face_det.py

import cv2
import os

def detect_faces(video_path, output_dir="./faces"):
    if not os.path.exists(video_path):
        raise FileNotFoundError(f"Video file not found: {video_path}")

    os.makedirs(output_dir, exist_ok=True)

    cap = cv2.VideoCapture(video_path)
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

    if not cap.isOpened():
        print("Error: Could not open video file.")
        return None

    frame_rate = 30
    frame_count = 0

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

        if frame_count % frame_rate == 0:
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            faces = face_cascade.detectMultiScale(
                gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE
            )

            for i, (x, y, w, h) in enumerate(faces):
                cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)

                face_image = frame[max(0, y): min(y + h, frame.shape[0]), max(0, x): min(x + w, frame.shape[1])]
                video_name = os.path.splitext(os.path.basename(video_path))[0]
                face_filename = os.path.join(output_dir, f"face_{video_name}_{frame_count}_{i}.jpg")
                cv2.imwrite(face_filename, face_image)

            cv2.imshow("Face Detection", frame)

        frame_count += 1

        if cv2.waitKey(1) & 0xFF == ord("q"):
            break

    cap.release()
    cv2.destroyAllWindows()
    
    return output_dir

# def count_face(video_path):
#     if not os.path.exists(video_path):
#         raise FileNotFoundError(f"Video file not found: {video_path}")

#     cap = cv2.VideoCapture(video_path)
#     face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

#     if not cap.isOpened():
#         print("Error: Could not open video file.")
#         return None

#     frame_count = 0
#     face_count = 0

#     while True:
#         ret, frame = cap.read()
#         if not ret:
#             break

#         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#         faces = face_cascade.detectMultiScale(
#             gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE
#         )

#         face_count += len(faces)
#         frame_count += 1
#         cv2.imshow("Face Detection", frame)

#     cap.release()
#     cv2.destroyAllWindows()
    
#     return face_count/frame_count