I'm trying to create an histogram using opencv. I have an image (32 bit) that came out from a blurring operation, so I just know that the values are in the range [-0.5; 0.5] but I don't know anything else about the starting data.
the problem is that I don't understand how to set the parameters to compute such histogram.
the code I wrote is:
int numbins=1000;
float range[]={-0.5, 0.5};
float *ranges[]={range};
CvHistogram *hist=cvCreateHist(1, &numbins, CV_HIST_ARRAY, ranges, 1);
cvCalcHist(&img, hist);
were img is the image I want to get the histogram. if I try to print the histogram I just get a black picture, while with the same function I get a correct histogram if use a grayscale 8bit image.
Have you looked at the calcHist example? Also, the camshiftdemo makes heavy use of histograms.
Are you normalizing the histogram output with normalize before display (camshiftdemo shows how to do this)? Values near 0 will appear black when displayed, but when normalized between say 0 and 255 will show up nicely.
Related
I have an uint16 satellite image whose values range from 0 to 3458 and its histogram is like this:
original histogram
I want to convert the image to float (range 0-1) but of course, I can simply divide everything by 3458 otherwise I will get a very dark image (because most pixels are below 500 as you can see from the histogram).
I would like to get a histogram like this:
new histogram
but I don't really know how to do it.
First of all you should convert your image type to float precision. If you are using MATLAB the function that can help you could be im2double().
Secondly, are you using imhist() function to show your histogram? If yes, It can easily get the range of bins you want to show your histogram.
Recently i read a paper , they extract depth intensity and distance of pixel from camera using depth image. But, as far I know, each pixel value in depth image represents distance in mm [range:0-65536] then how can they extract depth intensity within a range [0 to 255] from depth image. I don't understand it. kinect sensor returns uint16 depth frame which includes the each pixel distance from sensor. It does not return any intensity value, then how can the paper demonstrates that they extract depth intensity . I am really confused.
Here is the paper link
This is the graph what I want to extract(collected from the paper:
Since there is no an answer for this question , i will suggest you approach for getting your own depth image data .
One simple way can be scaling the image based on following formula:
Pixel_value=Pixel_value/4500*65535
If you want see the exact image that you get from uint8 ; I guess the following steps will work for you.
Probably while casting the image to uint8 matlab firstly clip the values above some threshold lets say 4095=2**12-1 (i'm not sure about value) and then it makes right shifts (4 shifts in our case) to make it inside the range of 0-255.
So i guess multiplying the value of uint8 with 256 and casting it as uint16 will help you get the same image
Pixel_uint16_value= Pixel_uint8_value*256 //or Pixel_uint16_value= Pixel_uint8_value<<8
//dont forget to cast the result as uint16
The other way to converting raw data to depth image in millimeters.
The depth image
should be stored in millimeters and as 16 bit unsigned
integers. The following two formulas can be used for
converting raw-data to millimeters .
distance = 1000/ (− 0.00307 ∗ rawDisparity + 3.33 )
distance = 123.6 ∗ tan ( rawDisparity/2842.5 + 1.1863 )
Save each distance value to coressponding rawdisparty pixel. Save them as 16 bit unsigned grayscale png images. Check this link for details.
Quick answer:
You can get the intensity by getting the intensity of corresponding IR pixel. let say you have a IR pixel array irdata,
then you can get the intensity of the ith pixel by
byte intensity = (byte)(irdata[i] >> 8);
In Kinect v2 only has two cameras, One is RGB camera and other one is IR camera. It uses IR camera to calculate the depth of the image by using the time-of-flight (TOF). If you need more information, please comment here or find my project on Kinect in github https://github.com/shanilfernando/VRInteraction. I'm more than happy to help you.
Edit
As you know depth is the distance between Kinect sensor to the object in a given space. The Kinect IR emitter emit bunch of IR rays and start counting time. Once the IR rays reflect back to the depth sensor(IR sensor) of the kinect, it stop the time counter. The time (t) between emission and receiving that specific ray is called the time-of-flight of that ray. Then distance (d) between kinect and the object can be calculated by
d = (t * speed-of-light)/2
This is done for all the rays it emits and build the IR image and depth image. Each and every ray represent a pixel in IR and depth images.
I read your reference paper, First of all, they are NOT using a depth image which is captured from the Kinect V2. It clearly said its resolution is 640 × 480 and effective distance range from 0.8 meters to 3.5 meters.
I want you to clearly understnad, the depth frame and depth image are two different things elements. If you check the depth frame, each pixel is a distance and in depth image each pixel is intensity(How much bright/brightness).
In this plot they are trying to plot intensity of the star point against the actual distance of the star point. They are starting with a depth (intensity) image, NOT depth frame. depth frame you can scale in to a depth image where values are 0 to 255 where near points has higher values and further points has lower values.
I guess you were trying to read depth from a Image file .png because of which the data is converted to binary form.
I would suggest you to save the depth image in .tiff format rather than png format.
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 using OpenNI and OpenCV (but without the latest code with openni support). If I just send the depth channel to the screen - it will look dark and difficult to understand something. So I want to show a depth channel for the user in a color but cannot find how to do that without losing of accuracy. Now I do it like that:
xn::DepthMetaData xDepthMap;
depthGen.GetMetaData(xDepthMap);
XnDepthPixel* depthData = const_cast<XnDepthPixel*>(xDepthMap.Data());
cv::Mat depth(frame_height, frame_width, CV_16U, reinterpret_cast<void*>(depthData));
cv::Mat depthMat8UC1;
depth.convertTo(depthMat8UC1, CV_8UC1);
cv::Mat falseColorsMap;
cv::applyColorMap(depthMat8UC1, falseColorsMap, cv::COLORMAP_AUTUMN);
depthWriter << falseColorsMap;
But in this case I get worse (loosing details) output than, for instance, kinects software for windows shows me. So I'm looking for a function in OpenNI or OpenCV with a better transformation.
ghttps://github.com/OpenNI/OpenNI2/blob/master/Samples/Common/OniSampleUtilities.h
the link is the code for histogram equalization. In short, it makes the probability of each level equal and optimizes mapping between 10,000 levels and 255 levels. That is why Kinect yellowish map looks better than naive I=255*z/z_range.
NOTE: don’t use color for visualization since a human eye is more sensitive to luminance change than to color variation. So with 255 levels of luminance you will get better contrast than with 255*255*255 levels of color. If you still decide to go along the color mapping avenue use HSV color space where you can manipulate Hue 0..360 deg, Value 1..0 and better set saturation to max. Map depth to hue and value, convert to RGB and display. Than go back to histogram equalization ;)
Try this:
const float scaleFactor = 0.05f;
depth.convertTo(depthMat8UC1, CV_8UC1, scaleFactor);
imshow("depth gray",depthMat8UC1);
Play with the value to get a result you're happy with
I have been trying to obtain the image brightness in Opencv, and so far I have used calcHist and considered the average of the histogram values. However, I feel this is not accurate, as it does not actually determine the brightness of an image. I performed calcHist over a gray scale version of the image, and tried to differentiate between the avergae values obtained from bright images over that of moderate ones. I have not been successful so far. Could you please help me with a method or algorithm, that can be realised through OpenCv, to estimate brightness of an image? Thanks in advance.
I suppose, that HSV color model will be usefull in your problem, where channel V is Value:
"Value is the brightness of the color and varies with color saturation. It ranges from 0 to 100%. When the value is ’0′ the color space will be totally black. With the increase in the value, the color space brightness up and shows various colors."
So use OpenCV method cvCvtColor(const CvArr* src, CvArr* dst, int code), that converts an image from one color space to another. In your case code = CV_BGR2HSV.Than calculate histogram of third channel V.
I was about to ask the same, but then found out, that similar question gave no satisfactory answers. All answers I've found on SO deal with human observation of a single pixel RGB vs HSV.
From my observations, the subjective brightness of an image also depends strongly on the pattern. A star in a dark sky may look more bright than a cloudy sky by day, while the average pixel value of the first image will be much smaller.
The images I use are grey-scale cell-images produced by a microscope. The forms vary considerably. Sometimes they are small bright dots on very black background, sometimes less bright bigger areas on not so dark background.
My approach is:
Find histogram maximum (HMax) using threshold for removing hot pixels.
Calculate mean values of all pixel between HMax * 2/3 and HMax
The ratio 2/3 could be also increased to 3/4 (which reduces the range of pixels considered as bright).
The approach works quite well, as different cell-patterns with same titration produce similar brightness.
P.S.: What I actually wanted to ask is, whether there is a similar function for such a calculation in OpenCV or SimpleCV. Many thanks for any comments!
I prefer Valentin's answer, but for 'yet another' way of determining average-per-pixel brightness, you can use numpy and a geometric mean instead of arithmetic. To me it has better results.
from numpy.linalg import norm
def brightness(img):
if len(img.shape) == 3:
# Colored RGB or BGR (*Do Not* use HSV images with this function)
# create brightness with euclidean norm
return np.average(norm(img, axis=2)) / np.sqrt(3)
else:
# Grayscale
return np.average(img)
A bit of OpenCV C++ source code for a trivial check to differentiate between light and dark images. This is inspired by the answer above provided years ago by #ann-orlova:
const int darkness_threshold = 128; // you need to determine what threshold to use
cv::Mat mat = get_image_from_device();
cv::Mat hsv;
cv::cvtColor(mat, hsv, CV_BGR2HSV);
const auto result = cv::mean(hsv);
// cv::mean() will return 3 numbers, one for each channel:
// 0=hue
// 1=saturation
// 2=value (brightness)
if (result[2] < darkness_threshold)
{
process_dark_image(mat);
}
else
{
process_light_image(mat);
}