『自己的工作7』依据多边形的顶点信息生成多边形mask(Python实现)！

朴灿烈づ我的快乐病毒、 2022-12-17 01:26 288阅读 0赞

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   <td><font>依据多边形的顶点信息生成多边形mask(Python实现)！</font> </td> 
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### 文章目录 ###

*  一. 安装labelme标定数据集
 *   *  1.1. 安装labelme
     *  1.2. 启动labelme标定
 *  二. 报纸版面使用labelme打标
 *   *  2.1. 转换json文件
     *  2.2. 多边形mask标定

# 一. 安装labelme标定数据集 #

## 1.1. 安装labelme ##

>  *  **首先安装一下`Anaconda`，然后创建一个`python3.6`的环境。命令如下：**

conda create -n lableme python=3.6

>  *  **然后`pip`安装`lableme`：**

pip install labelme

## 1.2. 启动labelme标定 ##

>  *  **标记好之后，会出现一个`json`文件，内容大致如下：**
> 
> ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FiYzEzNTI2MjIyMTYw_size_16_color_FFFFFF_t_70][]

{
        
      "version": "4.5.6",
      "flags": {
        },
      "shapes": [
        {
        
          "label": "1",
          "points": [
            [
              408.7391304347826,
              173.91304347826087
            ],
            [
              2404.391304347826,
              173.91304347826087
            ],
            [
              2402.2173913043475,
              1258.695652173913
            ],
            [
              415.2608695652174,
              1256.5217391304348
            ]
          ],
          "group_id": null,
          "shape_type": "polygon",
          "flags": {
        }
        },
        {
        
          "label": "2",
          "points": [
            [
              41.3478260869565,
              182.6086956521739
            ],
            [
              400.0434782608695,
              184.78260869565216
            ],
            [
              400.0434782608695,
              2176.086956521739
            ],
            [
              39.17391304347825,
              2167.391304347826
            ]
          ],
          "group_id": null,
          "shape_type": "polygon",
          "flags": {
        }
        },
        {
        
          "label": "3",
          "points": [
            [
              408.7391304347826,
              1289.1304347826087
            ],
            [
              1887.0,
              1293.4782608695652
            ],
            [
              1882.6521739130435,
              2171.7391304347825
            ],
            [
              430.47826086956513,
              2165.2173913043475
            ]
          ],
          "group_id": null,
          "shape_type": "polygon",
          "flags": {
        }
        },
        {
        
          "label": "4",
          "points": [
            [
              1913.086956521739,
              1306.5217391304348
            ],
            [
              2408.7391304347825,
              1308.695652173913
            ],
            [
              2404.391304347826,
              2230.4347826086955
            ],
            [
              1904.391304347826,
              2228.2608695652175
            ]
          ],
          "group_id": null,
          "shape_type": "polygon",
          "flags": {
        }
        },
        {
        
          "label": "5",
          "points": [
            [
              21.782608695652186,
              2204.3478260869565
            ],
            [
              421.78260869565213,
              2206.5217391304345
            ],
            [
              423.9565217391304,
              2717.391304347826
            ],
            [
              23.956521739130437,
              2717.391304347826
            ]
          ],
          "group_id": null,
          "shape_type": "polygon",
          "flags": {
        }
        },
        {
        
          "label": "6",
          "points": [
            [
              443.52173913043475,
              2202.173913043478
            ],
            [
              1882.6521739130435,
              2202.173913043478
            ],
            [
              1878.304347826087,
              2728.2608695652175
            ],
            [
              437.0,
              2721.7391304347825
            ]
          ],
          "group_id": null,
          "shape_type": "polygon",
          "flags": {
        }
        },
        {
        
          "label": "7",
          "points": [
            [
              1904.391304347826,
              2271.7391304347825
            ],
            [
              2410.9130434782605,
              2271.7391304347825
            ],
            [
              2404.391304347826,
              2758.695652173913
            ],
            [
              1902.2173913043475,
              2756.5217391304345
            ]
          ],
          "group_id": null,
          "shape_type": "polygon",
          "flags": {
        }
        },
        {
        
          "label": "8",
          "points": [
            [
              17.434782608695627,
              2758.695652173913
            ],
            [
              421.78260869565213,
              2756.5217391304345
            ],
            [
              421.78260869565213,
              3454.3478260869565
            ],
            [
              15.260869565217376,
              3454.3478260869565
            ]
          ],
          "group_id": null,
          "shape_type": "polygon",
          "flags": {
        }
        },
        {
        
          "label": "9",
          "points": [
            [
              443.52173913043475,
              2750.0
            ],
            [
              1834.8260869565215,
              2747.8260869565215
            ],
            [
              1834.8260869565215,
              2971.7391304347825
            ],
            [
              1037.0,
              2969.565217391304
            ],
            [
              1030.4782608695652,
              3447.8260869565215
            ],
            [
              441.3478260869565,
              3447.8260869565215
            ]
          ],
          "group_id": null,
          "shape_type": "polygon",
          "flags": {
        }
        },
        {
        
          "label": "10",
          "points": [
            [
              1060.9130434782608,
              2991.304347826087
            ],
            [
              1880.478260869565,
              2993.478260869565
            ],
            [
              1876.1304347826085,
              3452.173913043478
            ],
            [
              1047.8695652173913,
              3443.478260869565
            ]
          ],
          "group_id": null,
          "shape_type": "polygon",
          "flags": {
        }
        },
        {
        
          "label": "11",
          "points": [
            [
              1900.0434782608695,
              2786.9565217391305
            ],
            [
              2415.2608695652175,
              2791.304347826087
            ],
            [
              2408.7391304347825,
              3439.1304347826085
            ],
            [
              1895.695652173913,
              3432.6086956521735
            ]
          ],
          "group_id": null,
          "shape_type": "polygon",
          "flags": {
        }
        }
      ],
      "imagePath": "NBRB20110101A01.png",
      "imageData": "iVBORwmJ7I/wMfjEpe3nT0pwAAAABJRU5ErkJggg==",
      "imageHeight": 1469,
      "imageWidth": 1027
    }

# 二. 报纸版面使用labelme打标 #

## 2.1. 转换json文件 ##

>  *  **上面的标记json文件进行内容提取，转换为另一个json文件：**

import numpy as np
    import os
    import json
    import io
    import PIL.Image
    
    """
    这部分代码的功能就是从labelme标记好的JSON文件中，提取有用的坐标位置信息等。然后保存到另一个json文件。
    这部分内容参考了博客: https://blog.csdn.net/longzhinuhou/article/details/86634949
    """
    def shapes_to_label(json_file_path, savaFile):
        if not os.path.exists(savaFile):                             # 用来保存解析出来的xlsx文件目录
            os.mkdir(savaFile)
    
        list_path = os.listdir(json_file_path)
        for i in range(0, len(list_path)):
            path = os.path.join(json_file_path, list_path[i])
            file_name = list_path[i].replace(".json", "")            # 把后缀中的.json字符串去除
            file_name_path = os.path.join(savaFile, file_name)
            contents = []
            file_handle = open(file_name_path+'.json', mode='w')
    
            if os.path.isfile(path):
                with open(path, 'r', encoding='utf-8') as load_f:
                    strF = load_f.read()
                    if len(strF) > 0:  # 这里是为了防止为空
                        data = json.loads(strF)
                        for shape in data['shapes']:
                            label_name = shape['label']
                            polygons = shape['points']
                            contents.append({
        'name': file_name, 'label': label_name, 'points': polygons})
                    else:
                        data = {
        }
                json.dump(contents, file_handle)
        file_handle.close()
    
    
    if __name__ == '__main__':
        shapes_to_label('./dataset/JSON_Labelme', './dataset/JSON_Processed')      # json文件；

>  *  **转换的结果如下：**

[{
        "name": "NBRB20110101A02", "label": "1", "points": [[408.7391304347826, 173.91304347826087], [2404.391304347826, 173.91304347826087], [2402.2173913043475, 1258.695652173913], [415.2608695652174, 1256.5217391304348]]}, 
     {
        "name": "NBRB20110101A02", "label": "2", "points": [[41.3478260869565, 182.6086956521739], [400.0434782608695, 184.78260869565216], [400.0434782608695, 2176.086956521739], [39.17391304347825, 2167.391304347826]]}, 
     {
        "name": "NBRB20110101A02", "label": "3", "points": [[408.7391304347826, 1289.1304347826087], [1887.0, 1293.4782608695652], [1882.6521739130435, 2171.7391304347825], [430.47826086956513, 2165.2173913043475]]}, 
     {
        "name": "NBRB20110101A02", "label": "4", "points": [[1913.086956521739, 1306.5217391304348], [2408.7391304347825, 1308.695652173913], [2404.391304347826, 2230.4347826086955], [1904.391304347826, 2228.2608695652175]]}, 
     {
        "name": "NBRB20110101A02", "label": "5", "points": [[21.782608695652186, 2204.3478260869565], [421.78260869565213, 2206.5217391304345], [423.9565217391304, 2717.391304347826], [23.956521739130437, 2717.391304347826]]}, 
     {
        "name": "NBRB20110101A02", "label": "6", "points": [[443.52173913043475, 2202.173913043478], [1882.6521739130435, 2202.173913043478], [1878.304347826087, 2728.2608695652175], [437.0, 2721.7391304347825]]}, 
     {
        "name": "NBRB20110101A02", "label": "7", "points": [[1904.391304347826, 2271.7391304347825], [2410.9130434782605, 2271.7391304347825], [2404.391304347826, 2758.695652173913], [1902.2173913043475, 2756.5217391304345]]}, 
     {
        "name": "NBRB20110101A02", "label": "8", "points": [[17.434782608695627, 2758.695652173913], [421.78260869565213, 2756.5217391304345], [421.78260869565213, 3454.3478260869565], [15.260869565217376, 3454.3478260869565]]}, 
     {
        "name": "NBRB20110101A02", "label": "9", "points": [[443.52173913043475, 2750.0], [1834.8260869565215, 2747.8260869565215], [1834.8260869565215, 2971.7391304347825], [1037.0, 2969.565217391304], [1030.4782608695652, 3447.8260869565215], [441.3478260869565, 3447.8260869565215]]}, 
     {
        "name": "NBRB20110101A02", "label": "10", "points": [[1060.9130434782608, 2991.304347826087], [1880.478260869565, 2993.478260869565], [1876.1304347826085, 3452.173913043478], [1047.8695652173913, 3443.478260869565]]}, 
     {
        "name": "NBRB20110101A02", "label": "11", "points": [[1900.0434782608695, 2786.9565217391305], [2415.2608695652175, 2791.304347826087], [2408.7391304347825, 3439.1304347826085], [1895.695652173913, 3432.6086956521735]]}]

## 2.2. 多边形mask标定 ##

>  *  **进行多边形`mask`标定的时候，就是利用到了多边形的定点信息，这里由于一张图我划分了多个版面，我对每个版面分别天长不同的数值。代码如下：**

import numpy as np
    import json
    import cv2
    import os
    import matplotlib.pyplot as plt
    
    
    """
    @zhangkaifang 2020-10-22，本函数主要实现打开JSON_Processed文件，读取数据！
    """
    def open_json(filepath_json):
        data = json.load(open(filepath_json))
        polygon_set = []
        for ele in data:
            polygon_set.append([ele['points'], ele['label']])
        return polygon_set
    
    
    """ 
    @zhangkaifang 2020-10-23，这里是根据标签信息进行多边形mask标定, 这里的[1469,1027]是近似报纸pdf的尺寸大小！
    """
    def draw_mask(polygon_set):
        mask_all, i = np.zeros([3480,2433], dtype=np.uint8), 0
        for layout in polygon_set:                               ##### 依次遍历每个版面，每个版面是一个多边形。
            data = np.array([layout[0]], dtype=np.int32)         ##### shape(多边形的点, 2)
            label = int(layout[1])
            im = np.zeros([3480,2433], dtype=np.uint8)
            cv2.fillPoly(im, data, label)
            i = i + 1
            mask_all = mask_all+im
        plt.imshow(mask_all)
        plt.show()
        return mask_all, i                                       # i 表示版面的数量
    
    
    def main():
        path_files = '../dataset/JSON_Processed'
        for file in os.listdir(path_files):
            polygon_set = open_json(os.path.join(path_files, file))
            mask_all, i = draw_mask(polygon_set)
    
    
    if __name__ == '__main__':
        main()

> ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FiYzEzNTI2MjIyMTYw_size_16_color_FFFFFF_t_70 1][]

>  *  **以上对每个多边形mask区域内填充了不同的数值。**
>  *  **我实现这个的目的就是接下来对报纸进行版面分析！根据每个mask提取每篇文章的标题，副标题，作者，正文。**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FiYzEzNTI2MjIyMTYw_size_16_color_FFFFFF_t_70 2][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FiYzEzNTI2MjIyMTYw_size_16_color_FFFFFF_t_70]: /images/20221123/cc2bc55c82af47e39473320c5875d2a5.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FiYzEzNTI2MjIyMTYw_size_16_color_FFFFFF_t_70 1]: /images/20221123/3d11e4b6b52447618d0c4f6ae94655b5.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FiYzEzNTI2MjIyMTYw_size_16_color_FFFFFF_t_70 2]: /images/20221123/9d0f63cf22e1468ba4d9f5613c247722.png