Bruno Madeira
ec51afeba1
Updated requirements.txt to include opencv. Add patch_search option (named version) to existing nodes. Additional notes: - make_seamless.py should use the methods implemented in path_search.py (currently it is not, and its implementation has a bug). - consider importing cv only in functions, this way the prior solution can still be used w/o updating requirements... the speedup is so worth though... it might mislead potential users to keep using the 1st implementation... may should remove old solution and force opencv instead? ( to decide later, after seamless node implementation )
117 lines
5.3 KiB
Python
117 lines
5.3 KiB
Python
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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def find_patch_v1(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
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):
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"""
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Re-implementation of the version 1.0 solution using matchTemplate to improve performance.
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Uses the total instead of the mean for the errors matrix; other than that should be exactly the same.
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Does not output the same as version 1.0.
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"""
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blks_sqdiffs = []
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if ref_block_left is not None:
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blks_sqdiffs.append(cv.matchTemplate(
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image=texture[:, :-block_size + overlap],
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templ=ref_block_left[:, -overlap:], method=cv.TM_SQDIFF))
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if ref_block_right is not None:
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blks_sqdiffs.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=cv.TM_SQDIFF))
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if ref_block_top is not None:
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blks_sqdiffs.append(cv.matchTemplate(
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image=texture[:-block_size + overlap, :],
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templ=ref_block_top[-overlap:, :], method=cv.TM_SQDIFF))
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if ref_block_bottom is not None:
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blks_sqdiffs.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=cv.TM_SQDIFF))
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err_mat = np.add.reduce(blks_sqdiffs)
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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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def find_patch_v2(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
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):
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"""
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Same as find_patch_v1 but chooses maximum error instead of the sum of errors,
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when patching with multiple adjacent blocks.
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"""
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blks_sqdiffs = []
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if ref_block_left is not None:
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blks_sqdiffs.append(cv.matchTemplate(
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image=texture[:, :-block_size + overlap],
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templ=ref_block_left[:, -overlap:], method=cv.TM_SQDIFF))
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if ref_block_right is not None:
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blks_sqdiffs.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=cv.TM_SQDIFF))
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if ref_block_top is not None:
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blks_sqdiffs.append(cv.matchTemplate(
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image=texture[:-block_size + overlap, :],
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templ=ref_block_top[-overlap:, :], method=cv.TM_SQDIFF))
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if ref_block_bottom is not None:
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blks_sqdiffs.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=cv.TM_SQDIFF))
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err_mat = np.maximum.reduce(blks_sqdiffs)
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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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def find_patch_v3(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
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):
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"""
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This version makes use of TM_CCOEFF in matchTemplate instead of TM_SQDIFF.
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"""
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blks_ccs = []
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t_max = 0
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if ref_block_left is not None:
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blk_overlap = ref_block_left[:, -overlap:]
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blks_ccs.append(cv.matchTemplate(
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image=texture[:, :-block_size + overlap],
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templ=blk_overlap, method=cv.TM_CCOEFF))
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t_max = cv.matchTemplate(blk_overlap, blk_overlap, method=cv.TM_CCOEFF)[0]
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if ref_block_right is not None:
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blk_overlap = ref_block_right[:, :overlap]
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blks_ccs.append(cv.matchTemplate(
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image=np.roll(texture, -block_size + overlap, axis=1)[:, :-block_size + overlap],
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templ=blk_overlap, method=cv.TM_CCOEFF))
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t_max += cv.matchTemplate(blk_overlap, blk_overlap, method=cv.TM_CCOEFF)[0]
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if ref_block_top is not None:
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blk_overlap = ref_block_top[-overlap:, :]
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blks_ccs.append(cv.matchTemplate(
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image=texture[:-block_size + overlap, :],
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templ=blk_overlap, method=cv.TM_CCOEFF))
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t_max += cv.matchTemplate(blk_overlap, blk_overlap, method=cv.TM_CCOEFF)[0]
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if ref_block_bottom is not None:
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blk_overlap = ref_block_bottom[:overlap, :]
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blks_ccs.append(cv.matchTemplate(
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image=np.roll(texture, -block_size + overlap, axis=0)[:-block_size + overlap, :],
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templ=blk_overlap, method=cv.TM_CCOEFF))
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t_max += cv.matchTemplate(blk_overlap, blk_overlap, method=cv.TM_CCOEFF)[0]
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err_mat = t_max - np.add.reduce(blks_ccs)
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print(f"min val = {np.min(err_mat)}")
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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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