需要一个缓冲区协议的对象，这个怎么解决
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# Edge Impulse - OpenMV Image Classification Example import sensor, image, time, os, tf, uos, gc,pyb,machine from pyb import UART uart = UART(3,115200) green = (10,100,-50,15,-15,50) red = (0,100,-30,50,-30,30) blue = (10,100,-20,40,-10,40) orange = (10,80,-10,30,-20,35) brown = (0,80,-10,20,-20,30) yellow = (50,80,-10,10,-20,60) white = (10,100,-20,30,-30,20) gray = (20,80,-10,15,-10,25) 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) sensor.set_auto_gain(False) sensor.set_auto_whitebal(False) clock = time.clock() name = 'c' while(True): clock.tick() def rubbish(): net = None labels = None try: # load the model, alloc the model file on the heap if we have at least 64K free after loading net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64 * 1024))) except Exception as e: raise Exception( 'Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str( e) + ')') try: labels = [line.rstrip('\n') for line in open("labels.txt")] except Exception as e: raise Exception( 'Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str( e) + ')') image = sensor.snapshot() scores = net.classify(image) # default settings just do one detection... change them to search the image... for obj in net.classify(image, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5): image.draw_rectangle(obj.rect()) # This combines the labels and confidence values into a list of tuples predictions_list = list(zip(labels, obj.output())) name = predictions_list[0][0] for i in range(len(predictions_list)): max = 0.8 if predictions_list[i][1] > max: max = predictions_list[i][1] name = predictions_list[i][0] if name == 'KHSLJ': name = 'k' elif name == 'YHLJ': name = 'y' elif name == 'CYLJ': name = 'c' elif name == 'QTLJ': name = 'q' return name img = sensor.snapshot() blobs1 = img.find_blobs([green]) blobs2 = img.find_blobs([red]) blobs3 = img.find_blobs([blue]) blobs4 = img.find_blobs([orange]) blobs5 = img.find_blobs([brown]) blobs6 = img.find_blobs([yellow]) blobs7 = img.find_blobs([white]) blobs8 = img.find_blobs([gray]) if blobs1: rubbish() uart.write(name) if blobs2: rubbish() uart.write(name) if blobs3: rubbish() uart.write(name) if blobs4: rubbish() uart.write(name) if blobs5: rubbish() uart.write(name) if blobs6: rubbish() uart.write(name) if blobs7: rubbish() uart.write(name) if blobs8: rubbish() uart.write(name)
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你把name打印出来看看是啥。
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import sensor, image, time, os, tf, uos, gc,pyb,machine from pyb import UART uart = UART(3,115200) green = (10,100,-50,15,-15,50) red = (0,100,-30,50,-30,30) blue = (10,100,-20,40,-10,40) orange = (10,80,-10,30,-20,35) brown = (0,80,-10,20,-20,30) yellow = (50,80,-10,10,-20,60) white = (10,100,-20,30,-30,20) gray = (20,80,-10,15,-10,25) 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) sensor.set_auto_gain(False) sensor.set_auto_whitebal(False) clock = time.clock() while(True): clock.tick() def rubbish(): net = None labels = None try: # load the model, alloc the model file on the heap if we have at least 64K free after loading net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64 * 1024))) except Exception as e: raise Exception( 'Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str( e) + ')') try: labels = [line.rstrip('\n') for line in open("labels.txt")] except Exception as e: raise Exception( 'Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str( e) + ')') image = sensor.snapshot() scores = net.classify(image) # default settings just do one detection... change them to search the image... for obj in net.classify(image, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5): image.draw_rectangle(obj.rect()) # This combines the labels and confidence values into a list of tuples predictions_list = list(zip(labels, obj.output())) name = predictions_list[0][0] for i in range(len(predictions_list)): max = 0.8 if predictions_list[i][1] > max: max = predictions_list[i][1] name = predictions_list[i][0] if name == 'KHSLJ': name = 'k' elif name == 'YHLJ': name = 'y' elif name == 'CYLJ': name = 'c' elif name == 'QTLJ': name = 'q' return name name = rubbish() img = sensor.snapshot() blobs1 = img.find_blobs([green]) blobs2 = img.find_blobs([red]) blobs3 = img.find_blobs([blue]) blobs4 = img.find_blobs([orange]) blobs5 = img.find_blobs([brown]) blobs6 = img.find_blobs([yellow]) blobs7 = img.find_blobs([white]) blobs8 = img.find_blobs([gray]) if blobs1: rubbish() uart.write(name) print(name) if blobs2: rubbish() uart.write(name) if blobs3: rubbish() uart.write(name) if blobs4: rubbish() uart.write(name) if blobs5: rubbish() uart.write(name) if blobs6: rubbish() uart.write(name) if blobs7: rubbish() uart.write(name) if blobs8: rubbish() uart.write(name)
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前面打印name 一直是'c',我就把代码改成这样了，又提示了这个错误
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tf.load不要在死循环里调用，只需要在一开始调用一次。