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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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
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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