Question

我正在opencv python中创建预处理程序，以计算总面积，受感染区域和区域兴趣百分比。

import cv2
import numpy as np           
import argparse

img1 = cv2.imread('19.jpg')
img = cv2.resize(img1, (0,0), fx=0.5, fy=0.5)
original = img.copy()
neworiginal = img.copy()

blur1 = cv2.GaussianBlur(img,(3,3),1)

newimg = np.zeros((img.shape[0], img.shape[1],3),np.uint8)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER , 10 ,1.0)
img = cv2.pyrMeanShiftFiltering(blur1, 20, 30, newimg, 0, criteria)

blur = cv2.GaussianBlur(img,(11,11),1)

kernel = np.ones((5,5),np.uint8)
canny = cv2.Canny(blur, 200, 290)
res = cv2.morphologyEx(canny,cv2.MORPH_CLOSE, kernel)
canny = cv2.cvtColor(canny,cv2.COLOR_GRAY2BGR)
cv2.imshow('Canny',res)

hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lower = np.array([5,25,25])
upper = np.array([70,255,255])

mask = cv2.inRange(hsv, lower, upper)

res = cv2.bitwise_and(hsv,hsv, mask= mask)
gray = cv2.cvtColor(res,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
_, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)


for i in contours:
    cnt = cv2.contourArea(i)
    #M = cv2.momens(i)
    #cx = int(M['m10']/M['m00'])
    if cnt > 1000:  
        cv2.drawContours(img, [i], 0, (0,0,255), 2)

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
_, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours, key=cv2.contourArea)
Tarea = cv2.contourArea(cnt)
cv2.imshow('img', img)

height, width, _ = canny.shape
min_x, min_y = width, height
max_x = max_y = 0
frame = canny.copy()

for contour, hier in zip(contours, hierarchy):

 (x,y,w,h) = cv2.boundingRect(contour)
            min_x, max_x = min(x, min_x), max(x+w, max_x)
            min_y, max_y = min(y, min_y), max(y+h, max_y)
            if w > 80 and h > 80:
                cv2.rectangle(frame, (x,y), (x+w,y+h), (255, 0, 0), 2)
                roi = img[y:y+h , x:x+w]
                originalroi = original[y:y+h , x:x+w]
if max_x - min_x > 0 and max_y - min_y > 0:
    cv2.rectangle(frame, (min_x, min_y), (max_x, max_y), (255, 0, 0), 2)

img = roi

imghls = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)

imghls[np.where((imghls==[30,200,2]).all(axis=2))] = [0,200,0]

huehls = imghls[:,:,0]

huehls[np.where(huehls==[0])] = [35]

#Thresholding on hue image
ret, thresh = cv2.threshold(huehls,28,255,cv2.THRESH_BINARY_INV)
cv2.imshow('thresh', thresh)


mask = cv2.bitwise_and(originalroi,originalroi,mask = thresh)

_, contours,heirarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

Infarea = 0

for x in range(len(contours)):

    cv2.drawContours(originalroi,contours[x],-1,(0,0,255),2)

    cv2.imshow('Contour masked',originalroi)

    #Calculating area of infected region
    Infarea += cv2.contourArea(contours[x])




#if Infarea > Tarea:
    #Tarea = img.shape[0]*img.shape[1]

print ('_______________________\n| Total area: ' + str(Tarea) + '   |\n|_____________________|')

#Finding the percentage of infection in the banana
print ('\n__________________________\n| Infected area: ' + str(Infarea) + ' |\n|________________________|')

try:
    per = 100 * Infarea/Tarea

except ZeroDivisionError:
    per = 0

print( '\n_________________________________________________\n| Percentage of infection region: ' + str(per) + ' |\n|_______________________________________________|')


cv2.imshow('orig',original)

enter image description here

这是我在此特定代码中使用的图像，如果我们认为自己的感染区域必须在50％的范围内，那么我的问题是要获取感兴趣区域的不准确结果。因为结果也不准确，所以如何获得区域兴趣的最佳准确率的任何想法。

Answer 1

如果您的背景与受感染区域的颜色不同，则只需使用色差即可。我使用C ++，但可以轻松转换为Python。这是我的方法：

#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "highgui.h"
#include <stdlib.h>
#include <stdio.h>
#include <iostream>

using namespace cv;
using namespace std;

int main( int argc, char** argv )
{
    Mat src = imread( "/ur/image/directory/banana.jpg",CV_LOAD_IMAGE_GRAYSCALE );

    imshow( "Source", src );

    //check all pixels and change pixel values according to refrences
    for(int i=0; i<src.rows; i++)
    {
        for(int j=0;j<src.cols;j++)
        {
            if(src.at<uchar>(Point(j,i))>200)
                src.at<uchar>(Point(j,i)) = 0;
            else if(src.at<uchar>(Point(j,i))<30)
                src.at<uchar>(Point(j,i)) = 255;
            else
                src.at<uchar>(Point(j,i)) = 125;
        }
    }

    imshow( "First Process", src );

    Mat filtered_img;
    medianBlur ( src, filtered_img, 5 );

    imshow( "After Median", filtered_img );


    double deformation_counter = 0;
    double clean_counter = 0;

    //check all pixels and count them
    for(int i=0; i<src.rows; i++)
    {
        for(int j=0;j<src.cols;j++)
        {
            if(src.at<uchar>(Point(j,i))==255)
                deformation_counter++;
            if(src.at<uchar>(Point(j,i))==125)
                clean_counter++;
        }
    }

    double percentage = deformation_counter/(deformation_counter+clean_counter);

    cout<<percentage<<endl;

    waitKey(0);
    return 0;
}

源图像：

第一次更改：

结果：

如何获得感染区域的准确百分比？

1 个答案: