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Tensorflow对象检测API:多对象坐标

object-detection-api object-detection tensorflow opencv python

min2bro · 技术社区 · 8 年前

我在网络摄像头中使用tensorflow进行物体检测,而且我还必须识别图像中每个已识别物体的坐标。当我打印边界框,即“框:”时,我看到一个数组,如果我在框数组中拉第一个数组,它会给出[ymin,xmin,ymax,xmax],这是我猜测的第一个对象的坐标。

我的问题:如果识别出三个对象:Person、Chari、Backpack,那么如何从box数组中获取每个已识别对象的坐标

# Each box represents a part of the image where a particular object was detected.
    detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

# Actual detection.
      (boxes, scores, classes, num) = sess.run(
          [detection_boxes, detection_scores, detection_classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})

      ymin = int((boxes[0][0][0]*height))
      xmin = int((boxes[0][0][1]*width))
      ymax = int((boxes[0][0][2]*height))
      xmax = int((boxes[0][0][3]*width))

盒子阵列

[[[3.98103416e-01 2.58287102e-01 9.33272898e-01 7.91557074e-01]
  [5.12995601e-01 2.65186965e-01 8.48852396e-01 4.13343787e-01]
  [3.66972327e-01 7.32781529e-01 7.90232778e-01 9.99156952e-01]
  [3.98981631e-01 7.82709479e-01 7.35594213e-01 9.70018148e-01]
  [6.31434858e-01 6.56596124e-02 9.94844258e-01 9.91125226e-01]
  [4.03546631e-01 3.72542024e-01 8.93805206e-01 7.11475253e-01]
  [4.92211223e-01 2.52640486e-01 7.18696833e-01 4.07687068e-01]
  [6.85503840e-01 4.34535980e-01 8.68745089e-01 4.70350862e-01]
  [3.38426471e-01 5.05429626e-01 3.66816342e-01 5.16116381e-01]
  [5.56789041e-01 0.00000000e+00 8.72881413e-01 6.77806586e-02]
  [7.46141136e-01 4.60771352e-01 8.83682549e-01 5.43849111e-01]
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  [7.05794036e-01 1.78262621e-01 9.70153272e-01 2.98915476e-01]
  [5.08592129e-01 7.52241552e-01 7.34334707e-01 9.93464291e-01]
  [1.42102808e-01 0.00000000e+00 4.66604441e-01 7.86211610e-01]
  [5.85047305e-01 1.92644715e-01 6.23101652e-01 2.79952288e-01]
  [6.69153452e-01 7.77340114e-01 7.54663706e-01 8.55158150e-01]
  [3.93155396e-01 9.21279490e-01 7.05600083e-01 9.97392118e-01]]]
Counter({'person': 1, 'chair': 1, 'backpack': 1}) 2018-09-20 17:07:59.300454

0 回复 | 直到 8 年前

Jitesh Malipeddi 7 年前

你可以试试这个

image = Image.open(image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
image_np = load_image_into_numpy_array(image)  
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
output_dict = run_inference_for_single_image(image_np, detection_graph)

width = 1024
height = 600
threshold = 0.5

for i,j in zip(output_dict['detection_boxes'],output_dict['detection_scores']):
    if(j>threshold):
        print(i[1]*width,i[0]*height,i[3]*width,i[2]*height)

这将按的顺序输出框 xmin,ymin,xmax,ymax min_score_threshold . 希望这对你有帮助。如果您遇到任何问题,请告诉我