摘自https://docs.opencv.org/4.2.0/df/d9d/tutorial_py_colorspaces.html
Changing Color-space
There are more than 150 color-space conversion methods available in OpenCV. But we will look into only two: BGR ↔ Gray and BGR ↔ HSV.
For color conversion, we use the function cv.cvtColor(input_image, flag) where flag determines the type of conversion.
For BGR → Gray conversion, we use the flag cv.COLOR_BGR2GRAY. For BGR → HSV, we use the flag cv.COLOR_BGR2HSV.
For HSV, hue range is [0,179], saturation range is [0,255], and value range is [0,255]. Different software use different scales. So if you are comparing OpenCV values with them, you need to normalize these ranges.
Object Tracking
Now that we know how to convert a BGR image to HSV, we can use this to extract a colored object. In HSV, it is easier to represent a color than in BGR color-space.
How to find HSV values to track?
It is very simple and you can use the same function, cv.cvtColor(). Instead of passing an image, you just pass the BGR values you want. For example, to find the HSV value of Green:
>>> green = np.uint8([[[0,255,0 ]]])
>>> hsv_green = cv.cvtColor(green,cv.COLOR_BGR2HSV)
>>> print( hsv_green )
[[[ 60 255 255]]]Now you take [H-10, 100,100] and [H+10, 255, 255] as the lower bound and upper bound respectively. Apart from this method, you can use any image editing tools like GIMP or any online converters to find these values.