【PYTHON OPENCV】Detecting facial landmarks using dlib

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Detecting facial landmarks using dlib """ # Import required packages: import cv2 import dlib import numpy as np # Define what landmarks you want: JAWLINE_POINTS = list(range(0, 17)) RIGHT_EYEBROW_POINTS = list(range(17, 22)) LEFT_EYEBROW_POINTS = list(range(22, 27)) NOSE_BRIDGE_POINTS = list(range(27, 31)) LOWER_NOSE_POINTS = list(range(31, 36)) RIGHT_EYE_POINTS = list(range(36, 42)) LEFT_EYE_POINTS = list(range(42, 48)) MOUTH_OUTLINE_POINTS = list(range(48, 61)) MOUTH_INNER_POINTS = list(range(61, 68)) ALL_POINTS = list(range(0, 68)) def draw_shape_lines_all(np_shape, image): """Draws the shape using lines to connect between different parts of the face(e.g. nose, eyes, ...)""" draw_shape_lines_range(np_shape, image, JAWLINE_POINTS) draw_shape_lines_range(np_shape, image, RIGHT_EYEBROW_POINTS) draw_shape_lines_range(np_shape, image, LEFT_EYEBROW_POINTS) draw_shape_lines_range(np_shape, image, NOSE_BRIDGE_POINTS) draw_shape_lines_range(np_shape, image, LOWER_NOSE_POINTS) draw_shape_lines_range(np_shape, image, RIGHT_EYE_POINTS, True) draw_shape_lines_range(np_shape, image, LEFT_EYE_POINTS, True) draw_shape_lines_range(np_shape, image, MOUTH_OUTLINE_POINTS, True) draw_shape_lines_range(np_shape, image, MOUTH_INNER_POINTS, True) def draw_shape_lines_range(np_shape, image, range_points, is_closed=False): """Draws the shape using lines to connect the different points""" np_shape_display = np_shape[range_points] points = np.array(np_shape_display, dtype=np.int32) cv2.polylines(image, [points], is_closed, (255, 255, 0), thickness=1, lineType=cv2.LINE_8) def draw_shape_points_pos_range(np_shape, image, points): """Draws the shape using points and position for every landmark filtering by points parameter""" np_shape_display = np_shape[points] draw_shape_points_pos(np_shape_display, image) def draw_shape_points_pos(np_shape, image): """Draws the shape using points and position for every landmark""" for idx, (x, y) in enumerate(np_shape): # Draw the positions for every detected landmark: cv2.putText(image, str(idx + 1), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 255)) # Draw a point on every landmark position: cv2.circle(image, (x, y), 2, (0, 255, 0), -1) def draw_shape_points_range(np_shape, image, points): """Draws the shape using points for every landmark filtering by points parameter""" np_shape_display = np_shape[points] draw_shape_points(np_shape_display, image) def draw_shape_points(np_shape, image): """Draws the shape using points for every landmark""" # Draw a point on every landmark position: for (x, y) in np_shape: cv2.circle(image, (x, y), 2, (0, 255, 0), -1) def shape_to_np(dlib_shape, dtype="int"): """Converts dlib shape object to numpy array""" # Initialize the list of (x,y) coordinates coordinates = np.zeros((dlib_shape.num_parts, 2), dtype=dtype) # Loop over all facial landmarks and convert them to a tuple with (x,y) coordinates: for i in range(0, dlib_shape.num_parts): coordinates[i] = (dlib_shape.part(i).x, dlib_shape.part(i).y) # Return the list of (x,y) coordinates: return coordinates # Name of the two shape predictors: p = "shape_predictor_68_face_landmarks.dat" # p = "shape_predictor_5_face_landmarks.dat" # Initialize frontal face detector and shape predictor: detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(p) # Create VideoCapture object to get images from the webcam: video_capture = cv2.VideoCapture("istockphoto-1131308747-640_adpp_is.mp4") # You can use a test image for debugging purposes: test_face = cv2.imread("face_test.png") while True: # Capture frame from the VideoCapture object: ret, frame = video_capture.read() # Just for debugging purposes: # frame = test_face.copy() # Convert frame to grayscale: gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Detect faces: rects = detector(gray, 0) # For each detected face, find the landmark. for (i, rect) in enumerate(rects): # Draw a box around the face: cv2.rectangle(frame, (rect.left(), rect.top()), (rect.right(), rect.bottom()), (0, 255, 0), 1) # Get the shape using the predictor: shape = predictor(gray, rect) # Convert the shape to numpy array: shape = shape_to_np(shape) # Draw all lines connecting the different face parts: # draw_shape_lines_all(shape, frame) # Draw jaw line: # draw_shape_lines_range(shape, frame, JAWLINE_POINTS) # Draw all points and their position: # draw_shape_points_pos(shape, frame) # You can also use: # draw_shape_points_pos_range(shape, frame, ALL_POINTS) # Draw all shape points: draw_shape_points(shape, frame) # Draw left eye, right eye and bridge shape points and positions# draw_shape_points_pos_range(shape, frame, LEFT_EYE_POINTS + RIGHT_EYE_POINTS + NOSE_BRIDGE_POINTS) # Display the resulting frame cv2.imshow("Landmarks detection using dlib", frame) # Press 'q' key to exit if cv2.waitKey(1) & 0xFF == ord('q'): break # Release everything: video_capture.release() cv2.destroyAllWindows()