I have a question which i am unable to resolve. I am taking difference of two images using OpenCV. I am getting output in a seperate Mat. Difference method used is the AbsDiff method. Here is the code.
char imgName[15];
Mat img1 = imread(image_path1, COLOR_BGR2GRAY);
Mat img2 = imread(image_path2, COLOR_BGR2GRAY);
/*cvtColor(img1, img1, CV_BGR2GRAY);
cvtColor(img2, img2, CV_BGR2GRAY);*/
cv::Mat diffImage;
cv::absdiff(img2, img1, diffImage);
cv::Mat foregroundMask = cv::Mat::zeros(diffImage.rows, diffImage.cols, CV_8UC3);
float threshold = 30.0f;
float dist;
for(int j=0; j<diffImage.rows; ++j)
{
for(int i=0; i<diffImage.cols; ++i)
{
cv::Vec3b pix = diffImage.at<cv::Vec3b>(j,i);
dist = (pix[0]*pix[0] + pix[1]*pix[1] + pix[2]*pix[2]);
dist = sqrt(dist);
if(dist>threshold)
{
foregroundMask.at<unsigned char>(j,i) = 255;
}
}
}
sprintf(imgName,"D:/outputer/d.jpg");
imwrite(imgName, diffImage);
I want to bound the difference part in a rectangle. findContours is drawing too many contours. but i only need a particular portion. My diff image is
I want to draw a single rectangle around all the five dials.
Please point me to right direction.
Regards,
I would search for the highest value for i index giving a non black pixel; that's the right border.
The lowest non black i is the left border. Similar for j.
You can:
binarize the image with a threshold. Background will be 0.
Use findNonZero to retrieve all points that are not 0, i.e. all foreground points.
use boundingRect on the retrieved points.
Result:
Code:
#include <opencv2/opencv.hpp>
using namespace cv;
int main()
{
// Load image (grayscale)
Mat1b img = imread("path_to_image", IMREAD_GRAYSCALE);
// Binarize image
Mat1b bin = img > 70;
// Find non-black points
vector<Point> points;
findNonZero(bin, points);
// Get bounding rect
Rect box = boundingRect(points);
// Draw (in color)
Mat3b out;
cvtColor(img, out, COLOR_GRAY2BGR);
rectangle(out, box, Scalar(0,255,0), 3);
// Show
imshow("Result", out);
waitKey();
return 0;
}
Find contours, it will output a set of contours as std::vector<std::vector<cv::Point> let us call it contours:
std::vector<cv::Point> all_points;
size_t points_count{0};
for(const auto& contour:contours){
points_count+=contour.size();
all_points.reserve(all_points);
std::copy(contour.begin(), contour.end(),
std::back_inserter(all_points));
}
auto bounding_rectnagle=cv::boundingRect(all_points);
Related
I want to perform obstacle detection for a ground robot by using a picture taken by a drone of the area the ground robot will cover. Since I have limited background in image processing I am not sure how to carry this out. I tried using the following method but the result is not very accurate. It detects very small edges also and it does not work well with aerial images.
#pragma once
#include <string>
#include <iostream>
#include <vector>
#include "opencv2/opencv.hpp"
using namespace std;
using namespace cv;
//----------------------------------------------------------
// MAIN
//----------------------------------------------------------
int main(int argc, char* argv[])
{
// src image
Mat src;
//grayscale image
Mat gray;
// edges image
Mat edges;
//dst image
Mat dst;
//eroded image
Mat erosion;
//smoothed result
Mat result;
//----------------------------------------------------------
// Image loading
//----------------------------------------------------------
namedWindow("result");
namedWindow("src");
namedWindow("edges");
src = imread("C:/Users/HP/Desktop/SDP/obstacle detection/obstacle detection/obstacle detection/shapes.jpg");
//----------------------------------------------------------
//Specifying size and type of image
//----------------------------------------------------------
edges = Mat::zeros(src.size(), CV_8UC1);
dst = Mat::zeros(src.size(), CV_8UC1);
gray= Mat::zeros(src.size(), CV_8UC1);
erosion = Mat::zeros(src.size(), CV_8UC1);
result = Mat::zeros(src.size(), CV_8UC1);
//----------------------------------------------------------
//Converting from RGB to grayscale
//----------------------------------------------------------
cvtColor(src, gray, COLOR_BGR2GRAY);
//----------------------------------------------------------
//Edge Detetcion using OpenCV Canny Edge Detector function
//----------------------------------------------------------
Canny(gray, edges, 80, 255);
//----------------------------------------------------------
//Filling in the non-obstacle areas with white
//----------------------------------------------------------
for (int i = 0; i<edges.cols; ++i)
{
int j = edges.rows - 1;
for (j = edges.rows - 1; j>0; --j)
{
if (edges.at<uchar>(j, i)>0)
{
break;
}
}
dst(Range(j, dst.rows - 1), Range(i, i + 1)) = 255;
}
//----------------------------------------------------------
// Appying erosion function to remove noise
//----------------------------------------------------------
Mat element = getStructuringElement(MORPH_RECT, Size(10, 10));
erode(dst,erosion,element);
//----------------------------------------------------------
//Smoothing the edges to get result
//----------------------------------------------------------
GaussianBlur(erosion, result, Size(5,5), 4);
//----------------------------------------------------------
// Displaying the intermediate and final resulting images
//----------------------------------------------------------
namedWindow("src", WINDOW_NORMAL);
imshow("src", src);
namedWindow("edges", WINDOW_NORMAL);
imshow("edges", edges);
namedWindow("dst", WINDOW_NORMAL);
imshow("dst", dst);
namedWindow("erosion", WINDOW_NORMAL);
imshow("erosion", erosion);
namedWindow("result", WINDOW_NORMAL);
imshow("result", result);
//----------------------------------------------------------
// Wait key press
//----------------------------------------------------------
waitKey(0);
destroyAllWindows();
return 0;
}
The code takes in an image, converts it to grayscale. Then canny edge detection is used to detect edges of all the objects in the image. This edge detected image s filled with white color starting from the bottom until an edge is detected. The process continues until the whole image is covered. The result is a binary image with white color for areas without obstacles and and black color for obstacles. The opencv function erode is then used to remove unnecessary noise.
I would really appreciate it if I get suggestions on how to improve this or use any other technique.
I suggest thresholding the image for a color range matching the ground. This approach works well, if the color of your ground does not change too much (which is the case in your src image). You might want to check out this OpenCV example (Python).
How Can I detect the circles and count the number in this image. I'm new to open cv and c++.Can any one help with this issue. I tried with hough circle . But didn't work .
The skeletonized binary image is as follows.
Starting from this image (I removed the border):
You can follow this approach:
1) Use findContour to get the contours.
2) Keep only internal contours. You can do that checking the sign of the area returned by contourArea(..., true). You'll get the 2 internal contours:
3) Now that you have the two contours, you can find a circle with minEnclosingCircle (in blue), or fit an ellipse with fitEllipse (in red):
Here the full code for reference:
#include <opencv2/opencv.hpp>
#include <vector>
using namespace std;
using namespace cv;
int main()
{
Mat1b img = imread("path_to_image", IMREAD_GRAYSCALE);
// Get contours
vector<vector<Point>> contours;
findContours(img, contours, RETR_TREE, CHAIN_APPROX_NONE);
// Create output image
Mat3b out;
cvtColor(img, out, COLOR_GRAY2BGR);
Mat3b outContours = out.clone();
// Get internal contours
vector<vector<Point>> internalContours;
for (size_t i = 0; i < contours.size(); ++i) {
// Find orientation: CW or CCW
double area = contourArea(contours[i], true);
if (area >= 0) {
// Internal contour
internalContours.push_back(contours[i]);
// Draw with different color
drawContours(outContours, contours, i, Scalar(rand() & 255, rand() & 255, rand() & 255));
}
}
// Get circles
for (const auto& cnt : internalContours) {
Point2f center;
float radius;
minEnclosingCircle(cnt, center, radius);
// Draw circle in blue
circle(out, center, radius, Scalar(255, 0, 0));
}
// Get ellipses
for (const auto& cnt : internalContours) {
RotatedRect rect = fitEllipse(cnt);
// Draw ellipse in red
ellipse(out, rect, Scalar(0, 0, 255), 2);
}
imshow("Out", out);
waitKey();
return 0;
}
First of all you have to find all contours at your image (see function cv::findContours).
You have to analyse these contours (check it for accordance to your requirements).
P.S. The figure at the picture is definitely not circle. So I can't say exactly how do you have to check received contours.
I'm trying to count object from image. I use logs photo, and I use some steps to get a binary image.
This is my code:
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
#include <features2d.hpp>
using namespace cv;
using namespace std;
int main(int argc, char *argv[])
{
//load image
Mat img = imread("kayu.jpg", CV_LOAD_IMAGE_COLOR);
if(img.empty())
return -1;
//namedWindow( "kayu", CV_WINDOW_AUTOSIZE );
imshow("kayu", img);
//convert to b/w
Mat bw;
cvtColor(img, bw, CV_BGR2GRAY);
imshow("bw1", bw);
threshold(bw, bw, 40, 255, CV_THRESH_BINARY);
imshow("bw", bw);
//distance transform & normalisasi
Mat dist;
distanceTransform(bw, dist, CV_DIST_L2, 3);
normalize(dist, dist, 0, 2., NORM_MINMAX);
imshow("dist", dist);
//threshold to draw line
threshold(dist, dist, .5, 1., CV_THRESH_BINARY);
imshow("dist2", dist);
//dist = bw;
//dilasi
Mat dilation, erotion, element;
int dilation_type = MORPH_ELLIPSE;
int dilation_size = 17;
element = getStructuringElement(dilation_type, Size(2*dilation_size + 1, 2*dilation_size+1), Point(dilation_size, dilation_size ));
erode(dist, erotion, element);
int erotionCount = 0;
for(int i=0; i<erotionCount; i++){
erode(erotion, erotion, element);
}
imshow("erotion", erotion);
dilate(erotion, dilation, element);
imshow("dilation", dilation);
waitKey(0);
return 0;
}
As you can see, I use Erosion and Dilation to get better circular object of log. My problem is, I'm stuck at counting the object. I tried SimpleBlobDetector but I got nothing, because when I try to convert the result of "dilation" step to CV_8U, the white object disappear. I got error too when I use findContours(). It say something about channel of image. I can't show the error here, because that's too many step and I already delete it from my code.
Btw, at the end, i got 1 channel of image.
Can i just use it to counting, or am i have to convert it and what is the best method to do it?
Two simple steps:
Find contours for the binarized image.
Get the count of the contours.
Code:
int count_trees(const cv::Mat& bin_image){
cv::Mat img;
if(bin_image.channels()>1){
cv::cvtColor(bin_image,img,cv::COLOR_BGR2GRAY);
}
else{
img=bin_image.clone();;
}
if(img.type()!=CV_8UC1){
img*=255.f; //This could be stupid, but I do not have an environment to try it
img.convertTo(img,CV_8UC1);
}
std::vector<std::vector<cv::Point>> contours
std::vector<Vec4i> hierarchy;
cv::findContours( img, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
return contours.size();
}
I have the same problem, here's an idea I'm about to implement.
1) Represent your image as an array of integers; 0 = black, 1 = white.
2) set N = 2;
3) Scan your image, pixel-by-pixel. Whenever you find a white pixel, activate a flood-fill algorithm, starting at the pixel just found; paint the region with the value of N++;
4) Iterate 3 until you reach the last pixel. (N-2) is the number of regions found.
This method depends on the shape of the objects; mine are more chaotic than yours (wish me luck..). I'll make use of a recursive flood-fill recipe found somewhere (maybe Rosetta Code).
This solution also makes it easy to compute the size of each region.
try to apply that on the your deleted img
// count
for (int i = 0; i< contours.size(); i = hierarchy[i][0]) // iteration sur chaque contour .
{
Rect r = boundingRect(contours[i]);
if (hierarchy[i][2]<0) {
rectangle(canny_output, Point(r.x, r.y), Point(r.x + r.width, r.y + r.height), Scalar(20, 50, 255), 3, 8, 0);
count++;
}
}
cout << "Numeber of contour = " << count << endl;
imshow("src", src);
imshow("contour", dst);
waitKey(0);
I'm currently working on a project that uses a Lacatan Banana, and I would like to know how to further separate the foreground from the background:
I already got a segmented image of it using erosion, dilation, and thresholding only. The problem is that it is still not properly segmented.
Here is my code:
cv::Mat imggray, imgthresh, fg, bgt, bg;
cv::cvtColor(src, imggray, CV_BGR2GRAY); //Grayscaling the image from RGB color space
cv::threshold(imggray, imgthresh, 0, 255, CV_THRESH_BINARY_INV | CV_THRESH_OTSU); //Create an inverted binary image from the grayscaled image
cv::erode(imgthresh, fg, cv::Mat(), cv::Point(-1, -1), 1); //erosion of the binary image and setting it as the foreground
cv::dilate(imgthresh, bgt, cv::Mat(), cv::Point(-1, -1), 4); //dilation of the binary image to reduce the background region
cv::threshold(bgt, bg, 1, 128, CV_THRESH_BINARY); //we get the background by setting the threshold to 1
cv::Mat markers = cv::Mat::zeros(src.size(), CV_32SC1); //initializing the markers with a size same as the source image and setting its data type as 32-bit Single channel
cv::add(fg, bg, markers); //setting the foreground and background as markers
cv::Mat mask = cv::Mat::zeros(markers.size(), CV_8UC1);
markers.convertTo(mask, CV_8UC1); //converting the 32-bit single channel marker to a 8-bit single channel
cv::Mat mthresh;
cv::threshold(mask, mthresh, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU); //threshold further the mask to reduce the noise
// cv::erode(mthresh,mthresh,cv::Mat(), cv::Point(-1,-1),2);
cv::Mat result;
cv::bitwise_and(src, src, result, mthresh); //use the mask to subtrack the banana from the background
for (int x = 0; x < result.rows; x++) { //changing the black background to white
for (int y = 0; y < result.cols; y++) {
if (result.at<Vec3b>(x, y) == Vec3b(0, 0, 0)){
result.at<Vec3b>(x, y)[0] = 255;
result.at<Vec3b>(x, y)[1] = 255;
result.at<Vec3b>(x, y)[2] = 255;
}
}
}
This is my result:
As the background is near gray-color, try using Hue channel and Saturation channel instead of grayscale image.
You can get them easily.
cv::Mat hsv;
cv::cvtColor(src, hsv, CV_BGR2HSV);
std::vector<cv::Mat> channels;
cv::split(src, channels);
cv::Mat hue = channels[0];
cv::Mat saturation = channels[1];
// If you want to combine those channels, use this code.
cv::Mat hs = cv::Mat::zeros(src.size(), CV_8U);
for(int r=0; r<src.rows; r++) {
for(int c=0; c<src.cols; c++) {
int hp = h.at<uchar>(r,c);
int sp = s.at<uchar>(r,c);
hs.at<uchar>(r, c) = static_cast<uchar>((h+s)>>1);
}
}
adaptiveThreshold() should work better than just level-cut threshold(), because it does not consider absolute color levels, but rather a change in color in small area around the point being checked.
Try replacing your thresholding with adaptive one.
Use a top-hat instead of just erosion/dilation. It will take care of the background variations at the same time.
Then in your case a simple thresholding should be good enough to have an accurate segmentation. Else, you can couple it with a watershed.
(I will share some images asap).
Thanks guys, I tried to apply your advises and was able to come up with this
However as you can see there are still bits of the background,any ideas how to "clean" these further, i tried thresholding further but it would still have the bits.The Code I came up with is below and i apologize in advance if the variables and coding style is somewhat confusing didn't have the time to properly sort them.
#include <stdio.h>
#include <iostream>
#include <opencv2\core.hpp>
#include <opencv2\opencv.hpp>
#include <opencv2\highgui.hpp>
using namespace cv;
using namespace std;
Mat COLOR_MAX(Scalar(65, 255, 255));
Mat COLOR_MIN(Scalar(15, 45, 45));
int main(int argc, char** argv){
Mat src,hsv_img,mask,gray_img,initial_thresh;
Mat second_thresh,add_res,and_thresh,xor_thresh;
Mat result_thresh,rr_thresh,final_thresh;
// Load source Image
src = imread("sample11.jpg");
imshow("Original Image", src);
cvtColor(src,hsv_img,CV_BGR2HSV);
imshow("HSV Image",hsv_img);
//imwrite("HSV Image.jpg", hsv_img);
inRange(hsv_img,COLOR_MIN,COLOR_MAX, mask);
imshow("Mask Image",mask);
cvtColor(src,gray_img,CV_BGR2GRAY);
adaptiveThreshold(gray_img, initial_thresh, 255,ADAPTIVE_THRESH_GAUSSIAN_C,CV_THRESH_BINARY_INV,257,2);
imshow("AdaptiveThresh Image", initial_thresh);
add(mask,initial_thresh,add_res);
erode(add_res, add_res, Mat(), Point(-1, -1), 1);
dilate(add_res, add_res, Mat(), Point(-1, -1), 5);
imshow("Bitwise Res",add_res);
threshold(gray_img,second_thresh,170,255,CV_THRESH_BINARY_INV | CV_THRESH_OTSU);
imshow("TreshImge", second_thresh);
bitwise_and(add_res,second_thresh,and_thresh);
imshow("andthresh",and_thresh);
bitwise_xor(add_res, second_thresh, xor_thresh);
imshow("xorthresh",xor_thresh);
bitwise_or(and_thresh,xor_thresh,result_thresh);
imshow("Result image", result_thresh);
bitwise_and(add_res,result_thresh,final_thresh);
imshow("Final Thresh",final_thresh);
erode(final_thresh, final_thresh, Mat(), Point(-1,-1),5);
bitwise_and(src,src,rr_thresh,final_thresh);
imshow("Segmented Image", rr_thresh);
imwrite("Segmented Image.jpg", rr_thresh);
waitKey(0);
return 1;
}
I have image as follows:
I want to detect 5 dials for processing. Hough circles is detecting all other irrelevant circles. to solve this i created a plain image and generated absolute difference with this one. It gave this image:
I drew box around it and final image is:
My code is as follows:
Mat img1 = imread(image_path1, COLOR_BGR2GRAY);
Mat img2 = imread(image_path2, COLOR_BGR2GRAY);
cv::Mat diffImage;
cv::absdiff(img2, img1, diffImage);
cv::Mat foregroundMask = cv::Mat::zeros(diffImage.rows, diffImage.cols, CV_8UC3);
float threshold = 30.0f;
float dist;
for(int j=0; j<diffImage.rows; ++j)
{
for(int i=0; i<diffImage.cols; ++i)
{
cv::Vec3b pix = diffImage.at<cv::Vec3b>(j,i);
dist = (pix[0]*pix[0] + pix[1]*pix[1] + pix[2]*pix[2]);
dist = sqrt(dist);
if(dist>threshold)
{
foregroundMask.at<unsigned char>(j,i) = 255;
}
}
}
cvtColor(diffImage,diffImage,COLOR_BGR2GRAY);
Mat1b img = diffImage.clone();
// Binarize image
Mat1b bin = img > 70;
// Find non-black points
vector<Point> points;
findNonZero(bin, points);
// Get bounding rect
Rect box = boundingRect(points);
// Draw (in color)
rectangle(img1, box, Scalar(0,255,0), 3);
// Show
imshow("Result", img1);
Now the issue is i cant compare plain image with anyother iamge of different sizes. Any pointer to right direction will be very helpful.
Regards,
Saghir A. Khatr
Edit
My plain image is as follows
I want to create a standard sample plain image which can be used with any image to detect that portion...