How do I use np.linspace() to change the pixels colors of a patch in an image-CodePudding

The

Here is how I did it.

zeros = torch.zeros_like(img) #img is b,c,h,w x1, y1, x2, y2, r, g, b = 16, 50, 100, 100, 6, 2, 4 rgb = torch.tensor([r, g, b]) cx, cy = x2 - x1, y2 - y1 for t in np.linspace(0, 1, 50): px, py = x1 t * cx, y1 t * cy zeros[0, :, int(py), int(px)] = rgb mask = (zeros == 0).float() im = mask * img (1 - mask) *zeros

It is obvious in np.linspace(0, 1, 50) that I am changing 50 pixels. I want to use the same method to change a specific portion of the image, like a 14 x 14 patch of my image if the image size is 224 x 224 or even more. The range of pixels to cover now will be 196 -> (since there are 196 pixels in a 14 x 14 patch) instead of 50. How do I go about it, please?

CodePudding user response：

I assume that img dimensions are BCHW based on the comment in the first line. You can change values pixel by pixel in a for loop (which is quite inefficient):

img[0, :, y, x] = rgb

Or you can change the whole patch. Assuming img is Numpy ndarray and you want to reduce intensity:

img[0, :, 100:200, 100:200] = (img[0, :, 100:200, 100:200] / 1.3).astype(np.uint8)

Just put attention to dimensions and data types. You have to convert the result to uint8, same as original image.

To set some intensity gradient on the patch:

gradient = np.tile(np.linspace(0.2, 0.9, 100), (3, 100, 1)) img[0, :, 100:200, 100:200] = (img[0, :, 100:200, 100:200] * gradient).astype(np.uint8)

Here 0.2-0.9 is intensity and 100 is the patch size. So gradient is 3x100x100 tensor. I create it by taking a vector of intensities of shape (100,) and tiling it so it becomes a 3x100x100 tensor. The last line is a multiplication of each value in img by corresponding value in gradient.

When working with images it is much more convenient to represent them in HWC format. You can easily achieve that by writing

img = img[0].transpose(1,2,0)

This will swap dimensions CHW->HWC (as Batch is removed by indexing [0] ). Then you can easily write:

img[y:y patch_h, x:x patch_w] = rgb

and Python will automatically will try to fit missing dimension. In this case a 3-d vector rgb will be broadcasted to patch_hxpatch_wx3 tensor. Same logic works for Python native arrays, NumPy ndarray, Pytorch and TF tensors. Then you can (for example) flip a patch by

img[y:y patch_h, x:x patch_w] = np.fliplr(img[y:y patch_h, x:x patch_w])