I want to find local maxima of each '3X3' sized Window. So, How do we find that local maxima of each 3X3 sized Window in an image in OpenCV ?
You can use morphological operation dilate:
Mat img; // your input image that you should fill with values
Mat maxims(img.size(), img.type()); // container for all local maximums
dilate(img, maxims, Mat());
As a result each pixel of 'maxims' is maximum of appropriate 3x3 window in 'img'. Read more about morphological operation (dilatation, erosion, close, open, etc...) on Wikipedia or somewhere else.
Please see my answer to Find local maxima in grayscale image using OpenCV
The idea is to dilate with a kernel that has a "hole" in the middle (i.e. replace each pixel with the maximum of all its neighbors, excluding the pixel itself), and then compare the result to the original image.
Related
I want to normalize several images in imageJ using the mean pixel value in a ROI, so that after normalization the mean in this ROI has the same value in all the images. How can I do it? Thanks
It is hard to say with out a particular example but a priory I would select the ROI and press control + Mto measure the region. If it is grey scale image you should obtain the gray mean of the grey pixels. You can use then this value to divide all the pixels in you image using Divide function under the Process > Math menu. If you calculate the mean for each image and use that value to divide each corresponding image, your ROI should have the same mean value for all ROI in your pictures.
I hope it helps!
I have a vector of Point2f which have color space CV_8UC4 and need to convert them to CV_64F, is the following code correct?
points1.convertTo(points1, CV_64F);
More details:
I am trying to use this function to calculate the essential matrix (rotation/translation) through the 5-point algorithm, instead of using the findFundamentalMath included in OpenCV, which is based on the 8-point algorithm:
https://github.com/prclibo/relative-pose-estimation/blob/master/five-point-nister/five-point.cpp#L69
As you can see it first converts the image to CV_64F. My input image is a CV_8UC4, BGRA image. When I tested the function, both BGRA and greyscale images produce valid matrices from the mathematical point of view, but if I pass a greyscale image instead of color, it takes way more to calculate. Which makes me think I'm not doing something correctly in one of the two cases.
I read around that when the change in color space is not linear (which I suppose is the case when you go from 4 channels to 1 like in this case), you should normalize the intensity value. Is that correct? Which input should I give to this function?
Another note, the function is called like this in my code:
vector<Point2f>imgpts1, imgpts2;
for (vector<DMatch>::const_iterator it = matches.begin(); it!= matches.end(); ++it)
{
imgpts1.push_back(firstViewFeatures.second[it->queryIdx].pt);
imgpts2.push_back(secondViewFeatures.second[it->trainIdx].pt);
}
Mat mask;
Mat E = findEssentialMat(imgpts1, imgpts2, [camera focal], [camera principal_point], CV_RANSAC, 0.999, 1, mask);
The fact I'm not passing a Mat, but a vector of Point2f instead, seems to create no problems, as it compiles and executes properly.
Is it the case I should store the matches in a Mat?
I am no sure do you mean by vector of Point2f in some color space, but if you want to convert vector of points into vector of points of another type you can use any standard C++/STL function like copy(), assign() or insert(). For example:
copy(floatPoints.begin(), floatPoints.end(), doublePoints.begin());
or
doublePoints.insert(doublePoints.end(), floatPoints.begin(), floatPoints.end());
No, it is not. A std::vector<cv::Pointf2f> cannot make use of the OpenCV convertTo function.
I think you really mean that you have a cv::Mat points1 of type CV_8UC4. Note that those are RxCx4 values (being R and C the number of rows and columns), and that in a CV_64F matrix you will have RxC values only. So, you need to be more clear on how you want to transform those values.
You can do points1.convertTo(points1, CV_64FC4) to get a RxCx4 matrix.
Update:
Some remarks after you updated the question:
Note that a vector<cv::Point2f> is a vector of 2D points that is not associated to any particular color space, they are just coordinates in the image axes. So, they represent the same 2D points in a grey, rgb or hsv image. Then, the execution time of findEssentialMat doesn't depend on the image color space. Getting the points may, though.
That said, I think your input for findEssentialMat is ok (the function takes care of the vectors and convert them into their internal representation). In this cases, it is very useful to draw the points in your image to debug the code.
I'm writing an Android app in OpenCV to detect blobs. One task is to threshold the image to differentiate the foreground objects from the background (see image).
It works fine as long as the image is known and I can manually pass a threshold value to threshold()--in this particular image say, 200. But assuming that the image is not known with the only knowledge that there would be a dark solid background and lighter foreground objects how can I dynamically figure out the threshold value?
I've come across the histogram where I can compute the intensity distribution of the grayscale image. But I couldn't find a method to analyze the histogram and choose the value where the objects of interest (lighter) lies. That is; I want to differ the obviously dark background spikes from the lighter foreground spikes--in this case above 200, but in another case could be say, 100 if the objects are grayish.
If all your images are like this, or can be brought to this style, i think cv2.THRESHOLD_OTSU, ie otsu's tresholding algorithm is a good shot.
Below is a sample using Python in command terminal :
>>> import cv2
>>> import numpy as np
>>> img2 = cv2.imread('D:\Abid_Rahman_K\work_space\sofeggs.jpg',0)
>>> ret,thresh = cv2.threshold(img2,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
>>> ret
122.0
ret is the threshold value which is automatically calculated. We just pass '0' as threshold value for this.
I got 124 in GIMP ( which is comparable to result we got). And it also removes the noise. See result below:
If you say that the background is dark (black) and the foreground is lighter, then I recommend to use the YUV color space (or any other YXX like YCrCb, etc.), because the first component of such color spaces is luminance (or lightning).
So after the Y channel is extracted (via the extractChennel function) we need to analyse the histogram of this channel (image):
See the first (left) hump? It represents dark areas (the background in your situation) on your image. So our aim now is to find a segment (on abscissa, it's red part in the image) that contains this hump. Obviously the left point of this segment is zero. The right point is the first point where:
the (local) maximum of histogram is from the left of the point
the value of histogram is less than some small epsilon (you can set it to 10)
I drew a green vertical line to show the location of the right point of the segment in this histogram.
And that's it! This right point of the segment is the needed threshold. Here's the result (epsilon is 10 and the calculated threshold is 50):
I think that it's not a problem for you to delete the noise in the image above.
The following is a C++ implementation of Abid's answer that works with OpenCV 3.x:
// Convert the source image to a 1 channel grayscale:
Mat gray;
cvtColor(src, gray, CV_BGR2GRAY);
// Apply the threshold function with the CV_THRESH_OTSU setting as well
// You can skip having it return the value, but I include it for showing the
// results from OTSU
double thresholdValue = threshold(gray, gray, 0, 255, CV_THRESH_BINARY+CV_THRESH_OTSU);
// Present the threshold value
printf("Threshold value: %f\n", thresholdValue);
Running this against the original image, I get the following:
OpenCV calculated a threshold value of 122 for it, close to the value Abid found in his answer.
Just to verify, I altered the original image as seen here:
And produced the following, with a new threshold value of 178:
If I have an RGB image and a binary mask (1 channel), and I want to create contours for the RGB image based on the connected pixels of the binary mask. After that I want to compare the pixel values (e.g. check if each pixel in the contours is having a blue value > 150), then how can I implement the above by using OpenCV?
Thanks a lot!
Assuming the images are the same size and shape then simply scan over the pixels in the binary image looking for the contours and check the pixel values at the same row/col in the color image
See Fastest way to extract individual pixel data? for details
In particular, I have 2 vectors that have been filled by integers between 0 and 255 and a gray scale image.
I want to change the gray level of pixels of the image that matches with vec1[i] to vec2[i].
Do you know any function or fast procedure that can perform this in OpenCV?
I couldnt find a built-in function that returns all pixels with a specified gray level in OpenCV.
Best
Ali
That is known as a lookup-table transform, and that exists in opencv (link to documentation). You will have to adapt your input format a bit though .