2021电赛F题,openARTmini数字识别和循迹融合代码,数字识别准确率可达98.7%

#*********************引入库函数********************# import pyb import sensor, image, time, math import os, tf from machine import UART uart = UART(2, baudrate=9600) # 初始化串口 波特率设置为115200 TX是B12 RX是B13 #*********************神经网络********************# net_path = "trained.tflite" labels = ["1","2","3","4","5","6","7","8"] net = tf.load(net_path, load_to_fb=True) #*********************参数定义********************# value=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] mask = 0 x_1=x_2=x_3=0 #*********************众数滤波********************# def judge(x_2): global value global x_1 if x_1<25: value[x_1]=x_2 x_1=x_1+1 #print("value",value) else: x_3=max(value, default='list is empty', key=lambda v: value.count(v)) if x_3==0: pass #print("x_3:",x_3) uart_A.write(str(x_3)) #****为下次做准备****# value=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] x_1=0 TT = 0 clock = time.clock() def RGNN_IMG(): sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QVGA) # we run out of memory if the resolution is much bigger... sensor.set_brightness(500) # 设置图像亮度 越大越亮 sensor.skip_frames(time = 20) sensor.set_auto_gain(True) # must turn this off to prevent image washout... sensor.set_auto_whitebal(True,(0,0x80,0)) # must turn this off to prevent image washout... def RGNN_Find(): #*********************主循环********************# clock.tick() TT = 0 AA = 0 img = sensor.snapshot() for r in img.find_rects(threshold = 30000): # 在图像中搜索矩形 img.draw_rectangle(r.rect(), color = (255, 0, 0)) # 绘制矩形外框，便于在IDE上查看识别到的矩形位置 img1 = img.copy(r.rect()) # 拷贝矩形框内的图像 for obj in tf.classify(net, img, r.rect(), min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5): #img.draw_rectangle(obj.rect()) #print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect()) #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)): if predictions_list[AA][1] <= predictions_list[i][1]: TT = 1 AA = i print("%s = %f" % (predictions_list[i][0], predictions_list[i][1])) sorted_list = sorted(zip(labels, obj.output()), key = lambda x: x[1], reverse = True) for i in range(1): img.draw_string(r.x(), r.y(), "%s" % sorted_list[i][0], color = (255, 0, 0),scale=2) if TT == 1: FH = bytearray([0x79,0xaa,TT,AA + 1,0x85]) uart.write(FH) if TT == 0: FH = bytearray([0x79,0xaa,TT,AA,0x85]) uart.write(FH) print(TT) print(AA) print(AA+1)