Attribute Error如何解决
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使用IDE给OPENMV4 H7 PLUS写入代码时，运行到110行net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))时，IDE会出现Attribute Error:'module' object has no attribute 'load'报错，如何解决呢
''' openMV1 实现：识别路标 方式：神经网络数据集 ''' #------------------------------------------------------------ ##使用库导入## import sensor, image, time, os, tf, uos, gc from pyb import UART,Timer #------------------------------------------------------------ ##摄像头初始化## #像素与图片大小 sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QVGA)#320*240 #摄像头翻转 sensor.set_vflip (False) sensor.set_hmirror(False) #等待配置时间 sensor.skip_frames(time = 1000) #设置画面 sensor.set_windowing(0,30,320,180) #------------------------------------------------------------ ##通信功能## #-------------------- ##通信初始化 uart = UART(3, 9600) # 定义串口3变量 uart.init(9600, bits=8, parity=None, stop=1)#串口3初始化 #-------------------- ##通信协议 def send_data(mode,num,x,y): start = 0x24 end = 0x23 length = 5 class_num = 0x05 #例程编号 class_group = 0xBB #例程组 data_num = 0x00 #数据量 fenge = 0x2c #逗号 crc = 0 #校验位 data = [] #数据组 #参数都为0 if mode==0 and num==0 and x==0 and y ==0: pass else: #mode(小端模式) low = mode & 0xFF #低位 high = mode >> 8& 0xFF #高位 data.append(low) data.append(fenge) #增加"," data.append(high) data.append(fenge) #增加"," #num(小端模式) low = num & 0xFF #低位 high = num >> 8& 0xFF #高位 data.append(low) data.append(fenge) #增加"," data.append(high) data.append(fenge) #增加"," #x(小端模式) low = x & 0xFF #低位 high = x >> 8& 0xFF #高位 data.append(low) data.append(fenge) #增加"," data.append(high) data.append(fenge) #增加"," #y(小端模式) low = y & 0xFF #低位 high = y >> 8& 0xFF #高位 data.append(low) data.append(fenge) #增加"," data.append(high) data.append(fenge) #增加"," data_num = len(data) length += len(data) #print(length) send_merr = [length,class_num,class_group,data_num] for i in range(data_num): send_merr.append(data[i]) #print(send_merr) #不加上CRC位，进行CRC运算 for i in range(len(send_merr)): crc +=send_merr[i] crc = crc%256 send_merr.insert(0,start) #插入头部 send_merr.append(crc) send_merr.append(end) #print(send_merr) global send_buf send_buf = send_merr FH=bytearray(send_buf) print(send_buf) uart.write(FH) #-------------------- ##发送识别结果1 def send_temp(id): send_data(1,id,0,0) #------------------------------------------------------------ ##识别功能## #-------------------- net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024))) labels = [line.rstrip('\n') for line in open("labels.txt")] idbuffer=[0,0,0,0,0]#阈值清空buffer #色块识别阈值 red=(26, 100, 29, 127, -37, 52) blue=(0, 58, -38, 80, -128, -32) green=(10, 75, -128, 20, 20, 106) #阈值清空标准，减少误识别概率 buffer1=3 buffer2=3 buffer3=3 buffer4=3 #停止时间，防止识别同一个标识两次 sleep1=800 sleep2=600 sleep3=400 #------------------------------------------------------------ clock = time.clock() while (True): clock.tick() img = sensor.snapshot()#.lens_corr(strength = 1.8, zoom = 1.0) #视上车情况开抗鱼眼 #对数据集tensor进行匹配 for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5): data=obj.output() if (data[0]>0.35):#数据集匹配率 idbuffer[0]=idbuffer[0]+1 idbuffer[4]=0 elif (data[3]>0.55): idbuffer[3]=idbuffer[3]+1 idbuffer[4]=0 elif (data[1]>0.67): idbuffer[1]=idbuffer[1]+1 idbuffer[4]=0 elif (data[2]>0.75): idbuffer[2]=idbuffer[2]+1 idbuffer[4]=0 else:#无有效匹配值时，idbuffer[4]+1，用以清空idbuffer idbuffer[4]=idbuffer[4]+1 if idbuffer[0]>=buffer1:#红色标识1 blobs=img.find_blobs([red],pixels_threshold=3, area_threshold=9, merge=True, margin=3) #对色块进行额外匹配，防止误识别 if blobs: send_temp(1) idbuffer=[0,0,0,0,0] time.sleep_ms(sleep1) else: idbuffer[0]=0 elif idbuffer[1]>=buffer2:#蓝色标识2 blobs=img.find_blobs([blue],pixels_threshold=3, area_threshold=9, merge=True, margin=3) if blobs: send_temp(2) idbuffer=[0,0,0,0,0] time.sleep_ms(sleep2) else: idbuffer[1]=0 elif idbuffer[2]>=buffer3:#绿色标识3 blobs=img.find_blobs([green],pixels_threshold=3, area_threshold=9, merge=True, margin=3) if blobs: send_temp(3) idbuffer=[0,0,0,0,0] time.sleep_ms(sleep3) else: idbuffer[2]=0 elif idbuffer[3]>=buffer4:#红色标识停 blobs=img.find_blobs([red],pixels_threshold=3, area_threshold=9, merge=True, margin=3) if blobs: send_temp(4) idbuffer=[0,0,0,0,0] time.sleep_ms(sleep1) else: idbuffer[4]=0 elif idbuffer[4]>=2*buffer1:#无有效识别对象后清空阈值，防止后续干扰 idbuffer=[0,0,0,0,0] gc.collect()
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你用edge impulse自带的代码测试。