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.
Related
I have a 512x512 grayscale image (or MultiArray) which is the output of a CoreML depth estimation model.
In Python, one can use Matplotlib or other packages to visualise grayscale images in different colormaps, like so:
Grayscale
Magma
[Images from https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html]
I was wondering if there was any way to take said output and present it as a cmap in Swift/iOS?
If you make the model output an image, you get a CVPixelBuffer object. This is easy enough to draw on the screen by converting it to a CIImage and then a CGImage.
If you want to draw it with a colormap, you'll have to replace each of the grayscale values with a color manually. One way to do this is to output an MLMultiArray and loop through each of the output values, and use a lookup table for the colors. A quicker way is to do this in a Metal compute shader.
I am trying to understand the jpeg compression algorithm. If I have a 3-channel color image, do I have to take 3 different Discrete cosine transform (DCT) and quantize for each channel? And after taking inverse DCT, will the result be an jpeg image?
If I have a 3-channel color image, do I have to take 3 different Discrete cosine transform (DCT) and quantize for each channel?
Yes, except that the color values are normally converted from RGB to YCbCr first.
Then you have to do run-length compression and Huffman coding on the resulting values. The DCT alone negatively compresses.
I need to quantize the RGB values to 29 uniform color dictionary. I used rgb2ind(image,29) in Matlab.
So, is there any function or efficient way to quantize the image color in OpenCV?
(I need to quantize the image color because i want to get a 29-sized histogram of color)
You will have to make your own. I can reccomend using HSV instead of RGB (you can convert RGB to HSV with opencv). Once the image is converted, you can then simply use 29 ranges for the H value.
EDIT: I saw this answer might be a bit vague for those who have little experience in computer vision. This question gives a lot more information about the difference between HSV and RGB and why this is usefull.
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 am using SURF features in OpenCV where the input images are converted to GRAY image.
cvtColor(object, object, CV_RGB2GRAY);
When I went through the documentation of OpenSURF I realised that its not in grayscale.
My confusion is that can we apply SURF to any image formats (YUV, HSV, RGB) or we have to change and modify the program to achieve that?
Most feature detectors work on greyscale because they analyse the patterns of edges in the image patch. You can run SURF on any single colour channel from the colour formats you mention i.e. You can run it on Y, U or V from YUV images, or on H, S or V from HSV images. Not sure how OpenSURF treats this, but they must be using the greyscale image internally.
Like OpenCV if you given an image to OpenSURF that is not single channel, it calls cvtColor(src, dst, CV_BGR2GRAY). If you pass either a 3 channel image in a YUV, HSV, Lab etc, things will go horribly wrong because the image will have an inappropriate color conversion applied..