def peopleDetect():
CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
'tvmonitor')
cap = cv2.VideoCapture(1)
net = None
prefix = os.path.join(os.getcwd(), 'model', 'yolo2_darknet19_416')
epoch = 0
mean_pixels = (123, 117, 104)
ctx = mx.gpu(0)
global numPeople
global isNotQuit
count = 0
ret1, frame1 = cap.read()
detector = Detector(net,
prefix,
epoch,
data_shape,
mean_pixels,
ctx=ctx,
batch_size=batch)
while isNotQuit:
count += 1
ret, frame = cap.read()
ims = [
cv2.resize(frame, (data_shape, data_shape)) for i in range(batch)
]
data = None
data = get_batch(ims)
start = timer()
det_batch = mx.io.DataBatch(data, [])
detector.mod.forward(det_batch, is_train=False)
detections = detector.mod.get_outputs()[0].asnumpy()
result = []
for i in range(detections.shape[0]):
det = detections[i, :, :]
res = det[np.where(det[:, 0] >= 0)[0]]
result.append(res)
time_elapsed = timer() - start
# print("Detection time for {} images: {:.4f} sec , fps : {:.4f}".format(batch*1, time_elapsed , (batch*1/time_elapsed)))
numPeople, numChair = detector.show_result(frame, det, CLASSES, 0.5,
batch * 1 / time_elapsed)
# if count>40:
# isNotQuit = False
#break
cap.release()
cv2.destroyAllWindows()
