I have an image of a circle, I want to find the circle but not using hough circles.
I found a way, linked here.
But I can't find the transition coordinates from white to black as I don't know the x and y coordinates in the circle. What other methods are there, or how can I make that approach work?
This is my test image:
Another approach (that is useful for more than just circles) would be to find the image contours and do image moment analysis on the circle to find it's centre of mass:
I recommend learning them if you'e going to move forward with image processing. They're pretty helpful approaches that transform images into more useful structures.
One possible approach is to first threshold the image to get rid of some of the noise around the circle. Then you can extract the edge of the circle using Canny edge detection. Finally, findNonZero to get a list of pixel coordinates.
I first did a quick prototype with Python:
import cv2
import numpy as np
img = cv2.imread('circle.png', 0)
mask = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]
edges = cv2.Canny(mask, 20, 100)
points = np.array([p[0] for p in cv2.findNonZero(edges)])
And then ported it to C++, adding some extra code to save all the intermediate images and plot the found pixels.
#include <opencv2/opencv.hpp>
int main()
{
cv::Mat img(cv::imread("circle.png", 0));
cv::Mat mask;
cv::threshold(img, mask, 127, 255, cv::THRESH_BINARY);
cv::imwrite("circle_1.png", mask);
cv::Mat edges;
cv::Canny(mask, edges, 20, 100);
cv::imwrite("circle_2.png", edges);
std::vector<cv::Point2i> points;
cv::findNonZero(edges, points);
cv::Mat output(cv::Mat::zeros(edges.size(), CV_8UC3));
for (auto const& p : points) {
output.at<cv::Vec3b>(p) = cv::Vec3b(127, 255, 127);
}
cv::imwrite("circle_3.png", output);
}
Output of threshold:
Output of Canny:
Re-plotted pixels:
Related
]2
I would like to get a square shape from the right image above. But when I try to get it, it also includes other protruding parts because they have similar color. Are there any solutions to get the result like below? (The square lines are not 100 % straight. They are little distorted.)
This is the code I wrote.
cv::Mat img_gray, img, clahe_img, threshold_img, bitwise_img, morph_img;
cv::Mat rectified_CCD_img = cv::imread('img.png')
cv::Mat kernel = cv::Mat::ones(99, 99, CV_8U);
clahe = cv::createCLAHE(10, cv::Size(100, 100));
cv::cvtColor(rectified_CCD_img, img_gray, cv::COLOR_BGR2GRAY);
cv::medianBlur(img_gray, img, 33);
clahe->apply(img, clahe_img);
cv::threshold(clahe_img, threshold_img, 0, 255, cv::THRESH_OTSU);
cv::bitwise_not(threshold_img, bitwise_img);
cv::morphologyEx(bitwise_img, morph_img, cv::MORPH_OPEN, kernel);
That's the original image:
Google Drive link
For this specific image my pipeline would be very simple:
Binary threshold the image with a fixed threshold. The rectangle is quite dark compared to the rest of the image.
Morphological opening with a large rectangular kernel to get rid of the "noise".
To get a perfect rectangle, determine the bounding rectangle of the remaining part, and draw a white rectangle.
That'd be the whole code:
// Read image
cv::Mat img = cv::imread("OTH61.png", cv::IMREAD_GRAYSCALE);
// Binary threshold image at fixed threshold
cv::Mat img_thr;
cv::threshold(img, img_thr, 32, 255, cv::THRESH_BINARY_INV);
// Morphological opening with large rectangular kernel
cv::Mat img_mop;
cv::morphologyEx(img_thr, img_mop, cv::MORPH_OPEN, cv::Mat::ones(51, 51, CV_8UC1));
// Draw rectangle w.r.t. to the bounding rectangle of the remaining part
cv::rectangle(img_mop, cv::boundingRect(img_mop), 255, cv::FILLED);
The thresholded image:
The morphological opened image:
The cleaned image:
I have a camera under a glass with IR light to detect objects. I can find the contours and draw them using the following code (I just found some examples online and modified it to my need so I am not a master at all!).
using namespace cv;
cvtColor(mat, mat, COLOR_BGR2GRAY);
blur(mat, mat, Size(3,3));
erode(mat, mat, NULL, Point(-1,-1), 2);
dilate(mat, mat, NULL, Point(-1,-1), 2);
Canny(mat, mat, 100, 200);
auto contours = std::vector<std::vector<Point>>();
auto hierarchy = std::vector<Vec4i>();
findContours(mat, contours, hierarchy, CV_RETR_TREE,
CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
Mat drawing = Mat::zeros(mat.size(), CV_8UC3);
for( int i = 0; i< contours.size(); i++ ) {
Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0,255),
rng.uniform(0,255));
drawContours(drawing, contours, i, color, 2, 8, hierarchy, 0, Point());
}
putText(mat,
(QString("Blobs: %1").arg(contours.size())).toStdString(),
Point(25,175), cv::FONT_HERSHEY_PLAIN, 10, CV_RGB(0, 0, 255), 2);
This code results in a nice finding of the contours that I am quite happy with. Except the fact that my IR light somehow makes artifacts at the corners and bottom of the image.
You can see that I have used gimp to highlight the areas that I want to ignore while searching for contours. Under the gray shade you see the white pixels that my original code detects as contours. These areas are problematic and I want to exclude them from the either contour search or contour drawing (whichever is easier!)
I was thinking of cropping the image to get the ROI but the cropping is a rectangle while I (for example) could have things to be detected i.e. exactly at leftmost area.
I think there should be some data in the contour that tells me where are the pixels but I could not figure it out yet...
The easiest way would be to simply crop the image. Areas of the image are known as ROIs in OpenCV, which stands for Region of Interest.
So, you could simply say
cv::Mat image_roi = image(cv::Rect(x, y, w, h));
This basically makes a rectangular crop, with the top left corner at x,y, width w and height h.
Now, you might not want to reduce the size of the image. The next easiest way is to remove the artifacts is to set the borders to 0. Using ROIs, of course:
image(cv::Rect(x, y, w, h)).setTo(cv::Scalar(0, 0, 0));
This sets a rectangular region to black. You then have to define the 4 rectangular regions on the borders of your image that you want dark.
Note that all of the above is based on manual tuning and some experimentation, and it would work provided that your system is static.
image with two circles
I have an image that include two fibers (presenting as two circles in the image). How can I calculate the distance of two fibers?
I find it hard to detect the position of the fiber. I have tried to use the HoughCircles function, but the parameters are hard to optimize and it cannot locate the circle precisely in most times. Should I subtract the background first or is there any other methods? MANY Thanks!
Unfortunately, you haven't shown your preprocessing steps. In my approach, I'll do the following:
Convert input image to grayscale (see cvtColor).
Median blurring, maintains the "edges" (see medianBlur).
Adaptive thresholding (see adaptiveTreshold).
Morphological opening to get rid of small noise (see morphologyEx).
Find circles by HoughCircles.
Not done here: Possible refinements of the found circles. Exclude too small or too large circles. Use all prior information you have on that! For example, how large can the circles be at all?
Here's my whole code:
// Read image.
cv::Mat img = cv::imread("images/i7aJJ.jpg", cv::IMREAD_COLOR);
// Convert to grayscale for processing.
cv::Mat blk;
cv::cvtColor(img, blk, cv::COLOR_BGR2GRAY);
// Median blurring to improve following thresholding.
cv::medianBlur(blk, blk, 11);
// Adaptive thresholding.
cv::adaptiveThreshold(blk, blk, 255, cv::ADAPTIVE_THRESH_GAUSSIAN_C, cv::THRESH_BINARY, 51, -2);
// Morphological opening to get rid of small noise.
cv::morphologyEx(blk, blk, cv::MORPH_OPEN, cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(3, 3)));
// Find circles using Hough transform.
std::vector<cv::Vec4f> circles;
cv::HoughCircles(blk, circles, cv::HOUGH_GRADIENT, 1.0, 300, 50, 25, 100);
// TODO: Refinement of found circles, if there are more than two.
// For example, calculate areas: Neglect too small or too large areas.
// Compare all areas, and keep the two with nearly matching areas and
// suitable areas.
// Draw circles in input image.
for (Vec4f& circle : circles) {
cv::circle(img, cv::Point(circle[0], circle[1]), circle[2], cv::Scalar(0, 0, 255), 4);
cv::circle(img, cv::Point(circle[0], circle[1]), 5, cv::Scalar(0, 255, 0), cv::FILLED);
}
// --- Assuming there are only the two right circles left from here. --- //
// Draw some debug output in input image.
const cv::Point c1 = cv::Point(circles[0][0], circles[0][1]);
const cv::Point c2 = cv::Point(circles[1][0], circles[1][1]);
cv::line(img, c1, c2, cv::Scalar(255, 0, 0), 2);
// Calculate distance, and put in input image.
double dist = cv::norm(c1 - c2);
cv::putText(img, std::to_string(dist), cv::Point((c1.x + c2.x) / 2 + 20, (c1.y + c2.y) / 2 + 20), cv::FONT_HERSHEY_COMPLEX, 1.0, cv::Scalar(255, 0, 0));
The final output looks like this:
The intermediate image right before the HoughCircles operation looke like this:
In general, I'm not that skeptical about HoughCircles. You "just" have to pay attention to your preprocessing.
Hope that helps!
It's possible using hough circle detection but you should provide more images if you want a more stable detection. I just do denoising and go straight to circle detection. Using a non-local means denoising is pretty good at preserving edges which is in turn good for the canny edge algorithm included in the hough circle algorithm.
My code is written in Python but can easily be translated into C++.
import cv2
from matplotlib import pyplot as plt
IM_PATH = 'your image path'
DS = 2 # downsample the image
orig = cv2.imread(IM_PATH, cv2.IMREAD_GRAYSCALE)
orig = cv2.resize(orig, (orig.shape[1] // DS, orig.shape[0] // DS))
img = cv2.fastNlMeansDenoising(orig, h=3, templateWindowSize=20 // DS + 1, searchWindowSize=40 // DS + 1)
plt.imshow(orig, cmap='gray')
circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, dp=1, minDist=200 // DS, param1=40 // DS, param2=40 // DS, minRadius=210 // DS, maxRadius=270 // DS)
if circles is not None:
for x, y, r in circles[0]:
c = plt.Circle((x, y), r, fill=False, lw=1, ec='C1')
plt.gca().add_patch(c)
plt.gcf().set_size_inches((12, 8))
plt.show()
Important
Doing a bit of image processing is only the first step in a good (and stable!) object detection. You have to leverage every detail and property that you can get your hands on and apply some statistics to improve your results. For example:
Use Yves' approach as an addition and filter all detected circles that do not intersect the joints.
Is one circle always underneath the other? Filter out horizontally aligned pairs.
Can you reduce the ROI (are the circles always in a specific area in your image or can they be everywhere)?
Are both circles always the same size? Filter out pairs with different sizes.
...
If you can use multiple metrics you can apply a statistical model (ex. majority voting or knn) to find the best pair of circles.
Again: always think of what you know about your object, the environment and its behavior and take advantage of that knowledge.
I am trying to find triangles (blue contours) and trapezoids (yellow contours) in real time. In general it's okay.
But there is some problems. First it's a false positives. Triangles become trapezoids and vice versa. And I don't know how to how to solve this problem.
Second it's "noise". . I tried to check area of the figure, but the noise can be equal to the area. So it did not help so much. The noise depends on the thresholding parameters. cv::adaptiveThresholddoes not help at all. It's adds even more noise (and it so SLOW) erode and dilate cant fix it in a proper way
And here is my code.
cv::Mat detect(cv::Mat imageRGB)
{
//RGB -> GRAY
cv::Mat imageGray;
cv::cvtColor(imageRGB, imageGray, CV_BGR2GRAY);
//Bluring it
cv::Mat image;
cv::GaussianBlur(imageGray, image, cv::Size(5,5), 2);
//Thresholding
cv::threshold(image, image, 100, 255, CV_THRESH_BINARY_INV);
//SLOW and NOISE
//cv::adaptiveThreshold(image, image, 255.0, CV_ADAPTIVE_THRESH_GAUSSIAN_C, CV_THRESH_BINARY, 21, 0);
//Calculating canny params.
cv::Scalar mu;
cv::Scalar sigma;
cv::meanStdDev(image, mu, sigma);
cv::Mat imageCanny;
cv::Canny(image,
imageCanny,
mu.val[0] + sigma.val[0],
mu.val[0] - sigma.val[0]);
//Detecting conturs.
std::vector<std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours(imageCanny, contours, hierarchy,CV_RETR_TREE, CV_CHAIN_APPROX_NONE);
//Hierarchy is not needed here so clear it.
hierarchy.clear();
for (std::size_t i = 0; i < contours.size(); i++)
{
//fitEllipse need at last 5 points.
if (contours.at(i).size() < 5)
{
continue;
}
//Skip small contours.
if (std::fabs(cv::contourArea(contours.at(i))) < 800.0)
{
continue;
}
//Calculating RotatedRect from contours NOT from hull
//because fitEllipse need at last 5 points.
cv::RotatedRect bEllipse = cv::fitEllipse(contours.at(i));
//Finds the convex hull of a point set.
std::vector<cv::Point> hull;
cv::convexHull(contours.at(i), hull, true);
//Approx it, so we'll get 3 point for triangles
//and 4 points for trapez.
cv::approxPolyDP(hull, hull, 15, true);
//Is our contour convex. It's mast be.
if (!cv::isContourConvex(hull))
{
continue;
}
//Triangle
if (hull.size() == 3)
{
cv::drawContours(imageRGB, contours, i, cv::Scalar(255, 0, 0), 2);
cv::circle(imageRGB, bEllipse.center, 3, cv::Scalar(0, 255, 0), 2);
}
//trapez
if (hull.size() == 4)
{
cv::drawContours(imageRGB, contours, i, cv::Scalar(0, 255, 255), 2);
cv::circle(imageRGB, bEllipse.center, 3, cv::Scalar(0, 0, 255), 2);
}
}
return imageRGB;
}
So... In general all problems coused by wrong thresholding parameters, how can I calculete it in a proper way (automatically, of course)? And how can I can (lol, sorry for my english) prevent false positives?
Thesholding - i think that you should try Otsu binarization - here is some theory and a nice picture and here is documentation. This kind of thresholding generally is trying to find 2 most common values in image and use average value of them as a threshold value.
Alternatively consider using HSV color space, it might be easier to distinguish black and white regions from other regions. Another idea is to use inRange function (in RGB or in HSV color space - should work in woth situations) - you need to find 2 ranges (one from black regions and one for white) and search only for those regions (using inRange function) - look at this post.
Another way to accomplish this task might be using some library for blob extraction like this one or blob extractor which is part of OpenCV.
Distinguish triangle from trapezoid - i see 2 basic ways to improve you solution here:
in this line cv::approxPolyDP(hull, hull, 15, true); make third parameter (15 in this situation) not a constant value, but some part of contour area or length. Definitely it should adapt to contour size, it can't be just a canstant value. It's hard to say how to calculate it without some testing - try to start with 1-5% of contour area or length (i would start with length, but this is just my guess) and see whether this value is fine/to big/to small an check other values if needed. Unfortunetely there is no other way, but finding this equation manually shouldn't take very long time.
when you have 4 or 5 points calculate the equations of lines which join consecutive points (point 1 with point 2, point 2 with point 3, etc don't forget to calculate line between first point and last point), than check whether any 2 of those lines are parallel (or at least are close to being parallel - angle between them is close to 0 degress) - if you find any parallel lines than this contour is trapezoid, otherwise it's a triangle.
I have two almost similar images with the difference that the shapes in the second image are a little different. Most of the time smaller, but can be larger. Also the shape count in one image can range from ~10 to >100 and can get relatively close to each other.
It would look something like this (Notice: both images would be not transparent):
The black triangle is image 1, the grey triangle is image 2.
Now i want to add a predefined margin (3px here - to both sides of the contour) to the edges of image 1 and test if the edges of the second image are in "the same" range as the first image. If not, display that visually:
Top left: Small difference between the two images (visualized by red outline)
Bottom right: "Same" edge -> No difference
How can i best accomplish this?
I'm using OpenCV with C++
In case the shapes are at the same positions in both images and you just need the markers on an image without additional information, this simple trick could do it.
#include <opencv2/opencv.hpp>
using namespace cv;
int main()
{
Mat img1 = imread("D:/1.png");
Mat img2 = imread("D:/2.png");
Mat diff;
absdiff(img1, img2, diff);
cv::threshold(diff, diff, 128, 255, THRESH_BINARY);
Mat markers;
int minRadiusDiff = 2;
erode(diff, markers, Mat(), cv::Point(-1, -1), minRadiusDiff / 2);
imwrite("D:/out.png", markers);
}
Here are some example images:
The triangle becomes much bigger, the wobbly thing becomes much smaller and the quad ony shrinks slightly.
So we would like to have the triangle and the wobble marked, but not the quad.
And that is exactly our result.