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)
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)
cv2.imshow("detection result", draw)
writer.write(draw)
k = cv2.waitKey(1)
if k == 27 or k == 113: # Esc or q key to stop
writer.release()
cv2.destroyAllWindows()
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, 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(root_path,
dataset_path,
image_set,
prefix,
epoch,
batch_size,
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]
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)
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 = os.path.join(data_dir, 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 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 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=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
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
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)
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 '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 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 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(prefix, epoch, batch_size, ctx,
thresh=[0.6, 0.6, 0.7], min_face_size=24,
stride=2, slide_window=False, filename='test01.jpg'):
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)
img = cv2.imread(filename)
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('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)
f = filename.split('.')
cv2.imwrite(''.join([*f[:-1], "_annotated.", f[-1]]), draw)
cv2.waitKey(0)
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(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):
#img = cv2.imread("./1111.jpg")
detectors = [None, None, None]
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
print(model_path)
# 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 name in os.listdir("C:\\Users\\JINNIU\\Desktop\\liuzhen\\qinghua"):
img = cv2.imread(
os.path.join("C:\\Users\\JINNIU\\Desktop\\liuzhen\\qinghua", name))
boxes, boxes_c = mtcnn_detector.detect(img)
visssss(img, boxes_c, name, thresh=0.998)
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)
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(dataset_path,
prefix,
faceRecognize_model,
epoch,
batch_size,
test_mode="onet",
thresh=[0.6, 0.6, 0.7],
min_face_size=24,
margin=44,
stride=2,
slide_window=False):
detectors = [None, None, None]
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
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"]:
detectors[1] = Detector(R_Net, 24, batch_size[1], model_path[1])
# load onet model
if test_mode == "onet":
detectors[2] = Detector(O_Net, 48, batch_size[2], model_path[2])
# load faceRecognize model
face_rec = FcnRecognize(faceRecognize_model, data_size=67, batch_size=16)
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
######load data
input_dir0 = '/mllib/ALG/facenet-tensorflow/jz_80val'
classes1 = os.listdir(input_dir0)
message_path = []
for cls1 in classes1:
classes2_path = os.path.join(input_dir0, cls1)
try:
classes2 = os.listdir(classes2_path)
except Exception as e:
print e
continue
key = cv2.waitKey(1)
if key == 'q': ### when keycode is q
print('====------======\n')
img_list_tmp = []
num_id = 0
image_message = {}
message = []
meg_name = [
'exit_id', 'img_read', 'img_detect', 'face_num', 'face_roi',
'exit_person', 'score', 'person_name'
]
##########0 exit_id :Is there an id ; 1 ,0
##########1 img_read :Whether to read successfully image ; 1 ,0
##########2 img_detect :Whether to detect successfully image ; 1 ,0
##########3 face_num :The face amount ; 0, 1, 2, 3, ...
##########4 face_roi :The face of a coordinate ; (x1,y1,w1,h1),(x2,y2,w2,h2) ...
##########5 exit_person :The person amount ; 0, 1, 2, 3, ...
##########6 score :The person score ; 0, 1, 2, 3, ...
##########7 score :The person name ; name0, name1, name2, name3, ...
for cls2 in classes2:
classes3_path = os.path.join(classes2_path, cls2)
print(classes3_path)
if 1: #classes2_path == "emb_check" :
image_message[meg_name[0]] = 1
else:
num_id = 0
image_message[meg_name[0]] = 0
continue
try:
img = cv2.imread(classes3_path)
except Exception as e:
print e
image_message[meg_name[1]] = 0
continue
#img = cv2.imread(image_name)#('test01.jpg')
#img = cv2.imread(input_test+'.jpg')
#cv2.imshow("img", img)
#cv2.waitKey(0)
image_message[meg_name[0]] = 1
t1 = time.time()
try:
boxes, boxes_c = mtcnn_detector.detect_pnet(img)
except Exception as e:
image_message[meg_name[2]] = 0
print e
print(classes3_path)
continue
image_message[meg_name[2]] = 1
#message.append("img_available")
if boxes_c is None:
image_message[meg_name[3]] = 0
continue
boxes, boxes_c = mtcnn_detector.detect_rnet(img, boxes_c)
if boxes_c is None:
image_message[meg_name[3]] = 0
continue
boxes, boxes_c = mtcnn_detector.detect_onet(img, boxes_c)
if boxes_c is None:
image_message[meg_name[3]] = 0
continue
image_message[meg_name[3]] = len(boxes_c)
print('box_num:%d', len(boxes_c))
print 'time: ', time.time() - t1
message.append("have_face")
num_box = []
if boxes_c is not None:
img0 = misc.imread(classes3_path)
img_size = np.asarray(img0.shape)[0:2]
nrof_samples = len(boxes_c)
for det in boxes_c:
bb = np.zeros(4, dtype=np.int32)
margin_tmp = ((det[3] - det[1]) - (det[2] - det[0])) / 2
if margin_tmp > 0:
size_tmp = (det[3] - det[1]) * 0
bb[0] = np.maximum(det[0] - margin_tmp - size_tmp, 0)
bb[1] = np.maximum(det[1] - size_tmp, 0)
bb[2] = np.minimum(det[2] + margin_tmp + size_tmp,
img_size[1])
bb[3] = np.minimum(det[3] + size_tmp, img_size[0])
else:
size_tmp = (det[2] - det[0]) * 0
bb[0] = np.maximum(det[0] - size_tmp, 0)
bb[1] = np.maximum(det[1] + margin_tmp - size_tmp, 0)
bb[2] = np.minimum(det[2] + size_tmp, img_size[1])
bb[3] = np.minimum(det[3] - margin_tmp + size_tmp,
img_size[0])
cropped = img0[int(bb[1]):int(bb[3]),
int(bb[0]):int(bb[2]), :]
num_box.append(
"%d,%d,%d,%d" %
(int(bb[1]), int(bb[3]), int(bb[0]), int(bb[2])))
#misc.imsave('hebei/%d.png'%i, cropped)
#cropped=misc.imread('/home/ssd/fb_data/casic_cluster_china67/Jin Jong-oh/Jin Jong-oh_8.png')
#cv2.imshow("cropped", cropped)
#cv2.waitKey(500) ###
aligned = misc.imresize(cropped, (67, 67),
interp='bilinear')
#cv2.imshow("aligned", aligned)
#cv2.waitKey(500) ###
#misc.imsave('hebei/%d.png'%i, aligned)
prewhitened = faceRecognize.prewhiten(aligned)
img_list_tmp.append(prewhitened)
key = cv2.waitKey(1)
image_message[meg_name[4]] = num_box
print(image_message)
if len(img_list_tmp) < 2: #1 :
continue
img_list = [None] * len(img_list_tmp)
for i in range(len(img_list_tmp)):
img_list[i] = img_list_tmp[i]
images = np.stack(img_list)
t_emb = time.time()
emb = face_rec.recognize(images)
np.save("./emb_check.txt", emb)
print 'emb_1_time: ', time.time() - t_emb
for i in range(len(img_list_tmp)):
print('%1d ' % i)
score_tmp = 65
score2person = -1
for j in range(len(img_list_tmp)):
dist = np.sqrt(
np.sum(np.square(np.subtract(emb[i, :], emb[j, :]))))
print(' %1.4f ' % dict2score(dist))
print('')
if dict2score(dist) > score_tmp:
score_tmp = dict2score(dist)
score2person = j
if score_tmp > 65:
image_message[meg_name[6]] = score_tmp
image_message[meg_name[7]] = 'num_0'
else:
image_message[meg_name[6]] = 0
image_message[meg_name[7]] = 'num_0'
print('-----====end compare !!===-----')
print(image_message)
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: # 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)
class managerWindow(Ui_MainWindow):
def __init__(self):
super(managerWindow, self).__init__()
self.setupUi(self)
self.setlcd()
##
self.pushButton_selectid.clicked.connect(lambda: self.folder_msg(1))
self.pushButton_selectmodel.clicked.connect(lambda: self.folder_msg(2))
self.pushButton_renamefloder.clicked.connect(
lambda: self.progressBar_msg(1))
self.pushButton_resizeimage.clicked.connect(
lambda: self.progressBar_msg(2))
##
self.pushButton_loadmodel.clicked.connect(
lambda: self.progressBar_msg(4))
self.pushButton_opencam.clicked.connect(
lambda: self.progressBar_msg(5))
self.pushButton_closecam.clicked.connect(
lambda: self.progressBar_msg(6))
##
self.radioButton.toggled.connect(lambda: self.camnum_meg(0))
self.radioButton_2.toggled.connect(lambda: self.camnum_meg(1))
self.radioButton_3.toggled.connect(lambda: self.camnum_meg(2))
self.radioButton_4.toggled.connect(lambda: self.camnum_meg(3))
##
self.checkBox_fps.stateChanged.connect(lambda: self.checkbox_meg(1))
self.checkBox_box.stateChanged.connect(lambda: self.checkbox_meg(2))
self.checkBox_id.stateChanged.connect(lambda: self.checkbox_meg(3))
self.checkBox_landmarls.stateChanged.connect(
lambda: self.checkbox_meg(4))
self.checkBox_distance.stateChanged.connect(
lambda: self.checkbox_meg(5))
##
self.photo_match_id = []
self.img_paths = []
self.photo_bbox = []
self.photo_currentindex = -1
self.photo_dis = []
self.ret_dis = []
self.camnum = 0
self.modelcreat = False
self.boxid = []
self.boxidnum = 0
self.show_landmarks = False
self.show_bb = False
self.show_id = False
self.show_fps = False
self.show_distence = False
self.font = cv2.FONT_HERSHEY_SIMPLEX
self.pushButton_4.clicked.connect(self.show_manager_user)
self.pushButton_7.clicked.connect(self.show_searchlog)
def mat_inter(self, box1, box2):
# 判断两个矩形是否相交
# box=(xA,yA,xB,yB)
x01, y01, x02, y02 = box1
x11, y11, x12, y12 = box2
lx = abs((x01 + x02) / 2 - (x11 + x12) / 2)
ly = abs((y01 + y02) / 2 - (y11 + y12) / 2)
sax = abs(x01 - x02)
sbx = abs(x11 - x12)
say = abs(y01 - y02)
sby = abs(y11 - y12)
if lx <= (sax + sbx) / 2 and ly <= (say + sby) / 2:
return True
else:
return False
def solve_coincide(self, box1, box2):
# box=(xA,yA,xB,yB)
# 计算两个矩形框的重合度
if self.mat_inter(box1, box2) == True:
x01, y01, x02, y02 = box1
x11, y11, x12, y12 = box2
col = min(x02, x12) - max(x01, x11)
row = min(y02, y12) - max(y01, y11)
intersection = col * row
area1 = (x02 - x01) * (y02 - y01)
area2 = (x12 - x11) * (y12 - y11)
coincide = intersection / ((area1 + area2 - intersection) * 1.0)
return coincide
else:
return False
def detect_boxid(self, box, label):
result = False
for item in self.boxid:
tempbox = item.bbox[0]
bb = [
int(tempbox[0]),
int(tempbox[1]),
int(tempbox[2]),
int(tempbox[3])
]
bb2 = [int(box[0]), int(box[1]), int(box[2]), int(box[3])]
coincide = self.solve_coincide(bb, bb2)
#print(coincide)
if coincide > 0.618:
result = True
item.add_box(box)
item.add_label(label)
result_id = item.id_num
item.releasenum += 2
if len(item.labels) >= 10:
c = Counter(item.labels)
result_label = c.most_common()[0][0]
#print(result_label)
return result, result_id, result_label, self.boxid.index(
item)
else:
return result, result_id, "Null", self.boxid.index(item)
return result, 0, 0, -1
def setlcd(self):
db = pymysql.connect("localhost",
"root",
"root",
"sys",
charset='utf8')
cursor = db.cursor()
sql = "select * from Users where levUsers not like \'teacher\'"
if cursor.execute(sql):
data = cursor.fetchall()
self.lcdNumber.setProperty("intValue", int(len(data)))
def show_manager_user(self):
self.manager_userWindow = manager_userWindow()
QMessageBox.warning(
self, ("操作说明"),
("\n根据提示添加用户信息\n 删除用户可根据： \n 用户ID \n 用户姓名\n 二选一 \n"))
self.manager_userWindow.exec_()
def show_searchlog(self):
self.searchlog_Window = searchlog_Window()
QMessageBox.warning(self, ("操作说明"),
("\n根据提示查询信息\n 查询考勤可根据： \n 用户ID \n 用户姓名\n 二选一 \n"))
self.searchlog_Window.exec_()
def folder_msg(self, n): #文件夹按钮响应函数
if n == 1:
directory = QFileDialog.getExistingDirectory(
None, "选取ID文件夹", "/home")
self.label_idfolder.setText(directory)
if n == 2:
directory = QFileDialog.getExistingDirectory(
None, "选取模型文件夹", "/home")
self.label_modelfolder.setText(directory)
def checkbox_meg(self, n):
if n == 1:
if self.checkBox_fps.isChecked():
self.show_fps = True
else:
self.show_fps = False
if n == 2:
if self.checkBox_box.isChecked():
self.show_bb = True
else:
self.show_bb = False
if n == 3:
if self.checkBox_id.isChecked():
self.show_id = True
else:
self.show_id = False
if n == 4:
if self.checkBox_landmarls.isChecked():
self.show_landmarks = True
else:
self.show_landmarks = False
if n == 5:
if self.checkBox_distance.isChecked():
self.show_distence = True
else:
self.show_distence = False
def progressBar_msg(self, n):
if n == 1:
self.progressBar.setValue(0)
if self.label_idfolder.text() != "":
self.rename(self.label_idfolder.text())
else:
button = QMessageBox.warning(self, '警告', "目录尚未选择",
QMessageBox.Yes)
if n == 2: #resize images
self.progressBar.setValue(0)
if n == 4:
self.progressBar.setValue(0)
if self.label_idfolder.text(
) != "" and self.label_modelfolder.text() != "":
self.args = Arg_Data(
self.label_idfolder.text(),
self.label_modelfolder.text() + '/model,0')
self.loadModel()
self.modelcreat = True
else:
button = QMessageBox.warning(self, '警告', "目录尚未选择",
QMessageBox.Yes)
if n == 5:
self.timer = QTimer()
self.timer.setInterval(100)
self.start(self.camnum)
if n == 6:
self.closecap()
def camnum_meg(self, n):
if n == 0:
if self.radioButton.isChecked():
self.camnum = 0
else:
self.camnum = -1
if n == 1:
if self.radioButton_2.isChecked():
self.camnum = 1
else:
self.camnum = -1
if n == 2:
if self.radioButton_3.isChecked():
self.camnum = 2
else:
self.camnum = -1
if n == 3:
if self.radioButton_4.isChecked():
self.camnum = 3
else:
self.camnum = -1
def closecap(self):
self.cap.release()
self.boxid = []
self.boxidnum = 0
self.label_video.setText("视频画面")
def start(self, camnum):
if self.modelcreat:
#self.image_verify()
if self.camnum != -1 and self.lineEdit.text() == "":
devices = '/dev/video' + str(camnum)
self.cap = cv2.VideoCapture(devices)
else:
devices = self.lineEdit.text()
self.cap = cv2.VideoCapture(devices)
self.timer.start()
self.timer.timeout.connect(self.video_verify)
else:
button = QMessageBox.warning(self, '警告', "模型没有建立", QMessageBox.Yes)
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 find_matching_id(self, embedding):
threshold = 1.12 #1.1 if dist less than this ,believe person id
min_dist = 10.0 #10.0
matching_id = 'Unkown'
for id_data in self.id_dataset:
if id_data.embedding is not None:
dist = self.get_embedding_distance(id_data.embedding,
embedding)
if dist < threshold and dist < min_dist:
#if dist < threshold :
min_dist = dist
matching_id = id_data.name
#return matching_id, min_dist
return matching_id, min_dist
def get_embedding_distance(self, emb1, emb2):
dist = np.sqrt(np.sum(np.square(np.subtract(
emb1, emb2)))) #Euclodean distence
return dist
def get_id_data(self, id_folder):
id_dataset = []
ids = os.listdir(os.path.expanduser(id_folder))
ids.sort()
for i in range(len(ids)):
id_dir = os.path.join(id_folder, ids[i])
image_names = os.listdir(id_dir)
image_paths = [os.path.join(id_dir, img) for img in image_names]
for image_path in image_paths:
#print image_path
self.progressBar.setValue(i / len(ids))
img = cv2.imread(image_path.encode('utf-8'))
boxes, boxes_c = self.mtcnn_detector.detect_pnet(img)
if boxes_c is not None:
boxes, boxes_c = self.mtcnn_detector.detect_rnet(
img, boxes_c)
if boxes_c is not None:
boxes, boxes_c = self.mtcnn_detector.detect_onet(
img, boxes_c)
if boxes_c is not None:
for b in boxes_c:
bb = [int(b[0]), int(b[1]), int(b[2]), int(b[3])]
#cv2.rectangle(img, (int(b[0]), int(b[1])), (int(b[2]), int(b[3])), (0, 255, 255), 1)
temp_img = img[int(b[1]):int(b[3]),
int(b[0]):int(b[2])]
f1, _, _ = self.model.get_feature(temp_img)
#print f1
id_dataset.append(ID_Data(ids[i], image_path, f1))
self.progressBar.setValue(100)
return id_dataset, len(ids) ##
def video_verify(self): #视频检测
if (self.cap.isOpened()):
fla, img = self.cap.read()
frame_height = self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
start = time.time()
acc_box_id = -1
fonts = PIL.ImageFont.truetype("./fonts/simhei.ttf",
20,
encoding="utf-8")
if fla:
img = cv2.resize(img, (840, 560))
frame = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) ##第一次预检测
boxes, boxes_c = self.mtcnn_detector.detect_pnet(img)
if boxes_c is not None:
boxes, boxes_c = self.mtcnn_detector.detect_rnet(
img, boxes_c)
if boxes_c is not None:
boxes, boxes_c = self.mtcnn_detector.detect_onet(
img, boxes_c)
if boxes_c is not None:
for b in boxes_c:
bb = [int(b[0]), int(b[1]), int(b[2]), int(b[3])]
#cv2.rectangle(img, (int(b[0]), int(b[1])), (int(b[2]), int(b[3])), (0, 255, 255), 1)
temp_img = frame[int(b[1]):int(b[3]),
int(b[0]):int(b[2])]
f, bbox, points = self.model.get_feature(temp_img)
self.pil_im = PIL.Image.fromarray(frame)
draw = PIL.ImageDraw.Draw(
self.pil_im) # 括号中为需要打印的canvas，这里就是在图片上直接打印
matching_id = 'Unkown'
dist = self.args.threshold
if f is not None:
matching_id, dist = self.find_matching_id(f)
ret = self.detect_boxid(b, matching_id)
if ret[0] == False:
self.boxidnum += 1
tempitem = Box_id(self.boxidnum, b,
matching_id)
self.boxid.append(tempitem)
else:
matching_id = ret[2]
acc_box_id = ret[1]
index = ret[3]
if self.boxid[index].islog:
self.label_18.setText("已录入成功")
else:
if matching_id == "Null":
self.label_18.setText("识别中")
else:
if self.addlog2dataset(matching_id):
self.label_18.setText("录入成功")
self.boxid[index].islog = True
else:
self.label_18.setText("录入失败")
self.label_16.setText(matching_id.encode('utf-8'))
if self.show_id:
#cv2.putText(img, matching_id.encode('utf-8'), (bb[0], bb[3]), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA) #white
draw.text((bb[0], bb[3]),
matching_id, (255, 255, 255),
font=fonts)
#self.label_16.setText(matching_id.encode('utf-8'))
img = cv2.cvtColor(np.array(self.pil_im),
cv2.COLOR_RGB2BGR)
#cv2.putText(img, acc_matching_id, (bb[0], bb[3]), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA) #white
if self.show_bb:
cv2.rectangle(img, (bb[0], bb[1]),
(bb[2], bb[3]), (255, 0, 0), 2)
cv2.putText(img, 'BOX_ID: %d' % (acc_box_id),
(int(bb[0] + 5), int(bb[1])),
self.font, 0.5, (255, 255, 255), 1)
if self.show_distence:
cv2.putText(img, '%.3f' % dist,
(int(bb[0]), int(bb[1]) + 20),
self.font, 0.4, (255, 255, 255), 1)
if self.show_landmarks:
for j in range(
5
): # 0: righteye 1:lefteye 2:nose 3:rightmouth 4:leftmouth
cv2.circle(img, (bb[0] + points[j][0],
bb[1] + points[j][1]), 1,
(0, 0, 255), 2)
end = time.time()
seconds = end - start
fps = round(1 / seconds, 2)
if self.show_fps:
cv2.putText(img, str(fps), (0, int(frame_height) - 5),
self.font, 1, (255, 255, 255), 1, cv2.LINE_AA)
#out.write(img)
height, width, bytesPerComponent = img.shape
bytesPerLine = 3 * width
cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
QImg = QImage(img.data, width, height, bytesPerLine,
QImage.Format_RGB888)
pixmap = QPixmap.fromImage(QImg)
self.label_video.setPixmap(pixmap)
else:
for item in self.boxid:
item.releasenum -= 1
if item.releasenum <= 0:
self.boxid.remove(item)
if len(self.boxid) == 0:
self.boxidnum = 0
height, width, bytesPerComponent = img.shape
bytesPerLine = 3 * width
cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
QImg = QImage(img.data, width, height, bytesPerLine,
QImage.Format_RGB888)
pixmap = QPixmap.fromImage(QImg)
self.label_video.setPixmap(pixmap)
def addlog2dataset(self, name):
db = pymysql.connect("localhost",
"root",
"root",
"sys",
charset='utf8')
cursor = db.cursor()
sql = "select * from Users where nameUsers = \'%s\'" % (name)
if cursor.execute(sql):
data = cursor.fetchone()
userid = data[0]
time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
neartime = (
datetime.datetime.now() -
datetime.timedelta(hours=2)).strftime("%Y-%m-%d %H:%M:%S")
sqlnear = "select * from Log where nameLog = \'%s\' and timeLog >= \'%s\'" % (
name, neartime)
print(sqlnear)
if cursor.execute(sqlnear):
return True
else:
sql2 = "insert into Log(idLog,nameLog,timeLog) values(%d,\'%s\',\'%s\')" % (
userid, name, time)
if cursor.execute(sql2):
db.commit()
db.close()
return True
else:
db.close()
return False
else:
db.close()
return False
def rename(self, dirs):
image_rename_paths = []
image_paths = []
ids = os.listdir(os.path.expanduser(dirs))
for id_name in ids:
id_dir = os.path.join(dirs, id_name)
if os.path.isdir(id_dir):
#print(os.path.isdir(id_dir))
image_names = os.listdir(id_dir)
for img in image_names:
image_paths.append(
os.path.join(id_dir, img).encode('utf-8'))
else:
continue
filecount = 0
for img in image_names:
filecount = filecount + 1
filename = id_name + '_' + '%04d' % (filecount) + '.png'
image_rename_paths.append(
os.path.join(id_dir, filename.encode('utf-8')))
for i in range(len(image_paths)):
os.rename(image_paths[i], image_rename_paths[i])
self.progressBar.setValue(i * 100 / len(image_paths))
self.progressBar.setValue(100)
def detectOneImg(prefix,
epoch,
batch_size,
model,
imgPath,
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 densenet for classfication
model.load_model()
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
print(model_path)
# 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)
images_res = []
img = cv2.imread(os.path.join(imgPath))
boxes, boxes_c = mtcnn_detector.detect(img)
#print(boxes_c.shape)
#visssss(img, boxes_c, 'plain13134.jpg', thresh=0.998)
for i in range(boxes_c.shape[0]):
bbox = boxes_c[i, :4].astype('int32')
if bbox[1] < 0:
bbox[1] = 0
if bbox[0] < 0:
bbox[0] = 0
if bbox[2] > 2048:
bbox[2] = 2048
if bbox[3] > 2048:
bbox[3] = 2048
crop = img[bbox[1]:bbox[3], bbox[0]:bbox[2], :]
#print(boxes_c[i, :4])
#print('crop:',crop.shape)
crop = cv2.resize(crop, (48, 48))
#cv2.imwrite("C:\\Users\\JINNIU\\Desktop\\liuzhen\\qinghua\\temp\\"+str(i)+'.jpg',crop)
images_res.append(crop)
images_res = np.array(images_res).astype(np.float32)
images_res = normalize_images(images_res)
pred = model.test(images_res)
bg_box = np.where(
pred == 45
) #if the class is 45, the image is the background and not the traffic sign. we omit these in the next step
#print(pred)
#print(len(boxes_cc))
for ii in bg_box[0]:
boxes_c[ii, :] = 0
#print(boxes_c)
#del boxes_c[np.where(pred==45),:]
visssss(img, boxes_c, imgPath, pred, thresh=0.998)
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)
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()
