我在生成的ei_image_classification.py文件中修改roi边框内容，只有一个边框可以正常识别？

[pimy]

# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, uos, gc

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()

    img = sensor.snapshot()
    roil = (3,35,62,92)
    # default settings just do one detection... change them to search the image...
    for obj in net.classify(img,roi=roil,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()))
        for i in range(len(predictions_list)):
            print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
            
    roir = (126,35,62,92)
    # default settings just do one detection... change them to search the image...
    for obj in net.classify(img,roi=roir,min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
        print("**********\nPredictions2 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_list2 = list(zip(labels, obj.output()))
        for p in range(len(predictions_list2)):
            
            print("%s = %f" % (predictions_list2[p][0], predictions_list2[p][1]))
        print(clock.fps(), "fps")

Predictions at [x=3,y=35,w=62,h=92]
0 = 0.000000
4 = 0.000000
8 = 0.996094

---

Predictions2 at [x=126,y=35,w=62,h=92]
0 = 0.996094
4 = 0.000000
8 = 0.000000
5.22268 fps

左边识别8右边识别4 但是右边roi中没有识别到

[pimy]

别沉啊。。。。。。。。

[kidswong999]

我没办法复现问题, 没办法判断是模型的准确度问题, 还是代码的问题.