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ebd423139c
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@ -10,3 +10,4 @@ MY_INFO
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*.pyc
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*.pyx
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*.jpg
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*.csv
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@ -0,0 +1,821 @@
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[net]
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# Testing
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# batch=1
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# subdivisions=1
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# Training
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batch=64
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subdivisions=16
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width=608
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height=608
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channels=3
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momentum=0.9
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decay=0.0005
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angle=0
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saturation = 1.5
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exposure = 1.5
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hue=.1
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learning_rate=0.001
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burn_in=1000
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max_batches = 500200
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policy=steps
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steps=400000,450000
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scales=.1,.1
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[convolutional]
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batch_normalize=1
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filters=32
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size=3
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stride=1
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pad=1
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activation=leaky
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# Downsample
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[convolutional]
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batch_normalize=1
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filters=64
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size=3
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stride=2
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=32
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=64
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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# Downsample
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[convolutional]
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batch_normalize=1
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filters=128
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size=3
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stride=2
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=64
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=128
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=64
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=128
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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# Downsample
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=2
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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# Downsample
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=2
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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# Downsample
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[convolutional]
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batch_normalize=1
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filters=1024
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size=3
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stride=2
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=1024
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=512
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=1024
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=512
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=1024
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=512
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=1024
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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######################
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[convolutional]
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batch_normalize=1
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filters=512
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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size=3
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stride=1
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pad=1
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filters=1024
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=1
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stride=1
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pad=1
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activation=leaky
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### SPP ###
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[maxpool]
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stride=1
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size=5
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[route]
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layers=-2
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[maxpool]
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stride=1
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size=9
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[route]
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layers=-4
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[maxpool]
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stride=1
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size=13
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[route]
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layers=-1,-3,-5,-6
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### End SPP ###
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[convolutional]
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batch_normalize=1
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filters=512
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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size=3
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stride=1
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pad=1
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filters=1024
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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size=3
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stride=1
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pad=1
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filters=1024
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activation=leaky
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[convolutional]
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size=1
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stride=1
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pad=1
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filters=72
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activation=linear
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[yolo]
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mask = 6,7,8
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anchors=26,16, 19,45, 36,27, 54,32, 34,80, 60,49, 74,71, 96,105, 135,145
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classes=19
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num=9
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jitter=.3
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ignore_thresh = .7
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truth_thresh = 1
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random=1
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[route]
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layers = -4
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||||
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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[upsample]
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stride=2
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[route]
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layers = -1, 61
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||||
|
||||
|
||||
|
||||
[convolutional]
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||||
batch_normalize=1
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||||
filters=256
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size=1
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||||
stride=1
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||||
pad=1
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||||
activation=leaky
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||||
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||||
[convolutional]
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||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=72
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors=26,16, 19,45, 36,27, 54,32, 34,80, 60,49, 74,71, 96,105, 135,145
|
||||
classes=19
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=72
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors=26,16, 19,45, 36,27, 54,32, 34,80, 60,49, 74,71, 96,105, 135,145
|
||||
classes=19
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
classes=19
|
||||
train=/home/tomekb/yolov3/data/widok_01_19/widok_01_19_train_labels.txt
|
||||
valid=/home/tomekb/yolov3/data/widok_01_19/widok_01_19_test_labels.txt
|
||||
names=/home/tomekb/yolov3/data/widok_01_19/widok_01_19.names
|
||||
backup=backup/
|
||||
eval=coco
|
||||
|
|
@ -126,7 +126,7 @@ def detect(save_img=False):
|
|||
if save_txt: # Write to file
|
||||
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
|
||||
with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as file:
|
||||
file.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format
|
||||
file.write(('%g ' * 6 + '\n') % (cls, *xywh, conf)) # label format
|
||||
|
||||
if save_img or view_img: # Add bbox to image
|
||||
label = '%s %.2f' % (names[int(cls)], conf)
|
||||
|
|
|
|||
|
|
@ -1,18 +1,18 @@
|
|||
train:
|
||||
epochs: 2
|
||||
batch-size: 400
|
||||
cfg: ./cfg/yolov3-spp-18cls.cfg
|
||||
data: ./data/widok01-11.data
|
||||
epochs: 600
|
||||
batch-size: 8
|
||||
cfg: ./cfg/yolov3-spp-19cls.cfg
|
||||
data: ./data/widok_01_19.data
|
||||
multi-scale: false
|
||||
img-size: '64 128'
|
||||
img-size: '512 960'
|
||||
rect: false
|
||||
resume: false
|
||||
resume: true
|
||||
nosave: false
|
||||
notest: false
|
||||
evolve: false
|
||||
bucket:
|
||||
cache-images: false
|
||||
weights: /home/tomekb/yolov3/weights/yolov3-spp-ultralytics.pt
|
||||
weights: /home/tomekb/yolov3/weights/last.pt
|
||||
device: 1
|
||||
adam: true
|
||||
single-cls: false
|
||||
|
|
@ -41,16 +41,15 @@ train:
|
|||
experiments:
|
||||
dir: ./experiments
|
||||
detect:
|
||||
source: /home/michall/yolov3/data/widok01-11_test_labels.txt
|
||||
source: /home/tomekb/yolov3/data/widok_01_19/widok_01_19_test_labels.txt
|
||||
test-img-size: 1024
|
||||
conf-thres: 0.3
|
||||
iou-thres: 0.6
|
||||
save-txt: true
|
||||
classes:
|
||||
agnostic-nms:
|
||||
augment:
|
||||
confussion-matrix:
|
||||
labels-dir: ./data/widok01-11_labels
|
||||
labels-dir: /home/tomekb/yolov3/data/widok_01_19/widok_01_19_labels
|
||||
bayes:
|
||||
todo: todo
|
||||
|
||||
|
|
|
|||
|
|
@ -62,6 +62,8 @@ function findNearest(position, boxes) {
|
|||
}
|
||||
|
||||
function compare(labels, detections) {
|
||||
// console.log("LABELS", JSON.stringify(labels))
|
||||
// console.log("DETECTIONS", JSON.stringify(detections))
|
||||
const results = {}
|
||||
for(const label of labels) {
|
||||
const detection = findNearest(label, detections)
|
||||
|
|
@ -88,7 +90,7 @@ async function compareLabelsAndResults(txt) {
|
|||
const basename = path.basename(txt.split('.')[0])
|
||||
const labelPath = path.resolve(labelsDir, basename+'.txt')
|
||||
const [detectionData, labelData] = await Promise.all([fs.promises.readFile(detectionPath, 'utf8'), fs.promises.readFile(labelPath, 'utf8')])
|
||||
const detections = parseDetections(detectionData)
|
||||
const detections = parseLabels(detectionData)
|
||||
const labels = parseLabels(labelData)
|
||||
|
||||
return {
|
||||
|
|
|
|||
|
|
@ -46,7 +46,7 @@ def map_class_name_to_id(class_name, xml_document, class_distribution):
|
|||
elif class_name in ['6. osobowy van 7-9']:
|
||||
class_distribution[5] += 1
|
||||
return 5
|
||||
elif class_name in ['7. dostawczy blaszak']:
|
||||
elif class_name in ['7. dostawczy blaszak', '27. BUS sredni dostawczy']:
|
||||
class_distribution[6] += 1
|
||||
return 6
|
||||
elif class_name in ['8. dostawczy zabudowany']:
|
||||
|
|
@ -55,54 +55,51 @@ def map_class_name_to_id(class_name, xml_document, class_distribution):
|
|||
elif class_name in ['9. dostawczy pickup (w tym pomoc drog.)']:
|
||||
class_distribution[8] += 1
|
||||
return 8
|
||||
elif class_name in ['10. dostawczy VAN (osobowy)']:
|
||||
elif class_name in ['10. dostawczy VAN (osobowy)', '11. autobus maly 10-24', '28. BUS sredni osobowy']:
|
||||
class_distribution[9] += 1
|
||||
return 9
|
||||
elif class_name in ['11. autobus mały 10-24']:
|
||||
return -1
|
||||
elif class_name in ['12. autobus miejski']:
|
||||
elif class_name in ['12. autobus miejski', '13. autobus turystyczny i inny']:
|
||||
class_distribution[10] += 1
|
||||
return 10
|
||||
elif class_name in ['13. autobus turystyczny i inny']:
|
||||
return -1
|
||||
elif class_name in ['14. ciężarowy pow. 3,5t zabudowany']:
|
||||
elif class_name in ['14. ciezarowy pow. 3,5t zabudowany', '17. ciezarowy z widoczna przyczepa']:
|
||||
class_distribution[11] += 1
|
||||
return 11
|
||||
elif class_name in ['15. ciężarowy pow. 3,5t otwarty (w tym duży holownik)']:
|
||||
elif class_name in ['15. ciezarowy pow. 3,5t otwarty (w tym duzy holownik)']:
|
||||
class_distribution[12] += 1
|
||||
return 12
|
||||
elif class_name in ['16. ciężarowy pow. 3,5t inny (wanna, gruszka, dźwig itp.)']:
|
||||
elif class_name in ['16. ciezarowy pow. 3,5t inny (wanna, gruszka, dzwig itp.)']:
|
||||
class_distribution[13] += 1
|
||||
return 13
|
||||
elif class_name in ['17. ciężarowy z widoczną przyczepą']:
|
||||
return -1
|
||||
elif class_name in ['18. ciągnik siodłowy z widoczną naczepą']:
|
||||
elif class_name in ['18. ciagnik siodlowy z widoczna naczepa', '19. ciagnik siodlowy bez naczepy']:
|
||||
class_distribution[14] += 1
|
||||
return 14
|
||||
elif class_name in ['19. ciągnik siodłowy bez naczepy']:
|
||||
elif class_name in ['23. inne pojazdy silnikowe', '20. camper', '21. woz strazacki', '22. ciagnik roliczy, koparka, spychacz']:
|
||||
class_distribution[15] += 1
|
||||
return 15
|
||||
elif class_name in ['20. camper']:
|
||||
class_distribution[15] += 1
|
||||
return -1
|
||||
elif class_name in ['22. ciągnik roliczy, koparka, spychacz']:
|
||||
return -1
|
||||
elif class_name in ['23. inne pojazdy silnikowe']:
|
||||
return -1
|
||||
elif class_name in ['24. przyczepa']:
|
||||
class_distribution[16] += 1
|
||||
return 16
|
||||
elif class_name in ['25. BUS-karetka/policja']:
|
||||
class_distribution[17] += 1
|
||||
return 17
|
||||
elif class_name in ['26. BUS brygadowka']:
|
||||
class_distribution[18] += 1
|
||||
return 18
|
||||
else:
|
||||
raise Exception('Unknown Class ', xml_document, class_name)
|
||||
#print(f'{xml_document.split("/")[-1]} {class_name}')
|
||||
|
||||
|
||||
def generate_txt_from_xml():
|
||||
class_distribution = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
||||
class_distribution = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
||||
|
||||
filepaths = glob(join(annotations , '*.xml'))
|
||||
|
||||
duplicates = set([x for x in filepaths if filepaths.count(x) > 1])
|
||||
if duplicates:
|
||||
print('Duplicate files : ')
|
||||
print(duplicates)
|
||||
|
||||
filepaths = glob(annotations + '*.xml')
|
||||
for filepath in filepaths:
|
||||
txtpath = join(modified_annotations, re.sub(r"\.xml$", ".txt", os.path.basename(filepath)))
|
||||
|
||||
|
|
@ -110,6 +107,14 @@ def generate_txt_from_xml():
|
|||
|
||||
tree = ET.parse(in_file)
|
||||
root = tree.getroot()
|
||||
|
||||
print(filepath)
|
||||
|
||||
if root.attrib == {}:
|
||||
continue
|
||||
if root.attrib['verified'] != 'yes':
|
||||
continue
|
||||
|
||||
size = root.find('size')
|
||||
w = int(size.find('width').text)
|
||||
h = int(size.find('height').text)
|
||||
|
|
|
|||
|
|
@ -46,6 +46,7 @@ def move_training_results_to_experiments_dir(config):
|
|||
|
||||
|
||||
#for test purposes only
|
||||
#shutil.copy2('/home/tomekb/yolov3/experiments/1/best.pt', training_results_dir_path)
|
||||
|
||||
return weights_path, experiment_names_path, training_results_dir_path
|
||||
|
||||
|
|
@ -60,8 +61,8 @@ def call_detection_script(config, weights_path, names_path, dir):
|
|||
--weights {weights_path}
|
||||
--test-img-size {getattr(config.detect, 'test-img-size')}
|
||||
--conf-thres {getattr(config.detect, 'conf-thres')}
|
||||
--iou-thres {getattr(config.detect, 'iou-thres')}"""
|
||||
cmd += " --save-txt" if getattr(config.detect, 'save-txt') else ""
|
||||
--iou-thres {getattr(config.detect, 'iou-thres')}
|
||||
--save-txt"""
|
||||
cmd += " --agnostic-nms" if getattr(config.detect, 'agnostic-nms') else ""
|
||||
cmd += " --agument" if getattr(config.detect, 'augment') else ""
|
||||
cmd += f" --device {config.train.device}" if config.train.device else ""
|
||||
|
|
|
|||
|
|
@ -0,0 +1,24 @@
|
|||
import os
|
||||
import sys
|
||||
import csv
|
||||
# This should just be a file which groups files according to train, val datasets
|
||||
csv_file = sys.argv[1]
|
||||
jpg_files_dir = sys.argv[2]
|
||||
|
||||
output_train_labels = sys.argv[3]
|
||||
output_test_labels = sys.argv[4]
|
||||
|
||||
with open(csv_file, "r") as f:
|
||||
reader = csv.reader(f, delimiter=",")
|
||||
train_labels = open(output_train_labels, 'w')
|
||||
test_labels = open(output_test_labels, 'w')
|
||||
for i, line in enumerate(reader):
|
||||
if i != 0: # skip header
|
||||
file, train_or_test = line
|
||||
full_file_path = os.path.join(jpg_files_dir, file + '.jpg')
|
||||
to_write = [full_file_path, '\n']
|
||||
if train_or_test == 'train':
|
||||
train_labels.writelines(to_write)
|
||||
|
||||
else:
|
||||
test_labels.writelines(to_write)
|
||||
3
train.py
3
train.py
|
|
@ -117,6 +117,7 @@ def train(hyp):
|
|||
best_fitness = 0.0
|
||||
attempt_download(weights)
|
||||
if weights.endswith('.pt'): # pytorch format
|
||||
print("LOADIN MODEL")
|
||||
# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
|
||||
chkpt = torch.load(weights, map_location=device)
|
||||
|
||||
|
|
@ -397,7 +398,7 @@ if __name__ == '__main__':
|
|||
parser.add_argument('--adam', action='store_true', help='use adam optimizer')
|
||||
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
|
||||
opt = parser.parse_args()
|
||||
opt.weights = last if opt.resume else opt.weights
|
||||
#opt.weights = last if opt.resume else opt.weights
|
||||
check_git_status()
|
||||
opt.cfg = check_file(opt.cfg) # check file
|
||||
opt.data = check_file(opt.data) # check file
|
||||
|
|
|
|||
|
|
@ -830,10 +830,10 @@ def plot_one_box(x, img, color=None, label=None, line_thickness=None):
|
|||
cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
|
||||
if label:
|
||||
tf = max(tl - 1, 1) # font thickness
|
||||
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
|
||||
t_size = cv2.getTextSize(label, 0, fontScale=tl / 5, thickness=tf)[0]
|
||||
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
|
||||
cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
|
||||
cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
|
||||
cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 5, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
|
||||
|
||||
|
||||
def plot_wh_methods(): # from utils.utils import *; plot_wh_methods()
|
||||
|
|
|
|||
Loading…
Reference in New Issue