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,
stride=2,
slide_window=False,
shuffle=False,
vis=False):
detectors = [None, None, None]
# load pnet model
args, auxs = load_param(prefix[0], epoch[0], convert=False, ctx=ctx)
if slide_window:
PNet = Detector(P_Net("test"), 12, batch_size[0], ctx, args, auxs)
else:
PNet = FcnDetector(P_Net("test"), ctx, args, auxs)
detectors[0] = PNet
# load rnet model
if test_mode in ["rnet", "onet"]:
args, auxs = load_param(prefix[1], epoch[0], convert=False, 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=False, ctx=ctx)
ONet = Detector(O_Net("test"), 48, batch_size[2], ctx, args, auxs)
detectors[2] = ONet
mtcnn_detector = MtcnnDetector(detectors=detectors,
ctx=ctx,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
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.gt_imdb()
test_data = TestLoader(gt_imdb)
all_boxes = mtcnn_detector.detect_face(imdb, test_data, vis=vis)
imdb.write_results(all_boxes)
def test_net(root_path,
dataset_path,
prefix,
epoch,
batch_size,
test_mode="onet",
thresh=[0.6, 0.6, 0.7],
min_face_size=24,
stride=2,
slide_window=False,
shuffle=False,
vis=False):
detectors = [None, None, None]
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)
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.gt_imdb()
test_data = TestLoader(gt_imdb)
all_boxes = mtcnn_detector.detect_face(imdb, test_data, vis=vis)
imdb.write_results(all_boxes)
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,
stride=2, slide_window=False, shuffle=False, vis=False):
detectors = [None, None, None]
# load pnet model
args, auxs = load_param(prefix[0], epoch[0], convert=True, ctx=ctx)
if slide_window:
PNet = Detector(P_Net("test"), 12, batch_size[0], ctx, args, auxs)
else:
PNet = FcnDetector(P_Net("test"), ctx, args, auxs)
detectors[0] = PNet
# load rnet model
if test_mode in ["rnet", "onet"]:
args, auxs = load_param(prefix[1], epoch[0], 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"), 48, batch_size[2], ctx, args, auxs)
detectors[2] = ONet
mtcnn_detector = MtcnnDetector(detectors=detectors, ctx=ctx, min_face_size=min_face_size,
stride=stride, threshold=thresh, slide_window=slide_window)
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.gt_imdb()
test_data = TestLoader(gt_imdb)
all_boxes = mtcnn_detector.detect_face(imdb, test_data, vis=vis)
imdb.write_results(all_boxes)