垃圾分类,用的神经网络训练出来的代码,怎么跟舵机配合,使得识别到指定垃圾转动指定角度
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# Edge Impulse - OpenMV Image Classification Example import sensor, image, time, os, tf from pyb import Servo import time 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. s1 = Servo(1) s2 = Servo(2) net = "trained.tflite" labels = [line.rstrip('\n') for line in open("labels.txt")] n = 0 clock = time.clock() while(True): clock.tick() img = sensor.snapshot() # default settings just do one detection... change them to search the image... for obj in tf.classify(net, img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5): print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect()) img.draw_rectangle(obj.rect()) #我知道写控制舵机的语句在这些,我分了11个类,我想知道当openmv识别到指定垃圾时,程序根据哪个变量来知道识别 #的是哪种垃圾,因为我需要根据识别到的垃圾类型,来控制舵机转到相应的角度 # This combines the labels and confidence values into a list of tuples predictions_list = list(zip(labels, obj.output())) for i in range(len(predictions_list)): print("%s = %f" % (predictions_list[i][0], predictions_list[i][1])) print(clock.fps(), "fps")
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result = obj.output()
result[0]
result[1]
result[2]
result[3]
result[4]就是你labels.txt里每一个的概率