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facial_landmarks.py
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facial_landmarks.py
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# Facial landmarks with dlib, OpenCV, and PythonPython
# import the necessary packages
from imutils import face_utils
import numpy as np
import argparse
import imutils
import dlib
import cv2
def main():
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor", required=True, help="path to facial landmark predictor")
ap.add_argument("-i", "--image", required=True, help="path to input image")
ap.add_argument('--draw', nargs='?', const=True, type=bool, default=False, help="Fill landmarks")
args = vars(ap.parse_args())
# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])
# load the input image, resize it, and convert it to grayscale
image = cv2.imread(args["image"])
image = imutils.resize(image, width=500)
if args['draw']:
draw_individual_detections(image, detector, predictor)
else:
show_raw_detection(image, detector, predictor)
def show_raw_detection(image, detector, predictor):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale image
rects = detector(gray, 1)
# loop over the face detections
for (i, rect) in enumerate(rects):
# determine the facial landmarks for the face region, then
# convert the facial landmark (x, y)-coordinates to a NumPy
# array
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
# convert dlib's rectangle to a OpenCV-style bounding box
# [i.e., (x, y, w, h)], then draw the face bounding box
(x, y, w, h) = face_utils.rect_to_bb(rect)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
# show the face number
cv2.putText(image, "Face #{}".format(i + 1), (x - 10, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# loop over the (x, y)-coordinates for the facial landmarks
# and draw them on the image
for (x, y) in shape:
cv2.circle(image, (x, y), 1, (0, 0, 255), -1)
# show the output image with the face detections + facial landmarks
cv2.imshow("Output", image)
cv2.waitKey(0)
def draw_individual_detections(image, detector, predictor):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale image
rects = detector(gray, 1)
# loop over the face detections
for (i, rect) in enumerate(rects):
# determine the facial landmarks for the face region, then
# convert the landmark (x, y)-coordinates to a NumPy array
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
# loop over the face parts individually
for (name, (i, j)) in face_utils.FACIAL_LANDMARKS_IDXS.items():
# clone the original image so we can draw on it, then
# display the name of the face part on the image
clone = image.copy()
cv2.putText(clone, name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (0, 0, 255), 2)
# loop over the subset of facial landmarks, drawing the
# specific face part
for (x, y) in shape[i:j]:
cv2.circle(clone, (x, y), 1, (0, 0, 255), -1)
# extract the ROI of the face region as a separate image
(x, y, w, h) = cv2.boundingRect(np.array([shape[i:j]]))
roi = image[y:y + h, x:x + w]
roi = imutils.resize(roi, width=250, inter=cv2.INTER_CUBIC)
# show the particular face part
cv2.imshow("ROI", roi)
cv2.imshow("Image", clone)
cv2.waitKey(0)
# visualize all facial landmarks with a transparent overlay
output = face_utils.visualize_facial_landmarks(image, shape)
cv2.imshow("Image", output)
cv2.waitKey(0)
if __name__ == '__main__':
main()