dlib

import dlib import numpy as np # Used for dummy image # --- Quickstart: Face Detection and Landmark Prediction API Usage --- # Note: For this example to produce meaningful results, you need: # 1. A pre-trained facial landmark predictor model file (e.g., 'shape_predictor_68_face_landmarks.dat'). # Download from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 # Then decompress it using 'bunzip2'. # 2. An actual image file. Replace 'path/to/your/image.jpg' below. # --- 1. Face Detection --- # Initialize the frontal face detector detector = dlib.get_frontal_face_detector() # Load an image (replace with your actual image file path) # For demonstration, we use a placeholder: try: img = dlib.load_rgb_image("path/to/your/image.jpg") except RuntimeError: print("Warning: Could not load dummy image 'path/to/your/image.jpg'.") print("Using a blank NumPy array for demonstration purposes. Face detection will likely find nothing.") img = np.zeros((400, 400, 3), dtype=np.uint8) # Create a 400x400 black image # Detect faces in the image. The '1' argument means to upsample the image 1 time # (make it larger) to find smaller faces. dets = detector(img, 1) print(f"Detected {len(dets)} faces (or regions of interest) in the image.") # --- 2. Facial Landmark Prediction --- # Initialize the shape predictor (requires a downloaded model file) model_path = "shape_predictor_68_face_landmarks.dat" # Replace with actual path try: predictor = dlib.shape_predictor(model_path) except dlib.set_level_error as e: print(f"Error: Could not load shape predictor model from '{model_path}'.") print("Please download 'shape_predictor_68_face_landmarks.dat' and provide the correct path.") predictor = None # Prevent further errors if model loading failed if predictor: for i, d in enumerate(dets): # Predict landmarks for each detected face print(f" Processing face {i+1} at bounding box: Left: {d.left()}, Top: {d.top()}, Right: {d.right()}, Bottom: {d.bottom()}") try: shape = predictor(img, d) print(f" Found {shape.num_parts} facial landmarks for face {i+1}.") if shape.num_parts > 0: print(f" Example: First landmark point: ({shape.part(0).x}, {shape.part(0).y})") except Exception as e: print(f" Could not find landmarks for face {i+1}. This is expected if the image is blank or model is incorrect. Error: {e}") else: print("Skipping facial landmark prediction due to missing or invalid model.") print("\ndlib quickstart example finished.")