'''
Copyright (c) 2019 STMicroelectronics
This work is licensed under the MIT license
'''
# STM32Cube.AI on OpenMV MNIST Example
import sensor, image, time, nn_st
sensor.reset() # Reset and initialize the sensor.
sensor.set_contrast(3)
sensor.set_brightness(0)
sensor.set_auto_gain(True)
sensor.set_auto_exposure(True)
sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to Grays
cale
sensor.set_framesize(sensor.QQQVGA) # Set frame size to 80x60
sensor.skip_frames(time = 2000) # Wait for settings take effec
t.
clock = time.clock() # Create a clock object to track t
he FPS.
# [STM32Cube.AI] Initialize the network
net = nn_st.loadnnst('network')
nn_input_sz = 28 # The NN input is 28x28
while(True):
clock.tick() # Update the FPS clock.
img = sensor.snapshot() # Take a picture and return the image.
# Crop in the middle (avoids vignetting)
img.crop((img.width()//2-nn_input_sz//2,
img.height()//2-nn_input_sz//2,
nn_input_sz,
nn_input_sz))
# Binarize the image
img.midpoint(2, bias=0.5, threshold=True, offset=5, invert=Tru
e)
# [STM32Cube.AI] Run the inference
out = net.predict(img)
print('Network argmax output: {}'.format( out.index(max(out))
))
img.draw_string(0, 0, str(out.index(max(out))))
print('FPS {}'.format(clock.fps())) # Note: OpenMV Cam runs abo
ut half as fast when connected |
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