties": {"resultIndex": {"type": "integer"}, "resultMessage": {"type": "string"}, "similarPercent": {"type": "number"}}}, "imageSpecs": {"type": "array", "items": {"type": "object", "properties": {"leftTop": {"type": "object", "properties": {"isEmpty": {"type": "boolean"}, "x": {"type": "integer"}, "y": {"type": "integer"}}}, "rightTop": {"type": "object", "properties": {"isEmpty": {"type": "boolean"}, "x": {"type": "integer"}, "y": {"type": "integer"}}}, "rightBottom": {"type": "object", "properties": {"isEmpty": {"type": "boolean"}, "x": {"type": "integer"}, "y": {"type": "integer"}}}, "leftBottom": {"type": "object", "properties": {"isEmpty": {"type": "boolean"}, "x": {"type": "integer"}, "y": {"type": "integer"}}}}}}}} |
d9d164a7-8fdb-48e6-9f86-dcb76f9d2fe0/77d957b4-b075-4912-8f7e-1069bfdeee73/0/0 | Face Verification | PresentID Face verification API(photo id matching) evaluates whether two faces belong to the same person or not. | 9.4 | Send image with image file | Face verification API(photo id matching) evaluates whether two faces belong to the same person or not.
Image size should not exceed 8 MB
Also, the images should not be larger than 5000 pixels and smaller than 50 pixels | 200 | Face Verification | {"data": {"resultIndex": 0, "resultMessage": "The two faces belong to the same person. ", "similarPercent": 100}, "hasError": false, "imageSpecs": [{"leftBottom": {"isEmpty": false, "x": 381, "y": 251}, "leftTop": {"isEmpty": false, "x": 381, "y": 146}, "rightBottom": {"isEmpty": false, "x": 276, "y": 251}, "rightTop": {"isEmpty": false, "x": 276, "y": 146}}, {"leftBottom": {"isEmpty": false, "x": 305, "y": 297}, "leftTop": {"isEmpty": false, "x": 305, "y": 84}, "rightBottom": {"isEmpty": false, "x": 92, "y": 297}, "rightTop": {"isEmpty": false, "x": 92, "y": 84}}], "statusCode": 200, "statusMessage": "OK"} | {"properties": {"data": {"properties": {"resultIndex": {"type": "integer"}, "resultMessage": {"type": "string"}, "similarPercent": {"type": "integer"}}, "type": "object"}, "hasError": {"type": "boolean"}, "imageSpecs": {"items": {"properties": {"leftBottom": {"properties": {"isEmpty": {"type": "boolean"}, "x": {"type": "integer"}, "y": {"type": "integer"}}, "type": "object"}, "leftTop": {"properties": {"isEmpty": {"type": "boolean"}, "x": {"type": "integer"}, "y": {"type": "integer"}}, "type": "object"}, "rightBottom": {"properties": {"isEmpty": {"type": "boolean"}, "x": {"type": "integer"}, "y": {"type": "integer"}}, "type": "object"}, "rightTop": {"properties": {"isEmpty": {"type": "boolean"}, "x": {"type": "integer"}, "y": {"type": "integer"}}, "type": "object"}}, "type": "object"}, "type": "array"}, "statusCode": {"type": "integer"}, "statusMessage": {"type": "string"}}, "type": "object"} |
7c6decb9-750d-479a-84bd-6f5c2f1587ac/29ea23df-5fc1-4621-8b2e-8d1271e221d1/0/0 | Form Recognition | The world's most powerful form recognition capability, extracting text from form images/PDF format files in 52 languages, including wired tables, wireless tables, merged cell tables, as well as supporting the recognition of multiple table contents in a single image, returning the header and footer contents of each table, the cell text content, its row and column position information, and excel files. View Document | 7.2 | form 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": "table_ocr", "date": "11/19/2023 03:02:14 AM", "result": {"angle": 0, "excel": 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{"type": "object", "properties": {"right": {"type": "integer"}, "bottom": {"type": "integer"}, "left": {"type": "integer"}, "top": {"type": "integer"}}, "required": ["bottom", "left", "right", "top"]}, "position": {"type": "array", "items": {"type": "integer"}}, "end_col": {"type": "integer"}, "start_row": {"type": "integer"}, "start_col": {"type": "integer"}, "lines": {"type": "array", "items": {"type": "object", "properties": {"angle": {"type": "integer"}, "text": {"type": "string"}, "direction": {"type": "integer"}, "handwritten": {"type": "integer"}, "position": {"type": "array", "items": {"type": "integer"}}, "score": {"type": "number"}, "type": {"type": "string"}}, "required": ["angle", "direction", "handwritten", "position", "score", "text", "type"]}}, "text": {"type": "string"}}, "required": ["borders", "end_col", "end_row", "lines", "position", "start_col", "start_row", "text"]}}, "table_rows": {"type": "integer"}, "width_of_cols": {"type": "array", "items": {"type": "integer"}}, "position": {"type": "array", "items": {"type": "integer"}}, "lines": {"type": "array", "items": {"type": "object", "properties": {"angle": {"type": "integer"}, "text": {"type": "string"}, "direction": {"type": "integer"}, "handwritten": {"type": "integer"}, "position": {"type": "array", "items": {"type": "integer"}}, "score": {"type": "number"}, "type": {"type": "string"}}, "required": ["angle", "direction", "handwritten", "position", "score", "text", "type"]}}, "table_cols": {"type": "integer"}}, "required": ["height_of_rows", "lines", "position", "table_cells", "table_cols", "table_rows", "type", "width_of_cols"]}}, "width": {"type": "integer"}}, "required": ["angle", "excel", "height", "tables", "width"]}}, "required": ["code", "date", "result", "service", "status"]} |
78c2bc28-0568-4253-ab9c-2799a4a1e4b1/c4274a0f-9fbc-40ea-bffe-be56547e1905/0/0 | SolveMedia Solver | An API for bypassing and recognizing SolveMedia Captcha | 5.5 | byImageFile | Solve the SolveMedia captcha via Image upload | 200 | Response | {"status": "success", "result": {"response": "idk my bff jill", "verified": true}} | {"type": "object", "properties": {"status": {"type": "string"}, "result": {"type": "object", "properties": {"response": {"type": "string"}, "verified": {"type": "boolean"}}}}} |
78c2bc28-0568-4253-ab9c-2799a4a1e4b1/e4ecb91a-4016-469e-bc75-8657fce2e715/0/0 | SolveMedia Solver | An API for bypassing and recognizing SolveMedia Captcha | 5.5 | bySiteKey | Solve the SolveMedia via site key method | 200 | Response | {"status": "success", "result": {"challenge": "[email protected]@ZaqgdOMrVagENU8bgrS4Mn9APTC4lJjoEN6mJz8J69ReRJTWt1AFY.oiTmWxE2knvG.BvXSDAbeZZ4V0ndfdD.chZ3Tee1HBqekeCu8Uivn4.wPNWMPVXG0m81oVKAhiHbN41zP67JndOcn-LEAZy-smyT7TV7CgRbUr3DhRP4qMhqTzBRvoudSdbJmbYDOVdNRiF0vMfR.uqfWiUbEOFGa.P47gof-6AQREC4nB2rIylu9rCsz3PNvMtIlAhb4DvJPtTPvJndAiL8T0.otcaOzzyr2w2xhgJkhFXIK0uoA", "response": "never quit", "verified": true}} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"status": {"type": "string"}, "result": {"type": "object", "properties": {"challenge": {"type": "string"}, "response": {"type": "string"}, "verified": {"type": "boolean"}}, "required": ["challenge", "response", "verified"]}}, "required": ["result", "status"]} |
78c2bc28-0568-4253-ab9c-2799a4a1e4b1/aab67557-29bb-453a-af88-931a141e3bd9/0/0 | SolveMedia Solver | An API for bypassing and recognizing SolveMedia Captcha | 5.5 | byImageUrl | Solve the SolveMedia captcha via Image URL | 200 | Response | {"status": "success", "result": {"response": "sweypea", "verified": true}} | {"type": "object", "properties": {"status": {"type": "string"}, "result": {"type": "object", "properties": {"response": {"type": "string"}, "verified": {"type": "boolean"}}}}} |
d0a46374-a5fd-4f3a-a4b0-6d174e60802d/11de81d8-57f8-4f27-b3f1-29427777e664/0/0 | OCR - Extract text | Extract texts from images with very high accuracy and supports all languages of the earth. | 6.5 | Extract text from image url | Image must be a regular JPEG or PNG image (with or without transparency). | 200 | Response | {"ok": true, "text": "Maths, Maths, Maths !\n\nDown with old Pythagoras\nAnd down with rotten maths.\nDown with Archimedes,\nAnd drown him at the baths.\n\nIf anyone had to do it\nI'd make sure it was me\nFirst I'd wholly immerse him,\nThen Kick him up a tree.\n\nWhen he had been disposed of,\nYd turn on old Pythag\nVd drag him through a holly bush,\nAnd he'd come out like a rag.\n\nNow my pipe dreams over,\nAnd I\u2019ve nothing more to say\nExcept that Maths still lives on\nTo be taught another day."} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"ok": {"type": "boolean"}, "text": {"type": "string"}}, "required": ["ok", "text"]} |
705e7ed2-33dd-4bb9-9638-93b60849f4c9/504196a4-4b69-4ee5-b31b-1abb4b627259/0/0 | Insect Counter for yellow sticky traps | Send a yellow sticky trap image and find out the count of big, medium, small and white insects. | 0 | Create Detection | Create Detection | 201 | Create Detection Response | {"id": "11111111-2222-3333-4444-555555555555", "name": "1.jpg", "model": "DL", "createdAt": "2022-01-01T12:05:00.281629062", "count": {"small": 488, "medium": 182, "big": 6, "white": 163}, "images": {"original": {"url": "https://static.insect-counter.uolabs.tech/images/11111111-2222-3333-4444-555555555555/original/11111111-2222-3333-4444-555555555555"}, "processed": {"url": "https://static.insect-counter.uolabs.tech/images/11111111-2222-3333-4444-555555555555/processed/11111111-2222-3333-4444-555555555555"}}} | {"id": "string", "name": "string", "model": "string", "createdAt": "string", "count": {"small": "number", "medium": "number", "big": "number", "white": "number"}, "images": {"original": {"url": "string"}, "processed": {"url": "string"}}} |
571e1cf3-e5d6-4b12-9f53-5a18f0b20a9d/6a989c34-3494-4b22-ad97-d4487bf7cca4/0/0 | Cloudlabs Image OCR | Is an API used as OCR (Optical Character Recognition), to extract text in an image, supports handwriting | 9.1 | recognize (by Image URL) | This endpoint is used to extract text on the image using the image URL | 200 | Response | {"status": "success", "result": "how i improved my\nhandwriting"} | {"type": "object", "properties": {"status": {"type": "string"}, "result": {"type": "string"}}} |
571e1cf3-e5d6-4b12-9f53-5a18f0b20a9d/4ccdc249-f8cc-467f-9aa1-cf8078836619/0/0 | Cloudlabs Image OCR | Is an API used as OCR (Optical Character Recognition), to extract text in an image, supports handwriting | 9.1 | recognize (by Image File) | This endpoint is used to extract text on images through the upload process | 200 | New Example | {"status": "success", "result": "Optical Character Recognition is a software that converts text in an image or image file format into a text format that can be read and edited by computer applications"} | {"type": "object", "properties": {"status": {"type": "string"}, "result": {"type": "string"}}} |
c7a200f6-26d8-4e24-ba25-77d66df28845/1ad6699e-3c18-4471-845e-07a6fff94632/0/0 | Skin analyze Pro | Provides detailed multi-dimensional skin analysis of skin, comprehensive analysis of skin condition, such as skin color, skin texture, double eyelids, eye bags, dark circles, wrinkles, acne, spots, etc. | 7.8 | Skin analyze Pro | Skin analyze Pro | 200 | Success | {"request_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "log_id": "3A9BFC5E-3F7C-4D9A-9445-908C6D14AB5B", "error_code": 0, "face_rectangle": {"top": 0, "left": 0, "width": 0, "height": 0}, "left_side_result": {"left_jawline_quality": 1, "left_jawline_angle": 122.73, "left_jawline_mark": [{"x": 518, "y": 1256}, {"x": 531, "y": 1260}]}, "right_jawline_info": {"right_jawline_quality": 1, "right_jawline_angle": 122.73, "right_jawline_mark": [{"x": 518, "y": 1256}, {"x": 531, "y": 1260}]}, "result": {"image_quality": {"face_ratio:": 0.3, "face_orientation": {"yaw": 30.1, "pitch": 21.2, "roll": 89}, "face_rect": {"top": 808, "left": 677, "width": 800, "height": 800}, "hair_occlusion": 0.05, "glasses": 0}, "skin_type": {"skin_type": 0, "details": {"0": {"value": 0, "confidence": 0.89}, "1": {"value": 0, "confidence": 0.89}, "2": {"value": 1, "confidence": 0.89}, "3": {"value": 0, "confidence": 0.89}}}, "oily_intensity": {"t_zone": {"area": 0, "intensity": 0}, "left_cheek": {"area": 0, "intensity": 0}, "right_cheek": {"area": 0, "intensity": 0}, "chin_area": {"area": 0, "intensity": 0}}, "water": {"water_severity": 0, "water_area": 0, "water_forehead": {"area": 0}, "water_leftcheek": {"area": 0}, "water_rightcheek": {"area": 0}}, "skin_tone": {"value": 0, "confidence": 0}, "skintone_ita": {"ITA": 0, "skintone": 0}, "skin_hue_ha": {"HA": 0, "skin_hue": 0}, "blackhead": {"value": 0, "confidence": 0}, "blackhead_count": 0, "enlarged_pore_count": {"forehead": {"count": 0, "area": 0}, "left_cheek_count": {"count": 0, "area": 0}, "right_cheek_count": {"count": 0, "area": 0}, "chin_count": {"count": 0, "area": 0}}, "pores_forehead": {"value": 0, "confidence": 0}, "pores_rightcheek": {"value": 0, "confidence": 0}, "pores_leftcheek": {"value": 0, "confidence": 0}, "pores_jaw": {"value": 0, "confidence": 0}, "rough": {"rough_severity": 0, "rough_area": 0, "rough_forehead": {"area": 0}, "rough_leftcheek": {"area": 0}, "rough_rightcheek": {"area": 0}, "rough_jaw": {"area": 0}}, "melanin": {"brown_area": 0, "melanin_concentration": 0, "brown_forehead": 0, "brown_leftcheek": 0, "brown_rightcheek": 0}, "melanin_mark": {"polygon": [[{"x": 0, "y": 0}]]}, "mole": {"rectangle": [{"width": 0, "top": 0, "height": 0, "left": 0}], "confidence": [0], "polygon": [[{"x": 0, "y": 0}]]}, "brown_spot": {"rectangle": [{"width": 0, "top": 0, "height": 0, "left": 0}], "confidence": [0], "polygon": [[{"x": 0, "y": 0}]]}, "melasma": {"value": 0, "confidence": 0}, "freckle": {"value": 0, "confidence": 0}, "acne": {"rectangle": [{"width": 0, "top": 0, "height": 0, "left": 0}], "confidence": [0], "polygon": [[{"x": 0, "y": 0}]]}, "acne_pustule": {"rectangle": [{"width": 0, "top": 0, "height": 0, "left": 0}], "confidence": [0], "polygon": [[{"x": 0, "y": 0}]]}, "acne_nodule": {"rectangle": [{"width": 0, "top": 0, "height": 0, "left": 0}], "confidence": [0], "polygon": [[{"x": 0, "y": 0}]]}, "acne_mark": {"rectangle": [{"width": 0, "top": 0, "height": 0, "left": 0}], "confidence": [0], "polygon": [[{"x": 0, "y": 0}]]}, "closed_comedones": {"rectangle": [{"width": 0, "top": 0, "height": 0, "left": 0}], "confidence": [0], "polygon": [[{"x": 0, "y": 0}]]}, "sensitivity": {"sensitivity_area": 0, "sensitivity_intensity": 0}, "sensitivity_mark": {"polygon": [[{"x": 0, "y": 0}]]}, "skin_age": {"value": 0}, "forehead_wrinkle": {"value": 0, "confidence": 0}, "crows_feet": {"value": 0, "confidence": 0}, "eye_finelines": {"value": 0, "confidence": 0}, "glabella_wrinkle": {"value": 0, "confidence": 0}, "nasolabial_fold": {"value": 0, "confidence": 0}, "nasolabial_fold_severity": {"value": 0, "confidence": 0}, "left_mouth_wrinkle_severity": {"value": 0}, "right_mouth_wrinkle_severity": {"value": 0}, "forehead_wrinkle_severity": {"value": 0}, "left_crows_feet_severity": {"value": 0}, "right_crows_feet_severity": {"value": 0}, "left_eye_finelines_severity": {"value": 0}, "right_eye_finelines_severity": {"value": 0}, "glabella_wrinkle_severity": {"value": 0}, "left_nasolabial_fold_severity": {"value": 0}, "right_nasolabial_fold_severity": {"value": 0}, "left_cheek_wrinkle_severity": {"value": 0}, "right_cheek_wrinkle_severity": {"value": 0}, "fine_line": {"forehead_count": 0, "left_undereye_count": 0, "right_undereye_count": 0, "left_cheek_count": 0, "right_cheek_count": 0, "left_crowsfeet_count": 0, "right_crowsfeet_count": 0, "glabella_count": 0}, "wrinkle_count": {"forehead_count": 0, "left_undereye_count": 0, "right_undereye_count": 0, "left_mouth_count": 0, "right_mouth_count": 0, "left_nasolabial_count": 0, "right_nasolabial_count": 0, "glabella_count": 0, "left_cheek_count": 0, "right_cheek_count": 0, "left_crowsfeet_count": 0, "right_crowsfeet_count": 0}, "forehead_wrinkle_info": {"wrinkle_score": 0, "wrinkle_severity_level": 0, "wrinkle_norm_length": 0, "wrinkle_norm_depth": 0, "wrinkle_pixel_density": 0, "wrinkle_area_ratio": 0, "wrinkle_deep_ratio": 0, "wrinkle_deep_num": 0, "wrinkle_shallow_num": 0, "forehead_wrinkle_list": []}, "left_eye_wrinkle_info": {"wrinkle_score": 0, "wrinkle_severity_level": 0, "wrinkle_norm_length": 0, "wrinkle_norm_depth": 0, "wrinkle_pixel_density": 0, "wrinkle_area_ratio": 0, "wrinkle_deep_ratio": 0, "wrinkle_deep_num": 0, "wrinkle_shallow_num": 0, "left_eye_wrinkle_list": []}, "right_eye_wrinkle_info": {"wrinkle_score": 0, "wrinkle_severity_level": 0, "wrinkle_norm_length": 0, "wrinkle_norm_depth": 0, "wrinkle_pixel_density": 0, "wrinkle_area_ratio": 0, "wrinkle_deep_ratio": 0, "wrinkle_deep_num": 0, "wrinkle_shallow_num": 0, "right_eye_wrinkle_list": []}, "left_crowsfeet_wrinkle_info": {"wrinkle_score": 0, "wrinkle_severity_level": 0, "wrinkle_norm_length": 0, "wrinkle_norm_depth": 0, "wrinkle_pixel_density": 0, "wrinkle_area_ratio": 0, "wrinkle_deep_ratio": 0, "wrinkle_deep_num": 0, "wrinkle_shallow_num": 0, "left_crowsfeet_wrinkle_list": []}, "right_crowsfeet_wrinkle_info": {"wrinkle_score": 0, "wrinkle_severity_level": 0, "wrinkle_norm_length": 0, "wrinkle_norm_depth": 0, "wrinkle_pixel_density": 0, "wrinkle_area_ratio": 0, "wrinkle_deep_ratio": 0, "wrinkle_deep_num": 0, "wrinkle_shallow_num": 0, "right_crowsfeet_wrinkle_list": []}, "glabella_wrinkle_info": {"wrinkle_score": 0, "wrinkle_severity_level": 0, "wrinkle_norm_length": 0, "wrinkle_norm_depth": 0, "wrinkle_pixel_density": 0, "wrinkle_area_ratio": 0, "wrinkle_deep_ratio": 0, "wrinkle_deep_num": 0, "wrinkle_shallow_num": 0, "glabella_wrinkle_list": []}, "left_mouth_wrinkle_info": {"wrinkle_score": 0, "wrinkle_severity_level": 0, "wrinkle_norm_length": 0, "wrinkle_norm_depth": 0, "wrinkle_pixel_density": 0, "wrinkle_area_ratio": 0, "wrinkle_deep_ratio": 0, "wrinkle_deep_num": 0, "wrinkle_shallow_num": 0, "left_mouth_wrinkle_list": []}, "right_mouth_wrinkle_info": {"wrinkle_score": 0, "wrinkle_severity_level": 0, "wrinkle_norm_length": 0, "wrinkle_norm_depth": 0, "wrinkle_pixel_density": 0, "wrinkle_area_ratio": 0, "wrinkle_deep_ratio": 0, "wrinkle_deep_num": 0, "wrinkle_shallow_num": 0, "right_mouth_wrinkle_list": []}, "left_nasolabial_wrinkle_info": {"wrinkle_score": 0, "wrinkle_severity_level": 0, "wrinkle_norm_length": 0, "wrinkle_norm_depth": 0, "wrinkle_pixel_density": 0, "wrinkle_area_ratio": 0, "wrinkle_deep_ratio": 0, "wrinkle_deep_num": 0, "wrinkle_shallow_num": 0, "left_nasolabial_wrinkle_list": []}, "right_nasolabial_wrinkle_info": {"wrinkle_score": 0, "wrinkle_severity_level": 0, "wrinkle_norm_length": 0, "wrinkle_norm_depth": 0, "wrinkle_pixel_density": 0, "wrinkle_area_ratio": 0, "wrinkle_deep_ratio": 0, "wrinkle_deep_num": 0, "wrinkle_shallow_num": 0, "right_nasolabial_wrinkle_list": []}, "left_cheek_wrinkle_info": {"wrinkle_score": 0, "wrinkle_severity_level": 0, "wrinkle_norm_length": 0, "wrinkle_norm_depth": 0, "wrinkle_pixel_density": 0, "wrinkle_area_ratio": 0, "wrinkle_deep_ratio": 0, "wrinkle_deep_num": 0, "wrinkle_shallow_num": 0, "left_cheek_wrinkle_list": []}, "right_cheek_wrinkle_info": {"wrinkle_score": 0, "wrinkle_severity_level": 0, "wrinkle_norm_length": 0, "wrinkle_norm_depth": 0, "wrinkle_pixel_density": 0, "wrinkle_area_ratio": 0, "wrinkle_deep_ratio": 0, "wrinkle_deep_num": 0, "wrinkle_shallow_num": 0, "right_cheek_wrinkle_list": []}, "cheekbone_mark": {"left_cheekbone_mark": [], "right_cheekbone_mark": []}, "eye_pouch": {"value": 0, "confidence": 0}, "eye_pouch_severity": {"value": 0, "confidence": 0}, "left_eye_pouch_rectangle": [], "right_eye_pouch_rectangle": [], "dark_circle": {"value": 0, "confidence": 0}, "dark_circle_severity": {"value": 0, "confidence": 0}, "left_dark_circle_rete": {"value": 0}, "right_dark_circle_rete": {"value": 0}, "left_dark_circle_pigment": {"value": 0}, "right_dark_circle_pigment": {"value": 0}, "left_dark_circle_structural": {"value": 0}, "right_dark_circle_structural": {"value": 0}, "dark_circle_mark": {"left_eye_rect": {"left": 0, "top": 0, "width": 0, "height": 0}, "right_eye_rect": {"left": 0, "top": 0, "width": 0, "height": 0}}, "left_eye_pouch_rect": {"left": 0, "top": 0, "width": 0, "height": 0}, "right_eye_pouch_rect": {"left": 0, "top": 0, "width": 0, "height": 0}, "wrinkle_mark": {"left_cheek_wrinkle_outline": [], "right_cheek_wrinkle_outline": [], "head_wrinkle_outline": [], "left_nasolabial_wrinkle_outline": [], "right_nasolabial_wrinkle_outline": [], "glabella_wrinkle_outline": [], "left_crowsfeet_wrinkle_outline": [], "right_crowsfeet_wrinkle_outline": [], "left_mouth_wrinkle_outline": [], "right_mouth_wrinkle_outline": [], "left_eye_wrinkle_outline": [], "right_eye_wrinkle_outline": []}, "dark_circle_outline": {"left_dark_circle_outline": [], "right_dark_circle_outline": []}, "score_info": {"dark_circle_score": 0, "skin_type_score": 0, "wrinkle_score": 0, "oily_intensity_score": 0, "pores_score": 0, "blackhead_score": 0, "acne_score": 0, "sensitivity_score": 0, "melanin_score": 0, "water_score": 0, "rough_score": 0, "total_score": 0, "pores_type_score": {"pores_forehead_score": 0, "pores_leftcheek_score": 0, "pores_rightcheek_score": 0, "pores_jaw_score": 0}, "dark_circle_type_score": {"left_dark_circle_score": 0, "right_dark_circle_score": 0}}, "enhanced_bw_info": {"enhanced_bw_rect": {"left": 0, "top": 0, "width": 0, "height": 0}, "ratio": 0}, "face_maps": {"red_area": "", "brown_area": "", "texture_enhanced_pores": "", "texture_enhanced_blackheads": "", "texture_enhanced_oily_area": "", "texture_enhanced_lines": "", "water_area": "", "rough_area": "", "roi_outline_map": "", "texture_enhanced_bw": ""}}} | {"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."}} |
c7a200f6-26d8-4e24-ba25-77d66df28845/1ad6699e-3c18-4471-845e-07a6fff94632/0/1 | Skin analyze Pro | Provides detailed multi-dimensional skin analysis of skin, comprehensive analysis of skin condition, such as skin color, skin texture, double eyelids, eye bags, dark circles, wrinkles, acne, spots, etc. | 7.8 | Skin analyze Pro | Skin analyze Pro | 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."}} |
e7dacdb5-44b3-406e-9854-0a2524cb333a/2b79c04e-d16f-449b-9cb5-99f7fe29aac3/0/0 | Face Api | Facial features API.
Gender, emotion, landmarks, age. | 7.6 | Run | 200 | null | {"faces": [{"left": 0, "top": 0, "right": 0, "bottom": 0, "emotion": {}, "gender": {}, "age": 0, "ethnicity": {}, "landmarks": [{"isEmpty": true, "x": 0, "y": 0}]}]} | {"type": "object", "properties": {"faces": {"type": ["array", "null"], "items": {"type": "object", "properties": {"left": {"type": "integer", "description": "Left X-axis values of face rectangle", "format": "int32", "minimum": -2147483648, "maximum": 2147483647}, "top": {"type": "integer", "description": "Top Y-axis values of face rectangle", "format": "int32", "minimum": -2147483648, "maximum": 2147483647}, "right": {"type": "integer", "description": "Right X-axis values of face rectangle", "format": "int32", "minimum": -2147483648, "maximum": 2147483647}, "bottom": {"type": "integer", "description": "Bottom Y-axis values of face rectangle", "format": "int32", "minimum": -2147483648, "maximum": 2147483647}, "emotion": {"type": ["object", "null"], "additionalProperties": {"type": "number", "format": "float", "minimum": -3.402823669209385e+38, "maximum": 3.402823669209385e+38}, "description": "Emotion and its score. Values: happy, angry, neutral, disgusted, sad, fearful, surprised"}, "gender": {"type": ["object", "null"], "additionalProperties": {"type": "number", "format": "float", "minimum": -3.402823669209385e+38, "maximum": 3.402823669209385e+38}, "description": "Gender and its score. Values: male, female"}, "age": {"type": ["integer", "null"], "description": "Age", "format": "int32", "minimum": -2147483648, "maximum": 2147483647}, "ethnicity": {"type": ["object", "null"], "additionalProperties": {"type": "number", "format": "float", "minimum": -3.402823669209385e+38, "maximum": 3.402823669209385e+38}, "description": "Ethnicity and its score. Values: white, black, asian, indian, other"}, "landmarks": {"type": ["array", "null"], "items": {"type": "object", "properties": {"isEmpty": {"type": "boolean"}, "x": {"type": "integer", "format": "int32", "minimum": -2147483648, "maximum": 2147483647}, "y": {"type": "integer", "format": "int32", "minimum": -2147483648, "maximum": 2147483647}}, "additionalProperties": false}, "description": "Array of 192 facial landmarks"}}, "additionalProperties": false}, "description": "Array of detected faces"}}, "additionalProperties": false} |
|
d17be342-5fa1-4de0-8d52-b7a1ec64cb48/738551ad-a077-4f66-9cb7-dd84b2156aee/0/0 | AI Skin Beauty | AI-based algorithms automatically perform skin leveling and blemish removal (acne, pimple marks, freckles, etc.) on face areas, as well as skin area whitening on the whole body, while preserving skin texture as much as possible. Supports multi-person image processing. | 6.4 | AI Skin Beauty | AI Skin Beauty | 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."}}}} |
d17be342-5fa1-4de0-8d52-b7a1ec64cb48/738551ad-a077-4f66-9cb7-dd84b2156aee/0/1 | AI Skin Beauty | AI-based algorithms automatically perform skin leveling and blemish removal (acne, pimple marks, freckles, etc.) on face areas, as well as skin area whitening on the whole body, while preserving skin texture as much as possible. Supports multi-person image processing. | 6.4 | AI Skin Beauty | AI Skin Beauty | 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."}}}} |
d17be342-5fa1-4de0-8d52-b7a1ec64cb48/738551ad-a077-4f66-9cb7-dd84b2156aee/1/0 | AI Skin Beauty | AI-based algorithms automatically perform skin leveling and blemish removal (acne, pimple marks, freckles, etc.) on face areas, as well as skin area whitening on the whole body, while preserving skin texture as much as possible. Supports multi-person image processing. | 6.4 | AI Skin Beauty | AI Skin Beauty | 401 | Example | {"message": "Invalid API key in request"} | {"message": {"type": "String", "required": true, "example": "Invalid API key in request", "description": "Error Message."}} |
d7d00f4a-4ecb-4598-ae0a-8d3cc35e0a75/be220927-050d-4070-a976-52eede5f226d/0/0 | Macao Driving License OCR | Extract the 7 key fields from a Macau driving license, including name in Chinese, name in English, date of birth, date of issue, expiration date, agency of issue, license number and head portrait . | 6.3 | Macao 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": "11/22/2023 11:22:55 PM", "result": {"rotated_image_height": 474, "image_angle": 0, "rotated_image_width": 764, "type": "aomen_driving_license", "details": {"birthday": {"value": "12/04/1999", "position": {"bottom": 427, "left": 375, "right": 502, "top": 407}}, "head_portrait": {"value": 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", "position": {"bottom": 0, "left": 0, "right": 0, "top": 0}}, "name_pinyin": {"value": "XIAO XIAO", "position": {"bottom": 355, "left": 371, "right": 517, "top": 335}}, "expire_date": {"value": "08/06/2033", "position": {"bottom": 432, "left": 669, "right": 823, "top": 404}}, "license_number": {"value": "66554", "position": {"bottom": 428, "left": 143, "right": 221, "top": 404}}, "name": {"value": ""}, "issue_date": {"value": "08/06/2022", "position": {"bottom": 473, "left": 646, "right": 788, "top": 453}}, "issuing_agency": {"value": "\u6fb3\u9580\u7279\u5225\u884c\u653f\u533a\u4ea4\u901a\u4e8b\u52d9", "position": {"bottom": 477, "left": 159, "right": 416, "top": 453}}}}} | {"$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": {"birthday": {"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"]}, "name_pinyin": {"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"}}, "required": ["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"]}, "issuing_agency": {"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": ["birthday", "expire_date", "head_portrait", "issue_date", "issuing_agency", "license_number", "name", "name_pinyin"]}}, "required": ["details", "image_angle", "rotated_image_height", "rotated_image_width", "type"]}}, "required": ["code", "date", "result", "status"]} |
a386322c-b13b-4334-9e05-060e1e7979dc/b27e0899-96ca-4809-9261-7c400368aa7c/0/0 | Fashiontag | Tag images with fashion items | null | fashion tagging (will not work through the web interface of rapid-api) | Will return the following tags with a confidence score. | 200 | Response | {"labels": [{"confidence": 0.5428122282028198, "label": "Long Sleeves"}, {"confidence": 0.16284550726413727, "label": "Jacket"}, {"confidence": 0.1258610188961029, "label": "Short Length"}, {"confidence": 0.12292420864105225, "label": "High Collar"}, {"confidence": 0.11373768746852875, "label": "Shirt"}]} | {"properties": {"labels": {"items": {"properties": {"confidence": {"type": "number"}, "label": {"type": "string"}}, "type": "object"}, "type": "array"}}, "type": "object"} |
a386322c-b13b-4334-9e05-060e1e7979dc/99c6fb15-239f-4e67-b6b2-6d0d0e8123b3/0/0 | Fashiontag | Tag images with fashion items | null | Annotate from image_url | provide us with a valid img url to analyse | 200 | Response | {"request": {"image_url": "https://img01.ztat.net/article/spp-media-p1/1d271bf98fce36b49dc7de9a2f6ed047/863a067b4503495f9859bd523236f065.jpg?imwidth=1800", "response": 200}, "result": {"labels": [{"confidence": 0.6338905096054077, "label": "Long Sleeves"}, {"confidence": 0.24857348203659058, "label": "Jacket"}, {"confidence": 0.1558683067560196, "label": "Mandarin Collar"}, {"confidence": 0.13256564736366272, "label": "Floral"}, {"confidence": 0.11908653378486633, "label": "Scarf"}]}, "status": "ok"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"request": {"type": "object", "properties": {"image_url": {"type": "string"}, "response": {"type": "integer"}}, "required": ["image_url", "response"]}, "result": {"type": "object", "properties": {"labels": {"type": "array", "items": {"type": "object", "properties": {"confidence": {"type": "number"}, "label": {"type": "string"}}, "required": ["confidence", "label"]}}}, "required": ["labels"]}, "status": {"type": "string"}}, "required": ["request", "result", "status"]} |
7a743fef-9184-496b-acdb-aab51e74e91b/1acaf624-c1f8-411a-9ce7-0e0599372ba2/0/0 | Free Recognized Table Content | Free Recognized Table Content | null | /recognize-table | 200 | null | {"status": 1, "url": "http://vibktprfx-prod-prod-damo-eas-cn-shanghai.oss-cn-shanghai.aliyuncs.com/generative-cartoon/2023-09-09/1d73851a-7d53-416b-8606-01dc2f09db52/20230909_235408006889_dmbpxu56o3.jpg?Expires=1694276651&OSSAccessKeyId=LTAI4FoLmvQ9urWXgSRpDvh1&Signature=%2FcT4QU5F7%2FIR2J9Z9a3lvUnwdbI%3D"} | {"type": "object", "properties": {"status": {"type": "integer"}, "url": {"type": "string"}}, "required": ["status", "url"], "x-apifox-ignore-properties": [], "x-apifox-orders": ["status", "url"]} |
|
16d93fbd-9f1a-4d64-bed1-44cf8a37e2e4/a53c1dc8-a173-4531-8285-1528d2bb8690/0/0 | Face Enroll | PresentID Face enroll API allows you to register your users for future authentication. This API stores two image files along with the user's personal information. Before saving, it is checked whether these two images belong to one person or not. So allows the user to authenticate by sending the face and personID. | 5.8 | Search Person | Search person by face in your users group. | 200 | Face Enroll Search People | {"statusCode": 200, "statusMessage": "The two faces belong to the same person. ", "hasError": false, "data": {"personId": "Person Unique ID", "name": "Person Name", "group": "Group Name", "imageBase64": "Person Image in base64 data uri format", "imageMimeType": "image/jpeg"}} | {"type": "object", "properties": {"statusCode": {"type": "integer"}, "statusMessage": {"type": "string"}, "hasError": {"type": "boolean"}, "data": {"type": "object", "properties": {"personId": {"type": "string"}, "name": {"type": "string"}, "group": {"type": "string"}, "imageBase64": {"type": "string"}, "imageMimeType": {"type": "string"}}}}} |
16d93fbd-9f1a-4d64-bed1-44cf8a37e2e4/97b19846-6469-4397-b4bb-d4b94f36af1a/0/0 | Face Enroll | PresentID Face enroll API allows you to register your users for future authentication. This API stores two image files along with the user's personal information. Before saving, it is checked whether these two images belong to one person or not. So allows the user to authenticate by sending the face and personID. | 5.8 | Enroll by face | Enroll by face API allows you to register your users for future authentication. In this API, it stores two image files along with the user's personal information. Before saving, it is checked whether these two images belong to one person or not. The parameters must be submitted in multipart form. The API is called by POST method. | 200 | Face Enroll Registration | {"data": {"personID": "8000000070314049b090831f"}, "details": null, "hasError": false, "statusCode": 200, "statusMessage": "OK"} | {"properties": {"data": {"properties": {"personID": {"type": "string"}}, "type": "object"}, "details": {"type": "null"}, "hasError": {"type": "boolean"}, "statusCode": {"type": "integer"}, "statusMessage": {"type": "string"}}, "type": "object"} |
16d93fbd-9f1a-4d64-bed1-44cf8a37e2e4/f2bbb610-73db-4b71-9f56-c898082e4c5b/0/0 | Face Enroll | PresentID Face enroll API allows you to register your users for future authentication. This API stores two image files along with the user's personal information. Before saving, it is checked whether these two images belong to one person or not. So allows the user to authenticate by sending the face and personID. | 5.8 | Login by face | Identify your clients by their photo face. | 200 | Face Enroll Login | {"data": {"firstName": "hossein", "isLogin": true, "lastName": "shahabi", "personId": "8000000070314049b090831f"}, "hasError": false, "request": {"conversationId": null, "packageId": null, "userId": null}, "statusCode": 200, "statusMessage": "Login Successfully."} | {"properties": {"data": {"properties": {"firstName": {"type": "string"}, "isLogin": {"type": "boolean"}, "lastName": {"type": "string"}, "personId": {"type": "string"}}, "type": "object"}, "hasError": {"type": "boolean"}, "request": {"properties": {"conversationId": {"type": "null"}, "packageId": {"type": "null"}, "userId": {"type": "null"}}, "type": "object"}, "statusCode": {"type": "integer"}, "statusMessage": {"type": "string"}}, "type": "object"} |
bd425f7e-013d-4553-abf6-c78deec0e16f/d579f368-f3ad-436e-afaf-0fb7bc927608/0/0 | Face Pixelate | Anonymise faces from a photo.
Faces detected are pixelated.
A filter could only pixelate faces depending of the detected age. | 5.6 | /facepixelate/api | Pixelate all detected faces.
Optional set an age limit (for exemple only pixelate faces under 18years). | 200 | New Example | {"status": "ok", "imageUrl": "https://xxxxx/f9f64382d0154c54axxxaff67f8600bbeb8.jpg", "ageCondition": null, "modeAgeCondition": null, "detectedFaces": [{"left": 0.3684062063694, "top": 0.20878127217292786, "width": 0.05724090710282326, "height": 0.10473860055208206, "ageLow": 43, "ageHigh": 51}, {"left": 0.582745373249054, "top": 0.4258824288845062, "width": 0.04079504311084747, "height": 0.07189665734767914, "ageLow": 3, "ageHigh": 9}, {"left": 0.49995923042297363, "top": 0.4748813509941101, "width": 0.03866284340620041, "height": 0.06897767633199692, "ageLow": 6, "ageHigh": 12}, {"left": 0.5311446189880371, "top": 0.3407318890094757, "width": 0.005043273791670799, "height": 0.009110906161367893, "ageLow": 16, "ageHigh": 22}, {"left": 0.5491129755973816, "top": 0.336672842502594, "width": 0.004493224900215864, "height": 0.008679686114192009, "ageLow": 19, "ageHigh": 23}, {"left": 0.5696215629577637, "top": 0.3307998776435852, "width": 0.004920226987451315, "height": 0.007899490185081959, "ageLow": 20, "ageHigh": 28}], "imageUrlOutput": "https://xxxx/facepixelate/output/33e0a5686b2326e8a7e77ae3b726765c.jpg"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"status": {"type": "string"}, "imageUrl": {"type": "string"}, "ageCondition": {"type": "null"}, "modeAgeCondition": {"type": "null"}, "detectedFaces": {"type": "array", "items": {"type": "object", "properties": {"left": {"type": "number"}, "top": {"type": "number"}, "width": {"type": "number"}, "height": {"type": "number"}, "ageLow": {"type": "integer"}, "ageHigh": {"type": "integer"}}, "required": ["ageHigh", "ageLow", "height", "left", "top", "width"]}}, "imageUrlOutput": {"type": "string"}}, "required": ["ageCondition", "detectedFaces", "imageUrl", "imageUrlOutput", "modeAgeCondition", "status"]} |
88218db1-fcc8-4ac6-9a4f-cbcb741a5dfc/c0889839-8f3f-4ba1-97f5-315990c27818/0/0 | Real or Fake Image? | Our API identifies artificial or generative AI images, providing accurate detection to combat fake and misleading visuals. Using advanced algorithms, it analyzes and identifies unique patterns to offer an added layer of protection and verification for social media platforms, news organizations, and content creators. | 7.9 | /api-v1.0/SafeUnsafeImageWithTags | Our Artificial and Virtual Image Detector API accurately identifies artificial or virtual images. Simply upload your image file to our API endpoint, and you'll receive a JSON response with a label indicating if the image is artificial, along with a probability score. This helps you remove misleading visuals from your app or website, ensuring user trust. Stay tuned for our upcoming version, which will offer improved accuracy and performance, making it an invaluable tool for detecting fake images. | 200 | 0 | {"error_code": 0, "predictions": {"appearance_clothing_swimwear_bikini": 0.94, "appearance_clothing_underwear_lingerie": 0.94, "artificial-images": 0.99, "composition_one_female": 0.59, "unsafe": 1}} | {"type": "object", "properties": {"error_code": {"type": "integer"}, "predictions": {"type": "object", "properties": {"black bikini": {"type": "number"}, "muscular": {"type": "number"}, "thighs": {"type": "number"}, "black hair": {"type": "number"}, "dark skin": {"type": "number"}, "forehead": {"type": "number"}, "muscular female": {"type": "number"}, "unsafe": {"type": "number"}, "bikini": {"type": "number"}, "dark-skinned female": {"type": "number"}, "swimsuit": {"type": "number"}, "english commentary": {"type": "number"}, "jewelry": {"type": "number"}, "long hair": {"type": "number"}, "original": {"type": "number"}, "composition_one_female": {"type": "number"}, "toned": {"type": "number"}, "large breasts": {"type": "number"}, "solo": {"type": "number"}, "breasts": {"type": "number"}, "1girl": {"type": "number"}, "navel": {"type": "number"}, "lips": {"type": "number"}, "abs": {"type": "number"}, "choker": {"type": "number"}, "patreon username": {"type": "number"}, "looking at viewer": {"type": "number"}, "artificial-images": {"type": "number"}}}}} |
5651ae68e4b0f255c2d2bbc5/5651b2e2e4b0a4872350caf1/0/0 | Nude Detect | Use NetSpark's Nude and pornography image processing engine for high accuracy detection of nudity, sexuality and pornography in photos.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 image file types {WEBP, PNG, JPG/JPEG, BMP}. GIF and TIFF file types will return an error.For support for larger volumes or for inspection of vide... | null | Detect Nudity in Web-Hosted Image | Detect Nudity in Web-Hosted Image | 200 | Example_1 | {"status": "success", "photo url": "http://www.domain.com/yourimage.jpg", "is nude": {"confidence": "3.85%"}, "is minimal dress": {"confidence": "86.51%"}} | {"type": "object", "properties": {"status": {"type": "string"}, "photo url": {"type": "string"}, "is nude": {"type": "object", "properties": {"confidence": {"type": "string"}}}, "is minimal dress": {"type": "object", "properties": {"confidence": {"type": "string"}}}}} |
5651ae68e4b0f255c2d2bbc5/e6f17d6d-153e-4a15-b9c7-28df69f6fbc5/0/0 | Nude Detect | Use NetSpark's Nude and pornography image processing engine for high accuracy detection of nudity, sexuality and pornography in photos.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 image file types {WEBP, PNG, JPG/JPEG, BMP}. GIF and TIFF file types will return an error.For support for larger volumes or for inspection of vide... | null | Detect Illegal Web-Hosted Image | Detect image category from:
very likely nude, likely nude, very likely minimal dress, likely minimal dress, very likely partial dress, full dress, likely partial dress, men and objects, possible pedophilic, likely pedophilic, very likely pedophilic | 200 | New Example | {"result": "full dress", "url": "http://www.domain.com/yourimage.jpg"} | {"properties": {"result": {"type": "string"}, "url": {"type": "string"}}, "type": "object"} |
61ff43dc-78ea-4bc2-9e5e-50711d6bd289/f66058ff-6b18-4302-9add-98d737d0da53/0/0 | Indonesia Vehicle Analysis and LPR | An API which provides advanced capabilities to count, analyze, and perform License Plate Recognition (LPR) on vehicles within an image. It accurately identifies and outputs the locations of vehicles, extracts license plate numbers, determines vehicle color, and classifies vehicle types for comprehensive analysis and monitoring purposes. | 0.1 | Analyze | Count, Analyze, and run LPR for vehicles in an image
supported image format is JPEG and PNG
Output detected vehicles, with each vehicle'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
- License plate number
- Estimated vehicle type
- Estimated vehicle color | 200 | Result schema | {"status": "success", "image_file_name": "download.jpeg", "vehicle_count": 1, "analysis_result": [{"bbox": {"x1": 0.06178, "y1": 0, "x2": 0.87259, "y2": 0.93299}, "color": "gray", "color_prob": 0.9603, "type": "car", "type_prob": 0.8914, "plate_number": "D1509AEM", "plate_number_prob": 0.7}], "latency_ms": 1386.93} | {"type": "object", "properties": {"status": {"type": "string"}, "image_file_name": {"type": "string"}, "vehicle_count": {"type": "integer"}, "analysis_result": {"type": "array", "items": {"type": "object", "properties": {"bbox": {"type": "object", "properties": {"x1": {"type": "number"}, "y1": {"type": "integer"}, "x2": {"type": "number"}, "y2": {"type": "number"}}}, "color": {"type": "string"}, "color_prob": {"type": "number"}, "type": {"type": "string"}, "type_prob": {"type": "number"}, "plate_number": {"type": "string"}, "plate_number_prob": {"type": "number"}}}}, "latency_ms": {"type": "number"}}} |
61ff43dc-78ea-4bc2-9e5e-50711d6bd289/4bc0482d-98d3-4356-8ae2-1f0717880452/0/0 | Indonesia Vehicle Analysis and LPR | An API which provides advanced capabilities to count, analyze, and perform License Plate Recognition (LPR) on vehicles within an image. It accurately identifies and outputs the locations of vehicles, extracts license plate numbers, determines vehicle color, and classifies vehicle types for comprehensive analysis and monitoring purposes. | 0.1 | Licese Plate Recognition (LPR) Only | Only run LPR algorithm
supported image format is JPEG and PNG
Output detected license plates, with each license plate'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
- License plate number | 200 | New Example | {"status": "success", "image_file_name": "platnomor.jpg", "license_plate_count": 2, "lpr_result": [{"bbox": {"x1": 0.04767, "y1": 0.6305, "x2": 0.19633, "y2": 0.7035}, "plate_number": "B1314BBJ", "plate_number_prob": 0.88}, {"bbox": {"x1": 0.68567, "y1": 0.651, "x2": 0.90233, "y2": 0.726}, "plate_number": "B217TIO", "plate_number_prob": 0.77}], "latency_ms": 1292.9682731628418} | {"type": "object", "properties": {"status": {"type": "string"}, "image_file_name": {"type": "string"}, "license_plate_count": {"type": "integer"}, "lpr_result": {"type": "array", "items": {"type": "object", "properties": {"bbox": {"type": "object", "properties": {"x1": {"type": "number"}, "y1": {"type": "number"}, "x2": {"type": "number"}, "y2": {"type": "number"}}}, "plate_number": {"type": "string"}, "plate_number_prob": {"type": "number"}}}}, "latency_ms": {"type": "number"}}} |
373a07a4-b196-4448-95df-16d1817bd929/27d318e6-7660-452f-a1ed-f05150e2ad52/0/0 | People Detection | API for identifying and detecting the location of people in images. | null | Analyze Image | Detect the location of people in image. Returns the locations as bounding boxes. | 200 | Response | [{"id": "bb32399aa73d7c3e782535be617f0ef1", "region_info": {"bounding_box": {"top_row": 0.6631945, "left_col": 0.75529426, "bottom_row": 0.9394931, "right_col": 0.8360172}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.9278539, "app_id": "main"}]}, "value": 0.9278539}, {"id": "7788908b6e77af0a2fe049b26357ad80", "region_info": {"bounding_box": {"top_row": 0.64690113, "left_col": 0.6225491, "bottom_row": 0.9492987, "right_col": 0.68845844}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.9028303, "app_id": "main"}]}, "value": 0.9028303}, {"id": "67711fbc93c102e2a1f68cf4f2356c96", "region_info": {"bounding_box": {"top_row": 0.60346717, "left_col": 0.3610573, "bottom_row": 0.94157636, "right_col": 0.40356603}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.879238, "app_id": "main"}]}, "value": 0.879238}, {"id": "ea9e5d0208a8fb44ba83123182ed53ec", "region_info": {"bounding_box": {"top_row": 0.6322599, "left_col": 0.72713214, "bottom_row": 0.8802074, "right_col": 0.78370845}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.87085605, "app_id": "main"}]}, "value": 0.87085605}, {"id": "4ef87e993f961bf816b9c3540ffca8ce", "region_info": {"bounding_box": {"top_row": 0.6409835, "left_col": 0.5551642, "bottom_row": 0.9392192, "right_col": 0.62134427}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.81202334, "app_id": "main"}]}, "value": 0.81202334}, {"id": "3825289aa62cdafca0a56c029512df7b", "region_info": {"bounding_box": {"top_row": 0.6563121, "left_col": 0.24459152, "bottom_row": 0.888521, "right_col": 0.29402435}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.7285715, "app_id": "main"}]}, "value": 0.7285715}, {"id": "25aa3436d937439d9a77140993613cee", "region_info": {"bounding_box": {"top_row": 0.6482754, "left_col": 0.22244383, "bottom_row": 0.8831025, "right_col": 0.26849854}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.6911527, "app_id": "main"}]}, "value": 0.6911527}, {"id": "16a1b3b458ac6ccb0965c449c6fbc99f", "region_info": {"bounding_box": {"top_row": 0.6361816, "left_col": 0.51807445, "bottom_row": 0.876362, "right_col": 0.57179505}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.5947555, "app_id": "main"}]}, "value": 0.5947555}, {"id": "9fb7b9be0022b5a832702c2761ba7b4d", "region_info": {"bounding_box": {"top_row": 0.6349884, "left_col": 0.50031877, "bottom_row": 0.8781103, "right_col": 0.54863584}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.55083215, "app_id": "main"}]}, "value": 0.55083215}, {"id": "d6788fd9ab0eefb23039ab1092a92497", "region_info": {"bounding_box": {"top_row": 0.63005114, "left_col": 0.20553996, "bottom_row": 0.6527289, "right_col": 0.21416005}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.49524674, "app_id": "main"}]}, "value": 0.49524674}, {"id": "2ae9a0fb97829c41b33991d5dfb832d6", "region_info": {"bounding_box": {"top_row": 0.6366137, "left_col": 0.4821418, "bottom_row": 0.71085846, "right_col": 0.4956312}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.4587215, "app_id": "main"}]}, "value": 0.4587215}, {"id": "8ce1fd097a6845c3dadebe63d07ec93e", "region_info": {"bounding_box": {"top_row": 0.63241255, "left_col": 0.17452927, "bottom_row": 0.6752389, "right_col": 0.19006231}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.451904, "app_id": "main"}]}, "value": 0.451904}, {"id": "62a029ecd4cb2138e26bfc2286ca5364", "region_info": {"bounding_box": {"top_row": 0.650925, "left_col": 0.4414763, "bottom_row": 0.87524223, "right_col": 0.49947238}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.44376037, "app_id": "main"}]}, "value": 0.44376037}, {"id": "f7689831e9037edb21e824f124c77746", "region_info": {"bounding_box": {"top_row": 0.6338496, "left_col": 0.35141647, "bottom_row": 0.67407995, "right_col": 0.37131923}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.31530714, "app_id": "main"}]}, "value": 0.31530714}, {"id": "8ff11b0f3c2f9beed6e79d48b30cce46", "region_info": {"bounding_box": {"top_row": 0.6372098, "left_col": 0.65614766, "bottom_row": 0.7564742, "right_col": 0.67989314}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.30919585, "app_id": "main"}]}, "value": 0.30919585}, {"id": "f482f76636a23df5c72eaea3d3b1621b", "region_info": {"bounding_box": {"top_row": 0.6750108, "left_col": 0.83058554, "bottom_row": 0.7294058, "right_col": 0.8411717}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.2988781, "app_id": "main"}]}, "value": 0.2988781}, {"id": "9146014e3e4ac23d61458c200ef2246c", "region_info": {"bounding_box": {"top_row": 0.62929475, "left_col": 0.67763656, "bottom_row": 0.7652558, "right_col": 0.70437}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.28294367, "app_id": "main"}]}, "value": 0.28294367}, {"id": "931b2d1cff1e91167f328943bd9983e5", "region_info": {"bounding_box": {"top_row": 0.62714005, "left_col": 0.31403905, "bottom_row": 0.6565346, "right_col": 0.32203224}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.2813943, "app_id": "main"}]}, "value": 0.2813943}, {"id": "9b3a6c347cb6bb589d2d272283ae7a08", "region_info": {"bounding_box": {"top_row": 0.6521765, "left_col": 0.7971551, "bottom_row": 0.87120885, "right_col": 0.8408021}}, "data": {"concepts": [{"id": "ai_1Z4GZSNk", "name": "person", "value": 0.26122016, "app_id": "main"}]}, "value": 0.26122016}] | {"$schema": "http://json-schema.org/schema#", "type": "array", "items": {"type": "object", "properties": {"id": {"type": "string"}, "region_info": {"type": "object", "properties": {"bounding_box": {"type": "object", "properties": {"top_row": {"type": "number"}, "left_col": {"type": "number"}, "bottom_row": {"type": "number"}, "right_col": {"type": "number"}}, "required": ["bottom_row", "left_col", "right_col", "top_row"]}}, "required": ["bounding_box"]}, "data": {"type": "object", "properties": {"concepts": {"type": "array", "items": {"type": "object", "properties": {"id": {"type": "string"}, "name": {"type": "string"}, "value": {"type": "number"}, "app_id": {"type": "string"}}, "required": ["app_id", "id", "name", "value"]}}}, "required": ["concepts"]}, "value": {"type": "number"}}, "required": ["data", "id", "region_info", "value"]}} |
5cacd2ee-cde9-4cb8-85e4-14e4ad37cd51/3ab0bb7c-728d-4b49-9f72-62dca8b38204/0/0 | Passport/Visa/Id OCR | Asynchronous high-load API for recognition of proof of identity documents (ICAO 9303, ~200 countries). | 0 | mrz_id_get | Shows task status and results for your id | 200 | Example_1 | {"result": {"Document Type": "V", "Country": "UTO", "Surname": "ERIKSSON", "Name": "ANNA MARIA", "Doc. Number": "L8988901C", "Nationality": "XXX", "Birth Date": "400907", "Sex": "F", "Expiry Date": "961210"}, "current": 0, "total": 2} | {"type": "object", "properties": {"result": {"type": "object", "properties": {"Document Type": {"type": "string", "description": "Most common values: P - passport, V - visa, I - id, one or two capital letters", "format": "char[2]"}, "Country": {"type": "string", "description": "ISO 3166-1 alpha-3 codes (three-letter country codes)", "format": "char[3]"}, "Surname": {"type": "string", "description": "letters in uppercase"}, "Name": {"type": "string", "description": "letters in uppercase"}, "Doc. Number": {"type": "string"}, "Nationality": {"type": "string", "description": "ISO 3166-1 alpha-3 codes (three-letter country codes)", "format": "char[3]"}, "Birth Date": {"type": "string", "description": "YYMMDD", "format": "char[6]"}, "Sex": {"enum": ["M", "F"]}, "Expiry Date": {"type": "string", "description": "YYMMDD", "format": "char[6]"}}}, "current": {"type": "integer", "format": "Int8", "minimum": 0, "maximum": 2}, "total": {"type": "integer", "format": "Int8", "minimum": 1, "maximum": 3}}} |
5cacd2ee-cde9-4cb8-85e4-14e4ad37cd51/3ab0bb7c-728d-4b49-9f72-62dca8b38204/1/0 | Passport/Visa/Id OCR | Asynchronous high-load API for recognition of proof of identity documents (ICAO 9303, ~200 countries). | 0 | mrz_id_get | Shows task status and results for your id | 102 | Example_1 | {"state": "Pending", "current": 0, "total": 2} | {"type": "object", "properties": {"state": {"type": "string"}, "current": {"type": "integer", "format": "Int8", "minimum": 0, "maximum": 2}, "total": {"type": "integer", "format": "Int8", "minimum": 1, "maximum": 2}}} |
a5584ef5-532c-464a-9542-4736c987e820/871f55ae-e9ab-40c1-8031-d2983c267e2d/0/0 | Celebrities face recognition | Find name (of celebrities) and position of face in pictures | 0.2 | Find name and faces in picture | Find name and faces in picture | 200 | New Example | {"results": [{"face": {"x": 90, "y": 163, "w": 182, "h": 182}, "name": "Freya Allan"}, {"face": {"x": 190, "y": 283, "w": 300, "h": 252}, "name": "Anya Chalotra"}]} | {"type": "object", "properties": {"results": {"type": "array", "items": {"type": "object", "properties": {"face": {"type": "object", "properties": {"x": {"type": "integer"}, "y": {"type": "integer"}, "w": {"type": "integer"}, "h": {"type": "integer"}}}, "name": {"type": "string"}}}}}} |
a5584ef5-532c-464a-9542-4736c987e820/da2b3616-fd7a-45cf-9004-52dc27e8a36b/0/0 | Celebrities face recognition | Find name (of celebrities) and position of face in pictures | 0.2 | Wake up | Wake up the server if not running | 200 | New Example | {"Server": "Running..."} | {"type": "object", "properties": {"Server": {"type": "string"}}} |
d9b672cc-3dac-4166-ac37-f0cef3aee6bf/423ef22b-c2bf-4f69-8737-0c01b0d34ece/0/0 | Recaptcha V3 Solver (0.1 SCORE) | 0.50$/1000
Score is always 0.1, use if the site does not have a score requirement.
Solves Recaptcha V3 automatically and returns a token.
Latency displays average solve time. | 9.2 | Request Recaptcha V3 Token | Creates a request to solve Recaptcha V3 and returns a token. | 200 | New Example | {"token": "example"} | {"$schema": "http://json-schema.org/schema#", "type": "object", "properties": {"token": {"type": "string"}}, "required": ["token"]} |
f45f3739-0bc8-47fd-bd00-ecad84d2e5c4/8b80399e-47d7-436e-a8f8-ec58c4bf2218/0/0 | Masks detection | High-accuracy solution for medical masks detection. This solution analyzes a given image, detects people in it and reports if they wear protective masks or not. [](https://api4.ai/apis/mask-detection?utm_source=med_mask_rapidapi&utm_medium=endpoints&utm_campaign=rap... | 8.3 | Analyse image and return results | Performs actual image analysis and responds with results.
Image must be a regular JPEG or PNG image (with or without transparency) or PDF file.
Usually such images have extensions: .jpg, .jpeg, .png, .pdf. In case of PDF
each page will be converted to PNG image and processed separately.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
application/pdf
The size of image file must be less than 16Mb. | 200 | Successful detection | {"results": [{"status": {"code": "ok", "message": "Success"}, "name": "person.jpg", "md5": "11170166a6e894e74ff360ff3d01d6ef", "width": 1024, "height": 768, "entities": [{"kind": "objects", "name": "med-mask-detector", "objects": [{"box": [0.02378430962562561, 0.025364607572555542, 0.9789055287837982, 0.974009782075882], "entities": [{"kind": "classes", "name": "people-detector", "classes": {"person": 0.9750556349754333}}, {"kind": "classes", "name": "med-mask", "classes": {"mask": 4.6584673896177264e-07, "nomask": 0.9999995231628418}}]}]}]}]} | {"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"}, "objects": {"type": "array", "items": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "number"}}, "entities": {"type": "array", "items": {"type": "object", "properties": {"kind": {"type": "string"}, "name": {"type": "string"}, "classes": {"type": "object"}}}}}}}}}}}}}}} |
f45f3739-0bc8-47fd-bd00-ecad84d2e5c4/8b80399e-47d7-436e-a8f8-ec58c4bf2218/0/1 | Masks detection | High-accuracy solution for medical masks detection. This solution analyzes a given image, detects people in it and reports if they wear protective masks or not. [](https://api4.ai/apis/mask-detection?utm_source=med_mask_rapidapi&utm_medium=endpoints&utm_campaign=rap... | 8.3 | Analyse image and return results | Performs actual image analysis and responds with results.
Image must be a regular JPEG or PNG image (with or without transparency) or PDF file.
Usually such images have extensions: .jpg, .jpeg, .png, .pdf. In case of PDF
each page will be converted to PNG image and processed separately.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
application/pdf
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"}, "objects": {"type": "array", "items": {"type": "object", "properties": {"box": {"type": "array", "items": {"type": "number"}}, "entities": {"type": "array", "items": {"type": "object", "properties": {"kind": {"type": "string"}, "name": {"type": "string"}, "classes": {"type": "object"}}}}}}}}}}}}}}} |
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