【PYTHON OPENCV】 Face tracking using dlib frontal face detector for initialization and dlib discriminative correlation filter tracker

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Face tracking using dlib frontal face detector for initialization and dlib discriminative correlation filter tracker """ # Import required packages: import cv2 import dlib def draw_text_info(): """Draw text information""" # We set the position to be used for drawing text and the menu info: menu_pos_1 = (10, 20) menu_pos_2 = (10, 40) # Write text: cv2.putText(frame, "Use '1' to re-initialize tracking", menu_pos_1, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255)) if tracking_face: cv2.putText(frame, "tracking the face", menu_pos_2, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0)) else: cv2.putText(frame, "detecting a face to initialize tracking...", menu_pos_2, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255)) # Create the video capture to read from the webcam: capture = cv2.VideoCapture("istockphoto-1131308747-640_adpp_is.mp4") # Load frontal face detector from dlib: detector = dlib.get_frontal_face_detector() # We initialize the correlation tracker. tracker = dlib.correlation_tracker() # This variable will hold if we are currently tracking the face: tracking_face = False while True: # Capture frame from webcam: ret, frame = capture.read() # We draw basic info: draw_text_info() if tracking_face is False: gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Try to detect a face to initialize the tracker: rects = detector(gray, 0) # Check if we can start tracking (if we detected a face): if len(rects) > 0: # Start tracking: tracker.start_track(frame, rects[0]) tracking_face = True if tracking_face is True: # Update tracking and print the peak to side-lobe ratio (measures how confident the tracker is): print(tracker.update(frame)) # Get the position of the tracked object: pos = tracker.get_position() # Draw the position: cv2.rectangle(frame, (int(pos.left()), int(pos.top())), (int(pos.right()), int(pos.bottom())), (0, 255, 0), 3) # We capture the keyboard event key = 0xFF & cv2.waitKey(1) # Press '1' to re-initialize tracking (it will detect the face again): if key == ord("1"): tracking_face = False # To exit, press 'q': if key == ord('q'): break # Show the resulting image: cv2.imshow("Face tracking using dlib frontal face detector and correlation filters for tracking", frame) # Release everything: capture.release() cv2.destroyAllWindows()