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Both CIFAR and LENET work still. The smile network... I couldn;t really get to work before or afterwards. I noticed the Haar one has trouble finding my face. Maybe fix via using the contrast settings of the previous Haar scripts?
34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
# CIFAR10 Example
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import sensor, image, time, os, nn
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sensor.reset() # Reset and initialize the sensor.
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sensor.set_contrast(3)
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sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565
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sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
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sensor.set_windowing((128, 128)) # Set 128x128 window.
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sensor.skip_frames(time=1000)
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sensor.set_auto_gain(False)
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sensor.set_auto_exposure(False)
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# Load cifar10 network
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net = nn.load('/cifar10.network')
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# Faster, smaller and less accurate.
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#net = nn.load('/cifar10_fast.network')
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labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
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clock = time.clock() # Create a clock object to track the FPS.
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while(True):
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clock.tick() # Update the FPS clock.
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img = sensor.snapshot() # Take a picture and return the image.
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out = net.forward(img)
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max_idx = out.index(max(out))
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score = int(out[max_idx]*100)
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if (score < 70):
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score_str = "??:??%"
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else:
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score_str = "%s:%d%% "%(labels[max_idx], score)
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img.draw_string(0, 0, score_str, color=(255, 0, 0))
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print(clock.fps()) # Note: OpenMV Cam runs about half as fast when connected
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# to the IDE. The FPS should increase once disconnected.
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