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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}", [] | |
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() | |