Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/matplotlib/tests/test_compare_images.py

72 lines
3.2 KiB
Python

from pathlib import Path
import shutil
import pytest
from pytest import approx
from matplotlib.testing.compare import compare_images, make_test_filename
from matplotlib.testing.decorators import _image_directories
# Tests of the image comparison algorithm.
@pytest.mark.parametrize(
'im1, im2, tol, expect_rms',
[
# Comparison of an image and the same image with minor differences.
# This expects the images to compare equal under normal tolerance, and
# have a small RMS.
('basn3p02.png', 'basn3p02-minorchange.png', 10, None),
# Now test with no tolerance.
('basn3p02.png', 'basn3p02-minorchange.png', 0, 6.50646),
# Comparison with an image that is shifted by 1px in the X axis.
('basn3p02.png', 'basn3p02-1px-offset.png', 0, 90.15611),
# Comparison with an image with half the pixels shifted by 1px in the X
# axis.
('basn3p02.png', 'basn3p02-half-1px-offset.png', 0, 63.75),
# Comparison of an image and the same image scrambled.
# This expects the images to compare completely different, with a very
# large RMS.
# Note: The image has been scrambled in a specific way, by having
# each color component of each pixel randomly placed somewhere in the
# image. It contains exactly the same number of pixels of each color
# value of R, G and B, but in a totally different position.
# Test with no tolerance to make sure that we pick up even a very small
# RMS error.
('basn3p02.png', 'basn3p02-scrambled.png', 0, 172.63582),
# Comparison of an image and a slightly brighter image.
# The two images are solid color, with the second image being exactly 1
# color value brighter.
# This expects the images to compare equal under normal tolerance, and
# have an RMS of exactly 1.
('all127.png', 'all128.png', 0, 1),
# Now test the reverse comparison.
('all128.png', 'all127.png', 0, 1),
])
def test_image_comparison_expect_rms(im1, im2, tol, expect_rms):
"""
Compare two images, expecting a particular RMS error.
im1 and im2 are filenames relative to the baseline_dir directory.
tol is the tolerance to pass to compare_images.
expect_rms is the expected RMS value, or None. If None, the test will
succeed if compare_images succeeds. Otherwise, the test will succeed if
compare_images fails and returns an RMS error almost equal to this value.
"""
baseline_dir, result_dir = map(Path, _image_directories(lambda: "dummy"))
# Copy both "baseline" and "test" image to result_dir, so that 1)
# compare_images writes the diff to result_dir, rather than to the source
# tree and 2) the baseline image doesn't appear missing to triage_tests.py.
result_im1 = make_test_filename(result_dir / im1, "expected")
shutil.copyfile(baseline_dir / im1, result_im1)
result_im2 = result_dir / im1
shutil.copyfile(baseline_dir / im2, result_im2)
results = compare_images(
result_im1, result_im2, tol=tol, in_decorator=True)
if expect_rms is None:
assert results is None
else:
assert results is not None
assert results['rms'] == approx(expect_rms, abs=1e-4)