Improvement: Add predefined blur kernels
- Add a few predefined blur kernels, requested by jerri in #104. - Add compton-convgen.py to generate blur kernels.
This commit is contained in:
Richard Grenville
parent
abd559c512
commit
a3f6f4442b
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@ -0,0 +1,132 @@
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#! /usr/bin/env python3
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# -*- coding: utf-8 -*-
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# vim:fileencoding=utf-8
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import math, argparse
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class CGError(Exception):
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def __init__(self, value):
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self.value = value
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def __str__(self):
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return repr(self.value)
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class CGBadArg(CGError): pass
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class CGInternal(CGError): pass
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def mbuild(width, height):
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"""Build a NxN matrix filled with 0."""
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result = list()
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for i in range(height):
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result.append(list())
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for j in range(width):
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result[i].append(0.0)
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return result
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def mdump(matrix):
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"""Dump a matrix in natural format."""
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for col in matrix:
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print("[ ", end = '');
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for ele in col:
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print(format(ele, "13.6g") + ", ", end = " ")
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print("],")
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def mdumpcompton(matrix):
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"""Dump a matrix in compton's format."""
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width = len(matrix[0])
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height = len(matrix)
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print("{},{},".format(width, height), end = '')
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for i in range(height):
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for j in range(width):
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if int(height / 2) == i and int(width / 2) == j:
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continue;
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print(format(matrix[i][j], ".6f"), end = ",")
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print()
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def mnormalize(matrix):
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"""Scale a matrix according to the value in the center."""
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width = len(matrix[0])
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height = len(matrix)
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factor = 1.0 / matrix[int(height / 2)][int(width / 2)]
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if 1.0 == factor: return matrix
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for i in range(height):
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for j in range(width):
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matrix[i][j] *= factor
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return matrix
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def mmirror4(matrix):
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"""Do a 4-way mirroring on a matrix from top-left corner."""
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width = len(matrix[0])
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height = len(matrix)
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for i in range(height):
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for j in range(width):
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x = min(i, height - 1 - i)
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y = min(j, width - 1 - j)
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matrix[i][j] = matrix[x][y]
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return matrix
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def gen_gaussian(width, height, factors):
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"""Build a Gaussian blur kernel."""
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if width != height:
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raise CGBadArg("Cannot build an uneven Gaussian blur kernel.")
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size = width
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sigma = float(factors.get('sigma', 0.84089642))
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result = mbuild(size, size)
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for i in range(int(size / 2) + 1):
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for j in range(int(size / 2) + 1):
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diffx = i - int(size / 2);
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diffy = j - int(size / 2);
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result[i][j] = 1.0 / (2 * math.pi * sigma) * pow(math.e, - (diffx * diffx + diffy * diffy) / (2 * sigma * sigma))
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mnormalize(result)
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mmirror4(result)
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return result
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def gen_box(width, height, factors):
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"""Build a box blur kernel."""
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result = mbuild(width, height)
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for i in range(height):
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for j in range(width):
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result[i][j] = 1.0
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return result
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def gen_invalid(width, height, factors):
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raise CGBadArg("Unknown kernel type.")
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def args_readfactors(lst):
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"""Parse the factor arguments."""
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factors = dict()
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if lst:
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for s in lst:
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res = s.partition('=')
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if not res[0]:
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raise CGBadArg("Factor has no key.")
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if not res[2]:
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raise CGBadArg("Factor has no value.")
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factors[res[0]] = float(res[2])
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return factors
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parser = argparse.ArgumentParser(description='Build a convolution kernel.')
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parser.add_argument('type', help='Type of convolution kernel. May be "gaussian" (factor sigma = 0.84089642) or "box".')
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parser.add_argument('width', type=int, help='Width of convolution kernel. Must be an odd number.')
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parser.add_argument('height', nargs='?', type=int, help='Height of convolution kernel. Must be an odd number. Equals to width if omitted.')
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parser.add_argument('-f', '--factor', nargs='+', help='Factors of the convolution kernel, in name=value format.')
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parser.add_argument('--dump-compton', action='store_true', help='Dump in compton format.')
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args = parser.parse_args()
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width = args.width
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height = args.height
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if not height:
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height = width
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if not (width > 0 and height > 0):
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raise CGBadArg("Invalid width/height.")
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factors = args_readfactors(args.factor)
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funcs = dict(gaussian = gen_gaussian, box = gen_box)
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matrix = (funcs.get(args.type, gen_invalid))(width, height, factors)
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if args.dump_compton:
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mdumpcompton(matrix)
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else:
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mdump(matrix)
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@ -4237,6 +4237,9 @@ usage(void) {
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" --blur-background-fixed.\n"
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" A 7x7 Guassian blur kernel looks like:\n"
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" --blur-kern '7,7,0.000003,0.000102,0.000849,0.001723,0.000849,0.000102,0.000003,0.000102,0.003494,0.029143,0.059106,0.029143,0.003494,0.000102,0.000849,0.029143,0.243117,0.493069,0.243117,0.029143,0.000849,0.001723,0.059106,0.493069,0.493069,0.059106,0.001723,0.000849,0.029143,0.243117,0.493069,0.243117,0.029143,0.000849,0.000102,0.003494,0.029143,0.059106,0.029143,0.003494,0.000102,0.000003,0.000102,0.000849,0.001723,0.000849,0.000102,0.000003'\n"
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" May also be one the predefined kernels: 3x3box (default), 5x5box,\n"
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" 7x7box, 3x3gaussian, 5x5gaussian, 7x7gaussian, 9x9gaussian,\n"
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" 11x11gaussian.\n"
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"--blur-background-exclude condition\n"
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" Exclude conditions for background blur.\n"
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"--resize-damage integer\n"
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@ -4589,6 +4592,31 @@ parse_matrix_err:
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return NULL;
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}
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/**
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* Parse a convolution kernel.
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*/
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static inline XFixed *
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parse_conv_kern(session_t *ps, const char *src) {
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static const struct {
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const char *name;
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const char *kern_str;
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} CONV_KERN_PREDEF[] = {
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{ "3x3box", "3,3,1,1,1,1,1,1,1,1," },
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{ "5x5box", "5,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1," },
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{ "7x7box", "7,7,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1," },
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{ "3x3gaussian", "3,3,0.243117,0.493069,0.243117,0.493069,0.493069,0.243117,0.493069,0.243117," },
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{ "5x5gaussian", "5,5,0.003493,0.029143,0.059106,0.029143,0.003493,0.029143,0.243117,0.493069,0.243117,0.029143,0.059106,0.493069,0.493069,0.059106,0.029143,0.243117,0.493069,0.243117,0.029143,0.003493,0.029143,0.059106,0.029143,0.003493," },
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{ "7x7gaussian", "7,7,0.000003,0.000102,0.000849,0.001723,0.000849,0.000102,0.000003,0.000102,0.003493,0.029143,0.059106,0.029143,0.003493,0.000102,0.000849,0.029143,0.243117,0.493069,0.243117,0.029143,0.000849,0.001723,0.059106,0.493069,0.493069,0.059106,0.001723,0.000849,0.029143,0.243117,0.493069,0.243117,0.029143,0.000849,0.000102,0.003493,0.029143,0.059106,0.029143,0.003493,0.000102,0.000003,0.000102,0.000849,0.001723,0.000849,0.000102,0.000003," },
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{ "9x9gaussian", "9,9,0.000000,0.000000,0.000001,0.000006,0.000012,0.000006,0.000001,0.000000,0.000000,0.000000,0.000003,0.000102,0.000849,0.001723,0.000849,0.000102,0.000003,0.000000,0.000001,0.000102,0.003493,0.029143,0.059106,0.029143,0.003493,0.000102,0.000001,0.000006,0.000849,0.029143,0.243117,0.493069,0.243117,0.029143,0.000849,0.000006,0.000012,0.001723,0.059106,0.493069,0.493069,0.059106,0.001723,0.000012,0.000006,0.000849,0.029143,0.243117,0.493069,0.243117,0.029143,0.000849,0.000006,0.000001,0.000102,0.003493,0.029143,0.059106,0.029143,0.003493,0.000102,0.000001,0.000000,0.000003,0.000102,0.000849,0.001723,0.000849,0.000102,0.000003,0.000000,0.000000,0.000000,0.000001,0.000006,0.000012,0.000006,0.000001,0.000000,0.000000," },
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{ "11x11gaussian", "11,11,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000001,0.000006,0.000012,0.000006,0.000001,0.000000,0.000000,0.000000,0.000000,0.000000,0.000003,0.000102,0.000849,0.001723,0.000849,0.000102,0.000003,0.000000,0.000000,0.000000,0.000001,0.000102,0.003493,0.029143,0.059106,0.029143,0.003493,0.000102,0.000001,0.000000,0.000000,0.000006,0.000849,0.029143,0.243117,0.493069,0.243117,0.029143,0.000849,0.000006,0.000000,0.000000,0.000012,0.001723,0.059106,0.493069,0.493069,0.059106,0.001723,0.000012,0.000000,0.000000,0.000006,0.000849,0.029143,0.243117,0.493069,0.243117,0.029143,0.000849,0.000006,0.000000,0.000000,0.000001,0.000102,0.003493,0.029143,0.059106,0.029143,0.003493,0.000102,0.000001,0.000000,0.000000,0.000000,0.000003,0.000102,0.000849,0.001723,0.000849,0.000102,0.000003,0.000000,0.000000,0.000000,0.000000,0.000000,0.000001,0.000006,0.000012,0.000006,0.000001,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000," },
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};
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for (int i = 0;
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i < sizeof(CONV_KERN_PREDEF) / sizeof(CONV_KERN_PREDEF[0]); ++i)
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if (!strcmp(CONV_KERN_PREDEF[i].name, src))
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return parse_matrix(ps, CONV_KERN_PREDEF[i].kern_str);
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return parse_matrix(ps, src);
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}
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/**
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* Parse a condition list in configuration file.
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*/
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@ -5097,7 +5125,7 @@ get_cfg(session_t *ps, int argc, char *const *argv, bool first_pass) {
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case 301:
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// --blur-kern
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free(ps->o.blur_kern);
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if (!(ps->o.blur_kern = parse_matrix(ps, optarg)))
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if (!(ps->o.blur_kern = parse_conv_kern(ps, optarg)))
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exit(1);
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case 302:
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// --resize-damage
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