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
Related
I am working on processing images that consists of colors that have the same grayscale. In other words, each image is colored with random colors that have the same gray value.
When I converted the image using (rgb2grey() from skimage or cv2.cvtColor() from OpenCV), the resulted image has only one gray value (or slightly difference gray values (unperceivable by human eyes). Therefore, the resulted image details unrecognizable.
My questions are:
What are the best way to do before converting these images to grayscale ones? (Please note the colors of these images are not fixed)
Are there any color combinations for which the color-gray conversion algorithms won't work?
How about using YCbCr?
Y is intensity, Cb is the blue component relative to the green component and Cr is the red component relative to the green component.
So I think YCbCr can differentiate between multiple pixels with same grayscale value.
I have a problem with normalization.
Let me what the problem is and how I attempt to solve it.
I take a three-channel color image, convert it to grayscale and apply uniform or non-uniform quantization and the same thing.
To this image, I should apply the normalization, but I have a problem even if the image and grayscale and always has three channels.
How can I apply normalization having a three-channel image?
Should the min and the max all be in the three channels?
Could someone give me a hand?
The language I am using is processing 2.
P.S.
Can you do the same thing with a color image instead use a grayscale image?
You can convert between the 1-channel and 3-channel representations easily. I'd recommend scikit-image (http://scikit-image.org/).
from skimage.io import imread
from skimage.color import rgb2gray, gray2rgb
rgb_img = imread('path/to/my/image')
gray_img = rgb2gray(rgb_image)
# Now normalize gray image
gray_norm = gray_img / max(gray_img)
# Now convert back
rgb_norm = gray2rgb(gray_norm)
I worked with a similar problem sometime back. One of the good solutions to this was to:
Convert the image from RGB to HSI
Leaving the Hue and Saturation channels unchanged, simply normalize across the Intensity channel
Convert back to RGB
This logic can be applied accross several other image processing tasks, like for example, applying histogram equalization to RGB images.
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 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 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°).