Hi guys i'm new to OpenCV.
I'm using opencv to remove lines and get removed coordinates from image.
I'm using HoughlineP like this
Mat src = imread("F:/003-00.jpg", IMREAD_GRAYSCALE);
Mat bw;
blur(src, bw, Size(3, 3));
pyrDown(bw, bw);
//threshold(bw, bw, 170, 255, THRESH_BINARY_INV);
Canny(bw, bw, 100, 200, 3);
Mat color_dst = Mat::zeros(bw.size(), CV_8UC1);
vector<Vec4i> lines;
HoughLinesP(bw, lines, 1, CV_PI / 180, 400, 300, 20);
for (size_t i = 0; i < lines.size(); i++)
{
line(color_dst, Point(lines[i][0], lines[i][1]),
Point(lines[i][2], lines[i][3]), Scalar(255, 255, 255), 3);
}
imwrite("F:/result.jpg", color_dst);
and i got a result image like this
HoughlinesP Output
but one line actually contains many of child lines and group to one bold line
Now I want to erode,group, and normally it to one line with two Point begin , end for every line
Here is input image
Input image
Related
I am trying to port this response to c++ but I am not able to get past this cryptic exception (see image below). Not sure what is the limiting factor. I imagine it is the image color format or the corners parameter but nothing seems to be working. If it is related to converting color format please provide a small code snippet.
The python code provided by Anubhav Singh is working great however I would like to develop in c++. Any help would be greatly appreciated.
I am using OpenCV04.2.0
void CornerDetection(){
std::string image_path = samples::findFile("../wing.png");
Mat img = imread(image_path);
Mat greyMat;
Mat dst;
cv::cvtColor(img, greyMat, COLOR_BGR2GRAY);
threshold(greyMat, greyMat, 0, 255, THRESH_BINARY | THRESH_OTSU);
cornerHarris(greyMat, dst, 9, 5, 0.04);
dilate(dst, dst,NULL);
Mat img_thresh;
threshold(dst, img_thresh, 0.32 * 255, 255, 0);
img_thresh.convertTo(img_thresh, CV_8UC1);
Mat labels = Mat();
Mat stats = Mat();
Mat centroids = Mat();
cv::connectedComponentsWithStats(img_thresh, labels, stats, centroids, 8, CV_32S);
TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::MAX_ITER, 30, 0.001);
std::vector<Point2f> corners = std::vector<Point2f>();
Size winSize = Size(5, 5);
Size zeroZone = Size(-1, -1);
cornerSubPix(greyMat, corners, winSize, zeroZone, criteria);
for (int i = 0; i < corners.size(); i++)
{
circle(img, Point(corners[i].x, corners[i].y), 5, Scalar(0, 255, 0), 2);
}
imshow("img", img);
waitKey();
destroyAllWindows();
}
The solution was to iterate over the centroids to build the corners vector before passing the corners variable to the cornerSubPix(...) function.
std::vector<Point2f> corners = std::vector<Point2f>();
for (int i = 0; i < centroids.rows; i++)
{
double x = centroids.at<double>(i, 0);
double y = centroids.at<double>(i, 1);
corners.push_back(Point2f(x, y));
}
The output of the solution is still not exactly what the python output is, regardless it fixed this question in case anyone else ran across this issue.
I am processing video images and I would like to detect if the video contains any pixels of a certain range of red. Is this possible?
Here is the code I am adapting from a tutorial:
#ifdef __cplusplus
- (void)processImage:(Mat&)image;
{
cv::Mat orig_image = image.clone();
cv::medianBlur(image, image, 3);
cv::Mat hsv_image;
cv::cvtColor(image, hsv_image, cv::COLOR_BGR2HSV);
cv::Mat lower_red_hue_range;
cv::Mat upper_red_hue_range;
cv::inRange(hsv_image, cv::Scalar(0, 100, 100), cv::Scalar(10, 255, 255), lower_red_hue_range);
cv::inRange(hsv_image, cv::Scalar(160, 100, 100), cv::Scalar(179, 255, 255), upper_red_hue_range);
// Interpret values here
}
Interpreting values
I would like to detect if the results from the inRange operations are nil or not. In other words I want to understand if there are any matching pixels in the original image with a colour inRange from the given lower and upper red scale. How can I interpret the results?
First you need to OR the lower and upper mask:
Mat mask = lower_red_hue_range | upper_red_hue_range;
Then you can countNonZero to see if there are non zero pixels (i.e. you found something).
int number_of_non_zero_pixels = countNonZero(mask);
It could be better to first apply morphological erosion or opening to remove small (probably noisy) blobs:
Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(3, 3));
morphologyEx(mask, mask, MORPH_OPEN, kernel); // or MORPH_ERODE
or find connected components (findContours, connectedComponentsWithStats) and prune / search for according to some criteria:
vector<vector<Point>> contours
findContours(mask.clone(), contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
double threshold_on_area = 100.0;
for(int i=0; i<contours.size(); ++i)
{
double area = countourArea(contours[i]);
if(area < threshold_on_area)
{
// don't consider this contour
continue;
}
else
{
// do something (e.g. drawing a bounding box around the contour)
Rect box = boundingRect(contours[i]);
rectangle(hsv_image, box, Scalar(0, 255, 255));
}
}
I am a beginner in OpenCV, I need to remove the horizontal and vertical lines in the image so that only the text remains ( The lines were causing trouble when extracting text in ocr ). I am trying to extract text from the Nutrient Fact Table. Can anyone help me?
This was an interesting question, so I gave it a shot. Below I will show you how to extract and remove horizontal and vertical lines. You could extrapolate from it. Also, for sake of saving time, I did not preprocess your image to crop out the background as one should, which is an avenue for improvement.
The result:
The code (edit: added vertical lines):
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main(int, char** argv)
{
// Load the image
Mat src = imread(argv[1]);
// Check if image is loaded fine
if(!src.data)
cerr << "Problem loading image!!!" << endl;
Mat gray;
if (src.channels() == 3)
{
cvtColor(src, gray, CV_BGR2GRAY);
}
else
{
gray = src;
}
//inverse binary img
Mat bw;
//this will hold the result, image to be passed to OCR
Mat fin;
//I find OTSU binarization best for text.
//Would perform better if background had been cropped out
threshold(gray, bw, 0, 255, THRESH_BINARY_INV | THRESH_OTSU);
threshold(gray, fin, 0, 255, THRESH_BINARY | THRESH_OTSU);
imshow("binary", bw);
Mat dst;
Canny( fin, dst, 50, 200, 3 );
Mat str = getStructuringElement(MORPH_RECT, Size(3,3));
dilate(dst, dst, str, Point(-1, -1), 3);
imshow("dilated_canny", dst);
//bitwise_and w/ canny image helps w/ background noise
bitwise_and(bw, dst, dst);
imshow("and", dst);
Mat horizontal = dst.clone();
Mat vertical = dst.clone();
fin = ~dst;
//Image that will be horizontal lines
Mat horizontal = bw.clone();
//Selected this value arbitrarily
int horizontalsize = horizontal.cols / 30;
Mat horizontalStructure = getStructuringElement(MORPH_RECT, Size(horizontalsize,1));
erode(horizontal, horizontal, horizontalStructure, Point(-1, -1));
dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1), 1);
imshow("horizontal_lines", horizontal);
//Need to find horizontal contours, so as to not damage letters
vector<Vec4i> hierarchy;
vector<vector<Point> >contours;
findContours(horizontal, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_NONE);
for (const auto& c : contours)
{
Rect r = boundingRect(c);
float percentage_height = (float)r.height / (float)src.rows;
float percentage_width = (float)r.width / (float)src.cols;
//These exclude contours that probably are not dividing lines
if (percentage_height > 0.05)
continue;
if (percentage_width < 0.50)
continue;
//fills in line with white rectange
rectangle(fin, r, Scalar(255,255,255), CV_FILLED);
}
int verticalsize = vertical.rows / 30;
Mat verticalStructure = getStructuringElement(MORPH_RECT, Size(1,verticalsize));
erode(vertical, vertical, verticalStructure, Point(-1, -1));
dilate(vertical, vertical, verticalStructure, Point(-1, -1), 1);
imshow("verticalal", vertical);
findContours(vertical, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_NONE);
for (const auto& c : contours)
{
Rect r = boundingRect(c);
float percentage_height = (float)r.height / (float)src.rows;
float percentage_width = (float)r.width / (float)src.cols;
//These exclude contours that probably are not dividing lines
if (percentage_width > 0.05)
continue;
if (percentage_height < 0.50)
continue;
//fills in line with white rectange
rectangle(fin, r, Scalar(255,255,255), CV_FILLED);
}
imshow("Result", fin);
waitKey(0);
return 0;
}
The limitations of this approach are that the lines need to be straight. Due to the curve in the bottom line, it cuts slightly into "E" in "Energy". Perhaps with a hough line detection like suggested (I've never used it), a similar but more robust approach could be devised. Also, filling in the lines with rectangles probably is not the best approach.
I've been following this tutorial to get the skew angle of an image. It seems like HoughLinesP is struggling to find lines when characters are a bit scattered on the target image.
This is my input image:
This is the lines the HoughLinesP has found:
It's not really getting most of the lines and it seems pretty obvious to me why. This is because I've set my minLineWidth to be (size.width / 2.f). The point is that because of the few lines it has found it turns out that the skew angle is also wrong. (-3.15825 in this case, when it should be something close to 0.5)
I've tried to erode my input file to make characters get closer and in this case it seems to work out, but I don't feel this is best approach for situations akin to it.
This is my eroded input image:
This is the lines the HoughLinesP has found:
This time it has found a skew angle of -0.2185 degrees, which is what I was expecting but in other hand it is losing the vertical space between lines which in my humble opinion isn't a good thing.
Is there another to pre-process this kind of image to make houghLinesP get better results for scattered characters ?
Here is the source code I'm using:
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
static cv::Scalar randomColor( cv::RNG& rng )
{
int icolor = (unsigned) rng;
return cv::Scalar( icolor&255, (icolor>>8)&255, (icolor>>16)&255 );
}
void rotate(cv::Mat& src, double angle, cv::Mat& dst)
{
int len = std::max(src.cols, src.rows);
cv::Point2f pt(len/2., len/2.);
cv::Mat r = cv::getRotationMatrix2D(pt, angle, 1.0);
cv::warpAffine(src, dst, r, cv::Size(len, len));
}
double compute_skew(cv::Mat& src)
{
// Random number generator
cv::RNG rng( 0xFFFFFFFF );
cv::Size size = src.size();
cv::bitwise_not(src, src);
std::vector<cv::Vec4i> lines;
cv::HoughLinesP(src, lines, 1, CV_PI/180, 100, size.width / 2.f, 20);
cv::Mat disp_lines(size, CV_8UC3, cv::Scalar(0, 0, 0));
double angle = 0.;
unsigned nb_lines = lines.size();
for (unsigned i = 0; i < nb_lines; ++i)
{
cv::line(disp_lines, cv::Point(lines[i][0], lines[i][1]),
cv::Point(lines[i][2], lines[i][3]), randomColor(rng));
angle += atan2((double)lines[i][3] - lines[i][1],
(double)lines[i][2] - lines[i][0]);
}
angle /= nb_lines; // mean angle, in radians.
std::cout << angle * 180 / CV_PI << std::endl;
cv::imshow("HoughLinesP", disp_lines);
cv::waitKey(0);
return angle * 180 / CV_PI;
}
int main()
{
// Load in grayscale.
cv::Mat img = cv::imread("IMG_TESTE.jpg", 0);
cv::Mat rotated;
double angle = compute_skew(img);
rotate(img, angle, rotated);
//Show image
cv::imshow("Rotated", rotated);
cv::waitKey(0);
}
Cheers
I'd suggest finding individual components first (i.e., the lines and the letters), for example using cv::threshold and cv::findContours.
Then, you could drop the individual components that are narrow (i.e., the letters). You can do this using cv::floodFill for example. This should leave you with the lines only.
Effectively, getting rid of the letters might provide easier input for the Hough transform.
Try to detect groups of characters as blocks, then find contours of these blocks. Below I've done it using blurring, a morphological opening and a threshold operation.
Mat im = imread("yCK4t.jpg", 0);
Mat blurred;
GaussianBlur(im, blurred, Size(5, 5), 2, 2);
Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(3, 3));
Mat morph;
morphologyEx(blurred, morph, CV_MOP_OPEN, kernel);
Mat bw;
threshold(morph, bw, 0, 255, CV_THRESH_BINARY_INV | CV_THRESH_OTSU);
Mat cont = Mat::zeros(im.rows, im.cols, CV_8U);
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(bw, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for(int idx = 0; idx >= 0; idx = hierarchy[idx][0])
{
drawContours(cont, contours, idx, Scalar(255, 255, 255), 1);
}
Then use Hough line transform on contour image.
With accumulator threshold 80, I get following lines that results in an angle of -3.81. This is high because of the outlier line that is almost vertical. With this approach, majority of the lines will have similar angle values except few outliers. Detecting and discarding the outliers will give you a better approximation of the angle.
HoughLinesP(cont, lines, 1, CV_PI/180, 80, size.width / 4.0f, size.width / 8.0f);
I am trying to detect some lines using Hough Transform on a cv::gpu::GpuMat structure. I have tried using both gpu::HoughLines and gpu::HoughLinesP but even with extremely low thresholds, I am not getting any results at all. During debugging, I see that the container which should contain the results (houghLines) has only zeros stored inside it. The code I have written is given below,
static cv::Mat drawHoughLinesOnMat (cv::gpu::GpuMat hough_Mat, cv::gpu::GpuMat houghLines)
{
cv::Mat output_Mat;
cv::cvtColor(cv::Mat(hough_Mat), output_Mat, CV_GRAY2BGR);
std::vector<cv::Vec4i> lines_vector;
if (!houghLines.empty())
{
lines_vector.resize(houghLines.cols);
cv::Mat temp_Mat (1, houghLines.cols, CV_8UC3, &lines_vector[0]);
houghLines.download (temp_Mat);
}
for (size_t i=0; i<lines_vector.size(); ++i)
{
cv::Vec4i l = lines_vector[i];
cv::line(output_Mat, cv::Point(l[0], l[1]), cv::Point(l[2], l[3]), cv::Scalar(0, 0, 255), 1, 8);
}
return output_Mat;
}
int main()
{
cv::Mat input = cv::imread(INPUT_DATA_1->c_str(), CV_LOAD_IMAGE_GRAYSCALE);
std::string imageType = getImgType(input.type());
cv::gpu::GpuMat mat_input(input), bil_out, mat_thresh, hough_lines;
cv::gpu::HoughLinesBuf hough_buffer;
int bilateral_thresh = 15; // 5 == 0.085s; 15 == 0.467s at run-time
cv::gpu::bilateralFilter(mat_input, bil_out, bilateral_thresh, bilateral_thresh*2, bilateral_thresh/2);
//cv::gpu::threshold(bil_out, mat_thresh, 10, 255, CV_THRESH_BINARY);
cv::gpu::Canny(bil_out, mat_thresh, 10, 60, 5);
cv::gpu::HoughLinesP(mat_thresh, hough_lines, hough_buffer, 1.0f, (float)(CV_PI/180.0f), 5, 1);
//cv::Mat test_hough(hough_lines);
cv::Mat hough_Mat = drawHoughLinesOnMat(mat_input, hough_lines);
cv::gpu::HoughLines(mat_thresh, hough_lines, 1.0f, (float)(CV_PI/180.0f), 1, true);
/*cv::Mat */hough_Mat = drawHoughLinesOnMat(mat_input, hough_lines);
return EXIT_SUCCESS
}
The image I am using is,
Could someone tell me what it is that I am doing wrong..? Thanks in advance.!
The output of the Canny filter is,
EDIT:
I have tested on the CPU version of HoughLines and it seems to work just fine.
EDIT_2:
The solution posted by #jet47 works perfectly.
You use incorrect code for downloading results from GPU back to CPU:
lines_vector.resize(houghLines.cols);
cv::Mat temp_Mat (1, houghLines.cols, CV_8UC3, &lines_vector[0]);
houghLines.download (temp_Mat);
You use incorrect type for temp_Mat - CV_8UC3, it must be CV_32SC4.
The correct code is:
lines_vector.resize(houghLines.cols);
cv::Mat temp_Mat(1, houghLines.cols, CV_32SC4, &lines_vector[0]);
houghLines.download(temp_Mat);
My guess is that the Method you are using is outdated (but im not entirely sure).
This is how i would do it(as demonstrated in this Example Code):
//d_src filled with your image somewhere
GpuMat d_lines;
{
Ptr<cuda::HoughSegmentDetector> hough = cuda::createHoughSegmentDetector(1.0f, (float) (CV_PI / 180.0f), 50, 5);
hough->detect(d_src, d_lines);
}
vector<Vec4i> lines_gpu;
if (!d_lines.empty())
{
lines_gpu.resize(d_lines.cols);
Mat h_lines(1, d_lines.cols, CV_32SC4, &lines_gpu[0]);
d_lines.download(h_lines);
}
for (size_t i = 0; i < lines_gpu.size(); ++i)
{
Vec4i l = lines_gpu[i];
line(dst_gpu, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0, 0, 255), 3, LINE_AA);
}
EDIT The above uses the OpenCv 3.0 Interface