eeee... Is this code can only detect and tracking one video?

[qidian213]

eeee... Is this code can only detect and tracking one video?

[404hasbeenfound]

Then i delete the source code and git clone this code again, i tried the video again, firstly, i tried the second(former second video) video, but it has trouble as before, then i tried the first(former first video)video, it also worked. I'm curious about that, then i turned the second video again, it cannot work as before. the issue like the following(there is no error,but cnnot worked):

/home/adminroot/anaconda3/lib/python3.6/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
2018-10-09 19:11:59.537812: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-10-09 19:11:59.805751: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1392] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.6575
pciBusID: 0000:03:00.0
totalMemory: 10.91GiB freeMemory: 10.50GiB
2018-10-09 19:12:00.021861: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1392] Found device 1 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.6575
pciBusID: 0000:81:00.0
totalMemory: 10.92GiB freeMemory: 10.76GiB
2018-10-09 19:12:00.021963: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1471] Adding visible gpu devices: 0, 1
2018-10-09 19:12:00.716734: I tensorflow/core/common_runtime/gpu/gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-10-09 19:12:00.716792: I tensorflow/core/common_runtime/gpu/gpu_device.cc:958] 0 1
2018-10-09 19:12:00.716802: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: N N
2018-10-09 19:12:00.716809: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 1: N N
2018-10-09 19:12:00.717336: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10159 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1)
2018-10-09 19:12:00.885874: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10411 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:81:00.0, compute capability: 6.1)
model_data/yolo.h5 model, anchors, and classes loaded.
2018-10-09 19:12:16.803213: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1471] Adding visible gpu devices: 0, 1
2018-10-09 19:12:16.803361: I tensorflow/core/common_runtime/gpu/gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-10-09 19:12:16.803374: I tensorflow/core/common_runtime/gpu/gpu_device.cc:958] 0 1
2018-10-09 19:12:16.803381: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: N N
2018-10-09 19:12:16.803388: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 1: N N
2018-10-09 19:12:16.803786: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10159 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1)
2018-10-09 19:12:16.804005: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10411 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:81:00.0, compute capability: 6.1)

but the first video worked well, like following:
/home/adminroot/anaconda3/lib/python3.6/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
2018-10-09 19:09:32.120304: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-10-09 19:09:32.362899: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1392] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.6575
pciBusID: 0000:03:00.0
totalMemory: 10.91GiB freeMemory: 10.50GiB
2018-10-09 19:09:32.581086: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1392] Found device 1 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.6575
pciBusID: 0000:81:00.0
totalMemory: 10.92GiB freeMemory: 10.76GiB
2018-10-09 19:09:32.581193: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1471] Adding visible gpu devices: 0, 1
2018-10-09 19:09:33.303251: I tensorflow/core/common_runtime/gpu/gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-10-09 19:09:33.303309: I tensorflow/core/common_runtime/gpu/gpu_device.cc:958] 0 1
2018-10-09 19:09:33.303320: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: N N
2018-10-09 19:09:33.303327: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 1: N N
2018-10-09 19:09:33.303888: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10159 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1)
2018-10-09 19:09:33.493563: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10411 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:81:00.0, compute capability: 6.1)
model_data/yolo.h5 model, anchors, and classes loaded.
2018-10-09 19:09:49.842707: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1471] Adding visible gpu devices: 0, 1
2018-10-09 19:09:49.842868: I tensorflow/core/common_runtime/gpu/gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-10-09 19:09:49.842882: I tensorflow/core/common_runtime/gpu/gpu_device.cc:958] 0 1
2018-10-09 19:09:49.842890: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: N N
2018-10-09 19:09:49.842897: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 1: N N
2018-10-09 19:09:49.843231: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10159 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1)
2018-10-09 19:09:49.843472: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10411 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:81:00.0, compute capability: 6.1)
Optimizing fused batch norm node name: "net/ball/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/ball/Reshape"
input: "net/ball/Const"
input: "net/ball/beta"
input: "net/ball/moving_mean"
input: "net/ball/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
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attr {
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attr {
key: "is_training"
value {
b: false
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}

Optimizing fused batch norm node name: "net/fc1/fc1/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/fc1/fc1/bn/Reshape"
input: "net/fc1/fc1/bn/Const"
input: "net/fc1/fc1/bn/beta"
input: "net/fc1/fc1/bn/moving_mean"
input: "net/fc1/fc1/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
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attr {
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f: 0.001
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}
attr {
key: "is_training"
value {
b: false
}
}

Optimizing fused batch norm node name: "net/conv1_1/conv1_1/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/conv1_1/Conv2D"
input: "net/conv1_1/conv1_1/bn/Const"
input: "net/conv1_1/conv1_1/bn/beta"
input: "net/conv1_1/conv1_1/bn/moving_mean"
input: "net/conv1_1/conv1_1/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
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attr {
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f: 0.001
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}
attr {
key: "is_training"
value {
b: false
}
}

Optimizing fused batch norm node name: "net/conv1_2/conv1_2/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/conv1_2/Conv2D"
input: "net/conv1_2/conv1_2/bn/Const"
input: "net/conv1_2/conv1_2/bn/beta"
input: "net/conv1_2/conv1_2/bn/moving_mean"
input: "net/conv1_2/conv1_2/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
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attr {
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attr {
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f: 0.001
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attr {
key: "is_training"
value {
b: false
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}

Optimizing fused batch norm node name: "net/conv2_1/1/conv2_1/1/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/conv2_1/1/Conv2D"
input: "net/conv2_1/1/conv2_1/1/bn/Const"
input: "net/conv2_1/1/conv2_1/1/bn/beta"
input: "net/conv2_1/1/conv2_1/1/bn/moving_mean"
input: "net/conv2_1/1/conv2_1/1/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
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attr {
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value {
f: 0.001
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}
attr {
key: "is_training"
value {
b: false
}
}

Optimizing fused batch norm node name: "net/conv2_3/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/add"
input: "net/conv2_3/bn/Const"
input: "net/conv2_3/bn/beta"
input: "net/conv2_3/bn/moving_mean"
input: "net/conv2_3/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
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attr {
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value {
f: 0.001
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}
attr {
key: "is_training"
value {
b: false
}
}

Optimizing fused batch norm node name: "net/conv2_3/1/conv2_3/1/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/conv2_3/1/Conv2D"
input: "net/conv2_3/1/conv2_3/1/bn/Const"
input: "net/conv2_3/1/conv2_3/1/bn/beta"
input: "net/conv2_3/1/conv2_3/1/bn/moving_mean"
input: "net/conv2_3/1/conv2_3/1/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
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attr {
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value {
f: 0.001
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}
attr {
key: "is_training"
value {
b: false
}
}

Optimizing fused batch norm node name: "net/conv3_1/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/add_1"
input: "net/conv3_1/bn/Const"
input: "net/conv3_1/bn/beta"
input: "net/conv3_1/bn/moving_mean"
input: "net/conv3_1/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
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attr {
key: "epsilon"
value {
f: 0.001
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}
attr {
key: "is_training"
value {
b: false
}
}

Optimizing fused batch norm node name: "net/conv3_1/1/conv3_1/1/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/conv3_1/1/Conv2D"
input: "net/conv3_1/1/conv3_1/1/bn/Const"
input: "net/conv3_1/1/conv3_1/1/bn/beta"
input: "net/conv3_1/1/conv3_1/1/bn/moving_mean"
input: "net/conv3_1/1/conv3_1/1/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
attr {
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type: DT_FLOAT
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attr {
key: "data_format"
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s: "NHWC"
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}
attr {
key: "epsilon"
value {
f: 0.001
}
}
attr {
key: "is_training"
value {
b: false
}
}

Optimizing fused batch norm node name: "net/conv3_3/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/add_2"
input: "net/conv3_3/bn/Const"
input: "net/conv3_3/bn/beta"
input: "net/conv3_3/bn/moving_mean"
input: "net/conv3_3/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
attr {
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value {
type: DT_FLOAT
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value {
s: "NHWC"
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}
attr {
key: "epsilon"
value {
f: 0.001
}
}
attr {
key: "is_training"
value {
b: false
}
}

Optimizing fused batch norm node name: "net/conv3_3/1/conv3_3/1/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/conv3_3/1/Conv2D"
input: "net/conv3_3/1/conv3_3/1/bn/Const"
input: "net/conv3_3/1/conv3_3/1/bn/beta"
input: "net/conv3_3/1/conv3_3/1/bn/moving_mean"
input: "net/conv3_3/1/conv3_3/1/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
attr {
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attr {
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attr {
key: "epsilon"
value {
f: 0.001
}
}
attr {
key: "is_training"
value {
b: false
}
}

Optimizing fused batch norm node name: "net/conv4_1/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/add_3"
input: "net/conv4_1/bn/Const"
input: "net/conv4_1/bn/beta"
input: "net/conv4_1/bn/moving_mean"
input: "net/conv4_1/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
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s: "NHWC"
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attr {
key: "epsilon"
value {
f: 0.001
}
}
attr {
key: "is_training"
value {
b: false
}
}

Optimizing fused batch norm node name: "net/conv4_1/1/conv4_1/1/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/conv4_1/1/Conv2D"
input: "net/conv4_1/1/conv4_1/1/bn/Const"
input: "net/conv4_1/1/conv4_1/1/bn/beta"
input: "net/conv4_1/1/conv4_1/1/bn/moving_mean"
input: "net/conv4_1/1/conv4_1/1/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
attr {
key: "T"
value {
type: DT_FLOAT
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}
attr {
key: "data_format"
value {
s: "NHWC"
}
}
attr {
key: "epsilon"
value {
f: 0.001
}
}
attr {
key: "is_training"
value {
b: false
}
}

Optimizing fused batch norm node name: "net/conv4_3/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/add_4"
input: "net/conv4_3/bn/Const"
input: "net/conv4_3/bn/beta"
input: "net/conv4_3/bn/moving_mean"
input: "net/conv4_3/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "data_format"
value {
s: "NHWC"
}
}
attr {
key: "epsilon"
value {
f: 0.001
}
}
attr {
key: "is_training"
value {
b: false
}
}

Optimizing fused batch norm node name: "net/conv4_3/1/conv4_3/1/bn/FusedBatchNorm"
op: "FusedBatchNorm"
input: "net/conv4_3/1/Conv2D"
input: "net/conv4_3/1/conv4_3/1/bn/Const"
input: "net/conv4_3/1/conv4_3/1/bn/beta"
input: "net/conv4_3/1/conv4_3/1/bn/moving_mean"
input: "net/conv4_3/1/conv4_3/1/bn/moving_variance"
device: "/job:localhost/replica:0/task:0/device:GPU:0"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "data_format"
value {
s: "NHWC"
}
}
attr {
key: "epsilon"
value {
f: 0.001
}
}
attr {
key: "is_training"
value {
b: false
}
}

[404hasbeenfound]

what's wrong?

[404hasbeenfound]

I have found the reason, because i activate a environment without opencv and the base environmet is opencv2.4, so my code cannot read my video, then i install the opencv3.4, run this code, detection and tracking all finished, thanks @Qidian213 !