用 CSV 格式的框存储 Tensorflow 对象检测 API 图像输出 [英] Store Tensorflow object detection API image output with boxes in CSV format

问题描述

我指的是 Google 的 Tensor-Flow 对象检测 API.我已经成功地训练和测试了这些对象.我的问题是在测试后，我获得了在对象周围绘制框的输出图像，如何获得这些框的 csv 坐标?测试代码可以在 (https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb)

I am referring to Google's Tensor-Flow object detection API. I have successfully trained and tested the objects. My question is after testing I get output image with box drawn around an object, how do I get csv coordinates of these boxes? code for testing can be found on (https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb)

如果我看到辅助代码，它会将图像加载到 numpy 数组中:

If I see the helper code it loads the image into numpy array:

def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)

在检测中，它使用这组图像并给出如下绘制的框的输出

In detection it takes this array of images and give output with box drawn as follows

with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    # Definite input and output Tensors for detection_graph
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
    # 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')
    # Each score represent how level of confidence for each of the objects.
    # Score is shown on the result image, together with the class label.
    detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
    detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')
    for image_path in TEST_IMAGE_PATHS:
      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.
      (boxes, scores, classes, num) = sess.run(
          [detection_boxes, detection_scores, detection_classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
      # Visualization of the results of a detection.
      vis_util.visualize_boxes_and_labels_on_image_array(
          image_np,
          np.squeeze(boxes),
          np.squeeze(classes).astype(np.int32),
          np.squeeze(scores),
          category_index,
          use_normalized_coordinates=True,
          line_thickness=8)
      plt.figure(figsize=IMAGE_SIZE)
      plt.imshow(image_np)

我想将这些绿框的坐标存储在一个 csv 文件中.有什么方法可以做到?

I want to store the coordinates of these green boxes in a csv file.What is a way to do it?

推荐答案

boxes 数组([ymin, xmin, ymax, xmax])中的坐标被归一化.因此，您必须将它们与图像的宽度/高度相乘才能获得原始值.

The coordinates in the boxes array ([ymin, xmin, ymax, xmax]) are normalized. Therefore, you have to multiply them with the images width / height to obtain the original values.

要实现这一点，您可以执行以下操作:

To achieve this, you can do something like the following:

for box in np.squeeze(boxes):
    box[0] = box[0] * heigh
    box[1] = box[1] * width
    box[2] = box[2] * height
    box[3] = box[3] * width

然后您可以使用 numpy.savetxt() 方法将这些框保存到您的 csv:

Then you can save the boxes to your csv using the numpy.savetxt() method:

import numpy as np
np.savetxt('yourfile.csv', boxes, delimiter=',')

正如评论中所指出的，上面的方法给出了一个框坐标列表.这是因为框张量保存了每个检测到的区域的坐标.假设您使用默认的置信度接受阈值 0.5:

As pointed out in the comments, the approach above gives a list of box coordinates. This is due to the fact, that the boxes tensor holds the coordinates of every detected region. One quick fix for me is the following, assuming that you use the default confidence acceptance threshold of 0.5:

  for i, box in enumerate(np.squeeze(boxes)):
      if(np.squeeze(scores)[i] > 0.5):
          print("ymin={}, xmin={}, ymax={}, xmax{}".format(box[0]*height,box[1]*width,box[2]*height,box[3]*width))

这应该为您打印四个值，而不是四个框.每个值代表边界框的一个角.

This should print you the four values, and not four boxes. Each of the values represents one corner of the bounding box.

如果您使用另一个置信度接受阈值，则必须调整该值.也许你可以解析这个参数的模型配置.

If you use another confidence acceptance threshold you have to adjust this value. Maybe you can parse the model configuration for this parameter.

要将坐标存储为 CSV，您可以执行以下操作:

To store the coordinates as CSV, you can do something like:

new_boxes = []
for i, box in enumerate(np.squeeze(boxes)):
    if(np.squeeze(scores)[i] > 0.5):
        new_boxes.append(box)
np.savetxt('yourfile.csv', new_boxes, delimiter=',')

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