人形(不是人脸)检测如何返回人形在画面中的位置?如果用.rect()例程中返回的x,y为何都是0
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问题如题所示,麻烦回答一下
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运行的是那个代码?
如果涉及代码,需要报错提示与全部代码文本,请注意不要贴代码图片
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This post is deleted!
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# TensorFlow Lite Person Dection Example # # Google's Person Detection Model detects if a person is in view. # # In this example we slide the detector window over the image and get a list # of activations. Note that use a CNN with a sliding window is extremely compute # expensive so for an exhaustive search do not expect the CNN to be real-time. import sensor, image, time, os, tf sensor.reset() # Reset and initialize the sensor. sensor.set_pixformat(sensor.GRAYSCALE) # 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. # Load the built-in person detection network (the network is in your OpenMV Cam's firmware). net = tf.load('person_detection') labels = ['unsure', 'person', 'no_person'] clock = time.clock() while(True): clock.tick() img = sensor.snapshot() # net.classify() will run the network on an roi in the image (or on the whole image if the roi is not # specified). A classification score output vector will be generated for each location. At each scale the # detection window is moved around in the ROI using x_overlap (0-1) and y_overlap (0-1) as a guide. # If you set the overlap to 0.5 then each detection window will overlap the previous one by 50%. Note # the computational work load goes WAY up the more overlap. Finally, for multi-scale matching after # sliding the network around in the x/y dimensions the detection window will shrink by scale_mul (0-1) # down to min_scale (0-1). For example, if scale_mul is 0.5 the detection window will shrink by 50%. # Note that at a lower scale there's even more area to search if x_overlap and y_overlap are small... # default settings just do one detection... change them to search the image... for obj in net.classify(img, min_scale=1.0, scale_mul=0.5, x_overlap=0.0, y_overlap=0.0): print("**********\nDetections at [x=%d,y=%d,w=%d,h=%d]" % obj.rect()) for i in range(len(obj.output())): print("%s = %f" % (labels[i], obj.output()[i])) img.draw_rectangle(obj.rect()) img.draw_string(obj.x()+3, obj.y()-1, labels[obj.output().index(max(obj.output()))], mono_space = False) print(clock.fps(), "fps")
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net.classify这是分类器,可以判断区域内是否有人。
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@kidswong999 无法在人形上面画框是嘛?
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对,不能找到位置。