177 lines
6.2 KiB
Python
Executable File
177 lines
6.2 KiB
Python
Executable File
#!/usr/bin/python
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import unittest
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import math
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#import logging
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#logging.basicConfig(level = logging.DEBUG)
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from gi.repository import Vips
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Vips.leak_set(True)
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unsigned_formats = [Vips.BandFormat.UCHAR,
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Vips.BandFormat.USHORT,
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Vips.BandFormat.UINT]
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signed_formats = [Vips.BandFormat.CHAR,
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Vips.BandFormat.SHORT,
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Vips.BandFormat.INT]
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float_formats = [Vips.BandFormat.FLOAT,
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Vips.BandFormat.DOUBLE]
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complex_formats = [Vips.BandFormat.COMPLEX,
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Vips.BandFormat.DPCOMPLEX]
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int_formats = unsigned_formats + signed_formats
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noncomplex_formats = int_formats + float_formats
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all_formats = int_formats + float_formats + complex_formats
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# Run a function expecting a complex image on a two-band image
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def run_cmplx(fn, image):
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if image.format == Vips.BandFormat.FLOAT:
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new_format = Vips.BandFormat.COMPLEX
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elif image.format == Vips.BandFormat.DOUBLE:
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new_format = Vips.BandFormat.DPCOMPLEX
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else:
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raise "run_cmplx: not float or double"
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# tag as complex, run, revert tagging
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cmplx = image.copy(bands = 1, format = new_format)
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cmplx_result = fn(cmplx)
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return cmplx_result.copy(bands = 2, format = image.format)
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def to_polar(image):
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"""Transform image coordinates to polar.
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The image is transformed so that it is wrapped around a point in the
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centre. Vertical straight lines become circles or segments of circles,
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horizontal straight lines become radial spokes.
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"""
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# xy image, zero in the centre, scaled to fit image to a circle
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xy = Vips.Image.xyz(image.width, image.height)
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xy -= [image.width / 2.0, image.height / 2.0]
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scale = min(image.width, image.height) / float(image.width)
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xy *= 2.0 / scale
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# to polar, scale vertical axis to 360 degrees
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index = run_cmplx(lambda x: x.polar(), xy)
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index *= [1, image.height / 360.0]
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return image.mapim(index)
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def to_rectangular(image):
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"""Transform image coordinates to rectangular.
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The image is transformed so that it is unwrapped from a point in the
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centre. Circles or segments of circles become vertical straight lines,
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radial lines become horizontal lines.
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"""
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# xy image, vertical scaled to 360 degrees
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xy = Vips.Image.xyz(image.width, image.height)
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xy *= [1, 360.0 / image.height]
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# to rect, scale to image rect
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index = run_cmplx(lambda x: x.rect(), xy)
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scale = min(image.width, image.height) / float(image.width)
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index *= scale / 2.0
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index += [image.width / 2.0, image.height / 2.0]
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return image.mapim(index)
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# an expanding zip ... if either of the args is a scalar or a one-element list,
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# duplicate it down the other side
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def zip_expand(x, y):
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# handle singleton list case
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if isinstance(x, list) and len(x) == 1:
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x = x[0]
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if isinstance(y, list) and len(y) == 1:
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y = y[0]
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if isinstance(x, list) and isinstance(y, list):
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return list(zip(x, y))
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elif isinstance(x, list):
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return [[i, y] for i in x]
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elif isinstance(y, list):
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return [[x, j] for j in y]
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else:
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return [[x, y]]
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class TestResample(unittest.TestCase):
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# test a pair of things which can be lists for approx. equality
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def assertAlmostEqualObjects(self, a, b, places = 4, msg = ''):
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# print 'assertAlmostEqualObjects %s = %s' % (a, b)
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for x, y in zip_expand(a, b):
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self.assertAlmostEqual(x, y, places = places, msg = msg)
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def test_affine(self):
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im = Vips.Image.new_from_file("images/IMG_4618.jpg")
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# vsqbs is non-interpolatory, don't test this way
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for name in ["nearest", "bicubic", "bilinear", "nohalo", "lbb"]:
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x = im
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interpolate = Vips.Interpolate.new(name)
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for i in range(4):
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x = x.affine([0, 1, 1, 0], interpolate = interpolate)
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self.assertEqual((x - im).abs().max(), 0)
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def test_reduce(self):
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im = Vips.Image.new_from_file("images/IMG_4618.jpg")
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bicubic = Vips.Interpolate.new("bicubic")
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for fac in [1, 1.1, 1.5, 1.999]:
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for fmt in all_formats:
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x = im.cast(fmt)
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r = x.reduce(fac, fac)
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a = x.affine([1.0 / fac, 0, 0, 1.0 / fac],
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interpolate = bicubic,
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oarea = [0, 0, x.width / fac, x.height / fac])
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d = (r - a).abs().max()
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self.assertLess(d, 5)
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def test_resize(self):
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im = Vips.Image.new_from_file("images/IMG_4618.jpg")
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im2 = im.resize(0.25)
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self.assertEqual(im2.width, im.width // 4)
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self.assertEqual(im2.height, im.height // 4)
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def test_shrink(self):
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im = Vips.Image.new_from_file("images/IMG_4618.jpg")
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im2 = im.shrink(4, 4)
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self.assertEqual(im2.width, im.width // 4)
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self.assertEqual(im2.height, im.height // 4)
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self.assertTrue(abs(im.avg() - im2.avg()) < 1)
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im2 = im.shrink(2.5, 2.5)
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self.assertEqual(im2.width, im.width // 2.5)
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self.assertEqual(im2.height, im.height // 2.5)
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self.assertLess(abs(im.avg() - im2.avg()), 1)
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def test_similarity(self):
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im = Vips.Image.new_from_file("images/IMG_4618.jpg")
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im2 = im.similarity(angle = 90)
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im3 = im.affine([0, -1, 1, 0])
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# rounding in calculating the affine transform from the angle stops this
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# being exactly true
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self.assertLess((im2 - im3).abs().max(), 50)
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def test_similarity_scale(self):
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im = Vips.Image.new_from_file("images/IMG_4618.jpg")
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im2 = im.similarity(scale = 2)
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im3 = im.affine([2, 0, 0, 2])
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self.assertEqual((im2 - im3).abs().max(), 0)
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def test_mapim(self):
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im = Vips.Image.new_from_file("images/IMG_4618.jpg")
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p = to_polar(im)
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r = to_rectangular(p)
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# the left edge (which is squashed to the origin) will be badly
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# distorted, but the rest should not be too bad
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a = r.crop(50, 0, im.width - 50, im.height).gaussblur(2)
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b = im.crop(50, 0, im.width - 50, im.height).gaussblur(2)
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self.assertLess((a - b).abs().max(), 20)
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if __name__ == '__main__':
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unittest.main()
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