以 CSV 格式存储带框的 Tensorflow 对象检测 API 图像输出

Question

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对象。我的问题是在测试之后我得到了输出图像，并在一个对象周围绘制了方框，我如何获得这些方框的 csv 坐标？可以在 (https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb)

上找到测试代码

如果我看到辅助代码，它会将图像加载到 numpy 数组中：

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，它采用此图像数组并给出如下绘制框的输出

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)

我想将这些绿色框的坐标存储在 csv 文件中file.What有办法吗？

Answer 1

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宽度/高度相乘以获得原始值。

为此，您可以执行以下操作：

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() 方法将框保存到您的 csv：

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

编辑：

正如评论中指出的那样，上面的方法给出了一个框坐标列表。这是因为盒子张量包含每个检测到的区域的坐标。假设您使用默认置信度接受阈值 0.5，我的一个快速修复如下：

  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。

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

要将坐标存储为 CSV，您可以执行以下操作：

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=',')

以 CSV 格式存储带框的 Tensorflow 对象检测 API 图像输出

Store Tensorflow object detection API image output with boxes in CSV format

object-detection

computer-vision

python-3.x

deep-learning

tensorflow

编辑：