Status Updates From Learning OpenCV 4 Computer ...

Using itemset and set are faster but usually used in little portion of the image. When you need a large portion (ROI - region of interest) you can use openCV functions or numpy slides, prefer to use this.
img[:, :, 0] -> all y,x of blue channel
img[:, :, 1] -> all y,x of green channel
img[:, :, 2] -> all y,x of red channel
Having ROI you can modify just a part of the image.

FAMILIARIZE NUMPY AND NUMPY.ARRAY

Using itemset and set are faster but usually used in little portion of the image. When you need a large portion (ROI - region of interest) you can use openCV functions or numpy slides, prefer to use this.
img[:, :, 0] -> all y,x of blue channel
img[:, :, 1] -> all y,x of green channel
img[:, :, 2] -> all y,x of red channel
Having ROI you can modify just a part of the image.

Familiarize Numpy and numpy.array

We may access these values by using an expression such as image[0, 0] or image[0, 0, 0]. The first index is the pixel's y coordinate or row, 0 being the top. The second index is the pixel's x coordinate or column, 0 being the leftmost.
bytearray returns an array of bytes, this can be used to create random bytes to images, remember to use reshape with their proper channels.

An image is a multidimensional array, it has columns and rows of pixels and each pixel as a value.
0-255 is grayscale image, it has one channel.
You can convert an image using cv2.cvtColor.
BRG (Blue-Red-Green), it has three channels.
You can use img.shape to get shape properties.