显著性检测后处理

将显著性检测图像后处理，绘出一个矩形框，方便使用

先通过边缘检测，然后通过opencv接口实现

结果如下： 

# --coding:utf
# -8-*-
import os

import cv2 as cv
import numpy as np

# canny边缘检测
def canny_demo(image):
    t = 50
    canny_output = cv.Canny(image, t, 95)
    return canny_output

def image_target_detection(img, mask):
    # 读取图像
    src = cv.imread(mask)
    img = cv.imread(img)

    binary = canny_demo(src)
    k = np.ones((5, 5), dtype=np.uint8)
    binary = cv.morphologyEx(binary, cv.MORPH_DILATE, k)
    # kernel = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
    # binary = cv.morphologyEx(binary, cv.MORPH_OPEN, kernel)

    cv.namedWindow("input", cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
    cv.resizeWindow("input", 400, 300)
    cv.imshow("input", binary)

    image, contours, hierarchy = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
    print("contours", contours)
    print("contours", len(contours))
    print("contours", type(contours))
    for c in range(len(contours)):
        x, y, w, h = cv.boundingRect(contours[c])
        cv.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 10)

        rect = cv.minAreaRect(contours[c])  # 最小旋转矩形
        cx, cy = rect[0]
        box = cv.boxPoints(rect)            # 点集的最小外接矩形
        box = np.int0(box)
        cv.drawContours(src, [box], 0, (0, 0, 255), 10)
        cv.drawContours(img, [box], 0, (0, 0, 255), 10)
        cv.circle(src, (np.int32(cx), np.int32(cy)), 2, (255, 0, 0), 2, 8, 0)
        cv.drawContours(src, contours, c, (0, 0, 255), 2, 8)


    # 图像显示
    cv.namedWindow("contours_analysis", 0)
    cv.resizeWindow("contours_analysis", 400, 300)
    cv.imshow("contours_analysis", img)
    # cv.namedWindow("out", 0)
    # cv.resizeWindow("out", 400, 300)
    # cv.imshow("out", src)
    # cv.imwrite("D:/vsprojects/images/contours_analysis.png", src)
    cv.waitKey(0)
    cv.destroyAllWindows()

if __name__ == '__main__':
    imgdir = 'E:\\data\\'
    maskdir = 'E:\\data\\'
    img_list = [imgdir + f for f in os.listdir(imgdir) if f.endswith("jpg")]
    for img in img_list:
        mask = maskdir + img.split(os.sep)[-1].split('.')[0] + '.png'
        image_target_detection(img, mask)