导入环境：

# 导入docker镜像 vot_toolkit.tar 路径在/nas_dataset/docker_images/vot_toolkit.tar
docker load --input vot_toolkit.tar
# 根据docker镜像建立容器
 sudo docker run --gpus all --shm-size=60g -it -v /你的本地项目路径:/test  -w /test vot_toolkit

生成配置文件
在该目录下生成三个文件

1. trackers.ini:

   [TRACKALL]  # <tracker-name>
   label = TRACKALL
   protocol = traxpython
   
   command = tracker_vot
   
   # Specify a path to trax python wrapper if it is not visible (separate by ; if using multiple paths)
   paths = /test
   
   # Additional environment paths
   env_PATH = <additional-env-paths>;${PATH}
   

   tracker-name（中括号那个），label，根据自己跟踪器情况而定。

   paths：运行文件所在目录

   command：运行跟踪器的启动文件（需要在上面的paths下有command.py这个文件。在上面这个例子就是需要在 文件目录 下面有tracker_vot.py这个文件。

   然后参照官方调试用跟踪器的示例文件，修改自己的跟踪器的启动文件，添加trax等通信代码。

2. tracker_vot.py:
   模仿官方的tracker的输入输出去写即可

   class NCCTracker(object):
   
       def __init__(self, image, region):
       	"""
       	image： np.array->[h,w,3]
       	region: bbox
       	"""
       	
       	# 帧的初始化在init中完成 
           self.window = max(region.width, region.height) * 2
   		
           left = max(region.x, 0)
           top = max(region.y, 0)
   
           right = min(region.x + region.width, image.shape[1] - 1)
           bottom = min(region.y + region.height, image.shape[0] - 1)
   
           self.template = image[int(top):int(bottom), int(left):int(right)]
           self.position = (region.x + region.width / 2, region.y + region.height / 2)
           self.size = (region.width, region.height)
   
       def track(self, image):
   		
           left = max(round(self.position[0] - float(self.window) / 2), 0)
           top = max(round(self.position[1] - float(self.window) / 2), 0)
   
           right = min(round(self.position[0] + float(self.window) / 2), image.shape[1] - 1)
           bottom = min(round(self.position[1] + float(self.window) / 2), image.shape[0] - 1)
   
           if right - left < self.template.shape[1] or bottom - top < self.template.shape[0]:
               return vot.Rectangle(self.position[0] + self.size[0] / 2, self.position[1] + self.size[1] / 2, self.size[0], self.size[1])
   
           cut = image[int(top):int(bottom), int(left):int(right)]
   
           matches = cv2.matchTemplate(cut, self.template, cv2.TM_CCOEFF_NORMED)
           min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(matches)
   
           self.position = (left + max_loc[0] + float(self.size[0]) / 2, top + max_loc[1] + float(self.size[1]) / 2)
   		# 返回 (x_0,y_0,w,h) 和置信值
           return vot.Rectangle(left + max_loc[0], top + max_loc[1], self.size[0], self.size[1]), max_val```
   

3. vot.py:

   import sys
   import copy
   import collections
   try:
       import trax
   except ImportError:
       raise Exception('TraX support not found. Please add trax module to Python path.')
   
   Rectangle = collections.namedtuple('Rectangle', ['x', 'y', 'width', 'height'])
   Point = collections.namedtuple('Point', ['x', 'y'])
   Polygon = collections.namedtuple('Polygon', ['points'])
   
   class VOT(object):
       """ Base class for Python VOT integration """
       def __init__(self, region_format, channels=None):
           """ Constructor
           Args:
               region_format: Region format options
           """
           assert(region_format in [trax.Region.RECTANGLE, trax.Region.POLYGON])
   
           if channels is None:
               channels = ['color']
           elif channels == 'rgbd':
               channels = ['color', 'depth']
           elif channels == 'rgbt':
               channels = ['color', 'ir']
           elif channels == 'ir':
               channels = ['ir']
           else:
               raise Exception('Illegal configuration {}.'.format(channels))
   
           self._trax = trax.Server([region_format], [trax.Image.PATH], channels)
           request = self._trax.wait()
           assert(request.type == 'initialize')
           if isinstance(request.region, trax.Polygon):
               self._region = Polygon([Point(x[0], x[1]) for x in request.region])
           else:
               self._region = Rectangle(*request.region.bounds())
           self._image = [x.path() for k, x in request.image.items()]
           if len(self._image) == 1:
               self._image = self._image[0]
   
           self._trax.status(request.region)
   
       def region(self):
           """
           Send configuration message to the client and receive the initialization
           region and the path of the first image
           Returns:
               initialization region
           """
   
           return self._region
   
       def report(self, region, confidence = None):
           """
           Report the tracking results to the client
           Arguments:
               region: region for the frame
           """
           assert(isinstance(region, Rectangle) or isinstance(region, Polygon))
           if isinstance(region, Polygon):
               tregion = trax.Polygon.create([(x.x, x.y) for x in region.points])
           else:
               tregion = trax.Rectangle.create(region.x, region.y, region.width, region.height)
           properties = {}
           if not confidence is None:
               properties['confidence'] = confidence
           self._trax.status(tregion, properties)
   
       def frame(self):
           """
           Get a frame (image path) from client
           Returns:
               absolute path of the image
           """
           if hasattr(self, "_image"):
               image = self._image
               del self._image
               return image
   
           request = self._trax.wait()
   
           if request.type == 'frame':
               image = [x.path() for k, x in request.image.items()]
               if len(image) == 1:
                   return image[0]
               return image
           else:
               return None
   
   
       def quit(self):
           if hasattr(self, '_trax'):
               self._trax.quit()
   
       def __del__(self):
           self.quit()
   

安装vot工具包（docker 中使用可跳过已自带）：
pip install git+https://github.com/votchallenge/vot-toolkit-python

初始化vot workspace
在这一步的时候需要导入数据集 如果没有的话会自行下载
也可以自行导入数据集目录为 /sequences 格式如下

# 如果自行导入数据集可以在后面选择 增加–nodownload参数
vot initialize votlt2019 --workspace /test 

跑测试

# TRACKALL -> <tracker-name>
vot evaluate --workspace /test TRACKALL

测试结果保存在 result 文件夹，测试会默认在项目目录下寻找result

生成计算指标

# vot analysis --workspace 实验存放目录 NCCPython(跟踪器名称，即上面中括号的名称) --format {html,json等}
 vot analysis --workspace /test TRACKALL --format json

跑分结果保存在 analysis/运行日期的文件夹/
遇到过的问题：
下载数据集时速度慢，容易断：
1. 预先解析出图像包的下载链接，通过其他下载器预先下载好，并放置到对应的位置。如无法跳过时，可以参考将执行的源码文件中下载图片的代码，注释掉已跳过对应的下载步骤，并后期通过手动加入。此方法本人实验有效，但是操作时建议多写资料以便后期维护，仅供参考。

将shell的http链接通过指定端口进行下载。