utils/utils.py

import os
import requests
from dotenv import load_dotenv
import boto3
import supabase
import cv2

R2_ACCESS_KEY = os.getenv('R2_ACCESS_KEY')
R2_SECRET_KEY = os.getenv('R2_SECRET_KEY')
R2_BUCKET_NAME = os.getenv('R2_BUCKET_NAME')
R2_ENDPOINT_URL = os.getenv('R2_ENDPOINT_URL')

def download_video(video_url):
    if not os.path.exists('./input'):
        os.makedirs('./input')
    print(f'Downloading video from {video_url}')
    response = requests.get(video_url, stream=True)
    if response.status_code == 200:
        video_name = video_url.split('/')[-1]
        print(video_name)
        video_path = f'./input/{video_name}.mp4'
        print(video_path)
        with open(video_path, 'wb') as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)
        return video_path
    else:
        raise Exception(f"Failed to download video: {response.status_code}") 

def download_file(url, path):
    if not os.path.exists(path):
        os.makedirs(path)
    print(f'Downloading file from {url} to {path}')
    response = requests.get(url, stream=True)
    if response.status_code == 200:
        file_name = url.split('/')[-1]
        file_path = f'./{path}/{file_name}.mp4'
        with open(file_path, 'wb') as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)
        return file_path
    else:
        raise Exception(f"Failed to download file: {response.status_code}") 
    

def upload_file(file_path, bucket_name, object_name, endpoint_url, access_key, secret_key):
    s3 = boto3.client('s3', endpoint_url=endpoint_url, aws_access_key_id=access_key, aws_secret_access_key=secret_key)
    try:
        response =s3.upload_file(file_path, bucket_name, object_name)
        print(f'{file_path} uploaded to {bucket_name}/{object_name}')
        return response
    except Exception as e:
        print(f'Error uploading file: {e}')


def detect_faces_frames(video_url):
    video_name = video_url.split('/')[-1]
    print(video_name)
    video_path = download_video(video_url)
    
    frames =[]
   
    face_cascade = cv2.CascadeClassifier('./utils/face_detection.xml')

    # Open the video file
    cap = cv2.VideoCapture(video_path)

    # Get video properties
    fps = cap.get(cv2.CAP_PROP_FPS)
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    duration = total_frames / fps

    frame_count = 0
    time_count = 0
    while True:
        ret, frame = cap.read()
        if not ret:
            break

        # Process frame every 5 seconds
        if frame_count % int(fps * 5) == 0:
            # Convert frame to grayscale
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

            # Detect faces
            faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

            # Draw rectangles around the faces
            for (x, y, w, h) in faces:
                cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)

            # Save the frame with detected faces
            frame_name = f"./output/{video_name}_{time_count}.jpg"
            print(frame_name)
            frames.append(frame_name)
            cv2.imwrite(f"./output/{video_name}_{time_count}.jpg", frame)
            time_count += 1

        frame_count += 1

    cap.release()
    cv2.destroyAllWindows()

    print(f"Total video duration: {duration:.2f} seconds")
    print(f"Total frames processed: {time_count // 5}")

    res = []
    for frame in frames:
        upload_file(f'{frame}', 'outputs', frame.split('/')[-1] , 'https://c98643a1da5e9aa06b27b8bb7eb9227a.r2.cloudflarestorage.com/warden-ai', R2_ACCESS_KEY, R2_SECRET_KEY)
        res.append(f'https://pub-08a118f4cb7c4b208b55e6877b0bacca.r2.dev/outputs/{frame.split("/")[-1]}')
    
    return res