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landMarkDetector.py
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landMarkDetector.py
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import numpy as np
import dlib
import cv2
import utils
DEFAULT_PREDICTOR_PATH = "shape_predictor_68_face_landmarks.dat"
class FacialAnalysis:
def __init__(self,predictor_path=DEFAULT_PREDICTOR_PATH):
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor(predictor_path)
def DrawLandmarks(self,image):
'''
@parms: grayscale image
Predict the lanmarks on image and return the image.
'''
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale image
rects = self.detector(image, 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 = self.predictor(gray, rect)
shape = 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) = 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)
return image
fa = FacialAnalysis()
image = cv2.imread("sample.png")
fa.DrawLandmarks(image)
cv2.imshow("Output", image)
cv2.waitKey(0)