I'm trying to extract a series of random patches of image from a larger satellite image (from worldview3). I want to extract patches of uniform size (say 512x512 for example). If it were a normal image, any number of libraries could do this easily. But I want to use rasterio to retain the geographic information of the image patch.
So far the code I've written identifies the upper left pixel coordinate using
and using the resulting bounding box to get the height and the width. Then I use numpy to randomly generate a new random coordinate within the image extent:
new_col = np.random.randint(min(bounds.left, bounds.right), max(bounds.left, bounds.right)-(size+1)) new_row = np.random.randint(min(bounds.top, bounds.bottom), max(bounds.top, bounds.bottom)-(size+1))
And then I subtract the patch size (512) from the new row and column and use those new coordinates as the minx, miny, maxx, maxy and crop from there using
mask(image, shapes=coords, crop=True)
With a non-georeferenced image where the upper left is (0,0) and the lower right is (M,N) this works flawlessly. Similarly for a NAIP image this seems to work. But with the worldview3 image the size is not uniform. I'll get an image size like 700x2000 for example, where I want it to be 512x512. My thought was that the NAIP image I tested with was in UTM, but that the worldview3 image was in lat/long so that subtracting the patch size from the random row and column didn't translate to a uniform pixel size. I still think the error has to do with lat/long but after reprojecting the image to UTM the problem persists.
So, is there any way I can crop the worldview3 image using rasterio but using pixel coordinates instead of geographic coordinates so that I can crop a uniform image size from the larger image but still retain the geographic information of the cropped image patch?