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}", [] example_video_path = "examples/vid.mp4" if os.path.exists(example_video_path): with open(example_video_path, "rb") as file: example_video_bytes = file.read() else: example_video_bytes = None # Gradio Interface with gr.Blocks() as demo: gr.Markdown("### Video Uploader and Face Detector") gr.Markdown("Download the example video to try it out, or upload your own video to add your images to the dataset!") gr.Markdown("Make a short 5-7 seconds video as shown in the example, with good lighting and visible face for best results.") with gr.Row(): with gr.Column(): video = gr.File(label="Upload Your Video, Like This!", type="video", value=example_video_bytes) 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 for styling (optional) css = """ body { font-family: Arial, sans-serif; } """ demo.launch()