openmv/scripts/examples/03-Machine-Learning/00-TensorFlow/tf_image_classification.py
2024-07-18 16:36:16 -07:00

51 lines
1.8 KiB
Python

# This work is licensed under the MIT license.
# Copyright (c) 2013-2024 OpenMV LLC. All rights reserved.
# https://github.com/openmv/openmv/blob/master/LICENSE
#
# TensorFlow Lite Mobilenet V1 Example
#
# Google's Mobilenet is trained to detect 1000 classes of objects.
#
# NOTE: This example only works on boards that have enough memory to load the model.
# To get the models, please see the CNN Network library in OpenMV IDE under Tools->
# Machine Vision. The labels file (mobilenet_labels.txt) is included there as well,
# and it should be copied to the root of the filesystem for this script to work.
import sensor
import time
import ml
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
mobilenet_version = "1" # 1
mobilenet_width = "0.5" # 1.0, 0.75, 0.50, 0.25
mobilenet_resolution = "128" # 224, 192, 160, 128
mobilenet = "mobilenet_v%s_%s_%s_quant.tflite" % (
mobilenet_version,
mobilenet_width,
mobilenet_resolution,
)
model = ml.Model(mobilenet, load_to_fb=True)
labels = [line.rstrip("\n") for line in open("mobilenet_labels.txt")]
clock = time.clock()
while True:
clock.tick()
img = sensor.snapshot()
print("**********\nTop 5 Detections")
# This combines the labels and confidence values into a list of tuples
# and then sorts that list by the confidence values.
sorted_list = sorted(
zip(labels, model.predict([img])[0].flatten().tolist()), key=lambda x: x[1], reverse=True
)
for i in range(5):
print("%s = %f" % (sorted_list[i][0], sorted_list[i][1]))
print(clock.fps(), "fps")