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| import face_recognition | |
| import numpy as np | |
| import gradio as gr | |
| def read_files(files): | |
| images = [] | |
| encodings = [] | |
| no_face = [] | |
| more_face = [] | |
| for idx, image in enumerate(files): | |
| image = face_recognition.load_image_file(image.name) | |
| encoding = face_recognition.face_encodings(image) | |
| if len(encoding)==1: | |
| images.append(image) | |
| encodings.append(encoding[0]) | |
| elif len(encoding)==0: | |
| no_face.append(image) | |
| else: | |
| more_face.append(image) | |
| return images, encodings, no_face, more_face | |
| def read(profile, files): | |
| profile_encoding = face_recognition.face_encodings(profile) | |
| if len(profile_encoding)==1: | |
| images, encodings, no_face, more_face = read_files(files) | |
| face_distances = [] | |
| true_images = [] | |
| false_images = [] | |
| for index in range(len(images)): | |
| results = face_recognition.compare_faces(encodings[index], profile_encoding) | |
| if results[0] == True: | |
| face_distance = face_recognition.face_distance(profile_encoding, encodings[index]) | |
| face_distances.append(face_distance) | |
| true_images.append(images[index]) | |
| else: | |
| false_images.append(images[index]) | |
| score = len(face_distances)/(len(images)+len(no_face)+len(more_face)) | |
| text = "" | |
| vals, counts = np.unique(face_distances, return_counts=True) | |
| if (np.std(face_distances)<0.01) or max(counts)>((len(images)+len(no_face)+len(more_face))/2): | |
| text += "Most of the images look similar.\n\n" | |
| if len(false_images)>0: | |
| text += str(len(false_images)) + " of the images do not match with the profile picture.\n" | |
| if len(no_face)>0: | |
| text += "No faces were detected in " + str(len(no_face)) + " images.\n" | |
| if len(more_face)>0: | |
| text += "More than one face were detected in " + str(len(more_face)) + " images." | |
| return {"Percentage of matched images":score}, text, true_images, false_images, no_face, more_face | |
| else: | |
| return {"Percentage of matched images":0}, "No faces or more than one faces are detected in the profile picture", [], [], [], [] | |
| with gr.Blocks() as demo: | |
| gr.Markdown("""# Face Verification System""") | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("""### Upload the profile picture here""") | |
| profile = gr.Image(label="Profile picture") | |
| with gr.Column(): | |
| gr.Markdown("""### Upload the screenshots here""") | |
| files = gr.File(file_count="directory", label="Screenshots") | |
| btn = gr.Button(label="Verify").style(full_width=True) #show_progress=True | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("""### Report""") | |
| text = gr.Textbox(show_label=False).style(container=False) | |
| label = gr.Label(num_top_classes=1, show_label=False) | |
| with gr.Tab("Matched images"): | |
| gallery1 = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(columns=[5], height=3) | |
| with gr.Tab("Not matched"): | |
| gallery2 = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(columns=[5], height=3) | |
| with gr.Tab("No faces detected"): | |
| gallery3 = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(columns=[5], height=3) | |
| with gr.Tab("More than one face detected"): | |
| gallery4 = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(columns=[5], height=3) | |
| btn.click(read, [profile, files], [label, text, gallery1, gallery2, gallery3, gallery4], show_progress=True, scroll_to_output=True) | |
| demo.launch() |