Python中利用object_detection.core.modelDetectionModel()进行目标追踪的步骤解析

在Python中使用object_detection.core.modelDetectionModel()进行目标追踪，可以按照以下步骤进行操作：

1. 导入所需的库和模块：

from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
from object_detection.core import modelDetectionModel
import tensorflow as tf
import numpy as np
import cv2

2. 加载模型配置文件和标签文件：

MODEL_NAME = 'path_to_model_directory'
PATH_TO_LABELS = 'path_to_label_map.pbtxt'

detection_model = modelDetectionModel(MODEL_NAME)
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)

其中，MODEL_NAME是模型的目录路径，PATH_TO_LABELS是标签文件的路径。

3. 定义目标追踪函数：

def run_inference_for_single_image(image, detection_graph):
  with detection_graph.as_default():
    with tf.Session() as sess:
      # 获取输入和输出张量
      ops = tf.get_default_graph().get_operations()
      all_tensor_names = {output.name for op in ops for output in op.outputs}
      tensor_dict = {}
      for key in ['num_detections', 'detection_boxes', 'detection_scores',
                  'detection_classes', 'detection_masks']:
        tensor_name = key + ':0'
        if tensor_name in all_tensor_names:
          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(tensor_name)

      # 执行推理
      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
      output_dict = sess.run(tensor_dict, feed_dict={image_tensor: np.expand_dims(image, 0)})

      # 处理输出
      output_dict['num_detections'] = int(output_dict['num_detections'][0])
      output_dict['detection_classes'] = output_dict['detection_classes'][0].astype(np.uint8)
      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
      output_dict['detection_scores'] = output_dict['detection_scores'][0]

  return output_dict

4. 打开视频流或摄像头输入：

video = cv2.VideoCapture('path_to_video_file')  # 或 cv2.VideoCapture(0) 使用摄像头输入

5. 循环从视频流中读取帧并进行目标追踪：

while(video.isOpened()):
    ret, frame = video.read()
    if not ret:
        break

    # 转换颜色空间并调整尺寸
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    frame_expanded = np.expand_dims(frame_rgb, axis=0)

    # 进行目标追踪
    output_dict = run_inference_for_single_image(frame_expanded, detection_model)

    # 根据阈值筛选检测结果
    threshold = 0.5
    indices = np.where(output_dict['detection_scores'] > threshold)[0]
    boxes = output_dict['detection_boxes'][indices]
    classes = output_dict['detection_classes'][indices]
    scores = output_dict['detection_scores'][indices]

    # 在图像上绘制边界框和标签
    vis_util.visualize_boxes_and_labels_on_image_array(
      frame,
      boxes,
      classes,
      scores,
      category_index,
      use_normalized_coordinates=True,
      line_thickness=8)

    # 显示结果图像
    cv2.imshow('Object Detection', cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

video.release()
cv2.destroyAllWindows()

在这个示例中，我们首先导入了需要的库和模块。然后加载模型配置文件和标签文件，并使用modelDetectionModel()创建目标检测模型实例。接下来，定义了一个函数run_inference_for_single_image()，用于执行目标检测。在循环中，我们从视频流中读取每一帧图像，并将其转换为模型所需的输入格式。然后，调用run_inference_for_single_image()函数进行目标检测，并根据阈值筛选检测结果。最后，使用visualize_boxes_and_labels_on_image_array()函数在图像上绘制边界框和标签，然后显示结果图像。按下q键退出循环，并释放视频流和关闭窗口。

这是一个基本的使用object_detection.core.modelDetectionModel()进行目标追踪的例子。可以根据需要进行调整和扩展，例如可以对检测结果进行跟踪或将其保存到文件中。