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import gradio as gr
import cv2
import os
import boto3
s3_client = boto3.client(
's3',
aws_access_key_id='AKIAY5HVHYWVXRTEU6CB',
aws_secret_access_key='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',
region_name='eu-central-1'
)
def upload_to_s3(bucket_name, folder_name):
# Upload files in the folder to S3 bucket
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}")
def process_video(uploaded_video, name, surname, interval_ms):
try:
if uploaded_video is None:
return "No video file uploaded."
folder_name = f"{name}_{surname}"
os.makedirs(folder_name, exist_ok=True)
# The uploaded_video is a NamedString object, extract the file path
temp_video_path = uploaded_video.name
# Initialize face detector
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# Open and process the video
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 / 10000))
frame_count = 0
saved_image_count = 0
success, image = vidcap.read()
while success and saved_image_count < 86:
if frame_count % frame_interval == 0:
# Apply face detection
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
for (x, y, w, h) in faces:
# Crop and resize face
face = image[y:y+h, x:x+w]
face_resized = cv2.resize(face, (160, 160))
cv2.imwrite(os.path.join(folder_name, f"{name}_{surname}_{saved_image_count:04d}.png"), face_resized)
saved_image_count += 1
if saved_image_count >= 86:
break
success, image = vidcap.read()
frame_count += 1
vidcap.release()
bucket_name = 'imagefilessml' # Replace with your bucket name
upload_to_s3(bucket_name, folder_name)
return f"Saved and uploaded {saved_image_count} face images"
return f"Saved {saved_image_count} face images in the folder: {folder_name}"
except Exception as e:
return f"An error occurred: {e}"
with gr.Blocks() as demo:
with gr.Row():
video = gr.File(label="Upload Your Video")
name = gr.Textbox(label="Name")
surname = gr.Textbox(label="Surname")
interval = gr.Number(label="Interval in milliseconds", value=1000)
submit_button = gr.Button("Submit")
submit_button.click(
fn=process_video,
inputs=[video, name, surname, interval],
outputs=[gr.Text(label="Result")]
)
demo.launch()
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