Get the bounding box coordinates in the TensorFlow object detection API tutorial

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无人共我
无人共我 2021-01-31 10:48

I am new to both python and Tensorflow. I am trying to run the object_detection_tutorial file from the Tensorflow Object Detection API, but I cannot find where I can get the coo

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  • 2021-01-31 11:04

    I've got exactly the same story. Got an array with roughly hundred boxes (output_dict['detection_boxes']) when only one was displayed on an image. Digging deeper into code which is drawing a rectangle was able to extract that and use in my inference.py:

    #so detection has happened and you've got output_dict as a
    # result of your inference
    
    # then assume you've got this in your inference.py in order to draw rectangles
    vis_util.visualize_boxes_and_labels_on_image_array(
        image_np,
        output_dict['detection_boxes'],
        output_dict['detection_classes'],
        output_dict['detection_scores'],
        category_index,
        instance_masks=output_dict.get('detection_masks'),
        use_normalized_coordinates=True,
        line_thickness=8)
    
    # This is the way I'm getting my coordinates
    boxes = output_dict['detection_boxes']
    # get all boxes from an array
    max_boxes_to_draw = boxes.shape[0]
    # get scores to get a threshold
    scores = output_dict['detection_scores']
    # this is set as a default but feel free to adjust it to your needs
    min_score_thresh=.5
    # iterate over all objects found
    for i in range(min(max_boxes_to_draw, boxes.shape[0])):
        # 
        if scores is None or scores[i] > min_score_thresh:
            # boxes[i] is the box which will be drawn
            class_name = category_index[output_dict['detection_classes'][i]]['name']
            print ("This box is gonna get used", boxes[i], output_dict['detection_classes'][i])
    
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  • 2021-01-31 11:06

    I tried printing output_dict['detection_boxes'] but I am not sure what the numbers mean

    You can check out the code for yourself. visualize_boxes_and_labels_on_image_array is defined here.

    Note that you are passing use_normalized_coordinates=True. If you trace the function calls, you will see your numbers [ 0.56213236, 0.2780568 , 0.91445708, 0.69120586] etc. are the values [ymin, xmin, ymax, xmax] where the image coordinates:

    (left, right, top, bottom) = (xmin * im_width, xmax * im_width, 
                                  ymin * im_height, ymax * im_height)
    

    are computed by the function:

    def draw_bounding_box_on_image(image,
                               ymin,
                               xmin,
                               ymax,
                               xmax,
                               color='red',
                               thickness=4,
                               display_str_list=(),
                               use_normalized_coordinates=True):
      """Adds a bounding box to an image.
      Bounding box coordinates can be specified in either absolute (pixel) or
      normalized coordinates by setting the use_normalized_coordinates argument.
      Each string in display_str_list is displayed on a separate line above the
      bounding box in black text on a rectangle filled with the input 'color'.
      If the top of the bounding box extends to the edge of the image, the strings
      are displayed below the bounding box.
      Args:
        image: a PIL.Image object.
        ymin: ymin of bounding box.
        xmin: xmin of bounding box.
        ymax: ymax of bounding box.
        xmax: xmax of bounding box.
        color: color to draw bounding box. Default is red.
        thickness: line thickness. Default value is 4.
        display_str_list: list of strings to display in box
                          (each to be shown on its own line).
        use_normalized_coordinates: If True (default), treat coordinates
          ymin, xmin, ymax, xmax as relative to the image.  Otherwise treat
          coordinates as absolute.
      """
      draw = ImageDraw.Draw(image)
      im_width, im_height = image.size
      if use_normalized_coordinates:
        (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
                                      ymin * im_height, ymax * im_height)
    
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