mtcnn

import cv2 from mtcnn.mtcnn import MTCNN # Example image (replace with your path or download one) # For demonstration, we'll create a dummy image if file not found try: img_path = 'sample_image.jpg' # Replace with a path to a real image img = cv2.imread(img_path) if img is None: # Create a blank image with a simple 'face' if sample_image.jpg not found print(f"Warning: '{img_path}' not found. Creating a dummy image.") img = 255 * (cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (50, 50))) img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) # Add a simple rectangle to simulate a face cv2.rectangle(img, (100, 100), (200, 200), (0, 0, 255), 2) except Exception as e: print(f"Error loading image or creating dummy: {e}") # Fallback to a completely black image if even dummy creation fails img = (255 * (cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (50, 50)))) img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) # MTCNN expects RGB images, OpenCV loads BGR by default img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Initialize the MTCNN detector detector = MTCNN() # Detect faces in the image faces = detector.detect_faces(img_rgb) # Print detected faces (each face is a dict with 'box', 'confidence', 'keypoints') for face in faces: print(face) # Optional: Draw bounding boxes and keypoints on the original image # for face in faces: # x, y, width, height = face['box'] # cv2.rectangle(img, (x, y), (x + width, y + height), (0, 255, 0), 2) # for key, value in face['keypoints'].items(): # cv2.circle(img, value, 2, (0, 0, 255), 2) # cv2.imshow('Detected Faces', img) # cv2.waitKey(0) # cv2.destroyAllWindows()