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face_aligner_video.py
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face_aligner_video.py
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from imutils.face_utils import FaceAligner
from imutils.video import count_frames
import imutils
import numpy as np
import dlib
import cv2
import os
import shutil
# 利用 detector = dlib.get_frontal_face_detector()預測圖像,評估有幾張臉在這張圖像中。
detector = dlib.get_frontal_face_detector()
# 利用predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")在捕捉的臉部預測臉部 landmarks
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
fa = FaceAligner(predictor, desiredFaceWidth=64)
face_name = 1
def detect(img, idx, totle):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = detector(gray, 1)
print(totle)
for face in faces:
# fa.align參數分別是要擷取的圖像、要被辨識的圖像(灰階)、要對齊的圖像
faceAligned = fa.align(gray, gray, face)
global face_name
cv2.imwrite('./face/{0}.jpg'.format(face_name),faceAligned)
face_name += 1
print('Working with {0} frames. completed {1:.2f}'.format(idx, idx/float(totle)*100))
detect_video = 'onionman.mp4'
videoCapture = cv2.VideoCapture(detect_video)
success, frame = videoCapture.read()
frame_counter = 1
frame_totle = count_frames(detect_video)
path = 'face'
if not os.path.isdir(path):
os.mkdir(path)
else:
shutil.rmtree(path)
os.mkdir(path)
while success:
detect(frame, frame_counter, frame_totle)
success, frame = videoCapture.read()
frame_counter += 1
print('Done!')