一，2d图像mask输出boundingbox

代码实现：输入原图像地址，mask图像地址，输出一个yolov5格式的txt文件，一个不同分类不同颜色的原图像框选结果。

将2d的多分类图像mask输出boundingbox。

import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import shutil

def mask_find_bboxs(mask):

    retval, labels, stats, centroids = cv2.connectedComponentsWithStats(mask, connectivity=8) # connectivity参数的默认值为8
    # print(retval,labels,centroids)
    stats = stats[stats[:,4].argsort()]
    return stats[:-1] # 排除最外层的连通图

 上面的函数实现了对连通域进行划分，对结果进行输出。想了解搜一下cv2.connectedComponentsWithStats就行。

mask_ori = cv2.imread(r'.label_process_results
esults_compareTSE_10_label.png',
                          cv2.COLOR_BGR2GRAY)
    mask_rgb = cv2.imread(r'.label_process_results
esults_compareTSE_10.png')
    ret, mask = cv2.threshold(mask_ori, 20, 255, cv2.THRESH_BINARY)  # 30是最小分类,处理成灰度图才能进mask_find_bboxs函数
    bboxs = mask_find_bboxs(mask)  # 框选目标的参数
    width = len(mask[1, :])  # 宽
    height = len(mask[:, 1])  # 高
    file = open(r".	estyolo.txt", "w", encoding='utf-8')
    '''
    参数介绍b[0],b[1]:左上角坐标x,y x变大向右平移y变大向下平移
           b[2]     :矩形宽度
           b[3]     :矩形高度

        转换成yolo。txt格式
        类别（0开始） 方框中心点比例（x，y） width宽占比例 height 高占比例

    '''
    classification = 10  # 10表示出错

    for b in bboxs:
        pixel_catch = 0
        for i in range(3, 8):
            for j in range(3, 8):
                pixel_catch = max(mask_ori[int((b[1] + (b[3] * (j / 10)))), int((b[0] + (b[2] * (i / 10))))],
                                  pixel_catch)

        yolo_para_width = b[2] / width
        yolo_para_height = b[3] / height
        yolo_para_x_center = (b[0] / width) + (yolo_para_width / 2)
        yolo_para_y_center = (b[1] / height) + (yolo_para_height / 2)
        print(pixel_catch)
        if pixel_catch == 40:
            cv2.rectangle(mask_rgb, (b[0], b[1]), (b[0] + b[2], b[1] + b[3]), (255, 255, 0), 2)
            classification = 2
        if pixel_catch == 80:
            cv2.rectangle(mask_rgb, (b[0], b[1]), (b[0] + b[2], b[1] + b[3]), (255, 255, 0), 2)
            classification = 3
        if pixel_catch == 120:
            cv2.rectangle(mask_rgb, (b[0], b[1]), (b[0]+b[2], b[1]+b[3]), (0, 0, 255), 2)
            classification = 0
        if pixel_catch == 160:
            cv2.rectangle(mask_rgb, (b[0], b[1]), (b[0] + b[2], b[1] + b[3]), (255, 255, 0), 2)
            classification = 1
        if pixel_catch == 200:
            cv2.rectangle(mask_rgb, (b[0], b[1]), (b[0] + b[2], b[1] + b[3]), (255, 255, 0), 2)
            classification = 4
        if pixel_catch == 240:
            cv2.rectangle(mask_rgb, (b[0], b[1]), (b[0] + b[2], b[1] + b[3]), (255, 255, 0), 2)
            classification = 5

        file.write(str(classification) + ' ' + str(yolo_para_x_center) + ' ' + str(yolo_para_y_center) + ' ' + str(
            yolo_para_width) + ' ' + str(yolo_para_height) + '
')
    cv2.imwrite('./000_new.jpg', mask_rgb)
    file.close()

该代码通过输入原图像和mask图像，先把通过上面的函数把每一个连通域找到，再框选出图像，根据框选结果计算出yolo需要的参数进行输出。