# 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 from machine import LED, Pin led = LED("LED_BLUE") water = Pin("P0", Pin.OUT ) 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((1920, 1080)) # 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 = "person_detection.tflite" model = ml.Model(mobilenet, load_to_fb=True) labels = [line.rstrip("\n") for line in open("person_detection.txt")] clock = time.clock() shutoff = time.time() sensitivity = 0.1 delay = 2 #seconds 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(2): print("%s = %f" % (sorted_list[i][0], sorted_list[i][1])) print(clock.fps(), "fps") if sorted_list [1][1]shutoff+delay: water.off() led.off()