Python实现二值掩膜影像去噪与边缘强化方法详解

import cv2
import numpy as np
import os
def findFiles(root_dir, filter_type, reverse=False):
    print("Finding files ends with \'" + filter_type + "\' ...")
    separator = os.path.sep
    paths = []
    names = []
    files = []
    for parent, dirname, filenames in os.walk(root_dir):
        for filename in filenames:
            if filename.endswith(filter_type):
                paths.append(parent + separator)
                names.append(filename)
    for i in range(paths.__len__()):
        files.append(paths[i] + names[i])
    print(names.__len__().__str__() + " files have been found.")
    paths.sort()
    names.sort()
    files.sort()
    if reverse:
        paths.reverse()
        names.reverse()
        files.reverse()
    return paths, names, files
if __name__ == '__main__':
    root_dir = "./mask" # 影像的输入路径
    out_dir = "./mask/refine"   # 结果的输出路径
    file_type = ".jpg"  # 文件类型
    median_kernel = 7   # 中值滤波卷积核大小
    morph_kernel1 = 5   # 形态学操作卷积核大小1
    morph_kernel2 = 5   # 形态学操作卷积核大小2
    morph_iter1 = 3 # 形态学操作迭代次数1
    morph_iter2 = 3 # 形态学操作迭代次数2
    paths, names, files = findFiles(root_dir, file_type)
    kernel1 = np.ones((morph_kernel1, morph_kernel1), np.uint8)
    kernel2 = np.ones((morph_kernel2, morph_kernel2), np.uint8)
    for i in range(len(files)):
        img_gray = cv2.imread(files[i], cv2.IMREAD_GRAYSCALE)
        # 主要流程：先做一次中值滤波，去除零碎像素噪声点
        # 然后再做3次腐蚀操作去除一些噪声块，再做3次膨胀操作填充回来
        # 然后，做个Otsu二值化，把一些潜在的噪声去除
        # 最后，为了使掩膜尽可能包含真值，再做一次膨胀操作
        img_median = cv2.medianBlur(img_gray, ksize=median_kernel)
        img_erode = cv2.erode(img_median, kernel1, iterations=morph_iter1)
        img_dilate = cv2.dilate(img_erode, kernel1, iterations=morph_iter1)
        ret, img_threshold = cv2.threshold(img_dilate, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        img_mask = cv2.dilate(img_threshold, kernel2, iterations=morph_iter2)
        # 绘制轮廓，可视化，以及保存
        _, contours, _ = cv2.findContours(img_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
        img_rgb = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2BGR)
        img_contours = cv2.drawContours(img_rgb, contours, -1, (0, 0, 255))
        cv2.imwrite(out_dir + "/" + names[i].split(".")[0] + "_refine.jpg", img_mask)
        cv2.imwrite(out_dir + "/" + names[i].split(".")[0] + "_range.jpg", img_contours)
        # 如果需要查看每一步结果，可以把下面这些注释掉
        # cv2.imwrite(out_dir + "/" + names[i].split(".")[0] + "_median.jpg", img_median)
        # cv2.imwrite(out_dir + "/" + names[i].split(".")[0] + "_erode.jpg", img_erode)
        # cv2.imwrite(out_dir + "/" + names[i].split(".")[0] + "_dilate.jpg", img_dilate)
        # cv2.imwrite(out_dir + "/" + names[i].split(".")[0] + "_threshold.jpg", img_threshold)
        print(i + 1, "/", len(files))