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| import gradio as gr | |
| import cv2 | |
| import os | |
| import boto3 | |
| aws_access_key_id = os.getenv('AWS_ACCESS_KEY_ID') | |
| aws_secret_access_key = os.getenv('AWS_SECRET_ACCESS_KEY') | |
| s3_client = boto3.client( | |
| 's3', | |
| aws_access_key_id=aws_access_key_id, | |
| aws_secret_access_key=aws_secret_access_key, | |
| region_name='eu-central-1' | |
| ) | |
| def upload_to_s3(bucket_name, folder_name): | |
| image_paths = [] | |
| for filename in os.listdir(folder_name): | |
| if filename.endswith('.png'): | |
| file_path = os.path.join(folder_name, filename) | |
| s3_client.upload_file(file_path, bucket_name, f"{folder_name}/{filename}") | |
| image_paths.append(file_path) | |
| return image_paths | |
| def process_video(uploaded_video, name, surname, interval_ms): | |
| try: | |
| video_source = uploaded_video | |
| if video_source is None: | |
| return "No video file provided.", [] | |
| folder_name = f"{name}_{surname}" | |
| os.makedirs(folder_name, exist_ok=True) | |
| # Video processing logic | |
| # Use video_source directly as it's a file path (string) | |
| temp_video_path = video_source | |
| face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') | |
| vidcap = cv2.VideoCapture(temp_video_path) | |
| if not vidcap.isOpened(): | |
| raise Exception("Failed to open video file.") | |
| fps = vidcap.get(cv2.CAP_PROP_FPS) | |
| frame_interval = int(fps * (interval_ms / 1000)) | |
| frame_count = 0 | |
| saved_image_count = 0 | |
| success, image = vidcap.read() | |
| image_paths = [] | |
| while success and saved_image_count < 86: | |
| if frame_count % frame_interval == 0: | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| faces = face_cascade.detectMultiScale(gray, 1.2, 4) | |
| for (x, y, w, h) in faces: | |
| # Additional checks for face region validation | |
| aspect_ratio = w / h | |
| if aspect_ratio > 0.75 and aspect_ratio < 1.33 and w * h > 4000: # Example thresholds | |
| face = image[y:y+h, x:x+w] | |
| face_resized = cv2.resize(face, (160, 160)) | |
| image_filename = os.path.join(folder_name, f"{name}_{surname}_{saved_image_count:04d}.png") | |
| cv2.imwrite(image_filename, face_resized) | |
| image_paths.append(image_filename) | |
| saved_image_count += 1 | |
| if saved_image_count >= 86: | |
| break | |
| success, image = vidcap.read() | |
| frame_count += 1 | |
| vidcap.release() | |
| bucket_name = 'newimagesupload00' | |
| uploaded_images = upload_to_s3(bucket_name, folder_name) | |
| return f"Saved and uploaded {saved_image_count} face images", uploaded_images | |
| except Exception as e: | |
| return f"An error occurred: {e}", [] | |
| # Gradio Interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("### Video Uploader and Face Detector") | |
| gr.Markdown("Upload your own video to add your images to the dataset!") | |
| gr.Markdown("Make a short 10-15 seconds video of your front and side profiles, **slowly rotating your face**, with good lighting and visible face for best results.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| video = gr.File(label="Upload Your Video!") | |
| with gr.Column(): | |
| name = gr.Textbox(label="Name") | |
| surname = gr.Textbox(label="Surname") | |
| interval = gr.Number(label="Interval in milliseconds", value=100) | |
| submit_button = gr.Button("Submit") | |
| with gr.Column(): | |
| gallery = gallery = gr.Gallery( | |
| label="Generated images", show_label=False, elem_id="gallery" | |
| , columns=[3], rows=[1], object_fit="contain", height="auto") | |
| submit_button.click( | |
| fn=process_video, | |
| inputs=[video, name, surname, interval], | |
| outputs=[gr.Text(label="Result"), gallery] | |
| ) | |
| css = """ | |
| body { font-family: Arial, sans-serif; } | |
| """ | |
| # Demo Launching | |
| demo.launch() | |