脱机运行识别率太低,有六种图像通过edge impulse训练之后只能识别两种,图像2识别成图像3
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# Edge Impulse - OpenMV Image Classification Example import sensor, image, time, os, tf, uos, gc from pyb import UART 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. 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: print(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) + ')') clock = time.clock() while(True): clock.tick() uart = UART(3,9600) img = sensor.snapshot() # default settings just do one detection... change them to search the image... for obj in net.classify(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()) # This combines the labels and confidence values into a list of tuples predictions_list = list(zip(labels, obj.output())) predictions_list=sorted(predictions_list,key=lambda x:x[1],reverse=True) highest_prediction=predictions_list[0] print("The highest prediction is:%s=%f"%(highest_prediction[0],highest_prediction[1])) uart.write(highest_prediction[0]) uart.write("\r\n")
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增加图像数量,每个分类1000张,不同的角度,位置和光线都要。