def feature_extraction(self, feat_ext, images, rects, img):
if feat_ext == 'facenet':
return facenet_feature_extraction.feature_extraction(images)
elif feat_ext == 'vgg16':
return vgg16_feature_extraction.feature_extraction(images)
elif feat_ext == 'dlib_68':
return dlib_feature_extraction.feature_extraction(img, rects, "68")
else:
return []
def batch_feature_extraction(self, face_dst_path, batch_size=64):
batch = []
self.labels = []
self.src_features = None
dlib_src_img = []
dlib_src_rect = []
# 所有人脸文件的循环
for ii, file in enumerate(self.filenames, 1):
# 保存人脸文件名，不包含后缀，例：000001_2
self.labels.append(file.split("/")[-1].split(".")[0])
if self.feat_ext == 'dlib_68':
# dlib比较特殊，需要提供人脸所在帧的图片和人脸框
# 读取人脸所在帧的图片并保存到dlib_src_img中，例：000001.jpg
dlib_img = misc.imread(os.path.expanduser(os.path.join(self.dstpath, file.split("_")[0] + ".jpg")),
mode='RGB')
dlib_src_img.append(dlib_img)
# 保存人脸框
dlib_src_rect.append(self.dst_rects_lst[ii - 1])
else:
# 读取人脸图片并保存到batch中
img = misc.imread(os.path.expanduser(os.path.join(face_dst_path, file)), mode='RGB')
if self.feat_ext == 'facenet':
batch.append(img.reshape((160, 160, 3)))
else:
img = misc.imresize(img, (224, 224, 3), interp='bilinear')
batch.append(img.reshape((1, 224, 224, 3)))
# 如果读取的人脸数达到一个batch批次了，或者所有人脸都加载进来了，开始批量提取人脸特征
if ii % batch_size == 0 or ii == len(self.filenames):
if self.feat_ext == 'facenet':
images = np.stack(batch)
codes_batch = facenet_feature_extraction.feature_extraction(images)
elif self.feat_ext == 'vgg16':
images = np.concatenate(batch)
codes_batch = vgg16_feature_extraction.feature_extraction(images)
elif self.feat_ext == 'dlib_68':
images = np.stack(dlib_src_img) # concatenate
if images.ndim > 3:
codes_batch = []
for i in range(images.shape[0]):
feature = dlib_feature_extraction.feature_extraction_single(images[i], dlib_src_rect[i],
"68")
codes_batch.append(feature)
else:
codes_batch = dlib_feature_extraction.feature_extraction_single(images, dlib_src_rect[0], "68")
# 保存人脸特征
if self.src_features is None:
self.src_features = codes_batch
else:
self.src_features = np.concatenate((self.src_features, codes_batch))
# 重置变量，开始下一批次的循环
batch = []
dlib_src_img = []
dlib_src_rect = []
print('{} images processed'.format(ii))