Python TensorFlow对象检测API教程中获取bounding box坐标
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Get the bounding box coordinates in the TensorFlow object detection API tutorial
提问by Mandy
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 coordinates of the bounding boxes when objects are detected.
我是 python 和 Tensorflow 的新手。我正在尝试从Tensorflow 对象检测 API运行 object_detection_tutorial 文件，但是当检测到对象时，我找不到在哪里可以获得边界框的坐标。
Relevant code:
相关代码：
# The following processing is only for single image
detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
...
...
The place where I assume bounding boxes are drawn is like this:
我假设绘制边界框的地方是这样的：
# Visualization of the results of a detection.
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)
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_np)
I tried printing output_dict['detection_boxes'] but I am not sure what the numbers mean. There are a lot.
我尝试打印 output_dict['detection_boxes'] 但我不确定这些数字是什么意思。有很多。
array([[ 0.56213236, 0.2780568 , 0.91445708, 0.69120586],
[ 0.56261235, 0.86368728, 0.59286624, 0.8893863 ],
[ 0.57073039, 0.87096912, 0.61292225, 0.90354401],
[ 0.51422435, 0.78449738, 0.53994244, 0.79437423],
......
......
[ 0.32784131, 0.5461576 , 0.36972913, 0.56903434],
[ 0.03005961, 0.02714229, 0.47211722, 0.44683522],
[ 0.43143299, 0.09211366, 0.58121657, 0.3509962 ]], dtype=float32)
I found answers for similar questions, but I don't have a variable called boxes as they do. How can I get the coordinates? Thank you!
我找到了类似问题的答案，但我没有像他们那样有一个叫做 box 的变量。我怎样才能得到坐标？谢谢！
回答by MFisherKDX
I tried printing output_dict['detection_boxes'] but I am not sure what the numbers mean
我尝试打印 output_dict['detection_boxes'] 但我不确定这些数字是什么意思
You can check out the code for yourself. visualize_boxes_and_labels_on_image_arrayis defined here.
您可以自己查看代码。 在这里visualize_boxes_and_labels_on_image_array定义。
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:
请注意，您正在通过use_normalized_coordinates=True。如果您跟踪函数调用，您将看到您的数字[ 0.56213236, 0.2780568 , 0.91445708, 0.69120586]等是[ymin, xmin, ymax, xmax]图像坐标的值：
(left, right, top, bottom) = (xmin * im_width, xmax * im_width,
ymin * im_height, ymax * im_height)
are computed by the function:
由函数计算：
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)
回答by Vadim
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:
我有完全一样的故事。output_dict['detection_boxes']当图像上只显示一个框时，得到一个包含大约一百个框 ( )的数组。深入研究绘制矩形的代码能够提取它并在我的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])
