I want to use the function distanceTransform() to find the minimum distance of non-zero pixels to zeros pixels, but also the position of that closest zero pixel. I call the second version of the function with the labelType flag set to DIST_LABEL_PIXEL. Everything works fine and I get the distances to and indices of the closest zero pixels.
Now I want to convert the indices back to pixel locations and I thought the indexing would be like idx=(row*cols+col) or something like this but I had to find out that OpenCV is just counting the zero pixels and using this count as the index. So if I get 123 as the index of the closest pixel this means that the 123th zero pixel is the closest.
How is OpenCV counting them? Probably in a row-wise manner?
Is there an efficient way of mapping the indices back to the locations? Obviously I could recount them and keep track of the counts and positions, if I know how OpenCV counts them, but this seems stupid and not very efficient.
Is there a good reason to use the indexing they used? I mean, are there any advantages over using an absolute indexing?
Thanks in advance.
EDIT:
If you want to see what I mean, you can run this:
Mat mask = Mat::ones(100, 100, CV_8U);
mask.at<uchar>(50, 50) = 0;
Mat dist, labels;
distanceTransform(mask, dist, labels, CV_DIST_L2, CV_DIST_MASK_PRECISE, DIST_LABEL_PIXEL);
cout << labels.at<int>(0,0) << endl;
You will see that all the labels are 1 because there is only one zero pixel, but how am I supposed to find the location (50,50) with that information?
The zero pixels also get labelled - they will have the same label as the non-zero pixels to which they are closest.
So you will have a 2D array of labels, the same size as your source image. If you examine all of the zero pixels in the source image, you can then find the associated label from the 2D array returned. This can then allow you to find which non-zero pixels are associated with each zero pixel by matching the labels.
If you see what I mean.
In python you can use numpy to associate the labels and the coordinates:
import cv2
import numpy as np
# create an image with two 0-lines
a = np.ones((100,100), dtype=np.uint8)
a[50,:] = 0
a[:,70] = 0
dt,lbl = cv2.distanceTransformWithLabels(a, cv2.DIST_L2, 3, labelType=cv2.DIST_LABEL_PIXEL)
# coordinates of 0-value pixels
xy = np.where(a==0)
# print label id and coordinate
for i in range(len(np.unique(lbl))):
print(i,xy[0][i], xy[1][i])
Related
I have a Qt app where I have to find the HSV range of a couple of pixels around click coordinates, to track later on. This is how I do it:
cv::Mat temp;
cv::cvtColor(frame, temp, CV_BGR2HSV); //frame is pulled from a video or jpeg
cv::Vec3b hsv=temp.at<cv::Vec3b>(frameX,frameY); //sometimes SIGSEGV?
qDebug() << hsv.val[0]; //look up H
qDebug() << hsv.val[1]; //look up S
qDebug() << hsv.val[2]; //look up V
//just base values so far, will work on range later
emit hsvDownloaded(hsv.val[0], hsv.val[0]+5, hsv.val[1], 255, hsv.val[2], 255); //send to GUI which automaticly updates worker thread
Now, things are odd. Those are the results (red circle indicates the click location):
With red it's weird, upper half of the shape is detected correctly, lower half is not, despite it being a solid mass of the same colour.
And for an actual test
It detects HSV {95,196,248} which is frankly absurd (base values way too high). None of the pixels that were detected isn't even the one that was clicked. The best values to detect that ball 100% of the time are H:35-141 S:0-238 V:65-255. I've wanted to get a HSV range from a normalized histogram, but I can't even get the base values right. What's up? When OpenCV pulls a frame using kalibrowanyPlik.read(frame); , the default colour scheme is BGR, right?
Why would the colour detection work so randomly?
As berak has mentioned, your code looks like you've used the indices to access pixel in the wrong order.
That means your pixel locations are wrong, except for pixel that lie on the diagonal, so clicked objects that are around the diagonal will be detected correctly, while all the others won't.
To not get confused again and again, I want you to understand why OpenCV uses (row,col) ordering for indices:
OpenCV uses matrices to represent images. In mathematics, 2D matrices use (row,col) indexing, have a look at http://en.wikipedia.org/wiki/Index_notation#Two-dimensional_arrays and watch at the indices. So for matrices, it is typical to use the row index first, followed by the column index.
Unfortunately, images and pixel typically have a (x,y) indexing, which corresponds to x/y axis/direction in mathematical graphs and coordinate systems. So here the x position is used first, followed by the y position.
Luckily, OpenCV provides two different versions of .at method, one to access pixel-positions and one to access matrix elements (which are exactly the same elements in the end).
matrix.at<type>(row,column) // matrix indexing to access elements
// which equals
matrix.at<type>(y,x)
and
matrix.at<type>(cv::Point(x,y)) // pixel/position indexing to access elements
since the first version should be slightly more efficient it should be preferred if the positions aren't already given as cv::Point objects. So the best way often is to remember, that openCV uses matrices to represent images and it uses matric index notations to access elements.
btw, I've seen people wondering why matrix.at<type>(cv::Point(y,x)) doesn't work the way intended after they've learned that openCV images use the "wrong ordering". I hope this question doesn't come up after my explanation.
one more btw: in school I already wondered, why matrices index rows first, while graphs of functions index x axis first. I found it stupid to not use the "same" ordering for both but I still had to live with it :D (and at the end, both don't have much to do with the other)
I begin a project about the detection.
My idea is to rank every pixels of an image (Mat).
Then, I will be able to exit which colour is dominant.
The difficulty is a colour is not unic. For exemple, Green is rgb(0, 255, 0) but is almost rgb(10, 240, 20) too.
The goal of my ranking is to exit pixels which are almost same colour. Then, with a pourcentage, I think I can locate my object.
So, my question: Is it a way to ranking pixels by colour ?
Thx a lot in advance for your answers.
There isn't a straight method of ranking as you say of pixels in colours.
However, you can find an approximation to the most dominant one.
There are several way in which you can do it:
You can calculate the histogram for each colour channel - split it into the R,G,B and compute the histogram. Then you can see where the peaks of the resulting graphs are - e.g.
If you k-means cluster the pixels at the image - in other words, represent each pixel as a 3D point with coordinated (R, G, B). Then you can segment the pixels into k most occurring colours.
If you resize the image to a 1x1 pixel image, you'll find the average of all pixel values. If there is a dominant colour, where the majority of the pixels are in close proximity, it will give a good approximation.
There however, are all approximations. Your best choice would be to use k-means and to find the cluster that either has the most elements, or is the most dense.
In case you are looking for way to locate an object with a specific colour, you can use a maximum likelihood estimation. Something like this, which was used to classify different objects, such as grass, cars, building and pavement from satellite images. You can use it with a single colour and get a heat-map of where the object is in terms of likelihood (the percentage of probability) of that pixel belonging to your object.
In an ordinary image, there's always a number of colors involved. To best average the pixels carrying almost the same colors is done by color quantization which is reducing number of colors in an image using techniques like K-mean clustering. This is best explained here with Python code:
https://www.pyimagesearch.com/2014/07/07/color-quantization-opencv-using-k-means-clustering/
After successful quantization, you can just try the following code to rank the colors based on their frequencies in the image.
top_n_colors = []
n = 3
colors_count = {}
(channel_b, channel_g, channel_r) = cv2.split(_processed_image)
# Flattens the 2D single channel array so as to make it easier to iterate over it
channel_b = channel_b.flatten()
channel_g = channel_g.flatten()
channel_r = channel_r.flatten()
for i in range(len(channel_b)):
RGB = str(channel_r[i]) + " " + str(channel_g[i]) + " " + str(channel_b[i])
if RGB in colors_count:
colors_count[RGB] += 1
else:
colors_count[RGB] = 1
# taking the top n colors from the dictionary objects
_top_colors = sorted(colors_count.items(), key=lambda x: x[1], reverse=True)[0:n]
for _color in _top_colors:
_rgb = tuple([int(value) for value in _color[0].split()])
top_n_colors.append(_rgb)
print(top_n_colors)
In ImageJ you can adjust the brightness and contrast of an image using minimum and maximum values. You can also use the setMinAndMax() macro function. The dialog looks like this:
It maps each pixel to fit between the minimum and maximum values.
I'm trying to do the same thing in OpenCV (C++). I can change the contrast and brightness using the alpha and beta parameters of Mat::convertTo(), but I don't know how to do it with minimum and maximum values.
In my case I'm using a 12-bit image so the pixel values range from 0 to 4095. I'm not sure if that matters.
You do it like this.
First, find the current maximum and minimum.
Let's say the darkest and brightest are 80 and 220 respectively. Now you need to stretch this range 80..220 onto the full range 0..4095.
So you subtract 80 from every pixel in your image to shift down to zero at the left end of the histogram, so your range is now 0..140.
Now you need to multiply every pixel by 4095/140 to stretch the right end out to 4095.
Effectively, the formula you need is this:
newvalue = int((current value - darkest)*4095/(brightest-darkest))
You do it in three steps:
get the current min/max using minMaxLoc
adjust 'min' value by adding the difference to the image (no special function, just do 'image = image + offset' in C++ or python)
adjust 'max' value by scaling the image (no special function, 'image = image * scale')
I am trying to change the RGB for the overall image for a project. Currently I am working with a test file before I apply it to the actual Image. I want to test different values of RGB but would first like to start with the mean of all three. How would I go about doing this? I have other modules installed such as scipy, numpy, matplotlib, etc if those are needed. Thanks
from PIL import Image, ImageFilter
test = Image.open('/Users/MeganRCunninghan/Pictures/4th-of-July-Wallpaper.ppm')
test.show()
test.getrgb()
Assuming your image is stored as a numpy.ndarray (Test this with print type(test))...
Your image will be represented by an NxMx3 array. Basically this means you have a N by M image with a color depth of 3- your RGB values. Taking the mean of those 3 will leave you with an NxMx1 array, where the 1 is now the average intensity. Numpy does this very well:
test = test.mean(2)
The parameter given, 2, specifies the dimension to take the mean along. It could be either 0, 1, or 2, because your image matrix is 3 dimensional. This should return an NxM array. You basically will be left with a gray-scale, (color depth of 1) image. Try to show the value that gets returned! If you get Nx3 or Mx3, you know you have just taken the average along the wrong axis. Note that you can check the dimensions of a numpy array with:
test.shape
Shape will be a tuple describing the dimensions of your image.
Some details about my problem:
I'm trying to realize corner detector in openCV (another algorithm, that are built-in: Canny, Harris, etc).
I've got a matrix filled with the response values. The biggest response value is - the biggest probability of corner detected is.
I have a problem, that in neighborhood of a point there are few corners detected (but there is only one). I need to reduce number of false-detected corners.
Exact problem:
I need to walk through the matrix with a kernel, calculate maximum value of every kernel, leave max value, but others values in kernel make equal zero.
Are there build-in openCV functions to do this?
This is how I would do it:
Create a kernel, it defines a pixels neighbourhood.
Create a new image by dilating your image using this kernel. This dilated image contains the maximum neighbourhood value for every point.
Do an equality comparison between these two arrays. Wherever they are equal is a valid neighbourhood maximum, and is set to 255 in the comparison array.
Multiply the comparison array, and the original array together (scaling appropriately).
This is your final array, containing only neighbourhood maxima.
This is illustrated by these zoomed in images:
9 pixel by 9 pixel original image:
After processing with a 5 by 5 pixel kernel, only the local neighbourhood maxima remain (ie. maxima seperated by more than 2 pixels from a pixel with a greater value):
There is one caveat. If two nearby maxima have the same value then they will both be present in the final image.
Here is some Python code that does it, it should be very easy to convert to c++:
import cv
im = cv.LoadImage('fish2.png',cv.CV_LOAD_IMAGE_GRAYSCALE)
maxed = cv.CreateImage((im.width, im.height), cv.IPL_DEPTH_8U, 1)
comp = cv.CreateImage((im.width, im.height), cv.IPL_DEPTH_8U, 1)
#Create a 5*5 kernel anchored at 2,2
kernel = cv.CreateStructuringElementEx(5, 5, 2, 2, cv.CV_SHAPE_RECT)
cv.Dilate(im, maxed, element=kernel, iterations=1)
cv.Cmp(im, maxed, comp, cv.CV_CMP_EQ)
cv.Mul(im, comp, im, 1/255.0)
cv.ShowImage("local max only", im)
cv.WaitKey(0)
I didn't realise until now, but this is what #sansuiso suggested in his/her answer.
This is possibly better illustrated with this image, before:
after processing with a 5 by 5 kernel:
solid regions are due to the shared local maxima values.
I would suggest an original 2-step procedure (there may exist more efficient approaches), that uses opencv built-in functions :
Step 1 : morphological dilation with a square kernel (corresponding to your neighborhood). This step gives you another image, after replacing each pixel value by the maximum value inside the kernel.
Step 2 : test if the cornerness value of each pixel of the original response image is equal to the max value given by the dilation step. If not, then obviously there exists a better corner in the neighborhood.
If you are looking for some built-in functionality, FilterEngine will help you make a custom filter (kernel).
http://docs.opencv.org/modules/imgproc/doc/filtering.html#filterengine
Also, I would recommend some kind of noise reduction, usually blur, before all processing. That is unless you really want the image raw.