save_path = ''
idx = 0
for item in image_info:
idx += 1
image_file_name = os.path.join(data_dir, item[0], item[1])
if current_event != item[0]:
current_event = item[0]
save_path = os.path.join(output_file, item[0])
if not os.path.exists(save_path):
os.mkdir(save_path)
print('current path:', current_event)
# generate detection
img = cv2.imread(image_file_name)
all_boxes, _ = mtcnn_detector.detect_single_image(img)
f_name = item[1].split('.jpg')[0]
dets_file_name = os.path.join(save_path, f_name + '.txt')
fid = open(dets_file_name, 'w')
boxes = all_boxes[0]
if boxes is None:
fid.write(item[1] + '\n')
fid.write(str(1) + '\n')
fid.write('%f %f %f %f %f\n' % (0, 0, 0, 0, 0.99))
continue
fid.write(item[1] + '\n')
fid.write(str(len(boxes)) + '\n')
for box in boxes:
def run(prefix,
epoch,
batch_size,
output_dir,
test_mode="PNet",
thresh=[0.3, 0.1, 0.7],
min_face_size=20,
stride=2,
slide_window=False,
shuffle=False,
vis=False):
data_dir = '../../DATA/WIDER_val/images'
anno_file = 'wider_face_val.txt'
output_file = output_dir
if not os.path.exists(output_file):
os.mkdir(output_file)
test_mode = test_mode
thresh = thresh
min_face_size = min_face_size
stride = stride
slide_window = slide_window
shuffle = shuffle
vis = vis
detectors = [None, None, None]
# prefix is the model path
prefix = prefix
epoch = epoch
batch_size = batch_size
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
# load pnet model
if slide_window:
PNet = Detector(P_Net, 12, batch_size[0], model_path[0])
else:
PNet = FcnDetector(P_Net, model_path[0])
detectors[0] = PNet
# load rnet model
if test_mode in ["RNet", "ONet"]:
RNet = Detector(R_Net, 24, batch_size[1], model_path[1])
detectors[1] = RNet
# load onet model
if test_mode == "ONet":
ONet = Detector(O_Net, 48, batch_size[2], model_path[2])
detectors[2] = ONet
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
image_info = get_image_info(anno_file)
current_event = ''
save_path = ''
idx = 0
for item in image_info:
idx += 1
image_file_name = os.path.join(data_dir, item[0], item[1])
if current_event != item[0]:
current_event = item[0]
save_path = os.path.join(output_file, item[0])
if not os.path.exists(save_path):
os.mkdir(save_path)
print('current path:', current_event)
# generate detection
img = cv2.imread(image_file_name)
all_boxes, _ = mtcnn_detector.detect_single_image(img)
f_name = item[1].split('.jpg')[0]
dets_file_name = os.path.join(save_path, f_name + '.txt')
fid = open(dets_file_name, 'w')
boxes = all_boxes[0]
if boxes is None:
fid.write(item[1] + '\n')
fid.write(str(1) + '\n')
fid.write('%f %f %f %f %f\n' % (0, 0, 0, 0, 0.99))
continue
fid.write(item[1] + '\n')
fid.write(str(len(boxes)) + '\n')
for box in boxes:
fid.write(
'%f %f %f %f %f\n' %
(float(box[0]), float(box[1]), float(box[2] - box[0] + 1),
float(box[3] - box[1] + 1), box[4]))
fid.close()
if idx % 10 == 0:
print(idx)