def test_net(prefix,
epoch,
batch_size,
ctx,
thresh=[0.6, 0.6, 0.7],
min_face_size=24,
stride=4,
slide_window=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_Net20("test"), 20, batch_size[0], ctx, args, auxs)
else:
PNet = FcnDetector(P_Net20("test"), ctx, args, auxs)
detectors[0] = PNet
# load rnet model
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
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)
img = cv2.imread('test01.jpg')
t1 = time.time()
boxes, boxes_c = mtcnn_detector.detect_pnet20(img)
boxes, boxes_c = mtcnn_detector.detect_rnet(img, boxes_c)
boxes, boxes_c = mtcnn_detector.detect_onet(img, boxes_c)
print 'time: ', time.time() - t1
if boxes_c is not None:
draw = img.copy()
font = cv2.FONT_HERSHEY_SIMPLEX
for b in boxes_c:
cv2.rectangle(draw, (int(b[0]), int(b[1])), (int(b[2]), int(b[3])),
(0, 255, 255), 1)
cv2.putText(draw, '%.3f' % b[4], (int(b[0]), int(b[1])), font, 0.4,
(255, 255, 255), 1)
cv2.imshow("detection result", draw)
cv2.waitKey(0)
def creat_mtcnn_detector(prefix, epoch, batch_size, test_mode, thresh,
min_face_size, ctx):
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 ["onet", "hardrnet", "hardonet"]:
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 == "hardonet":
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)
return mtcnn_detector
def train_P_net20(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 = P_Net20()
train_net(sym, prefix, ctx, pretrained, epoch, begin_epoch, end_epoch,
gt_imdb, batch_size, thread_num, 20, True, True, False, frequent,
not resume, lr, lr_epoch)
def train_P_net20(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)
gt_imdb = imdb.gt_imdb()
gt_imdb = imdb.append_flipped_images(gt_imdb)
sym = P_Net20()
train_net(sym, prefix, ctx, pretrained, epoch, begin_epoch, end_epoch,
gt_imdb, batch_size, thread_num, 20, 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,
stride=4, 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_Net20("test"), 20, batch_size[0], ctx, args, auxs)
else:
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[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)
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,
stride=4,
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_Net20("test"), 20, batch_size[0], ctx, args, auxs)
else:
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[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)
imdb = IMDB("wider", image_set, root_path, dataset_path, 'test')
gt_imdb = imdb.gt_imdb()
test_data = TestLoader(gt_imdb)
detections = mtcnn_detector.detect_face(imdb, test_data, vis=vis)
if test_mode == "hardpnet20":
net = "pnet20"
if test_mode == "hardrnet":
net = "rnet"
elif test_mode == "hardonet":
net = "onet"
save_path = "%s/prepare_data/%s" % (config.root, net)
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:
cPickle.dump(detections, f, cPickle.HIGHEST_PROTOCOL)
save_hard_example(net)