I need to change the hue of some pixels of my image, but i don't know how to set them!
I converted the image in HSV with CV_BGR2HSV and now i'm cycling with a for by rows and cols...
how can I access each pixel's hue?
for setting RGB i'm using this code...
CvScalar s;
s=cvGet2D(imgRGB,i,j); // get the (i,j) pixel value
printf("B=%f, G=%f, R=%f\n",s.val[0],s.val[1],s.val[2]);
s.val[0]=240;
s.val[1]=100;
s.val[2]=100;
cvSet2D(imgRGB,i,j,s); // set the (i,j) pixel value
You already converted your image to HSV, so the 3 layers of the image now correspond to Hue, Saturation and Value:
s.val[0] is the hue.
s.val[1] is the saturation.
s.val[2] is the value.
So go ahead and use exactly the same method as for your RGB images to get and set the pixel values.
Yes, openCV uses 180° i.e., (0°-179°) cylinder of HSV; while normally its (0°-240°) in MS paint and ideally (0°-360°). So, openCV gives you result of hue from (0°-179°).
Related
I'm using an app for face redaction that doesn't allow access to the source code but only allows me to pass pixel values for red, green and blue channel upon which it creates a matrix with the same average RGB values for every ROI pixel value. For eg. if I give Red=32,Blue=123 and Green=233 it will assign these RGB values for every pixel of the ROI and then draws a colored patch on the face.
So I was wondering is there a general combination of RGB values of a pixel to distort it and make it look like it's blurred. I can also set the opacity value in the app.
Thanks.
Is there a way that compares histograms but for example white color to be excluded and so white color doesn't affect onto the comparison.
White pixels have Saturation, S = 0. So, it is very easy to remove the white pixels from being counted while creating histogram. Do the following:
Convert your image from BGR to HSV
Then split your HSV image into three individual channels i.e. H, S and V
Then, access each pixel of channel S and if the pixel value = 0 (means S = 0) then it mean that it is a white pixel.
If the pixel is white then do not consider its Hue value to create histogram and if not...then put its hue value into the corresponding bin (normal procedure to build histogram).
Summary: you just need to find white pixels by checking their Saturation value, which is S = 0.
PS: Have a look at this link to understand the HSV model.
I have images containing gray gradations and one another color. I'm trying to convert image to grayscale with opencv, also i want the colored pixels in the source image to become rather light in the output grayscale image, independently to the color itself.
The common luminosity formula is smth like 0.299R+0.587G+0.114B, according to opencv docs, so it gives very different luminosity to different colors.
I consider the solution is to set some custom weights in the luminosity formula.
Is it possible in opencv? Or maybe there is a better way to perform such selective desaturation?
I use python, but it doesnt matter
This is the perfect case for the transform() function. You can treat grayscale conversion as applying a 1x3 matrix transformation to each pixel of the input image. The elements in this matrix are the coefficients for the blue, green, and red components, respectively since OpenCV images are BGR by default.
im = cv2.imread(image_path)
coefficients = [1,0,0] # Gives blue channel all the weight
# for standard gray conversion, coefficients = [0.114, 0.587, 0.299]
m = np.array(coefficients).reshape((1,3))
blue = cv2.transform(im, m)
So you have custom formula,
Load source,
Mat src=imread(fileName,1);
Create gray image,
Mat gray(src.size(),CV_8UC1,Scalar(0));
Now in a loop, access BGR pixel of source like,
Vec3b bgrPixel=src.at<cv::Vec3b>(y,x); //gives you the BGR vector of type cv::Vec3band will be in row, column order
bgrPixel[0]= Blue//
bgrPixel[1]= Green//
bgrPixel[2]= Red//
Calculate new gray pixel value using your custom equation.
Finally set the pixel value on gray image,
gray.at<uchar>(y,x) = custom intensity value // will be in row, column order
I have a image and i want to detect a blue rectange in it. My teacher told me that:
convert it to HSV color model
define a thresh hold to make it become a binary image with the color we want to detect
So why do we do that ? why don't we direct thresh hold the rgb image ?
thanks for answer
You can find the answer to your question here
the basic summary is that HSV is better for object detection,
OpenCV usually captures images and videos in 8-bit, unsigned integer, BGR format. In other words, captured images can be considered as 3 matrices, BLUE,RED and GREEN with integer values ranges from 0 to 255.
How BGR image is formed
In the above image, each small box represents a pixel of the image. In real images, these pixels are so small that human eye cannot differentiate.
Usually, one can think that BGR color space is more suitable for color based segmentation. But HSV color space is the most suitable color space for color based image segmentation. So, in the above application, I have converted the color space of original image of the video from BGR to HSV image.
HSV color space is consists of 3 matrices, 'hue', 'saturation' and 'value'. In OpenCV, value range for 'hue', 'saturation' and 'value' are respectively 0-179, 0-255 and 0-255. 'Hue' represents the color, 'saturation' represents the amount to which that respective color is mixed with white and 'value' represents the amount to which that respective color is mixed with black.
According to http://en.wikipedia.org/wiki/HSL_and_HSV#Use_in_image_analysis :
Because the R, G, and B components of an object’s color in a digital image are all correlated with the amount of light hitting the object, and therefore with each other, image descriptions in terms of those components make object discrimination difficult. Descriptions in terms of hue/lightness/chroma or hue/lightness/saturation are often more relevant.
Also some good info here
The HSV color space abstracts color (hue) by separating it from saturation and pseudo-illumination. This makes it practical for real-world applications such as the one you have provided.
R, G, B in RGB are all co-related to the color luminance( what we loosely call intensity),i.e., We cannot separate color information from luminance. HSV or Hue Saturation Value is used to separate image luminance from color information. This makes it easier when we are working on or need luminance of the image/frame. HSV also used in situations where color description plays an integral role.
Cheers
I am a beginner in both Image Processing and Opencv. I am trying to find out the individual pixel intensities of an image, using OPENCV#. There is assistance here: http://docs.opencv.org/doc/user_guide/ug_mat.html?highlight=pixel%20intensity for the same issue. But I am not sure how to use it in OPENCV#.
I know this is a very basic query. Please try to help out. Thanks in advance.
Pixel intensity is the same thing as that pixel's grayscale value. To get a grayscale (pixel intensity) version of an BGR image you can do this:
cv::cvtColor(bgr_mat,gray_mat,CV_BGR2GRAY);
Now the 3 channel BGR image has been converted to a 1 channel GRAYSCALE image. To find the intensity of pixel (x,y) in the gray image you can do this:
//NOTE: in OpenCV pixels are accessed in (row,col) format
int intensity = (int)gray_mat.at<uchar>(y,x);
Since each grayscale pixel is stored as uchar, the value of intensity will range from (0-255) where 255 is maximum intensity (seen as a completely white pixel).
in emgu cv, you can do it like this.
//Color
//Red
byte Red_val = My_Image.Data[y,x,0];
//Green
byte Green_val = My_Image.Data[y,x,1];
//Blue
byte Blue_val = My_Image.Data[y,x,2];
//Greyscale
byte Gray_val = My_Image.Data[y,x,0];