【coco】掩膜mask影像转coco格式txt（含python代码）

本文主要是介绍【coco】掩膜mask影像转coco格式txt（含python代码），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

最近在做实例分割，遇到二值掩膜影像——coco格式txt的实例分割转换问题，困扰很久，不知道怎么转换，转出来的txt没法用代码成功读取。一系列问题，索性记录下自己的结局路程，方便大家python代码自取。

📞📞1.coco格式示例

📗 images模块

📘 categories模块

📙annotation模块

📷📷2.环境准备

📢📢3.maskToanno函数定义

⏰⏰4.images模块内容写入txt

📡📡5.categories模块内容写入txt

🛁🛁6.annotation模块内容写入txt

🔋🔋7.完整python代码

整理不易，欢迎一键三连！！！

送你们一条美丽的--分割线--

📞📞1.coco格式示例

coco格式txt文件示例：

主要包含三个模块：

images
categories
annotations

其中每个模块都由好多个分块组成，images和categories比较简单。

📗 images模块

images里主要记录的是每张image的长宽，id和文件名信息，注意此处的文件名必须是images文件名，labels也得是相同的文件名，不然索引不到。id从1开始，依次向下编号。

images[
{"height": 512,"width": 512,"id": 1,"file_name": "1.png"
}
...
...
...
{"height": 512,"width": 512,"id": 100,"file_name": "100.png"
}]

📘 categories模块

categories模块记录的是所有样本的类别信息，name为类别名称，id从1开始，依次向下编号，supercategory表示该类别的从属类别，理解起来比较简单，比如name为bus,supercategory就可以为car，name为cat，supercategory就可以为animal。如果没有多级类别，可以将name和supercategory写出相同的，像我下面写的。

"categories": [
{"supercategory": "land","id": 1,"name": "land"
}
{"supercategory": "land","id": 2,"name": "land2"
}
...
...
...
{"supercategory": "land","id": n,"name": "landn"
}
],

📙annotation模块

annotation模块主要记录的是label信息，也是最关键的内容，此处以实例分割为例进行讲解，因为coco格式可以做的任务太多，此处仅限实例分割或者语义分割。

annotation模块的一个完整内容包括：

segmentation记录目标的边界坐标点位置信息，可以是很长但是要记得是双[[...]];
area记录得是目标得面积信息，这个可以自动计算，后面会细讲；
iscrowd代表一个目标是否被切分成多块，比如一个猫得身体和尾巴被一只狗头挡住，分开成2部分。0代表没有切分，1代表切分；
image_id表示这个目标所对应得原始影像得id编号，与images模块里的id是一一对应的关系；
bbox指这个目标的外界矩形框的位置信息；
category_id表示这个目标的类别信息，与categories模块里的id是一一对应的关系；
id代表目标的编号信息，可以与images个数不一致，因为一张图上很可能会有多个目标。

"annotations": [
{"segmentation": [[276,286,275,287,274,287,273,287,]],"area": 2148,"iscrowd": 0,"image_id": 2,"bbox": [233.0,286.0,49.0,68.0],"category_id": 1,"id": 1
},...{"segmentation": [[276,286,275,287,274,287,273,287,]],"area": 248,"iscrowd": 0,"image_id": 5,"bbox": [233.0,286.0,49.0,68.0],"category_id": 2,"id":100
},

📷📷2.环境准备

代码所需环境包有：json、numpy、pycocotools、OpenCV、os、sys。

包导入命令：

import jsonimport numpy as npfrom pycocotools import maskimport cv2import osimport sys

📢📢3.maskToanno函数定义

输入：round_truth_binary_mask, ann_count, category_id

输出：annotations

python代码如下：

def maskToanno(ground_truth_binary_mask, ann_count, category_id):contours, _ = cv2.findContours(ground_truth_binary_mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)  # 根据二值图找轮廓annotations = [] #一幅图片所有的annotatonsglobal segmentation_id# print(ann_count)# 对每个实例进行处理for i in range(len(contours)):# print(i)# 生成二值的黑色图片x = np.zeros((512, 512))cv2.drawContours(x, contours, i, (1, 1, 1), -1)  # 将单个mask表示为二值图的形式ground_truth_binary_mask_id = np.array(x, dtype=object).astype(np.uint8)fortran_ground_truth_binary_mask = np.asfortranarray(ground_truth_binary_mask_id)# 求每个mask的面积和框encoded_ground_truth = mask.encode(fortran_ground_truth_binary_mask)ground_truth_area = mask.area(encoded_ground_truth)ground_truth_bounding_box = mask.toBbox(encoded_ground_truth)contour, _ = cv2.findContours(ground_truth_binary_mask_id, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)# contour = measure.find_contours(ground_truth_binary_mask_id, 0.5)# print(contour)annotation = {"segmentation": [],"area": ground_truth_area.tolist(),"iscrowd": 0,"image_id": ann_count,"bbox": ground_truth_bounding_box.tolist(),"category_id": category_id,"id": segmentation_id}#print(contour)# 求segmentation部分contour = np.flip(contour, axis=0)segmentation = contour.ravel().tolist()if len(segmentation)<=4:continueannotation["segmentation"].append(segmentation)annotations.append(annotation)segmentation_id = segmentation_id + 1return annotations

⏰⏰4.images模块内容写入txt

输入：jsonpath

输出：jsonpath

将jsonpath路径下的txt文件打开，若image存在且对应文件名的label文件存在，就可以写image的images模块信息，python代码如下：

with io.open(jsonPath, 'w', encoding='utf8') as output:# 那就全部写在一个文件夹好了# 先写images的信息output.write(unicode('{\n'))output.write(unicode('"images": [\n'))for image in rgb_image_files:if os.path.exists(os.path.join(block_mask_path, image)):output.write(unicode('{'))annotation = {"height": 512,"width": 512,"id": imageCount,"file_name": image}str_ = json.dumps(annotation, indent=4)str_ = str_[1:-1]if len(str_) > 0:output.write(unicode(str_))imageCount = imageCount + 1if (image == rgb_image_files[-1]):output.write(unicode('}\n'))else:output.write(unicode('},\n'))

📡📡5.categories模块内容写入txt

输入：jsonpath

输出：jsonpath

将jsonpath路径下的txt文件打开，将categories模块里的supercategory、id、name信息写入txt，此处的categories信息只是示例，可以根据自己的类别信息修改，python代码如下：

with io.open(jsonPath, 'w', encoding='utf8') as output:output.write(unicode('"categories": [\n'))output.write(unicode('{\n'))categories = {"supercategory": "land","id": 1,"name": "land"}str_ = json.dumps(categories, indent=4)str_ = str_[1:-1]if len(str_) > 0:output.write(unicode(str_))output.write(unicode('}\n'))output.write(unicode('],\n'))

🛁🛁6.annotation模块内容写入txt

输入：jsonpath

输出：jsonpath

将jsonpath路径下的txt文件打开，若label存在且对应文件名的image文件存在，就可以把annotation模块里的信息写入txt，python代码如下：

with io.open(jsonPath, 'w', encoding='utf8') as output:output.write(unicode('"annotations": [\n'))for i in range(len(block_mask_image_files)):if os.path.exists(os.path.join(path, block_mask_image_files[i])):block_image = block_mask_image_files[i]# 读取二值图像block_im = cv2.imread(os.path.join(block_mask_path, block_image), 0)_, block_im = cv2.threshold(block_im, 100, 1, cv2.THRESH_BINARY)if not block_im is None:block_im = np.array(block_im, dtype=object).astype(np.uint8)block_anno = maskToanno(block_im, annCount, 1)for b in block_anno:str_block = json.dumps(b, indent=4)str_block = str_block[1:-1]if len(str_block) > 0:output.write(unicode('{\n'))output.write(unicode(str_block))if (block_image == rgb_image_files[-1] and b == block_anno[-1]):output.write(unicode('}\n'))else:output.write(unicode('},\n'))annCount = annCount + 1else:print(block_image)

🔋🔋7.完整python代码

二值掩膜mask影像转coco格式的实例分割txt完整python代码如下：

import json
import numpy as np
from pycocotools import mask
import cv2
import os
import sysif sys.version_info[0] >= 3:unicode = strimport io
# 实例的id，每个图像有多个物体每个物体的唯一id
global segmentation_id
segmentation_id = 1
# annotations部分的实现
def maskToanno(ground_truth_binary_mask, ann_count, category_id):contours, _ = cv2.findContours(ground_truth_binary_mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)  # 根据二值图找轮廓annotations = [] #一幅图片所有的annotatonsglobal segmentation_id# print(ann_count)# 对每个实例进行处理for i in range(len(contours)):# print(i)# 生成二值的黑色图片x = np.zeros((512, 512))cv2.drawContours(x, contours, i, (1, 1, 1), -1)  # 将单个mask表示为二值图的形式ground_truth_binary_mask_id = np.array(x, dtype=object).astype(np.uint8)fortran_ground_truth_binary_mask = np.asfortranarray(ground_truth_binary_mask_id)# 求每个mask的面积和框encoded_ground_truth = mask.encode(fortran_ground_truth_binary_mask)ground_truth_area = mask.area(encoded_ground_truth)ground_truth_bounding_box = mask.toBbox(encoded_ground_truth)contour, _ = cv2.findContours(ground_truth_binary_mask_id, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)# contour = measure.find_contours(ground_truth_binary_mask_id, 0.5)# print(contour)annotation = {"segmentation": [],"area": ground_truth_area.tolist(),"iscrowd": 0,"image_id": ann_count,"bbox": ground_truth_bounding_box.tolist(),"category_id": category_id,"id": segmentation_id}#print(contour)# 求segmentation部分contour = np.flip(contour, axis=0)segmentation = contour.ravel().tolist()if len(segmentation)<=4:continueannotation["segmentation"].append(segmentation)annotations.append(annotation)segmentation_id = segmentation_id + 1return annotations# mask图像路径
block_mask_path = '/labels_512'
block_mask_image_files = sorted(os.listdir(block_mask_path))# coco json保存的位置
jsonPath = "/data/temp.json"
annCount = 1
imageCount = 1
# 原图像的路径， 原图像和mask图像的名称是一致的。
path = "/images_512"
rgb_image_files = sorted(os.listdir(path))with io.open(jsonPath, 'w', encoding='utf8') as output:# 那就全部写在一个文件夹好了# 先写images的信息output.write(unicode('{\n'))output.write(unicode('"images": [\n'))for image in rgb_image_files:if os.path.exists(os.path.join(block_mask_path, image)):output.write(unicode('{'))annotation = {"height": 512,"width": 512,"id": imageCount,"file_name": image}str_ = json.dumps(annotation, indent=4)str_ = str_[1:-1]if len(str_) > 0:output.write(unicode(str_))imageCount = imageCount + 1if (image == rgb_image_files[-1]):output.write(unicode('}\n'))else:output.write(unicode('},\n'))output.write(unicode('],\n'))# 接下来写cateoutput.write(unicode('"categories": [\n'))output.write(unicode('{\n'))categories = {"supercategory": "land","id": 1,"name": "land"}str_ = json.dumps(categories, indent=4)str_ = str_[1:-1]if len(str_) > 0:output.write(unicode(str_))output.write(unicode('}\n'))output.write(unicode('],\n'))# 写annotationsoutput.write(unicode('"annotations": [\n'))for i in range(len(block_mask_image_files)):if os.path.exists(os.path.join(path, block_mask_image_files[i])):block_image = block_mask_image_files[i]# 读取二值图像block_im = cv2.imread(os.path.join(block_mask_path, block_image), 0)_, block_im = cv2.threshold(block_im, 100, 1, cv2.THRESH_BINARY)if not block_im is None:block_im = np.array(block_im, dtype=object).astype(np.uint8)block_anno = maskToanno(block_im, annCount, 1)for b in block_anno:str_block = json.dumps(b, indent=4)str_block = str_block[1:-1]if len(str_block) > 0:output.write(unicode('{\n'))output.write(unicode(str_block))if (block_image == rgb_image_files[-1] and b == block_anno[-1]):output.write(unicode('}\n'))else:output.write(unicode('},\n'))annCount = annCount + 1else:print(block_image)output.write(unicode(']\n'))output.write(unicode('}\n'))

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