识别到的目标不能全部框出来，我的情况跟教程系列的42一样，就是不能完全圈出来

尽管识别到的目标不是占据整个画面的全部也是这样

# Edge Impulse - OpenMV Image Classification Example

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

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
min_confidence = 0.8

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) + ')')

colors = [(255,0,0),(0,255,0),(255,255,0)]


clock = time.clock()
while(True):
    clock.tick()
    
    img = sensor.snapshot()
    
    result =net.detect(img,thresholds=[(math.ceil(min_confidence*255),255)])
    print(result)
    for i,detection_list in enumerate(result):
        if(len(detection_list)==0):continue # no detections for this class?
        
        print("********** %s **********" %labels[i])
        for d in detection_list:
            [x,y,w,h] = d.rect()
            center_x = math.floor(x+(w/2))
            center_y = math.floor(y+(h/2))
            print('x %d\ty %d' % (center_x,center_y))
            img.draw_circle((center_x,center_y,12),color=colors[i],thickness=2)
  
    print(clock.fps(), "fps", end="\n\n")


这样简单的图也一样吗，我差不多每张远近不同角度拍200张

如果只是简单的线条折线之类的呢