def gen_onet_data(data_dir,
anno_file,
pnet_model_file,
rnet_model_file,
prefix_path='',
use_cuda=True,
vis=False):
mtcnn_detector = MtcnnDetector(p_model_path=pnet_model_file,
r_model_path=rnet_model_file,
o_model_path=None,
min_face_size=12,
use_cuda=True)
imagedb = ImageDB(anno_file, mode="test", prefix_path=prefix_path)
imdb = imagedb.load_imdb()
image_reader = TestImageLoader(imdb, 1, False)
all_boxes = list()
batch_idx = 0
for databatch in image_reader:
if batch_idx % 100 == 0:
print("%d images done" % batch_idx)
im = databatch
t = time.time()
# detect an image by pnet and rnet
p_boxes, p_boxes_align = mtcnn_detector.detect_pnet(im=im)
boxes, boxes_align = mtcnn_detector.detect_rnet(im=im,
dets=p_boxes_align)
if boxes_align is None:
all_boxes.append(np.array([]))
batch_idx += 1
continue
if vis:
vision.vis_face(im, boxes_align)
t1 = time.time() - t
print('time cost for image ', batch_idx, '/', image_reader.size, ': ',
t1)
all_boxes.append(boxes_align)
batch_idx += 1
save_path = config.TRAIN_DATA_DIR
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path,
"pnet_rnet_detections_%d.pkl" % int(time.time()))
with open(save_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
# save_file = '/home/liujing/Codes/MTCNN/data/pnet_detections_1532582821.pkl'
get_onet_sample_data(data_dir, anno_file, save_file, prefix_path)
def gen_rnet_data(data_dir,
anno_file,
pnet_model_file,
prefix_path='',
use_cuda=True,
vis=False):
# load the pnet and pnet_detector ,利用刚刚训练的pnet网络生成rnet的训练数据
mtcnn_detector = MtcnnDetector(
p_model_path=pnet_model_file, # pnet_model_file自行设置
r_model_path=None,
o_model_path=None,
min_face_size=12,
use_cuda=True)
device = mtcnn_detector.device
# 生成rnet网络的训练集
imagedb = ImageDB(anno_file, mode="test", prefix_path=prefix_path)
imdb = imagedb.load_imdb()
image_reader = TestImageLoader(imdb, 1, False)
all_boxes = []
batch_idx = 0
for databatch in image_reader:
if batch_idx % 100 == 0:
print("%d images done" % batch_idx)
im = databatch
t = time.time()
boxes, boxes_align = mtcnn_detector.detect_pnet(im)
if boxes_align is None:
all_boxes.append(np.array([]))
continue
if vis:
vision.vis_face(im, boxes_align)
t1 = time.time() - t
print('time cost for image {} / {} : {:.4f}'.format(
batch_idx, image_reader.size, t1))
all_boxes.append(boxes_align)
batch_idx += 1
save_path = config.TRAIN_DATA_DIR
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path,
"pnet_detections_%d.pkl" % int(time.time()))
with open(save_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
# save_file = '/home/liujing/Codes/MTCNN/data/pnet_detections_1532530263.pkl'
get_rnet_sample_data(data_dir, anno_file, save_file, prefix_path)