mirror of
https://github.com/opencv/opencv.git
synced 2026-07-26 22:03:04 +04:00
Matt Van Horn
5e6a591357
imgproc: fix INTER_NEAREST_EXACT to match Pillow (5.x retarget of #28661) #28873 ## Summary Fixes `INTER_NEAREST_EXACT` producing different results from Pillow for images with dimensions that are multiples of 64 (e.g., 128, 192). Retargets #28661 to 5.x per core team request. ## Root cause The old 16-bit fixed-point arithmetic (`ifx`, `ifx0`, `>> 16`) rounds differently from Pillow's pixel-center coordinate mapping. At exact pixel boundaries (e.g., y=7 with 128 to 160: `0.4 + 7*0.8 = 5.999999999999999`), Pillow's truncation drops to 5 while the old opencv formula rounds to 6. ## Changes - `resize.cpp`: Replace `ifx/ifx0/ify/ify0` 16-bit fixed-point with an int64 arithmetic formula equivalent to `floor((i + 0.5) * src / dst)`, computed as `((int64_t)(i * 2 + 1) * src) / (dst * 2)`. This matches Pillow's pixel-center mapping exactly, is deterministic across platforms, and avoids FP rounding divergence entirely. - `test_resize_bitexact.cpp`: Add `NearestExact_PillowCompat` covering 4 dimension pairs including the reproducer from #28429. ## Testing - All 3 `Resize_Bitexact` tests pass (Linear8U, Nearest8U, NearestExact_PillowCompat) on this branch built against 5.x - Verified bit-exact match to PIL output (Python 3.14, Pillow) on 49 dimension combinations including random pairs - Built on macOS ARM64 with HAL (carotene/KleidiCV) active Fixes #28429
49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
#!/usr/bin/env python
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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try:
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from PIL import Image
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except ImportError:
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Image = None
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from tests_common import NewOpenCVTests
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class Imgproc_Tests(NewOpenCVTests):
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def test_python_986(self):
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cntls = []
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img = np.zeros((100,100,3), dtype=np.uint8)
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color = (0,0,0)
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cnts = np.array(cntls, dtype=np.int32).reshape((1, -1, 2))
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try:
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cv.fillPoly(img, cnts, color)
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assert False
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except:
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assert True
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def test_filter2d(self):
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img = self.get_sample('samples/data/lena.jpg', 1)
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eps = 0.001
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# compare 2 ways of computing 3x3 blur using the same function
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kernel = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype='float32')
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img_blur0 = cv.filter2D(img, cv.CV_32F, kernel*(1./9))
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img_blur1 = cv.filter2Dp(img, kernel, ddepth=cv.CV_32F, scale=1./9)
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self.assertLess(cv.norm(img_blur0 - img_blur1, cv.NORM_INF), eps)
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def test_resize_pillow(self):
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if Image is None:
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self.skipTest("Pillow is not available")
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r = np.random.randint(0, 255, size=(128, 147, 3), dtype="uint8")
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target_size=[(128,128), (129,129), (160,160)]
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for ts in target_size:
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pil_result = np.array(Image.fromarray(r).resize(ts, Image.NEAREST))
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ocv_result = cv.resize(r, dsize=ts, interpolation=cv.INTER_NEAREST_EXACT)
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status = np.all(pil_result == ocv_result)
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print(ts, status)
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self.assertTrue(status, "resize result differs from Pillow for target size %s" % (ts,))
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