4. fomo检测可以分区域检测吗？为什么对roi区域划分循环识别无法实现？没有报错但是实现不了分区域，可以怎么改进？
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    • fomo检测可以分区域检测吗？为什么对roi区域划分循环识别无法实现？没有报错但是实现不了分区域，可以怎么改进？

    • D
       

      import sensor, image, time, os, tf, math, uos, gc, ustruct
from pyb import UART,LED
from image import SEARCH_EX, SEARCH_DS

sensor.reset()                         # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE)    # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)      # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240))       # Set 240x240 window.
sensor.skip_frames(time=2000)          # Let the camera adjust.
sensor.set_contrast(1)
sensor.set_gainceiling(16)

net = None
labels = None
min_confidence = 0.5
rois = [(0,30,80,80),(70,30,85,85)]

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) + ')')

colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
    (255,   0,   0),
    (  0, 255,   0),
    (255, 255,   0),
    (  0,   0, 255),
    (255,   0, 255),
    (  0, 255, 255),
    (255, 255, 255),
]

#rois = [(0,20,190,200),(150,35,160,170)]
for roi in rois:
  clock = time.clock()

  num = 0
  while(True):
      clock.tick()

      img = sensor.snapshot()
    
    # detect() returns all objects found in the image (splitted out per class already)
    # we skip class index 0, as that is the background, and then draw circles of the center
    # of our objects

      for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
          if (i == 0): continue # background class
          if (len(detection_list) == 0): continue # no detections for this class?
        
          print(" %s " % labels[i])
          tem = labels[i]
          #print(tem)
          num = 1
       
          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)
           
      if (num != 0):break
      0

    • 因为你代码写的不对。

      可以运行的代码：

      import sensor, image, time, os, tf, math, uos, gc, ustruct
from pyb import UART,LED
from image import SEARCH_EX, SEARCH_DS

sensor.reset()                         # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE)    # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)      # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240))       # Set 240x240 window.
sensor.skip_frames(time=2000)          # Let the camera adjust.
sensor.set_contrast(1)
sensor.set_gainceiling(16)

net = None
labels = None
min_confidence = 0.5
rois = [(0,30,80,80),(70,30,85,85)]

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) + ')')

colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
    (255,   0,   0),
    (  0, 255,   0),
    (255, 255,   0),
    (  0,   0, 255),
    (255,   0, 255),
    (  0, 255, 255),
    (255, 255, 255),
]


num = 0
while(True):
    
    img = sensor.snapshot()
    
    for roi in rois:
        clock = time.clock()
    
        for i, detection_list in enumerate(net.detect(img, roi=roi, thresholds=[(math.ceil(min_confidence * 255), 255)])):
            if (i == 0): continue # background class
            print(detection_list)
            if (len(detection_list) == 0): continue # no detections for this class?
            
            print(" %s " % labels[i])
            tem = labels[i]
            #print(tem)
            num = 1
            
            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)
           
        if (num != 0):break
      0