Initial commit

app.py

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+import gradio as gr
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow as tf
+from sklearn.metrics.pairwise import cosine_similarity
+
+import matplotlib.patches as patches
+from facenet_pytorch import InceptionResnetV1, MTCNN
+import mtcnn
+import torch
+import shutil
+from PIL import Image
+import ssl
+ssl._create_default_https_context = ssl._create_unverified_context
+
+# Current directory
+abspath = os.path.abspath(__file__)
+dname = os.path.dirname(abspath)
+os.chdir(dname)
+
+def save_uploaded_files(uploaded_file_paths, folder):
+    if not os.path.exists(folder):
+        os.makedirs(folder)
+    else:
+        shutil.rmtree(folder, ignore_errors=True)
+        os.makedirs(folder)
+    
+    for uploaded_file_path in uploaded_file_paths:
+        shutil.move(uploaded_file_path, os.path.join(folder, os.path.basename(uploaded_file_path)))
+
+def face_detection(img, threshold=0.9, return_coords=True):
+    # Detection
+    detector = mtcnn.MTCNN()
+    faces = detector.detect_faces(img)
+
+    # Create a list to store face coordinates and cropped faces
+    face_list = []
+    face_coords = []
+
+    # Draw bounding boxes and save each face as a separate image
+    for i, face in enumerate(faces):
+        if face['confidence']>= threshold:
+            x, y, width, height = face['box']
+            # Append face coordinates to the list
+            face_coords.append((x, y, width, height))
+
+            # Create a rectangle patch
+            rect = patches.Rectangle((x, y), width, height, linewidth=2, edgecolor='orange', facecolor='none')
+
+            # Add the rectangle to the plot
+            plt.gca().add_patch(rect)
+
+            # Crop the face and append to the list
+            face_img = img[y:y+height, x:x+width]
+            face_list.append(face_img)
+        else:
+            continue
+    
+    if return_coords:
+        return face_list, face_coords
+    else:
+        return face_list
+
+def generate_combined_reference():
+    image_paths = []
+    for image in os.listdir("reference"):
+        image_paths.append("reference/" + image)
+    
+    # Open each image and resize or pad to the dimensions of the largest image
+    max_width, max_height = 0, 0
+    images_resized = []
+
+    for path in image_paths:
+        image = Image.open(path)
+        width, height = image.size
+        max_width = max(max_width, width)
+        max_height = max(max_height, height)
+        images_resized.append(image.resize((max_width, max_height)))
+    
+    # Create a new blank image with the combined width and the total height
+    combined_image = Image.new("RGB", (max_width * len(images_resized), max_height))
+
+    # Paste each image into the combined image
+    for i, image in enumerate(images_resized):
+        combined_image.paste(image, (i * max_width, 0))
+
+    # Save the combined image
+    combined_image.save("combined_reference.jpg")
+
+    return "Created combined reference image."
+
+def img_to_encoding(img):
+    model = InceptionResnetV1(pretrained='vggface2').eval()
+    img = cv2.resize(img, (160, 160))
+    img = np.expand_dims(img, axis = 0)
+    img = img / 255.0  # Normalize pixel values
+    img = torch.tensor(img.transpose(0, 3, 1, 2), dtype = torch.float32)  # Adjust image format for PyTorch
+    encoding = model(img)
+
+    return encoding.flatten().detach().numpy()
+
+def process_image():
+    # Load group photo in "testing" folder
+    group_photo_path = "testing/"
+    group_photo = None
+
+    for image in os.listdir(group_photo_path):
+        group_photo = cv2.imread(group_photo_path + image)
+        break
+
+    if group_photo is None:
+        return "No image found in testing folder."
+    elif len(os.listdir(group_photo_path)) > 1:
+        return "Can only process one image at a time."
+
+    # Face detection
+    group_photo_faces, group_photo_face_coords = face_detection(group_photo)
+
+    # Generate reference image & do face detection
+    generate_combined_reference()
+    reference_photo = plt.imread("combined_reference.jpg")
+    reference_faces = face_detection(reference_photo, threshold=0.9, return_coords=False)
+
+    # Convert the reference faces & group photo into 128 dimensional vector
+    ref_encodings = [img_to_encoding(face) for face in reference_faces]
+    face_encodings = [img_to_encoding(face) for face in group_photo_faces]
+
+    # Calculate cosine similarity between each face in the group photo and each reference face
+    similarities = cosine_similarity(ref_encodings, face_encodings)
+
+    # compute the average similarity for each face in face_list across all reference faces
+    average_similarities = np.mean(similarities, axis=0)
+
+    # Blur the face with the highest average similarity in group photo
+    max_avg_similarity_idx = np.argmax(average_similarities)
+
+    # Coordinates of the face with the highest average similarity
+    (x, y, w, h) = group_photo_face_coords[max_avg_similarity_idx]
+
+    # Blur the corresponding region in group photo
+    img_obstruct = cv2.imread(group_photo_path + image)
+    img_obstruct[y:y+h, x:x+w] = cv2.blur(img_obstruct[y:y+h, x:x+w], (50, 50))
+    img_obstruct = cv2.cvtColor(img_obstruct, cv2.COLOR_BGR2RGB)
+
+    # Delete all photos in reference and testing folder after processing
+    shutil.rmtree('reference', ignore_errors=True)
+    shutil.rmtree('testing', ignore_errors=True)
+    
+    return img_obstruct
+
+# Setting up Gradio Interface
+def interface_fn(reference_images, group_photo):
+    save_uploaded_files(reference_images, 'reference')
+    save_uploaded_files(group_photo, 'testing')
+    return process_image()
+
+iface = gr.Interface(
+    fn=interface_fn,
+    inputs=[
+        gr.File(file_types=["image"], file_count="multiple"),
+        gr.File(file_types=["image"], file_count="multiple")
+    ],
+    outputs=gr.Image(),
+    title="Face Detection and Blurring",
+    description="Upload multiple reference images and a group photo. The app will process the images and return the group photo with blurred faces."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

@@ -0,0 +1,134 @@
+absl-py==2.0.0
+aiofiles==23.2.1
+altair==5.1.2
+annotated-types==0.6.0
+anyio==3.7.1
+appnope==0.1.3
+asttokens==2.4.1
+astunparse==1.6.3
+attrs==23.1.0
+cachetools==5.3.2
+certifi==2023.7.22
+charset-normalizer==3.3.2
+click==8.1.7
+colorama==0.4.6
+comm==0.1.4
+contourpy==1.2.0
+cycler==0.12.1
+debugpy==1.8.0
+decorator==5.1.1
+dlib==19.24.2
+exceptiongroup==1.1.3
+executing==2.0.1
+face-recognition-models==0.3.0
+facenet-pytorch==2.5.3
+fastapi==0.104.1
+ffmpy==0.3.1
+filelock==3.13.1
+flatbuffers==23.5.26
+fonttools==4.44.0
+fsspec==2023.10.0
+gast==0.5.4
+google-auth==2.23.4
+google-auth-oauthlib==1.0.0
+google-pasta==0.2.0
+gradio==4.2.0
+gradio_client==0.7.0
+grpcio==1.59.2
+h11==0.14.0
+h5py==3.10.0
+httpcore==1.0.2
+httpx==0.25.1
+huggingface-hub==0.19.0
+idna==3.4
+importlib-resources==6.1.1
+ipykernel==6.26.0
+ipython==8.17.2
+jedi==0.19.1
+Jinja2==3.1.2
+joblib==1.3.2
+jsonschema==4.19.2
+jsonschema-specifications==2023.7.1
+jupyter_client==8.5.0
+jupyter_core==5.5.0
+keras==2.14.0
+kiwisolver==1.4.5
+libclang==16.0.6
+Markdown==3.5.1
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+matplotlib==3.8.1
+matplotlib-inline==0.1.6
+mdurl==0.1.2
+ml-dtypes==0.2.0
+mpmath==1.3.0
+mtcnn==0.1.1
+nest-asyncio==1.5.8
+networkx==3.2.1
+numpy==1.26.1
+oauthlib==3.2.2
+opencv-python==4.8.1.78
+opt-einsum==3.3.0
+orjson==3.9.10
+packaging==23.2
+pandas==2.1.3
+parso==0.8.3
+pexpect==4.8.0
+Pillow==10.1.0
+platformdirs==3.11.0
+prompt-toolkit==3.0.39
+protobuf==4.25.0
+psutil==5.9.6
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyasn1==0.5.0
+pyasn1-modules==0.3.0
+pydantic==2.4.2
+pydantic_core==2.10.1
+pydub==0.25.1
+Pygments==2.16.1
+pyparsing==3.1.1
+python-dateutil==2.8.2
+python-multipart==0.0.6
+pytz==2023.3.post1
+PyYAML==6.0.1
+pyzmq==25.1.1
+referencing==0.30.2
+requests==2.31.0
+requests-oauthlib==1.3.1
+rich==13.6.0
+rpds-py==0.12.0
+rsa==4.9
+scikit-learn==1.3.2
+scipy==1.11.3
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.16.0
+sniffio==1.3.0
+stack-data==0.6.3
+starlette==0.27.0
+sympy==1.12
+tensorboard==2.14.1
+tensorboard-data-server==0.7.2
+tensorflow==2.14.0
+tensorflow-estimator==2.14.0
+tensorflow-io-gcs-filesystem==0.34.0
+tensorflow-macos==2.14.0
+termcolor==2.3.0
+threadpoolctl==3.2.0
+tomlkit==0.12.0
+toolz==0.12.0
+torch==2.1.0
+torchvision==0.16.0
+tornado==6.3.3
+tqdm==4.66.1
+traitlets==5.13.0
+typer==0.9.0
+typing_extensions==4.8.0
+tzdata==2023.3
+urllib3==2.0.7
+uvicorn==0.24.0.post1
+wcwidth==0.2.9
+websockets==11.0.3
+Werkzeug==3.0.1
+wrapt==1.14.1