import math from helper import unittest, PillowTestCase, hopper from PIL import Image class TestImageTransform(PillowTestCase): def test_sanity(self): from PIL import ImageTransform im = Image.new("L", (100, 100)) seq = tuple(range(10)) transform = ImageTransform.AffineTransform(seq[:6]) im.transform((100, 100), transform) transform = ImageTransform.ExtentTransform(seq[:4]) im.transform((100, 100), transform) transform = ImageTransform.QuadTransform(seq[:8]) im.transform((100, 100), transform) transform = ImageTransform.MeshTransform([(seq[:4], seq[:8])]) im.transform((100, 100), transform) def test_extent(self): im = hopper('RGB') (w, h) = im.size transformed = im.transform(im.size, Image.EXTENT, (0, 0, w//2, h//2), # ul -> lr Image.BILINEAR) scaled = im.resize((w*2, h*2), Image.BILINEAR).crop((0, 0, w, h)) # undone -- precision? self.assert_image_similar(transformed, scaled, 23) def test_quad(self): # one simple quad transform, equivalent to scale & crop upper left quad im = hopper('RGB') (w, h) = im.size transformed = im.transform(im.size, Image.QUAD, (0, 0, 0, h//2, # ul -> ccw around quad: w//2, h//2, w//2, 0), Image.BILINEAR) scaled = im.transform((w, h), Image.AFFINE, (.5, 0, 0, 0, .5, 0), Image.BILINEAR) self.assert_image_equal(transformed, scaled) def test_fill(self): for mode, pixel in [ ['RGB', (255, 0, 0)], ['RGBA', (255, 0, 0, 255)], ['LA', (76, 0)] ]: im = hopper(mode) (w, h) = im.size transformed = im.transform(im.size, Image.EXTENT, (0, 0, w*2, h*2), Image.BILINEAR, fillcolor='red') self.assertEqual(transformed.getpixel((w-1, h-1)), pixel) def test_mesh(self): # this should be a checkerboard of halfsized hoppers in ul, lr im = hopper('RGBA') (w, h) = im.size transformed = im.transform(im.size, Image.MESH, [((0, 0, w//2, h//2), # box (0, 0, 0, h, w, h, w, 0)), # ul -> ccw around quad ((w//2, h//2, w, h), # box (0, 0, 0, h, w, h, w, 0))], # ul -> ccw around quad Image.BILINEAR) scaled = im.transform((w//2, h//2), Image.AFFINE, (2, 0, 0, 0, 2, 0), Image.BILINEAR) checker = Image.new('RGBA', im.size) checker.paste(scaled, (0, 0)) checker.paste(scaled, (w//2, h//2)) self.assert_image_equal(transformed, checker) # now, check to see that the extra area is (0, 0, 0, 0) blank = Image.new('RGBA', (w//2, h//2), (0, 0, 0, 0)) self.assert_image_equal(blank, transformed.crop((w//2, 0, w, h//2))) self.assert_image_equal(blank, transformed.crop((0, h//2, w//2, h))) def _test_alpha_premult(self, op): # create image with half white, half black, # with the black half transparent. # do op, # there should be no darkness in the white section. im = Image.new('RGBA', (10, 10), (0, 0, 0, 0)) im2 = Image.new('RGBA', (5, 10), (255, 255, 255, 255)) im.paste(im2, (0, 0)) im = op(im, (40, 10)) im_background = Image.new('RGB', (40, 10), (255, 255, 255)) im_background.paste(im, (0, 0), im) hist = im_background.histogram() self.assertEqual(40*10, hist[-1]) def test_alpha_premult_resize(self): def op(im, sz): return im.resize(sz, Image.BILINEAR) self._test_alpha_premult(op) def test_alpha_premult_transform(self): def op(im, sz): (w, h) = im.size return im.transform(sz, Image.EXTENT, (0, 0, w, h), Image.BILINEAR) self._test_alpha_premult(op) def test_blank_fill(self): # attempting to hit # https://github.com/python-pillow/Pillow/issues/254 reported # # issue is that transforms with transparent overflow area # contained junk from previous images, especially on systems with # constrained memory. So, attempt to fill up memory with a # pattern, free it, and then run the mesh test again. Using a 1Mp # image with 4 bands, for 4 megs of data allocated, x 64. OMM (64 # bit 12.04 VM with 512 megs available, this fails with Pillow < # a0eaf06cc5f62a6fb6de556989ac1014ff3348ea # # Running by default, but I'd totally understand not doing it in # the future pattern = [ Image.new('RGBA', (1024, 1024), (a, a, a, a)) for a in range(1, 65) ] # Yeah. Watch some JIT optimize this out. pattern = None self.test_mesh() def test_missing_method_data(self): im = hopper() self.assertRaises(ValueError, im.transform, (100, 100), None) class TestImageTransformAffine(PillowTestCase): transform = Image.AFFINE def _test_image(self): im = hopper('RGB') return im.crop((10, 20, im.width - 10, im.height - 20)) def _test_rotate(self, deg, transpose): im = self._test_image() angle = - math.radians(deg) matrix = [ round(math.cos(angle), 15), round(math.sin(angle), 15), 0.0, round(-math.sin(angle), 15), round(math.cos(angle), 15), 0.0, 0, 0] matrix[2] = (1 - matrix[0] - matrix[1]) * im.width / 2 matrix[5] = (1 - matrix[3] - matrix[4]) * im.height / 2 if transpose is not None: transposed = im.transpose(transpose) else: transposed = im for resample in [Image.NEAREST, Image.BILINEAR, Image.BICUBIC]: transformed = im.transform(transposed.size, self.transform, matrix, resample) self.assert_image_equal(transposed, transformed) def test_rotate_0_deg(self): self._test_rotate(0, None) def test_rotate_90_deg(self): self._test_rotate(90, Image.ROTATE_90) def test_rotate_180_deg(self): self._test_rotate(180, Image.ROTATE_180) def test_rotate_270_deg(self): self._test_rotate(270, Image.ROTATE_270) def _test_resize(self, scale, epsilonscale): im = self._test_image() size_up = int(round(im.width * scale)), int(round(im.height * scale)) matrix_up = [ 1 / scale, 0, 0, 0, 1 / scale, 0, 0, 0] matrix_down = [ scale, 0, 0, 0, scale, 0, 0, 0] for resample, epsilon in [(Image.NEAREST, 0), (Image.BILINEAR, 2), (Image.BICUBIC, 1)]: transformed = im.transform( size_up, self.transform, matrix_up, resample) transformed = transformed.transform( im.size, self.transform, matrix_down, resample) self.assert_image_similar(transformed, im, epsilon * epsilonscale) def test_resize_1_1x(self): self._test_resize(1.1, 6.9) def test_resize_1_5x(self): self._test_resize(1.5, 5.5) def test_resize_2_0x(self): self._test_resize(2.0, 5.5) def test_resize_2_3x(self): self._test_resize(2.3, 3.7) def test_resize_2_5x(self): self._test_resize(2.5, 3.7) def _test_translate(self, x, y, epsilonscale): im = self._test_image() size_up = int(round(im.width + x)), int(round(im.height + y)) matrix_up = [ 1, 0, -x, 0, 1, -y, 0, 0] matrix_down = [ 1, 0, x, 0, 1, y, 0, 0] for resample, epsilon in [(Image.NEAREST, 0), (Image.BILINEAR, 1.5), (Image.BICUBIC, 1)]: transformed = im.transform( size_up, self.transform, matrix_up, resample) transformed = transformed.transform( im.size, self.transform, matrix_down, resample) self.assert_image_similar(transformed, im, epsilon * epsilonscale) def test_translate_0_1(self): self._test_translate(.1, 0, 3.7) def test_translate_0_6(self): self._test_translate(.6, 0, 9.1) def test_translate_50(self): self._test_translate(50, 50, 0) class TestImageTransformPerspective(TestImageTransformAffine): # Repeat all tests for AFFINE transformations with PERSPECTIVE transform = Image.PERSPECTIVE if __name__ == '__main__': unittest.main()