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Merge pull request #882 from hugovk/effects
Tests and access functions for Effects.c
This commit is contained in:
wiredfool
commit
86d5d8abed
39
PIL/Image.py
39
PIL/Image.py
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@ -1910,6 +1910,16 @@ class Image:
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im = self.im.transpose(method)
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return self._new(im)
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def effect_spread(self, distance):
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"""
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Randomly spread pixels in an image.
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:param distance: Distance to spread pixels.
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"""
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self.load()
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im = self.im.effect_spread(distance)
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return self._new(im)
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# --------------------------------------------------------------------
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# Lazy operations
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@ -2419,3 +2429,32 @@ def _show(image, **options):
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def _showxv(image, title=None, **options):
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from PIL import ImageShow
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ImageShow.show(image, title, **options)
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# --------------------------------------------------------------------
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# Effects
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def effect_mandelbrot(size, extent, quality):
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"""
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Generate a Mandelbrot set covering the given extent.
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:param size: The requested size in pixels, as a 2-tuple:
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(width, height).
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:param extent: The extent to cover, as a 4-tuple:
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(x0, y0, x1, y2).
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:param quality: Quality.
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"""
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return Image()._new(core.effect_mandelbrot(size, extent, quality))
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def effect_noise(size, sigma):
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"""
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Generate Gaussian noise centered around 128.
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:param size: The requested size in pixels, as a 2-tuple:
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(width, height).
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:param sigma: Standard deviation of noise.
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"""
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return Image()._new(core.effect_noise(size, sigma))
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# End of file
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BIN
Tests/images/effect_mandelbrot.png
Normal file
BIN
Tests/images/effect_mandelbrot.png
Normal file
Binary file not shown.
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After Width: | Height: | Size: 13 KiB |
BIN
Tests/images/effect_spread.png
Normal file
BIN
Tests/images/effect_spread.png
Normal file
Binary file not shown.
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After Width: | Height: | Size: 42 KiB |
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@ -1,6 +1,7 @@
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from helper import unittest, PillowTestCase, lena
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from PIL import Image
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import sys
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class TestImage(PillowTestCase):
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@ -140,6 +141,60 @@ class TestImage(PillowTestCase):
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img_colors = sorted(img.getcolors())
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self.assertEqual(img_colors, expected_colors)
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def test_effect_mandelbrot(self):
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# Arrange
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size = (512, 512)
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extent = (-3, -2.5, 2, 2.5)
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quality = 100
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# Act
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im = Image.effect_mandelbrot(size, extent, quality)
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# Assert
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self.assertEqual(im.size, (512, 512))
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im2 = Image.open('Tests/images/effect_mandelbrot.png')
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self.assert_image_equal(im, im2)
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def test_effect_mandelbrot_bad_arguments(self):
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# Arrange
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size = (512, 512)
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# Get coordinates the wrong way round:
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extent = (+3, +2.5, -2, -2.5)
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# Quality < 2:
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quality = 1
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# Act/Assert
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self.assertRaises(
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ValueError,
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lambda: Image.effect_mandelbrot(size, extent, quality))
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@unittest.skipUnless(sys.platform.startswith('win32'),
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"Stalls on Travis CI, passes on Windows")
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def test_effect_noise(self):
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# Arrange
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size = (100, 100)
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sigma = 128
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# Act
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im = Image.effect_noise(size, sigma)
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# Assert
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self.assertEqual(im.size, (100, 100))
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self.assertEqual(im.getpixel((0, 0)), 60)
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self.assertEqual(im.getpixel((0, 1)), 28)
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def test_effect_spread(self):
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# Arrange
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im = lena()
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distance = 10
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# Act
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im2 = im.effect_spread(distance)
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# Assert
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self.assertEqual(im.size, (128, 128))
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im3 = Image.open('Tests/images/effect_spread.png')
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self.assert_image_similar(im2, im3, 80)
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if __name__ == '__main__':
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unittest.main()
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@ -48,25 +48,25 @@ ImagingEffectMandelbrot(int xsize, int ysize, double extent[4], int quality)
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for (y = 0; y < ysize; y++) {
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UINT8* buf = im->image8[y];
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for (x = 0; x < xsize; x++) {
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x1 = y1 = xi2 = yi2 = 0.0;
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cr = x*dr + extent[0];
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ci = y*di + extent[1];
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for (k = 1;; k++) {
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y1 = 2*x1*y1 + ci;
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x1 = xi2 - yi2 + cr;
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xi2 = x1*x1;
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yi2 = y1*y1;
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if ((xi2 + yi2) > radius) {
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buf[x] = k*255/quality;
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break;
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}
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if (k > quality) {
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buf[x] = 0;
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break;
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}
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}
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}
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for (x = 0; x < xsize; x++) {
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x1 = y1 = xi2 = yi2 = 0.0;
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cr = x*dr + extent[0];
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ci = y*di + extent[1];
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for (k = 1;; k++) {
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y1 = 2*x1*y1 + ci;
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x1 = xi2 - yi2 + cr;
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xi2 = x1*x1;
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yi2 = y1*y1;
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if ((xi2 + yi2) > radius) {
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buf[x] = k*255/quality;
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break;
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}
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if (k > quality) {
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buf[x] = 0;
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break;
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}
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}
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}
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}
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return im;
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}
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@ -74,7 +74,7 @@ ImagingEffectMandelbrot(int xsize, int ysize, double extent[4], int quality)
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Imaging
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ImagingEffectNoise(int xsize, int ysize, float sigma)
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{
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/* Generate gaussian noise centered around 128 */
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/* Generate Gaussian noise centered around 128 */
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Imaging imOut;
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int x, y;
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@ -83,19 +83,19 @@ ImagingEffectNoise(int xsize, int ysize, float sigma)
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imOut = ImagingNew("L", xsize, ysize);
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if (!imOut)
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return NULL;
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return NULL;
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next = 0.0;
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nextok = 0;
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for (y = 0; y < imOut->ysize; y++) {
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UINT8* out = imOut->image8[y];
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for (x = 0; x < imOut->xsize; x++) {
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for (x = 0; x < imOut->xsize; x++) {
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if (nextok) {
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this = next;
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nextok = 0;
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} else {
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/* after numerical recepies */
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/* after numerical recipes */
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double v1, v2, radius, factor;
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do {
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v1 = rand()*(2.0/32767.0) - 1.0;
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@ -113,14 +113,6 @@ ImagingEffectNoise(int xsize, int ysize, float sigma)
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return imOut;
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}
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Imaging
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ImagingEffectPerlinTurbulence(int xsize, int ysize)
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{
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/* Perlin turbulence (In progress) */
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return NULL;
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}
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Imaging
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ImagingEffectSpread(Imaging imIn, int distance)
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{
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@ -132,11 +124,11 @@ ImagingEffectSpread(Imaging imIn, int distance)
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imOut = ImagingNew(imIn->mode, imIn->xsize, imIn->ysize);
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if (!imOut)
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return NULL;
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return NULL;
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#define SPREAD(type, image)\
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#define SPREAD(type, image)\
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for (y = 0; y < imIn->ysize; y++)\
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for (x = 0; x < imIn->xsize; x++) {\
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for (x = 0; x < imIn->xsize; x++) {\
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int xx = x + (rand() % distance) - distance/2;\
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int yy = y + (rand() % distance) - distance/2;\
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if (xx >= 0 && xx < imIn->xsize && yy >= 0 && yy < imIn->ysize) {\
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@ -147,9 +139,9 @@ ImagingEffectSpread(Imaging imIn, int distance)
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}
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if (imIn->image8) {
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SPREAD(UINT8, image8);
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SPREAD(UINT8, image8);
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} else {
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SPREAD(INT32, image32);
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SPREAD(INT32, image32);
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}
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ImagingCopyInfo(imOut, imIn);
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@ -157,217 +149,4 @@ ImagingEffectSpread(Imaging imIn, int distance)
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return imOut;
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}
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/* -------------------------------------------------------------------- */
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/* Taken from the "C" code in the W3C SVG specification. Translated
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to C89 by Fredrik Lundh */
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#if 0
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/* Produces results in the range [1, 2**31 - 2].
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Algorithm is: r = (a * r) mod m
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where a = 16807 and m = 2**31 - 1 = 2147483647
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See [Park & Miller], CACM vol. 31 no. 10 p. 1195, Oct. 1988
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To test: the algorithm should produce the result 1043618065
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as the 10,000th generated number if the original seed is 1.
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*/
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#define RAND_m 2147483647 /* 2**31 - 1 */
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#define RAND_a 16807 /* 7**5; primitive root of m */
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#define RAND_q 127773 /* m / a */
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#define RAND_r 2836 /* m % a */
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static long
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perlin_setup_seed(long lSeed)
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{
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if (lSeed <= 0) lSeed = -(lSeed % (RAND_m - 1)) + 1;
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if (lSeed > RAND_m - 1) lSeed = RAND_m - 1;
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return lSeed;
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}
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static long
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perlin_random(long lSeed)
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{
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long result;
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result = RAND_a * (lSeed % RAND_q) - RAND_r * (lSeed / RAND_q);
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if (result <= 0) result += RAND_m;
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return result;
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}
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#define BSize 0x100
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#define BM 0xff
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#define PerlinN 0x1000
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#define NP 12 /* 2^PerlinN */
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#define NM 0xfff
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static int perlin_uLatticeSelector[BSize + BSize + 2];
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static double perlin_fGradient[4][BSize + BSize + 2][2];
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typedef struct
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{
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int nWidth; /* How much to subtract to wrap for stitching. */
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int nHeight;
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int nWrapX; /* Minimum value to wrap. */
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int nWrapY;
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} StitchInfo;
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static void
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perlin_init(long lSeed)
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{
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double s;
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int i, j, k;
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lSeed = perlin_setup_seed(lSeed);
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for(k = 0; k < 4; k++)
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{
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for(i = 0; i < BSize; i++)
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{
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perlin_uLatticeSelector[i] = i;
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for (j = 0; j < 2; j++)
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perlin_fGradient[k][i][j] = (double)(((lSeed = perlin_random(lSeed)) % (BSize + BSize)) - BSize) / BSize;
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s = (double) (sqrt(perlin_fGradient[k][i][0] * perlin_fGradient[k][i][0] + perlin_fGradient[k][i][1] * perlin_fGradient[k][i][1]));
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perlin_fGradient[k][i][0] /= s;
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perlin_fGradient[k][i][1] /= s;
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}
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}
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while(--i)
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{
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k = perlin_uLatticeSelector[i];
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perlin_uLatticeSelector[i] = perlin_uLatticeSelector[j = (lSeed = perlin_random(lSeed)) % BSize];
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perlin_uLatticeSelector[j] = k;
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}
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for(i = 0; i < BSize + 2; i++)
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{
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perlin_uLatticeSelector[BSize + i] = perlin_uLatticeSelector[i];
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for(k = 0; k < 4; k++)
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for(j = 0; j < 2; j++)
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perlin_fGradient[k][BSize + i][j] = perlin_fGradient[k][i][j];
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}
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}
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#define s_curve(t) ( t * t * (3. - 2. * t) )
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#define lerp(t, a, b) ( a + t * (b - a) )
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static double
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perlin_noise2(int nColorChannel, double vec[2], StitchInfo *pStitchInfo)
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{
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int bx0, bx1, by0, by1, b00, b10, b01, b11;
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double rx0, rx1, ry0, ry1, *q, sx, sy, a, b, t, u, v;
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register int i, j;
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t = vec[0] + (double) PerlinN;
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bx0 = (int)t;
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bx1 = bx0+1;
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rx0 = t - (int)t;
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rx1 = rx0 - 1.0f;
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t = vec[1] + (double) PerlinN;
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by0 = (int)t;
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by1 = by0+1;
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ry0 = t - (int)t;
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ry1 = ry0 - 1.0f;
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/* If stitching, adjust lattice points accordingly. */
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if(pStitchInfo != NULL)
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{
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if(bx0 >= pStitchInfo->nWrapX)
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bx0 -= pStitchInfo->nWidth;
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if(bx1 >= pStitchInfo->nWrapX)
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bx1 -= pStitchInfo->nWidth;
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if(by0 >= pStitchInfo->nWrapY)
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by0 -= pStitchInfo->nHeight;
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if(by1 >= pStitchInfo->nWrapY)
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by1 -= pStitchInfo->nHeight;
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}
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bx0 &= BM;
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bx1 &= BM;
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by0 &= BM;
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by1 &= BM;
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i = perlin_uLatticeSelector[bx0];
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j = perlin_uLatticeSelector[bx1];
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b00 = perlin_uLatticeSelector[i + by0];
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b10 = perlin_uLatticeSelector[j + by0];
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b01 = perlin_uLatticeSelector[i + by1];
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b11 = perlin_uLatticeSelector[j + by1];
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sx = (double) (s_curve(rx0));
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sy = (double) (s_curve(ry0));
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q = perlin_fGradient[nColorChannel][b00]; u = rx0 * q[0] + ry0 * q[1];
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q = perlin_fGradient[nColorChannel][b10]; v = rx1 * q[0] + ry0 * q[1];
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a = lerp(sx, u, v);
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q = perlin_fGradient[nColorChannel][b01]; u = rx0 * q[0] + ry1 * q[1];
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q = perlin_fGradient[nColorChannel][b11]; v = rx1 * q[0] + ry1 * q[1];
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b = lerp(sx, u, v);
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return lerp(sy, a, b);
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}
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double
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perlin_turbulence(
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int nColorChannel, double *point, double fBaseFreqX, double fBaseFreqY,
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int nNumOctaves, int bFractalSum, int bDoStitching,
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double fTileX, double fTileY, double fTileWidth, double fTileHeight)
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{
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StitchInfo stitch;
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StitchInfo *pStitchInfo = NULL; /* Not stitching when NULL. */
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double fSum = 0.0f;
|
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double vec[2];
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double ratio = 1;
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int nOctave;
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vec[0] = point[0] * fBaseFreqX;
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vec[1] = point[1] * fBaseFreqY;
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/* Adjust the base frequencies if necessary for stitching. */
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if(bDoStitching)
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{
|
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/* When stitching tiled turbulence, the frequencies must be adjusted */
|
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/* so that the tile borders will be continuous. */
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if(fBaseFreqX != 0.0)
|
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{
|
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double fLoFreq = (double) (floor(fTileWidth * fBaseFreqX)) / fTileWidth;
|
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double fHiFreq = (double) (ceil(fTileWidth * fBaseFreqX)) / fTileWidth;
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if(fBaseFreqX / fLoFreq < fHiFreq / fBaseFreqX)
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fBaseFreqX = fLoFreq;
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else
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fBaseFreqX = fHiFreq;
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}
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if(fBaseFreqY != 0.0)
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{
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double fLoFreq = (double) (floor(fTileHeight * fBaseFreqY)) / fTileHeight;
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double fHiFreq = (double) (ceil(fTileHeight * fBaseFreqY)) / fTileHeight;
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if(fBaseFreqY / fLoFreq < fHiFreq / fBaseFreqY)
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fBaseFreqY = fLoFreq;
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else
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fBaseFreqY = fHiFreq;
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}
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/* Set up initial stitch values. */
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pStitchInfo = &stitch;
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stitch.nWidth = (int) (fTileWidth * fBaseFreqX + 0.5f);
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stitch.nWrapX = (int) (fTileX * fBaseFreqX + PerlinN + stitch.nWidth);
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stitch.nHeight = (int) (fTileHeight * fBaseFreqY + 0.5f);
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stitch.nWrapY = (int) (fTileY * fBaseFreqY + PerlinN + stitch.nHeight);
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}
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for(nOctave = 0; nOctave < nNumOctaves; nOctave++)
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{
|
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if(bFractalSum)
|
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fSum += (double) (perlin_noise2(nColorChannel, vec, pStitchInfo) / ratio);
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else
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fSum += (double) (fabs(perlin_noise2(nColorChannel, vec, pStitchInfo)) / ratio);
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vec[0] *= 2;
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vec[1] *= 2;
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ratio *= 2;
|
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|
||||
if(pStitchInfo != NULL)
|
||||
{
|
||||
/* Update stitch values. Subtracting PerlinN before the multiplication and */
|
||||
/* adding it afterward simplifies to subtracting it once. */
|
||||
stitch.nWidth *= 2;
|
||||
stitch.nWrapX = 2 * stitch.nWrapX - PerlinN;
|
||||
stitch.nHeight *= 2;
|
||||
stitch.nWrapY = 2 * stitch.nWrapY - PerlinN;
|
||||
}
|
||||
}
|
||||
return fSum;
|
||||
}
|
||||
|
||||
#endif
|
||||
// End of file
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user