openmv/scripts/examples/03-Machine-Learning/01-ST-CubeAI/nn_stm32cubeai.py

51 lines
1.6 KiB
Python

# This work is licensed under the MIT license.
# Copyright (c) 2013-2023 OpenMV LLC. All rights reserved.
# https://github.com/openmv/openmv/blob/master/LICENSE
#
# STM32 CUBE.AI on OpenMV MNIST Example
# See https://github.com/openmv/openmv/blob/master/src/stm32cubeai/README.MD
import sensor
import time
import 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 Grayscale
sensor.set_framesize(sensor.QQQVGA) # Set frame size to 80x60
sensor.skip_frames(time=2000) # Wait for settings take effect.
clock = time.clock() # Create a clock object to track the FPS.
# [CUBE.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=True)
# [CUBE.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 about half as fast when connected