Python opencv怎么实现医学处理

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题目描述

利用opencv或其他工具编写程序实现医学处理。

实现过程

# -*- coding: utf-8 -*-'''作者 : 丁毅开发时间 : 2021/5/9 16:30'''import cv2import numpy as np# 图像细化def VThin(image, array):    rows, cols = image.shape    NEXT = 1    for i in range(rows):        for j in range(cols):            if NEXT == 0:                NEXT = 1            else:                M = int(image[i, j - 1]) + int(image[i, j]) + int(image[i, j + 1]) if 0 < j < cols - 1 else 1                if image[i, j] == 0 and M != 0:                    a = [0]*9                    for k in range(3):                        for l in range(3):                            if -1 < (i - 1 + k) < rows and -1 < (j - 1 + l) < cols and image[i - 1 + k, j - 1 + l] == 255:                                a[k * 3 + l] = 1                    sum = a[0] * 1 + a[1] * 2 + a[2] * 4 + a[3] * 8 + a[5] * 16 + a[6] * 32 + a[7] * 64 +  a[8] * 128                    image[i, j] = array[sum]*255                    if array[sum] == 1:                        NEXT = 0    return imagedef HThin(image, array):    rows, cols = image.shape    NEXT = 1    for j in range(cols):        for i in range(rows):            if NEXT == 0:                NEXT = 1            else:                M = int(image[i-1, j]) + int(image[i, j]) + int(image[i+1, j]) if 0 < i < rows-1 else 1                if image[i, j] == 0 and M != 0:                    a = [0]*9                    for k in range(3):                        for l in range(3):                            if -1 < (i-1+k) < rows and -1 < (j-1+l) < cols and image[i-1+k, j-1+l] == 255:                                a[k*3+l] = 1                    sum = a[0]*1+a[1]*2+a[2]*4+a[3]*8+a[5]*16+a[6]*32+a[7]*64+a[8]*128                    image[i, j] = array[sum]*255                    if array[sum] == 1:                        NEXT = 0    return imagearray = [0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,         1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,         0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,         1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,         1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,         1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1,         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,         0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,         1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,         0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,         1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,         1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,         1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,         1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0,         1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0]# 显示灰度图img = cv2.imread(r"C:UserspcDesktopvas0.png",0)cv2.imshow("img1",img)# 自适应阈值分割img2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 17, 4)cv2.imshow('img2', img2)# 图像反色img3 = cv2.bitwise_not(img2)cv2.imshow("img3", img3)# 图像扩展img4 = cv2.copyMakeBorder(img3, 1, 1, 1, 1, cv2.BORDER_REFLECT)cv2.imshow("img4", img4)contours, hierarchy = cv2.findContours(img4, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)# 消除小面积img5 = img4for i in range(len(contours)):    area = cv2.contourArea(contours[i])    if (area < 80) | (area > 10000):        cv2.drawContours(img5, [contours[i]], 0, 0, -1)cv2.imshow("img5", img5)num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(img5, connectivity=8, ltype=None)# print(stats)s = sum(stats)img6 = np.ones(img5.shape, np.uint8) * 0for (i, label) in enumerate(np.unique(labels)):    # 如果是背景，忽略    if label == 0:        # print("[INFO] label: 0 (background)")        continue    numPixels = stats[i][-1]    div = (stats[i][4]) / s[4]    # print(div)    # 判断区域是否满足面积要求    if round(div, 3) > 0.002:        color = 255        img6[labels == label] = colorcv2.imshow("img6", img6)# 图像反色img7 = cv2.bitwise_not(img6)# 图像细化for i in range(10):    VThin(img7, array)    HThin(img7, array)cv2.imshow("img7",img7)# 边缘检测img8 = cv2.Canny(img6, 80, 255)cv2.imshow("img8", img8)# 使灰度图黑白颠倒img9 = cv2.bitwise_not(img8)cv2.imshow("img9", img9)cv2.waitKey(0)

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