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Added second attribute to avoid unstable nature of qsort
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Tests/images/hopper_resized.gif
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Tests/images/hopper_resized.gif
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@ -7,7 +7,12 @@ import pytest
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from PIL import Image
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from .helper import assert_image_equal, assert_image_similar, hopper
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from .helper import (
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assert_image_equal,
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assert_image_equal_tofile,
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assert_image_similar,
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hopper,
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)
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class TestImagingCoreResize:
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@ -135,6 +140,17 @@ class TestImagingCoreResize:
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with pytest.raises(ValueError):
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self.resize(hopper(), (10, 10), 9)
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def test_cross_platform(self, tmp_path):
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# This test is intended for only check for consistent behaviour across
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# platforms. So if a future Pillow change requires that the test file
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# be updated, that is okay.
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im = hopper().resize((64, 64))
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temp_file = str(tmp_path / "temp.gif")
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im.save(temp_file)
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with Image.open(temp_file) as reloaded:
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assert_image_equal_tofile(reloaded, "Tests/images/hopper_resized.gif")
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@pytest.fixture
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def gradients_image():
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@ -753,11 +753,19 @@ annotate_hash_table(BoxNode *n, HashTable *h, uint32_t *box) {
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return 1;
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}
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typedef struct {
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uint32_t *distance;
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uint32_t index;
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} DistanceWithIndex;
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static int
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_sort_ulong_ptr_keys(const void *a, const void *b) {
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uint32_t A = **(uint32_t **)a;
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uint32_t B = **(uint32_t **)b;
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return (A == B) ? 0 : ((A < B) ? -1 : +1);
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DistanceWithIndex *A = (DistanceWithIndex *)a;
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DistanceWithIndex *B = (DistanceWithIndex *)b;
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if (*A->distance == *B->distance) {
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return A->index < B->index ? -1 : +1;
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}
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return *A->distance < *B->distance ? -1 : +1;
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}
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static int
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@ -793,24 +801,42 @@ static int
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build_distance_tables(
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uint32_t *avgDist, uint32_t **avgDistSortKey, Pixel *p, uint32_t nEntries) {
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uint32_t i, j;
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DistanceWithIndex *dwi;
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dwi = calloc(nEntries * nEntries, sizeof(DistanceWithIndex));
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if (!dwi) {
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return 0;
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}
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for (i = 0; i < nEntries; i++) {
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avgDist[i * nEntries + i] = 0;
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avgDistSortKey[i * nEntries + i] = &(avgDist[i * nEntries + i]);
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dwi[i * nEntries + i] = (DistanceWithIndex){
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&(avgDist[i * nEntries + i]),
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i * nEntries + i
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};
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for (j = 0; j < i; j++) {
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avgDist[j * nEntries + i] = avgDist[i * nEntries + j] =
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_DISTSQR(p + i, p + j);
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avgDistSortKey[j * nEntries + i] = &(avgDist[j * nEntries + i]);
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avgDistSortKey[i * nEntries + j] = &(avgDist[i * nEntries + j]);
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dwi[j * nEntries + i] = (DistanceWithIndex){
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&(avgDist[j * nEntries + i]),
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j * nEntries + i
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};
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dwi[i * nEntries + j] = (DistanceWithIndex){
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&(avgDist[i * nEntries + j]),
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i * nEntries + j
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};
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}
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}
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for (i = 0; i < nEntries; i++) {
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qsort(
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avgDistSortKey + i * nEntries,
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dwi + i * nEntries,
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nEntries,
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sizeof(uint32_t *),
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sizeof(DistanceWithIndex),
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_sort_ulong_ptr_keys);
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for (j = 0; j < nEntries; j++) {
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avgDistSortKey[i * nEntries + j] = dwi[i * nEntries + j].distance;
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}
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}
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free(dwi);
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return 1;
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}
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@ -1176,8 +1202,10 @@ k_means(
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if (!built) {
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compute_palette_from_quantized_pixels(
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pixelData, nPixels, paletteData, nPaletteEntries, avg, count, qp);
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build_distance_tables(
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avgDist, avgDistSortKey, paletteData, nPaletteEntries);
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if (!build_distance_tables(
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avgDist, avgDistSortKey, paletteData, nPaletteEntries)) {
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goto error_3;
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}
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built = 1;
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} else {
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recompute_palette_from_averages(paletteData, nPaletteEntries, avg, count);
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