Commit edfb6f8c authored by jack's avatar jack

update tiny model

parent 0a7abc2c
[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=64
subdivisions=4
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 4000
policy=steps
steps=3200,3600
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[route]
layers=-1
groups=2
group_id=1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[route]
layers = -1,-2
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[route]
layers = -6,-1
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[route]
layers=-1
groups=2
group_id=1
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[route]
layers = -1,-2
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[route]
layers = -6,-1
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[route]
layers=-1
groups=2
group_id=1
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[route]
layers = -1,-2
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[route]
layers = -6,-1
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
##################################
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=21
activation=linear
[yolo]
mask = 3,4,5
anchors = 9, 19, 16, 30, 28, 49, 49, 84, 90,155, 192,296
classes=2
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
ignore_thresh = .7
truth_thresh = 1
random=1
resize=1.5
nms_kind=greedynms
beta_nms=0.6
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 23
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=21
activation=linear
[yolo]
mask = 1,2,3
anchors = 9, 19, 16, 30, 28, 49, 49, 84, 90,155, 192,296
classes=2
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
ignore_thresh = .7
truth_thresh = 1
random=1
resize=1.5
nms_kind=greedynms
beta_nms=0.6
...@@ -44,16 +44,18 @@ def show_frame(frame): ...@@ -44,16 +44,18 @@ def show_frame(frame):
while True: while True:
data = frame.get() data = frame.get()
image = data["image"] image = data["image"]
cv2.putText(image, "fps:{fps}".format(fps=data["fps"]), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0 ,0)) fps=data["fps"]
cv2.putText(image, "fps:{fps}".format(fps), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0 ,0))
cv2.namedWindow("camera", cv2.WINDOW_AUTOSIZE) cv2.namedWindow("camera", cv2.WINDOW_AUTOSIZE)
cv2.imshow("camera", image) cv2.imshow("camera", image)
if cv2.waitKey(1)& 0xFF == ord('q'): if cv2.waitKey(fps)& 0xFF == ord('q'):
break break
frame_queue.task_done() frame_queue.task_done()
if __name__ == "__main__": if __name__ == "__main__":
frame_queue = Queue() frame_queue = Queue()
cam = Camera("rtsp://admin:admin12345@192.168.77.19:554/h264/ch1/main/av_stream", frame_queue) #cam = Camera("rtsp://admin:admin12345@192.168.77.19:554/h264/ch1/main/av_stream", frame_queue)
cam = Camera(0, frame_queue)
cam.run() cam.run()
thread_show = Thread(target=show_frame, args=(frame_queue,)) thread_show = Thread(target=show_frame, args=(frame_queue,))
thread_show.start() thread_show.start()
......
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