Spaces:
Running
Running
Josh Brown Kramer
commited on
Commit
·
d6c0ea5
1
Parent(s):
21ff0ae
Have a locally working gradio demo
Browse files- .gitignore +179 -0
- align.py +26 -2
- app.py +5 -2
- requirements.txt +0 -2
.gitignore
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@@ -0,0 +1,179 @@
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# Created by https://www.toptal.com/developers/gitignore/api/python
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# Edit at https://www.toptal.com/developers/gitignore?templates=python
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### Python ###
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target/
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# Jupyter Notebook
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profile_default/
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ipython_config.py
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ENV/
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venv.bak/
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/site
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.mypy_cache/
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.dmypy.json
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dmypy.json
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poetry.toml
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.vscode/
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# End of https://www.toptal.com/developers/gitignore/api/python
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align.py
CHANGED
@@ -1,9 +1,34 @@
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import numpy as np
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import mediapipe as mp
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mp_face_detection = mp.solutions.face_detection
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face_detection = mp_face_detection.FaceDetection(min_detection_confidence=0.5, model_selection=0)
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def get_locations(numpy_array,model_type="dlib"):
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'''
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model_type can be "dlib" or "mediapipe"
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return None
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areas = [(l[2] - l[0])*(l[1] - l[3]) for l in locations]
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i = np.argmax(areas)
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t0 = time.time()
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face_landmarks_list = get_landmarks(numpy_im,[locations[i]],context,model_type)#face_recognition.face_landmarks(numpy_im,[locations[i]])
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im,quad = image_align(Image.fromarray(numpy_im),face_landmarks_list[0],enable_padding=enable_padding,output_size=output_size,transform_size=4*output_size)
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return im,quad*scale
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@@ -127,7 +151,7 @@ def image_align(img, lm, output_size=1024, transform_size=4096, enable_padding=T
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# Transform.
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img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
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if output_size < transform_size:
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img = img.resize((output_size, output_size), PIL.Image.
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# Save aligned image.
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return img,original_quad
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import numpy as np
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import mediapipe as mp
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from PIL import Image
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import PIL
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mp_face_detection = mp.solutions.face_detection
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face_detection = mp_face_detection.FaceDetection(min_detection_confidence=0.5, model_selection=0)
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def get_landmarks(numpy_array,locations,context,model_type="dlib"):
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'''
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model_type can be "dlib" or "mediapipe"
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context is the second result from get_locations
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'''
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assert(model_type in ["dlib","mediapipe"])
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if model_type == "dlib":
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return face_recognition.face_landmarks(numpy_array,locations)
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else:
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return [context[tuple(l)] for l in locations]
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def landmarks_from_result(result,np_array):
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keypoint_names = ["left_eye", "right_eye", "nose" ,"mouth", "left_ear", "right_ear"]
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landmarks = {}
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for i,k in enumerate(result.location_data.relative_keypoints):
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x = round(k.x * np_array.shape[1])
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y = round(k.y * np_array.shape[0])
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landmarks[keypoint_names[i]] = np.array([x,y])
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return landmarks
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def get_locations(numpy_array,model_type="dlib"):
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'''
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model_type can be "dlib" or "mediapipe"
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return None
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areas = [(l[2] - l[0])*(l[1] - l[3]) for l in locations]
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i = np.argmax(areas)
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face_landmarks_list = get_landmarks(numpy_im,[locations[i]],context,model_type)#face_recognition.face_landmarks(numpy_im,[locations[i]])
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im,quad = image_align(Image.fromarray(numpy_im),face_landmarks_list[0],enable_padding=enable_padding,output_size=output_size,transform_size=4*output_size)
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return im,quad*scale
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# Transform.
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img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
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if output_size < transform_size:
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img = img.resize((output_size, output_size), PIL.Image.LANCZOS)
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# Save aligned image.
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return img,original_quad
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app.py
CHANGED
@@ -43,11 +43,14 @@ title = "pix2pixHD Image-to-Image Translation"
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description = "Upload an image to see the pix2pixHD model in action."
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article = "<p style='text-align: center'>Model based on the <a href='https://github.com/NVIDIA/pix2pixHD' target='_blank'>pix2pixHD repository</a>.</p>"
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gr.Interface(
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fn=predict,
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inputs=gr.Image(type="pil", label="Input Image"),
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outputs=gr.Image(type="pil", label="Output Image"),
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title=title,
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description=description,
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article=article,
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-
)
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description = "Upload an image to see the pix2pixHD model in action."
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article = "<p style='text-align: center'>Model based on the <a href='https://github.com/NVIDIA/pix2pixHD' target='_blank'>pix2pixHD repository</a>.</p>"
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demo = gr.Interface(
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fn=predict,
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inputs=gr.Image(type="pil", label="Input Image"),
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outputs=gr.Image(type="pil", label="Output Image"),
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title=title,
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description=description,
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article=article,
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)
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demo.launch()
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#demo.launch(debug=True)
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requirements.txt
CHANGED
@@ -2,6 +2,4 @@ gradio
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onnxruntime-gpu
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opencv-python
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numpy
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#face_recognition
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#dlib
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mediapipe
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onnxruntime-gpu
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opencv-python
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numpy
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mediapipe
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