def adaptiveselection()

in src/controlnet_aux/leres/leres/depthmap.py [0:0]

• 27 lines of code
• 11 McCabe index (conditional complexity)

def adaptiveselection(integral_grad, patch_bound_list, gf):
    patchlist = {}
    count = 0
    height, width = integral_grad.shape

    search_step = int(32/factor)

    # Go through all patches
    for c in range(len(patch_bound_list)):
        # Get patch
        bbox = patch_bound_list[str(c)]['rect']

        # Compute the amount of gradients present in the patch from the integral image.
        cgf = getGF_fromintegral(integral_grad, bbox)/(bbox[2]*bbox[3])

        # Check if patching is beneficial by comparing the gradient density of the patch to
        # the gradient density of the whole image
        if cgf >= gf:
            bbox_test = bbox.copy()
            patchlist[str(count)] = {}

            # Enlarge each patch until the gradient density of the patch is equal
            # to the whole image gradient density
            while True:

                bbox_test[0] = bbox_test[0] - int(search_step/2)
                bbox_test[1] = bbox_test[1] - int(search_step/2)

                bbox_test[2] = bbox_test[2] + search_step
                bbox_test[3] = bbox_test[3] + search_step

                # Check if we are still within the image
                if bbox_test[0] < 0 or bbox_test[1] < 0 or bbox_test[1] + bbox_test[3] >= height \
                        or bbox_test[0] + bbox_test[2] >= width:
                    break

                # Compare gradient density
                cgf = getGF_fromintegral(integral_grad, bbox_test)/(bbox_test[2]*bbox_test[3])
                if cgf < gf:
                    break
                bbox = bbox_test.copy()

            # Add patch to selected patches
            patchlist[str(count)]['rect'] = bbox
            patchlist[str(count)]['size'] = bbox[2]
            count = count + 1
    
    # Return selected patches
    return patchlist