save_hard_example()利用PNet识别结果(困难样本)来确定RNet数据结果:
def save_hard_example(net, data,save_path):
# load ground truth from annotation file
# format of each line: image/path [x1,y1,x2,y2] for each gt_box in this image
im_idx_list = data['images']
# print(images[0])
gt_boxes_list = data['bboxes']
num_of_images = len(im_idx_list)
print("processing %d images in total" % num_of_images)
# save files
neg_label_file = "../../DATA/no_LM%d/neg_%d.txt" % (net, image_size)
neg_file = open(neg_label_file, 'w')
pos_label_file = "../../DATA/no_LM%d/pos_%d.txt" % (net, image_size)
pos_file = open(pos_label_file, 'w')
part_label_file = "../../DATA/no_LM%d/part_%d.txt" % (net, image_size)
part_file = open(part_label_file, 'w')
#read detect result
det_boxes = pickle.load(open(os.path.join(save_path, 'detections.pkl'), 'rb'))
# print(len(det_boxes), num_of_images)
print(len(det_boxes))
print(num_of_images)
assert len(det_boxes) == num_of_images, "incorrect detections or ground truths"
# index of neg, pos and part face, used as their image names
n_idx = 0
p_idx = 0
d_idx = 0
image_done = 0
#im_idx_list image index(list)
#det_boxes detect result(list)
#gt_boxes_list gt(list)
for im_idx, dets, gts in zip(im_idx_list, det_boxes, gt_boxes_list):
gts = np.array(gts, dtype=np.float32).reshape(-1, 4)
if image_done % 2 == 0:
print("%d images done" % image_done)
image_done += 1
if dets.shape[0] == 0:
continue
img = cv2.imread(im_idx)
#change to square
dets = convert_to_square(dets)
dets[:, 0:4] = np.round(dets[:, 0:4])
neg_num = 0
for box in dets:
x_left, y_top, x_right, y_bottom, _ = box.astype(int)
width = x_right - x_left + 1
height = y_bottom - y_top + 1
# ignore box that is too small or beyond image border
if width < 20 or x_left < 0 or y_top < 0 or x_right > img.shape[1] - 1 or y_bottom > img.shape[0] - 1:
continue
# compute intersection over union(IoU) between current box and all gt boxes
Iou = IoU(box, gts)
cropped_im = img[y_top:y_bottom + 1, x_left:x_right + 1, :]
resized_im = cv2.resize(cropped_im, (image_size, image_size),
interpolation=cv2.INTER_LINEAR)
# save negative images and write label
# Iou with all gts must below 0.3
if np.max(Iou) < 0.3 and neg_num < 10:
#save the examples
save_file = get_path(neg_dir, "%s.jpg" % n_idx)
# print(save_file)
neg_file.write(save_file + ' 0\n')
cv2.imwrite(save_file, resized_im)
n_idx += 1
neg_num += 1
else:
# find gt_box with the highest iou
idx = np.argmax(Iou)
assigned_gt = gts[idx]
x1, y1, x2, y2 = assigned_gt
# compute bbox reg label
offset_x1 = (x1 - x_left) / float(width)
offset_y1 = (y1 - y_top) / float(height)
offset_x2 = (x2 - x_right) / float(width)
offset_y2 = (y2 - y_bottom) / float(height)
# save positive and part-face images and write labels
if np.max(Iou) >= 0.65:
save_file = get_path(pos_dir, "%s.jpg" % p_idx)
pos_file.write(save_file + ' 1 %.2f %.2f %.2f %.2f\n' % (
offset_x1, offset_y1, offset_x2, offset_y2))
cv2.imwrite(save_file, resized_im)
p_idx += 1
elif np.max(Iou) >= 0.4:
save_file = os.path.join(part_dir, "%s.jpg" % d_idx)
part_file.write(save_file + ' -1 %.2f %.2f %.2f %.2f\n' % (
offset_x1, offset_y1, offset_x2, offset_y2))
cv2.imwrite(save_file, resized_im)
d_idx += 1
neg_file.close()
part_file.close()
pos_file.close()
def t_net(prefix, epoch,
batch_size, test_mode="PNet",
thresh=[0.6, 0.6, 0.7], min_face_size=25,
stride=2, slide_window=False, shuffle=False, vis=False):
detectors = [None, None, None]
print("Test model: %s"% test_mode)
#PNet-echo
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
print(model_path[0])
# load pnet model
if slide_window:
PNet = Detector(P_Net, 12, batch_size[0], model_path[0])
else:
print("model_path[0]................%s"%model_path[0])
PNet = FcnDetector(P_Net, model_path[0])
detectors[0] = PNet
# load rnet model
if test_mode in ["RNet", "ONet"]:
print("==========================----------", test_mode)
RNet = Detector(R_Net, 24, batch_size[1], model_path[1])
detectors[1] = RNet
# load onet model
if test_mode == "ONet":
print("==================================", test_mode)
ONet = Detector(O_Net, 48, batch_size[2], model_path[2])
detectors[2] = ONet
basedir = '../../DATA/'
#anno_file
filename = 'wider_face_train_bbx_gt.txt'
#read anotation(type:dict), include 'images' and 'bboxes'
data = read_annotation(basedir,filename)
mtcnn_detector = MtcnnDetector(detectors=detectors, min_face_size=min_face_size,
stride=stride, threshold=thresh, slide_window=slide_window)
print("==================================")
# 注意是在“test”模式下
# imdb = IMDB("wider", image_set, root_path, dataset_path, 'test')
# gt_imdb = imdb.gt_imdb()
print('load test data')
test_data = TestLoader(data['images'])
print("test_data --------------------------------------%s"%test_data)
print ('finish loading')
#list
print ('start detecting....')
detections,_ = mtcnn_detector.detect_face(test_data)
print ('finish detecting ')
save_net = 'RNet'
if test_mode == "PNet":
save_net = "RNet"
elif test_mode == "RNet":
save_net = "ONet"
#save detect result
save_path = os.path.join(data_dir, save_net)
print ('save_path is :')
print(save_path)
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections.pkl")
with open(save_file, 'wb') as f:
pickle.dump(detections, f,1)
print("%s测试完成开始OHEM" % image_size)
save_hard_example(image_size, data, save_path)
def parse_args():
parser = argparse.ArgumentParser(description='Test mtcnn',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--test_mode', dest='test_mode', help='test net type, can be pnet, rnet or onet',
default='RNet', type=str)
parser.add_argument('--prefix', dest='prefix', help='prefix of model name', nargs="+",
default=['../data/MTCNN_model/PNet_landmark/PNet', '../data/MTCNN_model/RNet_Landmark/RNet', '../data/MTCNN_model/ONet_Landmark/ONet'],
type=str)
parser.add_argument('--epoch', dest='epoch', help='epoch number of model to load', nargs="+",
default=[18, 14, 16], type=int)
parser.add_argument('--batch_size', dest='batch_size', help='list of batch size used in prediction', nargs="+",
default=[64, 64, 16], type=int) #2048
parser.add_argument('--thresh', dest='thresh', help='list of thresh for pnet, rnet, onet', nargs="+",
default=[0.3, 0.1, 0.7], type=float)
parser.add_argument('--min_face', dest='min_face', help='minimum face size for detection',
default=20, type=int)
parser.add_argument('--stride', dest='stride', help='stride of sliding window',
default=2, type=int)
parser.add_argument('--sw', dest='slide_window', help='use sliding window in pnet', action='store_true')
#parser.add_argument('--gpu', dest='gpu_id', help='GPU device to train with',default=0, type=int)
parser.add_argument('--shuffle', dest='shuffle', help='shuffle data on visualization', action='store_true')
parser.add_argument('--vis', dest='vis', help='turn on visualization', action='store_true')
args = parser.parse_args()
return args
if __name__ == '__main__':
net = 'RNet'
if net == "RNet":
image_size = 24
if net == "ONet":
image_size = 48
base_dir = '../../DATA/WIDER_train' #data\MTCNN_model\PNet_landmark
data_dir = '../../DATA/no_LM%s' % str(image_size)
print(data_dir)
neg_dir = get_path(data_dir, 'negative')
pos_dir = get_path(data_dir, 'positive')
part_dir = get_path(data_dir, 'part')
#create dictionary shuffle
for dir_path in [neg_dir, pos_dir, part_dir]:
if not os.path.exists(dir_path):
os.makedirs(dir_path)
args = parse_args()
print('Called with argument:')
print(args)
t_net(args.prefix,#model param's file
args.epoch, #final epoches
args.batch_size, #test batch_size
args.test_mode,#test which model
args.thresh, #cls threshold
args.min_face, #min_face
args.stride,#stride
args.slide_window,
args.shuffle,
vis=False)