语义分割结果可视化（画目标轮廓线与最小包围框）

效果如图：

#文件夹预测
import math
from unet import Unet
from PIL import Image
import numpy
import os
import os.path
import numpy as np
import cv2

unet = Unet()
# 指明被遍历的文件夹
maindir = r'G:\\DL\\unet-keras\\unet-keras-master\\stone_predict\\ture\\stone'
blenddir= r'G:\\DL\\unet-keras\\unet-keras-master\\stone_predict\\ture\\add1207'


def drawDetect(img,predict_image,image_bgr):
    shape = predict_image.shape
    w = shape[1]
    h = shape[0]
    threshold=h/30 * w/30
    blurred = cv2.GaussianBlur(img, (11, 11), 0)
    edged = cv2.Canny(blurred, 30, 150)  # 用Canny算子提取边缘
    contours, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)  # 寻找封闭区域
    maxCnt=-1
    if contours is not None:
        for i in range(len(contours)):
            area = cv2.contourArea(contours[i])  # 计算轮廓所占面积
            if area > threshold:  # 将area小于阈值区域填充背景色，由于OpenCV读出的是BGR值

                # cv2.drawContours(img, [contours[i]], -1, (84, 1, 68), thickness=-1)  # 原始图片背景BGR值(84,1,68)
                rect = cv2.minAreaRect(contours[i])  # 最小外接矩形
                rect_w, rect_h = (rect[1][0]) + 1.0, (rect[1][1]) + 1.0  # 最小外接矩形的宽和高
                if rect_w > rect_h:
                    angle = 90 - rect[2]
                    L = rect_w
                    Ly = rect_w * math.cos(math.radians(angle))
                    Lx = rect_w * math.sin(math.radians(angle))
                else:
                    angle = rect[2]
                    L = rect_h
                    Ly = rect_h * math.cos(math.radians(angle))
                    Lx = rect_h * math.sin(math.radians(angle))
                box = np.int0(cv2.boxPoints(rect))  # 矩形的四个角点取整
                if box is not None:
                    image_bgr = cv2.drawContours(image_bgr, [box], 0, (0, 0, 255), 2)
                image_bgr = cv2.drawContours(image_bgr, contours[i], -1, (0, 255, 0), 3)
                continue
        # predict_image = cv2.drawContours(image_bgr, contours, -1, (0, 255, 0), 3)

        # return contours, box
        return image_bgr


# while True:
for parent, dirnames, filenames in os.walk(maindir):  # 遍历每一张图片
    for filename in filenames:
        pic_name = os.path.join(parent, filename)
        image = Image.open(pic_name)
        r_image = unet.detect_image(image)
        # predict_image = cv2.imread(r_image)
        image_bgr = cv2.cvtColor(numpy.asarray(image), cv2.COLOR_RGB2BGR)  # PIL转为cv.bgr
        predict_image = cv2.cvtColor(numpy.asarray(r_image), cv2.COLOR_RGB2BGR)  # PIL转为cv.bgr
        predict_gray = cv2.cvtColor(predict_image, cv2.COLOR_BGR2GRAY)  # 转为灰度图
        # contours, box = drawDetect(predict_gray,predict_image)
        predict_image2 = drawDetect(predict_gray,predict_image,image_bgr)
        if predict_image2 is None:
            predict_image2 = image_bgr
        # print(type(predict_image2))
        blend_image = Image.fromarray(cv2.cvtColor(predict_image2,cv2.COLOR_BGR2RGB))
        blend_image.save(blenddir +'\\' +  pic_name.split('.')[0].strip(parent) + ".png")  # 存储裁剪得到的图像,需要先创建文件夹