def test_net(root_path,
dataset_path,
image_set,
prefix,
epoch,
batch_size,
ctx,
test_mode="hardpnet20",
thresh=[0.6, 0.6, 0.7],
min_face_size=24):
thread_num = len(ctx)
mtcnn_detectors = list()
for i in range(thread_num):
mtcnn_detectors.append(
creat_mtcnn_detector(prefix, epoch, batch_size, test_mode, thresh,
min_face_size, ctx[i]))
imdb = IMDB("wider", image_set, root_path, dataset_path, 'test')
annotations = imdb.get_annotations()
image_num = len(annotations)
test_batch_size = 10 * thread_num
start_idx = 0
detections = list()
while start_idx < image_num:
end_idx = min(start_idx + test_batch_size, image_num)
cur_annotations = annotations[start_idx:end_idx]
cur_detections = test_minibatch(cur_annotations, mtcnn_detectors)
detections = detections + cur_detections
start_idx = end_idx
print '%d images done' % start_idx
return detections
def train_O_net(image_set, root_path, dataset_path, prefix, ctx,
pretrained, epoch, begin_epoch, end_epoch, batch_size, thread_num,
frequent, lr,lr_epoch, resume, with_landmark):
imdb = IMDB("mtcnn", image_set, root_path, dataset_path, 'train')
gt_imdb = imdb.get_annotations()
sym = O_Net('train',with_landmark)
train_net(sym, prefix, ctx, pretrained, epoch, begin_epoch, end_epoch, gt_imdb, batch_size, thread_num,
48, True, True, with_landmark, frequent, not resume, lr, lr_epoch)
def train_R_net(image_set, root_path, dataset_path, prefix, ctx,
pretrained, epoch, begin_epoch, end_epoch, batch_size, thread_num,
frequent, lr, lr_epoch, resume):
imdb = IMDB("mtcnn", image_set, root_path, dataset_path, 'train')
gt_imdb = imdb.get_annotations()
sym = R_Net()
train_net(sym, prefix, ctx, pretrained, epoch, begin_epoch, end_epoch, gt_imdb, batch_size, thread_num,
24, True, True, False, frequent, not resume, lr, lr_epoch)
def test_net(root_path,
dataset_path,
prefix,
epoch,
batch_size,
ctx,
test_mode="onet",
thresh=[0.6, 0.6, 0.7],
min_face_size=24):
detectors = [None, None, None]
# load pnet model
args, auxs = load_param(prefix[0], epoch[0], convert=True, ctx=ctx)
PNet = FcnDetector(P_Net20("test"), ctx, args, auxs)
detectors[0] = PNet
# load rnet model
if test_mode in ["rnet", "onet"]:
args, auxs = load_param(prefix[1], epoch[1], convert=True, ctx=ctx)
RNet = Detector(R_Net("test"), 24, batch_size[1], ctx, args, auxs)
detectors[1] = RNet
# load onet model
if test_mode == "onet":
args, auxs = load_param(prefix[2], epoch[2], convert=True, ctx=ctx)
ONet = Detector(O_Net("test", False), 48, batch_size[2], ctx, args,
auxs)
detectors[2] = ONet
mtcnn_detector = MtcnnDetector(detectors=detectors,
ctx=ctx,
min_face_size=min_face_size,
stride=4,
threshold=thresh,
slide_window=False)
for i in range(1, 11):
image_set = "fold-" + str(i).zfill(2)
imdb = IMDB("fddb", image_set, root_path, dataset_path, 'test')
gt_imdb = imdb.get_annotations()
test_data = TestLoader(gt_imdb)
all_boxes = mtcnn_detector.detect_face(imdb, test_data, vis=False)
imdb.write_results(all_boxes)
