I am promoting my comment to an answer:
The easy way is:
You could draw in the original 'frame' itself instead of using gray image.
The hard way (method you were trying to implement):
Copybacktorgb = cv2.cvtColor(gray,cv2.COLOR_GRAY2RGB)
is the correct syntax.
Answer from Anoop K. Prabhu on Stack OverflowI am promoting my comment to an answer:
The easy way is:
You could draw in the original 'frame' itself instead of using gray image.
The hard way (method you were trying to implement):
Copybacktorgb = cv2.cvtColor(gray,cv2.COLOR_GRAY2RGB)
is the correct syntax.
Alternatively, cv2.merge() can be used to turn a single channel binary mask layer into a three channel color image by merging the same layer together as the blue, green, and red layers of the new image. We pass in a list of the three color channel layers - all the same in this case - and the function returns a single image with those color channels. This effectively transforms a grayscale image of shape (height, width, 1) into (height, width, 3)
To address your problem
I did some thresholding on an image and want to label the contours in green, but they aren't showing up in green because my image is in black and white.
This is because you're trying to display three channels on a single channel image. To fix this, you can simply merge the three single channels
Copyimage = cv2.imread('image.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray_three = cv2.merge([gray,gray,gray])
Example
We create a color image with dimensions (200,200,3)
Copyimage = (np.random.standard_normal([200,200,3]) * 255).astype(np.uint8)
Next we convert it to grayscale and create another image using cv2.merge() with three gray channels
Copygray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray_three = cv2.merge([gray,gray,gray])
We now draw a filled contour onto the single channel grayscale image (left) with shape (200,200,1) and the three channel grayscale image with shape (200,200,3) (right). The left image showcases the problem you're experiencing since you're trying to display three channels on a single channel image. After merging the grayscale image into three channels, we can now apply color onto the image
Copycontour = np.array([[10,10], [190, 10], [190, 80], [10, 80]])
cv2.fillPoly(gray, [contour], [36,255,12])
cv2.fillPoly(gray_three, [contour], [36,255,12])
Full code
Copyimport cv2
import numpy as np
# Create random color image
image = (np.random.standard_normal([200,200,3]) * 255).astype(np.uint8)
# Convert to grayscale (1 channel)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Merge channels to create color image (3 channels)
gray_three = cv2.merge([gray,gray,gray])
# Fill a contour on both the single channel and three channel image
contour = np.array([[10,10], [190, 10], [190, 80], [10, 80]])
cv2.fillPoly(gray, [contour], [36,255,12])
cv2.fillPoly(gray_three, [contour], [36,255,12])
cv2.imshow('image', image)
cv2.imshow('gray', gray)
cv2.imshow('gray_three', gray_three)
cv2.waitKey()
python - Opencv - Grayscale mode Vs gray color conversion - Stack Overflow
[Question] Grayscale to bgr/rgb
Convert Image Color from Grayscale to RGB OpenCV C++ - Stack Overflow
Converting Grayscale to Color Image using Color Maps
Videos
To illustrate, I've opened up this same color JPEG image:
once using the conversion
img = cv2.imread(path)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
and another by loading it in gray scale mode
img_gray_mode = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
Like you've documented, the diff between the two images is not perfectly 0, I can see diff pixels in towards the left and the bottom
I've summed up the diff too to see
import numpy as np
np.sum(diff)
# I got 6143, on a 494 x 750 image
I tried all cv2.imread() modes
Among all the IMREAD_ modes for cv2.imread(), only IMREAD_COLOR and IMREAD_ANYCOLOR can be converted using COLOR_BGR2GRAY, and both of them gave me the same diff against the image opened in IMREAD_GRAYSCALE
The difference doesn't seem that big. My guess is comes from the differences in the numeric calculations in the two methods (loading grayscale vs conversion to grayscale)
Naturally what you want to avoid is fine tuning your code on a particular version of the image just to find out it was suboptimal for images coming from a different source.
In brief, let's not mix the versions and types in the processing pipeline.
So I'd keep the image sources homogenous, e.g. if you have capturing the image from a video camera in BGR, then I'd use BGR as the source, and do the BGR to grayscale conversion cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
Vice versa if my ultimate source is grayscale then I'd open the files and the video capture in gray scale cv2.imread(path, cv2.IMREAD_GRAYSCALE)
cv2.imread(path, 0) (or rather cv2.imread(path, cv2.IMREAD_GRAYSCALE)) asks the image file reading code to load the image as grayscale. For most file types, some 3rd party library is used to read them. If this library supports grayscale conversion, we’ll be using that library’s conversion routine.
Otherwise, we’re using OpenCV’s implementation of the conversion to grayscale.
There’s no reason to assume one implementation is better than the other, the differences observed are likely due to a different computation order, or to a different assumption about the whitepoint. But note that if the file has a color profile embedded, the 3rd party library might be able to use it to do the conversion, and so will have whitepoint information available. This color profile does not get loaded into OpenCV, so OpenCV will always make an assumption about the whitepoint.
Reading the image directly as grayscale is possibly a bit more efficient. If the 3rd party library converts each pixel as it’s read, we won’t need a temporary memory space to store the full color image (which takes up 3x as much memory as the grayscale image). For a format such as JPEG, which stores intensity and color information separately, reading as grayscale also avoids a lot of computation (we’re directly outputting the intensity value, rather than computing the RGB values and then converting those back to intensity).
Reading directly as grayscale has the possibility of giving different results if the image file gets converted to a different format, as then a different grayscale conversion will be used. Say, you convert your JPEG file to PNG, then using IMREAD_GRAYSCALE will use different libraries to do the grayscale conversion, using OpenCV’s conversion code will ensure both files read identically.
Hey people, I have a question, I wanted to ask that why grayscale to rgb or bgr image conversion with proper colors with opencv in-built functions without a deep learning model is not possible? And if so why does opencv have cvtColor functions with gray2bgr or gray2rgb functions? Under which circumstances do they work?
This is currently set up to expect a grayscale image as input. I think that you are asking how to adapt it to accept a colour input image and return a colour output image. You don't need to change much:
cv::Mat CVCircles::detectedCirclesInImage(cv::Mat img, double dp, double minDist, double param1, double param2, int min_radius, int max_radius) {
if(img.empty())
{
cout << "can not open image " << endl;
return img;
}
Mat img;
if (img.type()==CV_8UC1) {
//input image is grayscale
cvtColor(img, cimg, CV_GRAY2RGB);
} else {
//input image is colour
cimg = img;
cvtColor(img, img, CV_RGB2GRAY);
}
the rest stays as is.
If your input image is colour, you are converting it to gray for processing by HoughCircles, and applying the found circles to the original colour image for output.
The cvtImage routine will simply copy your gray element to each of the three elements R, G, and B for each pixel. In other words if the pixel gray value is 26, then the new image will have R = 26, G = 26, B = 26.
The image presented will still LOOK grayscale even though it contains all 3 color components, all you have essentially done is to triple the space necessary to store the same image.
If indeed you want color to appear in the image (when you view it), this is truly impossible to go from grayscale back to the ORIGINAL colors. There are however means of pseudo-coloring or false coloring the image.
http://en.wikipedia.org/wiki/False_color
http://blog.martinperis.com/2011/09/opencv-pseudocolor-and-chroma-depth.html
http://podeplace.blogspot.com/2012/11/opencv-pseudocolors.html