i don't understand when opencv documentation mention the term "channel" . Does it mean the channel as in digital image ? or it is something else ?
So as OpenCV is an image processing Library, So A given image can be assumed as 2D matrix with each element as a pixel. Now since there are various types of image formats like Gray, RGB or RGBA, etc. each format is different as to how many colors it(pixel) can support. For example the pixels of Gray image take values in range 0-255 so to represent each gray pixel we need single uchar value, so it has single channel, similarly the pixels of RGB image can take values from 0-16777216 and to represent each RGB pixel, we need 3 uchar values, (256^3 = 16777216), hence it is 3 channels, similarly RGBA has 4 channels, the last channel is used for storing the alpha(transparency) value.
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Does the second channel of a C2 image represent the alpha channel or do they just fill the gap between C1-C3,C4?
You are mistaking colorspaces with channels. For example you have a greyscale colorspace, which is represented with 1 channel. Then you have BGR with 3 channels, and BGRA with 4. Here the 4th channel is the Alpha value. OpenCV supports several types of colorspaces.
OpenCV is opened to your needs, in some cases you have a mat with 2 values per pixel, for example Dense Optical Flow results, which have a vector of movement of each pixel (x,y vector). You may even create a greyscale image with alpha value for whatever reason or algorithm you have... in this case it will be a CV_8UC2. However this is not a standard colorspace in OpenCV, and a lot of the algorithms have hard constraints on the color space so they may not work with this Mat type.
A cv::Mat can have more than 4 channels even (up to 512 the last time I checked, for more info check the constant CV_CN_MAX), but beware that this may not work with all of OpenCV functions and it will more like a container to your custom algorithms.
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 working with opencv 2.4 and numpy. I would like to open an image and get all the information about it (8 bit - if its RGB-BGR etc) and also try to change the color space.
I have this code:
if __name__ == '__main__':
img = cv2.imread('imL.png')
conv= cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
cv2.imwrite('lab.png', conv )
When I open the file lab.png I get the image with different colors!
I check the value of BGR to LAB in: http://www.brucelindbloom.com/
For this I would like to know all the information about one image.
That rigtht you will get a different image colour because imwrite() saves file in the format specified (PNG, JPEG 2000, and TIFF) single-channel or 3-channel (with ‘BGR’ channel order). images can be saved using this function. imwrite() doesn't know the format LAB to save image as it always expect the image in BGR.
If the format, depth or channel order is different, use Mat::convertTo() , and cvtColor() to convert it before saving.
Lab is another color space, like the BGR color space which is gained from cv2.imread(). It just like you convert temperature from Fahrenheit to Celsius.
32 Fahrenheit and 0 Celsius is the same temperature but in different unit.
cv2.imwrite() dose not know if the values are in BGR color space or not. When it get a 3 dimension array, it assume that it is a BGR color space while your conv variable contains Lab color space. This is why your color of your image is changed.
For your information, Each layer of BGR color space contains blue, green and red colors while layers of Lab contains lightness (0-100), a* and b* respectively. For more information, please see "Lab color space" in Wikipedia.
i want to compare to components with their filled colors if they are equal or not
i do the following algorithm , i do averaging for the rgb as following
double avg1 =(comp[0].Red+comp[0].Blue+comp[0].Green)/3;
double avg2 =(comp[1].Red+comp[1].Blue+comp[1].Green)/3;
then compare them as following
double ratio = avg1/avg2 ;
if(ratio > 0.8 && ratio < 1.2){} //then they are supposed to be equal
but this way isn't accurate at all
after searching i found that the best way is converting the image to HSL space and compare
but i can't get how i compare 2 colors ?!! here
in other words after converting the image into HSL space what can i do ?!
help please !!
modification to the question for more clarification
i mean with component (sequence of points) so in the averaging step actually i revisit all the points calculating the sum of the average of rgb for each pixel , then do averaging over the total number of the points
Convert to HSL and use the difference in H (hue) to group colors.
So if your question is "after converting the image into HSL space what can i do ?!" then here goes:
convert the RGB image you've loaded to HSL using cvCvtColor() with the CV_RGB2HLS flag (the HSL image should be 3-channel, naturally)
make three single-channel images (of same size) for the H, L, S channels to be separated into
cvSplit( hls, h, l, s, 0 ) to separate the HSL image into channels
Now the h_image will be just like any single-channel grayscale image. So after extracting components (do this from thresholding the RGB image, sometimes Hue channel image looks weird :P) simply compare the colors in the hue image that correspond to their co-ordinates.
Hope this helps.