我的 Yolo 面部检测中的 Facail 地标

Question

我想使用人脸地标来对齐从我的人脸检测代码中提取的人脸(使用 yolov3(。

这是我的代码：

import argparse
import sys
import os
from utils import *
#####################################################################
parser = argparse.ArgumentParser()
parser.add_argument('--model-cfg', type=str, default='./cfg/yolov3- 
face.cfg',
help='path to config file')
parser.add_argument('--model-weights', type=str,
default='./model-weights/yolov3-wider_16000.weights',
help='path to weights of model')
parser.add_argument('--image', type=str, default='',
help='path to image file')
parser.add_argument('--video', type=str, default='',
help='path to video file')
parser.add_argument('--src', type=int, default=0,
help='source of the camera')
parser.add_argument('--output-dir', type=str, default='outputs/',
help='path to the output directory')
args = parser.parse_args()
#####################################################################
# print the arguments
print('----- info -----')
print('[i] The config file: ', args.model_cfg)
print('[i] The weights of model file: ', args.model_weights)
print('[i] Path to image file: ', args.image)
print('[i] Path to video file: ', args.video)
print('###########################################################n')
# check outputs directory
if not os.path.exists(args.output_dir):
print('==> Creating the {} directory...'.format(args.output_dir))
os.makedirs(args.output_dir)
else:
print('==> Skipping create the {} 
directory...'.format(args.output_dir))
# Give the configuration and weight files for the model and load the 
network
# using them.
net = cv2.dnn.readNetFromDarknet(args.model_cfg, args.model_weights)
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

def _main():
wind_name = 'face detection using YOLOv3'
cv2.namedWindow(wind_name, cv2.WINDOW_NORMAL)
output_file = ''
if args.image:
if not os.path.isfile(args.image):
print("[!] ==> Input image file {} doesn't 
exist".format(args.image))
sys.exit(1)
cap = cv2.VideoCapture(args.image)
output_file = args.image[:-4].rsplit('/')[-1] + '_yoloface.jpg'
elif args.video:
if not os.path.isfile(args.video):
print("[!] ==> Input video file {} doesn't 
exist".format(args.video))
sys.exit(1)
cap = cv2.VideoCapture(args.video)
output_file = args.video[:-4].rsplit('/')[-1] + '_yoloface.avi'
else:
# Get data from the camera
cap = cv2.VideoCapture(args.src)
# Get the video writer initialized to save the output video
if not args.image:
video_writer = cv2.VideoWriter(os.path.join(args.output_dir, 
output_file),
cv2.VideoWriter_fourcc('M', 'J', 
'P', 'G'),
cap.get(cv2.CAP_PROP_FPS), (
round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))
while True:
has_frame, frame = cap.read()
# Stop the program if reached end of video
if not has_frame:
print('[i] ==> Done processing!!!')
print('[i] ==> Output file is stored at', 
os.path.join(args.output_dir, output_file))
cv2.waitKey(1000)
break
# Create a 4D blob from a frame.
blob = cv2.dnn.blobFromImage(frame, 1 / 255, (IMG_WIDTH, 
IMG_HEIGHT),
[0, 0, 0], 1, crop=False)
# Sets the input to the network
net.setInput(blob)
# Runs the forward pass to get output of the output layers
outs = net.forward(get_outputs_names(net))
# Remove the bounding boxes with low confidence
faces = post_process(frame, outs, CONF_THRESHOLD, NMS_THRESHOLD)
print('[i] ==> # detected faces: {}'.format(len(faces)))
print('#' * 60)
# initialize the set of information we'll displaying on the frame
info = [
('number of faces detected', '{}'.format(len(faces)))
]
for (i, (txt, val)) in enumerate(info):
text = '{}: {}'.format(txt, val)
cv2.putText(frame, text, (10, (i * 20) + 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, COLOR_RED, 2)
# Save the output video to file
if args.image:
cv2.imwrite(os.path.join(args.output_dir, output_file), 
frame.astype(np.uint8))
else:
video_writer.write(frame.astype(np.uint8))
cv2.imshow(wind_name, frame)
key = cv2.waitKey(1)
if key == 27 or key == ord('q'):
print('[i] ==> Interrupted by user!')
break
cap.release()
cv2.destroyAllWindows()
print('==> All done!')
print('***********************************************************')

if __name__ == '__main__':
_main()

我的问题是我想使用面部地标来对齐由 Yoloface 检测器提取的面部，但我不知道如何在我的代码中使用代码进行 Facail Ladmark

请帮助我 感谢

Answer 1

你可以使用 dlib 和 imutils。该模型可在此处下载：http://dlib.net/files/

import dlib
from imutils import face_utils
import cv2
shape_predictor = dlib.shape_predictor('shape_predictor_5_face_landmarks.dat')
fa = face_utils.facealigner.FaceAligner(self.shape_predictor, desiredLeftEye=(0.2, 0.2))

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
aligned_face = self.fa.align(frame, gray, dlib.rectangle(left = box[0], top=box[1], right=box[2], bottom=box[3]))