Bruno Madeira
0287e2d4a2
* refactored patch_search.py * fixed H Seam position on v2 * fixed seamless node bar total steps not accounting for batch size * added uicd logic to make_seamless2.py * removed mains from make_seamless.py & make_seamless2.py * other minor cleanups - triggered error in find_patch_vx due to random gen having a value equal or lesser than zero as argument. Was not able to reproduce the error again so far. Conjecture: it may be the case that the min error was negative due to lack of precision when using patch v3, resulting in an empty list of candidates if tolerance is not zero; if this is the case the error should happen only when using version 3.
111 lines
3.9 KiB
Python
111 lines
3.9 KiB
Python
from .jena2020.generate import findPatchVertical, findPatchHorizontal, findPatchBoth
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import numpy as np
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import cv2 as cv
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epsilon = np.finfo(float).eps
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# region get methods by version
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def get_find_patch_to_the_right_method(version: int):
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match version:
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case 0:
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return findPatchHorizontal
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case _:
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def vx_right(left_block, image, block_size, overlap, tolerance, rng):
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return find_patch_vx(left_block, None, None, None,
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image, block_size, overlap, tolerance, rng, version)
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return vx_right
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def get_find_patch_below_method(version: int):
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match version:
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case 0:
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return findPatchVertical
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case _:
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def vx_below(top_block, image, block_size, overlap, tolerance, rng):
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return find_patch_vx(None, None, top_block, None,
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image, block_size, overlap, tolerance, rng, version)
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return vx_below
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def get_find_patch_both_method(version: int):
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match version:
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case 0:
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return findPatchBoth
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case _:
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def vx_both(left_block, top_block, image, block_size, overlap, tolerance, rng):
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return find_patch_vx(left_block, None, top_block, None,
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image, block_size, overlap, tolerance, rng, version)
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return vx_both
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def get_generic_find_patch_method(version: int):
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def vx_patch_find(ref_block_left, ref_block_right, ref_block_top, ref_block_bottom,
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texture, block_size, overlap, tolerance, rng):
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return find_patch_vx(ref_block_left, ref_block_right, ref_block_top, ref_block_bottom,
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texture, block_size, overlap, tolerance, rng, version)
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return vx_patch_find
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def compute_errors(diffs: list[np.ndarray], version: int) -> np.ndarray:
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match version:
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case 1:
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return np.add.reduce(diffs)
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case 2:
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return np.maximum.reduce(diffs)
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case 3:
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return 1 - np.minimum.reduce(diffs) # values from 0 to 2
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case _:
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raise NotImplemented()
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def get_match_template_method(version: int) -> int:
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match version:
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case 1:
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return cv.TM_SQDIFF
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case 2:
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return cv.TM_SQDIFF
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case 3:
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return cv.TM_CCORR_NORMED
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case _:
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raise NotImplemented()
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# endregion
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def find_patch_vx(ref_block_left, ref_block_right, ref_block_top, ref_block_bottom,
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texture, block_size, overlap, tolerance,
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rng: np.random.Generator, version):
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blks_diffs = []
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template_method = get_match_template_method(version)
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if ref_block_left is not None:
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blks_diffs.append(cv.matchTemplate(
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image=texture[:, :-block_size + overlap],
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templ=ref_block_left[:, -overlap:], method=template_method))
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if ref_block_right is not None:
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blks_diffs.append(cv.matchTemplate(
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image=np.roll(texture, -block_size + overlap, axis=1)[:, :-block_size + overlap],
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templ=ref_block_right[:, :overlap], method=template_method))
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if ref_block_top is not None:
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blks_diffs.append(cv.matchTemplate(
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image=texture[:-block_size + overlap, :],
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templ=ref_block_top[-overlap:, :], method=template_method))
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if ref_block_bottom is not None:
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blks_diffs.append(cv.matchTemplate(
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image=np.roll(texture, -block_size + overlap, axis=0)[:-block_size + overlap, :],
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templ=ref_block_bottom[:overlap, :], method=template_method))
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err_mat = compute_errors(blks_diffs, version)
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min_val = np.min(err_mat[err_mat > 0 if tolerance > 0 else True]) # ignore zeroes to enforce tolerance usage
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y, x = np.nonzero(err_mat <= (1.0 + tolerance) * min_val)
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c = rng.integers(len(y))
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y, x = y[c], x[c]
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return texture[y:y + block_size, x:x + block_size]
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