Python 保存在数据帧中检测到的对象：tensorflow对象检测

我正在运行github存储库tensorflow/object_deteciton中的典型代码：

特别是“object\u detection\u tutorial.ipynb”文件。主回路在此部分中：

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)

我只是在寻找一些建议，告诉你如何将图像识别的内容保存到一个数据框中，这个数据框可以理想地存储图像中检测到的每个对象的对象类别

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任何帮助都将不胜感激（：

好吧，我想已经很晚了，但我现在正在做这件事。所以我在几天的时间里经历了同样的痛苦，最后得到了一些工作。我只限于您的代码片段，添加了一些片段，得到了以下结果：

# Initialize hitlist
hitf = open("hitlist.csv",'w')
hitf.write('image,class,score,bb0,bb1,bb2,bb3\n')
hitlim = 0.5

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})

          # Write the results to hitlist - one line per hit over the 0.5
            nprehit = scores.shape[1] # 2nd array dimension
            for j in range(nprehit):
                fname = "image"+str(i)
                classid = int(classes[i][j])
                classname = category_index[classid]["name"]
                score = scores[i][j]
                if (score>=hitlim):
                    sscore = str(score)
                    bbox = boxes[i][j]
                    b0 = str(bbox[0])
                    b1 = str(bbox[1])
                    b2 = str(bbox[2])
                    b3 = str(bbox[3])
                    line = ",".join([fname,classname,sscore,b0,b1,b2,b3])
                    hitf.write(line+"\n")

          # 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)                      

# close hitlist
hitf.flush()
hitf.close()

注:

• 添加的代码有三个部分，一个初始化

  hitlist.csv

  ，一个在置信限0.5以上的“预命中”中添加一行，一个关闭文件

• 它故意不太“pythonic”，它使用简单而明显的结构来说明正在发生的事情。除了

  “，”.join（…）

  ，我非常喜欢它，我无法抗拒

• 通过查看分数的第二维度或ClassID，可以找到预命中数

• 返回的classid是

  float

  s，尽管您通常需要将它们作为整数。转换很容易

• 这里可能有一些小的复制和粘贴错误，因为我没有一个真正的MVE（最小可验证示例）

• 我使用的是

  rfcn\u resnet101\u coco\u 2017\u 11\u 08

  目标检测模型，而不是

  ssd\u mobilenet\u v1\u coco\u 2017\u 11\u 17

  ，所以我的命中列表和分数有点不同（实际上更糟）

以下是csv的外观：

image,class,score,bb0,bb1,bb2,bb3
image0,kite,0.997912,0.086756825,0.43700624,0.1691603,0.4966739
image0,person,0.9968072,0.7714941,0.15771112,0.945292,0.20014654
image0,person,0.9858992,0.67766637,0.08734644,0.8385928,0.12563995
image0,kite,0.9683157,0.26249793,0.20640253,0.31359094,0.2257214
image0,kite,0.8578382,0.3803091,0.42938906,0.40701985,0.4453904
image0,person,0.85244817,0.5692219,0.06266626,0.6282138,0.0788657
image0,kite,0.7622662,0.38192448,0.42580333,0.4104231,0.442965
image0,person,0.6722884,0.578461,0.022049228,0.6197509,0.036917627
image0,kite,0.6671517,0.43708095,0.80048573,0.47312954,0.8156846
image0,person,0.6577289,0.5996533,0.13272598,0.63358027,0.1430584
image0,kite,0.5893124,0.3790631,0.3451705,0.39845183,0.35965574
image0,person,0.51051,0.57377476,0.025907507,0.6221084,0.04294989

对于此图像（来自ipython笔记本-但具有不同的对象检测模型）

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我认为所描述的object\u detection.py文件的语法发生了一些变化。我对所描述的答案进行了一些调整，以适应新的语法：

这是您应该在代码中找到的位置：

   # Actual detection.
      output_dict = run_inference_for_single_image(image_np, detection_graph)

然后可以添加以下内容：

  # store boxes in dataframe!
  cut_off_scores = len(list(filter(lambda x: x >= 0.1, output_dict['detection_scores'])))
  detect_scores = []
  detect_classes = []
  detect_ymin = []
  detect_xmin = []
  detect_ymax = []
  detect_xmax = []
  for j in range(cut_off_scores):
      detect_scores.append(output_dict['detection_scores'][j])
      detect_classes.append(output_dict['detection_classes'][j])
       # Assumption: ymin, xmin, ymax, xmax:
      boxes = output_dict['detection_boxes'][j]
      detect_ymin.append(boxes[0])
      detect_xmin.append(boxes[1])
      detect_ymax.append(boxes[2])
      detect_xmax.append(boxes[3])
      # Assumption: your files are named image1, image2, etc.
      Identifier = ("image" + str(n))
      Id_list = [Identifier] * cut_off_scores
      Detected_objects = pd.DataFrame(
        {'Image': Id_list,
         'Score': detect_scores,
         'Class': detect_classes,
         'Ymin':  detect_ymin,
         'Xmax': detect_xmax,
         'Ymax': detect_ymax,
         'Xmax':  detect_xmax
        })

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我尝试了以上两种方法，但都没有成功。 和User27074，在附加xmax、xmin等值时出现问题

我尝试了一个简单的代码，它的工作只是检测到的物体的坐标保存在百分比，需要乘以高度和宽度的图像后

detected_boxes = []
h = image_height = 500 #Change accordingly
w = image_width = 500 #change accordingly
#Columns' format 'ymin','xmin','ymax', 'xmax', 'class', 'Detection score'
for i, box in enumerate(np.squeeze(boxes)):
    if (np.squeeze(scores)[i] > 0.85):
        box[0] = int(box[0] * h)
        box[1] = int(box[1] * w)
        box[2] = int(box[2] * h)
        box[3] = int(box[3] * w)
        box = np.append(box, np.squeeze(classes)[i])
        box = np.append(box, np.squeeze(scores)[i]*100)
        detected_boxes.append(box)
np.savetxt('detection_coordinates.csv', detected_boxes, fmt='%i', delimiter=',')

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我可以说，你有太多的评论。好的评论是稀疏的，因为好的代码是自我记录的。任何时候你想表达为什么你选择了一个不明显的特定算法或技术，你应该留下一条评论，讨论为什么代码是这样写的。任何时候你有一段代码，尽管名字很好iers，没有明确的目的。您可以留下注释来记录代码的功能。不要留下包含通过阅读代码可以了解的信息的注释。变量名，可理解的操作，等等。我不确定您是否知道，但这段代码取自Github的教程，这就是为什么它有c我想这里面有什么内容……有什么反馈吗？我想新TF版本中的代码随着时间的推移已经发生了变化，但是您显示的这段代码不在您描述的文件中。@user27074那么答案是否仍然有用呢？我会在有机会的时候看一看。