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 mtcnn_model(prefix, epoch, batch_size, ctx, thresh, min_face, stride,
slide_window):
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
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
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,
stride=stride,
threshold=thresh,
slide_window=slide_window)
return mtcnn_detector
def test_net(gpuId):
prefix = [
os.path.join(mtnnDir, 'pnet'),
os.path.join(mtnnDir, 'rnet'),
os.path.join(mtnnDir, 'onet')
]
epoch = [16, 16, 16]
batch_size = [2048, 256, 16]
ctx = mx.gpu(gpuId)
thresh = [0.5, 0.5, 0.7]
min_face_size = 40
stride = 2
args_p, auxs_p = load_param(prefix[0], epoch[0], convert=True, ctx=ctx)
PNet = FcnDetector(P_Net("test"), ctx, args_p, auxs_p)
# load rnet model
args_r, auxs_r = load_param(prefix[1], epoch[0], convert=True, ctx=ctx)
RNet = Detector(R_Net("test"), 24, batch_size[1], ctx, args_r, auxs_r)
# load onet model
args_o, auxs_o = load_param(prefix[2], epoch[2], convert=True, ctx=ctx)
ONet = Detector(O_Net("test"), 48, batch_size[2], ctx, args_o, auxs_o)
return MtcnnDetector(
detectors=[PNet, RNet, ONet],
ctx=ctx,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=False)
def onet_detector(image):
"""
:param image: 输入为48*48大小的图像
:return: 返回概率值
"""
sym = O_Net('test')
ctx = mx.cpu()
# ctx = mx.gpu()
# ctx = [mx.gpu(int(i)) for i in [0,1,2,3]]
args, auxs = load_param('model/onet', 9, convert=False, ctx=ctx)
data_size = 48 # landmark net 输入的图像尺寸为48*48
data_shapes = {'data': (1, 3, data_size, data_size)}
# # img_resized = cv2.resize(image, (48, 48))
newimg = transform(image)
args['data'] = mx.nd.array(newimg, ctx)
executor = sym.simple_bind(ctx, grad_req='null', **dict(data_shapes))
executor.copy_params_from(args, auxs)
executor.forward(is_train=False) # inference
out_list = [[] for _ in range(len(executor.outputs))]
for o_list, o_nd in zip(out_list, executor.outputs):
o_list.append(o_nd.asnumpy())
out = list()
for o in out_list:
out.append(np.vstack(o))
cls_pro = out[0][0][1]
return out
def test_net(root_path,
dataset_path,
image_set,
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[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"), 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("fddb", image_set, root_path, dataset_path, 'test')
gt_imdb = imdb.gt_imdb()
test_data = TestLoader(gt_imdb)
print('1')
# 得到的detections格式为:[[第一张图的所有预测框],[第二张图片的所有预测框],[第三张图片的所有预测框],...,[最后一张图片的所有预测框]],每个预测框为[x1,y1,x2,y2,score],score为该预测框为人脸的分数
detections = mtcnn_detector.detect_face(imdb, test_data, vis=vis)
save_path = "/Users/qiuxiaocong/Downloads/mtcnn1"
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections_onet_0009_givenPRnet.pkl")
with open(save_file, 'wb') as f:
pickle.dump(detections, f, pickle.HIGHEST_PROTOCOL)
print('detections saved done!')
def train_O_net(image_set, root_path, dataset_path, prefix, ctx, pretrained,
epoch, begin_epoch, end_epoch, frequent, lr, resume):
imdb = IMDB("mtcnn", image_set, root_path, dataset_path)
gt_imdb = imdb.gt_imdb()
gt_imdb = imdb.append_flipped_images(gt_imdb)
sym = O_Net()
train_net(sym, prefix, ctx, pretrained, epoch, begin_epoch, end_epoch,
gt_imdb, 48, frequent, not resume, lr)
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 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 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, image_set, prefix, epoch,
batch_size, ctx, test_mode="rnet",
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)
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 == "pnet":
net = "rnet"
elif test_mode == "rnet":
net = "onet"
save_path = "./prepare_data/%s"%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)
def loadModel(self):
self.model = face_embedding.FaceModel(self.args)
detectors = [None, None, None]
ctx = mx.gpu(0)
prefix = ['mtcnnmodel/pnet', 'mtcnnmodel/rnet', 'mtcnnmodel/onet']
epoch = [16, 16, 16]
batch_size = [2048, 256, 16]
thresh = [0.6, 0.6, 0.7]
min_face_size = 24
stride = 2
slide_window = False
# 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
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
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
self.mtcnn_detector = MtcnnDetector(detectors=detectors,
ctx=ctx,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
#print (self.model)
self.id_dataset, self.idnums = self.get_id_data(self.args.id_dir)
def test_net(prefix,
epoch,
batch_size,
ctx,
thresh=[0.6, 0.6, 0.7],
min_face_size=24,
stride=2,
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: # 使用滑动窗口(MTCNN的P_NET不使用了滑动窗口,而是全卷积网络)
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
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 = MtcnnDetector1(detectors=detectors,
ctx=ctx,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
# img = cv2.imread('test01.jpg') # 读取图片
# img = cv2.imread('zhang.jpeg') # 读取图片
# img = cv2.imread('curry.jpg') # 读取图片
# img = cv2.imread('physics.jpg') # 读取图片
# img = cv2.imread('000007.jpg') # 读取图片
# img = cv2.imread('test01.jpg') # 读取图片
# img = cv2.imread('NBA98.jpg')
# img = cv2.imread('download.jpg')
# img = cv2.imread('/Users/qiuxiaocong/Downloads/WIDER_train/images/7--Cheering/7_Cheering_Cheering_7_16.jpg')
# img = cv2.imread('/Users/qiuxiaocong/Downloads/WIDER_train/images/11--Meeting/11_Meeting_Meeting_11_Meeting_Meeting_11_77.jpg')
# img = cv2.imread('/Users/qiuxiaocong/Downloads/3Dfacedeblurring/dataset_test/falling1/input/00136.png')
img = cv2.imread('/Users/qiuxiaocong/Downloads/facetrack_python/error.jpg')
boxes, boxes_c = mtcnn_detector.detect_pnet(img)
boxes, boxes_c = mtcnn_detector.detect_rnet(img, boxes_c)
boxes, boxes_c = mtcnn_detector.detect_onet(img, boxes_c)
# print(boxes_c) # x1 y1 x2 y2
original_detect = []
crop_list = []
detect_len_list = []
nd_array = []
score_list = []
if boxes_c is not None:
draw = img.copy()
font = cv2.FONT_HERSHEY_SIMPLEX # Python 一种字体
idx = 0
for b in boxes_c: # nms和bbr之后的结果
# 在draw上绘制矩形框(左上角坐标+右下角坐标)
b_new0 = np.array(b[0:4]) # 添加检测框
original_detect.append(b_new0)
b_new = convert_to_square(b_new0) # 添加送入到landmark net的48*48大小的框
crop_list.append(b_new)
score_list.append(b[4])
# cv2.rectangle(draw, (int(b_new[0]), int(b_new[1])), (int(b_new[2]), int(b_new[3])),
# (0, 255, 255), 1)
# # 在draw上添加文字
# cv2.putText(draw, '%.3f'%b[4], (int(b[0]), int(b[1])), font, 0.4, (255, 255, 255), 1)
# cv2.imshow("detection result", draw)
img_draw = img[int(b_new[1]):int(b_new[3]),
int(b_new[0]):int(b_new[2])]
detect_len = min(img_draw.shape[0], img_draw.shape[1])
# print(img_draw.shape[0], img_draw.shape[1])
if detect_len != 0:
detect_len_list.append(detect_len)
img_resized = cv2.resize(img_draw, (48, 48))
# cv2.imshow("detection result", draw)
# print('img_resized type is :{}'.format(type(img_resized)))
nd_array.append(img_resized)
# cv2.imwrite("detection_result{}.jpg".format(idx), img_resized)
# cv2.waitKey(0)
idx = idx + 1
return crop_list, detect_len_list, original_detect, idx, img, nd_array
def test_net(prefix,
epoch,
batch_size,
ctx,
thresh=[0.6, 0.6, 0.7],
min_face_size=24,
stride=2,
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: # 使用滑动窗口(MTCNN的P_NET不使用了滑动窗口,而是全卷积网络)
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
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') # 读取图片
# img = cv2.imread('000007.jpg') # 读取图片
# img = cv2.imread('crow.jpg') # 读取图片
img = cv2.imread('physics.jpg') # 读取图片
t1 = time.time() # 开始计时
boxes, boxes_c = mtcnn_detector.detect_pnet(img)
boxes, boxes_c = mtcnn_detector.detect_rnet(img, boxes_c)
boxes, boxes_c = mtcnn_detector.detect_onet(img, boxes_c)
print(boxes_c)
print('time: ', time.time() - t1)
if boxes_c is not None:
draw = img.copy()
font = cv2.FONT_HERSHEY_SIMPLEX # Python 一种字体
for b in boxes_c: # nms和bbr之后的结果
# for b in boxes: # nms和bbr之前的结果
# 在draw上绘制矩形框(左上角坐标+右下角坐标)
cv2.rectangle(draw, (int(b[0]), int(b[1])), (int(b[2]), int(b[3])),
(0, 255, 255), 1)
# 在draw上添加文字
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 test_net(prefix=['model/pnet', 'model/rnet', 'model/onet'],
epoch=[16, 16, 16],
batch_size=[2048, 256, 16],
ctx=mx.cpu(0),
thresh=[0.6, 0.6, 0.7],
min_face_size=24,
stride=2,
camera_path='0'):
# load pnet model
args, auxs = load_param(prefix[0], epoch[0], convert=True, ctx=ctx)
PNet = FcnDetector(P_Net("test"), ctx, args, auxs)
# load rnet model
args, auxs = load_param(prefix[1], epoch[0], convert=True, ctx=ctx)
RNet = Detector(R_Net("test"), 24, batch_size[1], ctx, args, auxs)
# 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)
mtcnn_detector = MtcnnDetector(detectors=[PNet, RNet, ONet],
ctx=ctx,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=False)
try:
capture = cv2.VideoCapture(int(camera_path))
except ValueError as e:
capture = cv2.VideoCapture(camera_path)
try:
first_loop = True
while (capture.isOpened()):
ret, img = capture.read()
if img is None:
continue
# Initialize video writing
if (first_loop):
first_loop = False
fourcc = cv2.VideoWriter_fourcc(*'H264')
h, w = img.shape[:2]
writer = cv2.VideoWriter('test.mkv', fourcc, 10, (w, h), True)
t1 = time.time()
boxes, boxes_c = mtcnn_detector.detect_pnet(img)
boxes, boxes_c = mtcnn_detector.detect_rnet(img, boxes_c)
boxes, boxes_c = mtcnn_detector.detect_onet(img, boxes_c)
print('shape: ', img.shape, '--', 'time: ', time.time() - t1)
draw = img.copy()
if boxes_c is not None:
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)
writer.write(draw)
except KeyboardInterrupt as e:
print("KeyboardInterrupt")
writer.release()
def test_net(prefix,
epoch,
batch_size,
ctx,
thresh=[0.6, 0.6, 0.7],
min_face_size=24,
stride=2,
slide_window=False,
camera_path='0'):
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
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
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)
try:
capture = cv2.VideoCapture(int(camera_path))
except ValueError as e:
capture = cv2.VideoCapture(camera_path)
while (capture.isOpened()):
ret, img = capture.read()
if img is None:
continue
# img = cv2.imread('test01.jpg')
t1 = time.time()
boxes, boxes_c = mtcnn_detector.detect_pnet(img)
boxes, boxes_c = mtcnn_detector.detect_rnet(img, boxes_c)
boxes, boxes_c = mtcnn_detector.detect_onet(img, boxes_c)
print('shape: ', img.shape, '--', '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)
else:
cv2.imshow("detection result", img)
k = cv2.waitKey(1)
if k == 27 or k == 113: # Esc or q key to stop
break
def test_net(prefix,
epoch,
batch_size,
ctx,
thresh=[0.6, 0.6, 0.7],
min_face_size=24,
stride=2,
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_Net("test"), 12, batch_size[0], ctx, args, auxs)
else:
PNet = FcnDetector(P_Net("test"), ctx, args, auxs)
detectors[0] = PNet
# load rnet model
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
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)
video_capture = cv2.VideoCapture(0)
boxes = []
boxes_c = []
while True:
#img = cv2.imread('/home/zzg/Opensource/mtcnn-master/data/custom/02.jpg')
_, img = video_capture.read()
t1 = time.time()
boxes, boxes_c = mtcnn_detector.detect_pnet(img)
if boxes_c is None:
continue
boxes, boxes_c = mtcnn_detector.detect_rnet(img, boxes_c)
if boxes_c is None:
continue
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.imwrite("o12.jpg",draw)
cv2.waitKey(10)