batuhan-basoglu/Vehicle-Anti-Theft-Face-Recognition-System
1
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions
Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/matplotlib/tests/test_image.py

1139 lines
35 KiB
Python
Raw Normal View History

Fixed database typo and removed unnecessary class identifier.
2020-10-14 14:10:37 +00:00
from contextlib import ExitStack
from copy import copy
import io
import os
from pathlib import Path
import platform
import sys
import urllib.request
import numpy as np
from numpy.testing import assert_array_equal
from PIL import Image
from matplotlib import (
colors, image as mimage, patches, pyplot as plt, style, rcParams)
from matplotlib.image import (AxesImage, BboxImage, FigureImage,
NonUniformImage, PcolorImage)
from matplotlib.testing.decorators import check_figures_equal, image_comparison
from matplotlib.transforms import Bbox, Affine2D, TransformedBbox
import pytest
@image_comparison(['image_interps'], style='mpl20')
def test_image_interps():
"""Make the basic nearest, bilinear and bicubic interps."""
# Remove this line when this test image is regenerated.
plt.rcParams['text.kerning_factor'] = 6
X = np.arange(100)
X = X.reshape(5, 20)
fig = plt.figure()
ax1 = fig.add_subplot(311)
ax1.imshow(X, interpolation='nearest')
ax1.set_title('three interpolations')
ax1.set_ylabel('nearest')
ax2 = fig.add_subplot(312)
ax2.imshow(X, interpolation='bilinear')
ax2.set_ylabel('bilinear')
ax3 = fig.add_subplot(313)
ax3.imshow(X, interpolation='bicubic')
ax3.set_ylabel('bicubic')
@image_comparison(['interp_alpha.png'], remove_text=True)
def test_alpha_interp():
"""Test the interpolation of the alpha channel on RGBA images"""
fig, (axl, axr) = plt.subplots(1, 2)
# full green image
img = np.zeros((5, 5, 4))
img[..., 1] = np.ones((5, 5))
# transparent under main diagonal
img[..., 3] = np.tril(np.ones((5, 5), dtype=np.uint8))
axl.imshow(img, interpolation="none")
axr.imshow(img, interpolation="bilinear")
@image_comparison(['interp_nearest_vs_none'],
extensions=['pdf', 'svg'], remove_text=True)
def test_interp_nearest_vs_none():
"""Test the effect of "nearest" and "none" interpolation"""
# Setting dpi to something really small makes the difference very
# visible. This works fine with pdf, since the dpi setting doesn't
# affect anything but images, but the agg output becomes unusably
# small.
rcParams['savefig.dpi'] = 3
X = np.array([[[218, 165, 32], [122, 103, 238]],
[[127, 255, 0], [255, 99, 71]]], dtype=np.uint8)
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax1.imshow(X, interpolation='none')
ax1.set_title('interpolation none')
ax2 = fig.add_subplot(122)
ax2.imshow(X, interpolation='nearest')
ax2.set_title('interpolation nearest')
@pytest.mark.parametrize('suppressComposite', [False, True])
@image_comparison(['figimage'], extensions=['png', 'pdf'])
def test_figimage(suppressComposite):
fig = plt.figure(figsize=(2, 2), dpi=100)
fig.suppressComposite = suppressComposite
x, y = np.ix_(np.arange(100) / 100.0, np.arange(100) / 100)
z = np.sin(x**2 + y**2 - x*y)
c = np.sin(20*x**2 + 50*y**2)
img = z + c/5
fig.figimage(img, xo=0, yo=0, origin='lower')
fig.figimage(img[::-1, :], xo=0, yo=100, origin='lower')
fig.figimage(img[:, ::-1], xo=100, yo=0, origin='lower')
fig.figimage(img[::-1, ::-1], xo=100, yo=100, origin='lower')
def test_image_python_io():
fig, ax = plt.subplots()
ax.plot([1, 2, 3])
buffer = io.BytesIO()
fig.savefig(buffer)
buffer.seek(0)
plt.imread(buffer)
@pytest.mark.parametrize(
"img_size, fig_size, interpolation",
[(5, 2, "hanning"), # data larger than figure.
(5, 5, "nearest"), # exact resample.
(5, 10, "nearest"), # double sample.
(3, 2.9, "hanning"), # <3 upsample.
(3, 9.1, "nearest"), # >3 upsample.
])
@check_figures_equal(extensions=['png'])
def test_imshow_antialiased(fig_test, fig_ref,
img_size, fig_size, interpolation):
np.random.seed(19680801)
dpi = plt.rcParams["savefig.dpi"]
A = np.random.rand(int(dpi * img_size), int(dpi * img_size))
for fig in [fig_test, fig_ref]:
fig.set_size_inches(fig_size, fig_size)
axs = fig_test.subplots()
axs.set_position([0, 0, 1, 1])
axs.imshow(A, interpolation='antialiased')
axs = fig_ref.subplots()
axs.set_position([0, 0, 1, 1])
axs.imshow(A, interpolation=interpolation)
@check_figures_equal(extensions=['png'])
def test_imshow_zoom(fig_test, fig_ref):
# should be less than 3 upsample, so should be nearest...
np.random.seed(19680801)
dpi = plt.rcParams["savefig.dpi"]
A = np.random.rand(int(dpi * 3), int(dpi * 3))
for fig in [fig_test, fig_ref]:
fig.set_size_inches(2.9, 2.9)
axs = fig_test.subplots()
axs.imshow(A, interpolation='antialiased')
axs.set_xlim([10, 20])
axs.set_ylim([10, 20])
axs = fig_ref.subplots()
axs.imshow(A, interpolation='nearest')
axs.set_xlim([10, 20])
axs.set_ylim([10, 20])
@check_figures_equal()
def test_imshow_pil(fig_test, fig_ref):
style.use("default")
png_path = Path(__file__).parent / "baseline_images/pngsuite/basn3p04.png"
tiff_path = Path(__file__).parent / "baseline_images/test_image/uint16.tif"
axs = fig_test.subplots(2)
axs[0].imshow(Image.open(png_path))
axs[1].imshow(Image.open(tiff_path))
axs = fig_ref.subplots(2)
axs[0].imshow(plt.imread(png_path))
axs[1].imshow(plt.imread(tiff_path))
def test_imread_pil_uint16():
img = plt.imread(os.path.join(os.path.dirname(__file__),
'baseline_images', 'test_image', 'uint16.tif'))
assert img.dtype == np.uint16
assert np.sum(img) == 134184960
def test_imread_fspath():
img = plt.imread(
Path(__file__).parent / 'baseline_images/test_image/uint16.tif')
assert img.dtype == np.uint16
assert np.sum(img) == 134184960
@pytest.mark.parametrize("fmt", ["png", "jpg", "jpeg", "tiff"])
def test_imsave(fmt):
has_alpha = fmt not in ["jpg", "jpeg"]
# The goal here is that the user can specify an output logical DPI
# for the image, but this will not actually add any extra pixels
# to the image, it will merely be used for metadata purposes.
# So we do the traditional case (dpi == 1), and the new case (dpi
# == 100) and read the resulting PNG files back in and make sure
# the data is 100% identical.
np.random.seed(1)
# The height of 1856 pixels was selected because going through creating an
# actual dpi=100 figure to save the image to a Pillow-provided format would
# cause a rounding error resulting in a final image of shape 1855.
data = np.random.rand(1856, 2)
buff_dpi1 = io.BytesIO()
plt.imsave(buff_dpi1, data, format=fmt, dpi=1)
buff_dpi100 = io.BytesIO()
plt.imsave(buff_dpi100, data, format=fmt, dpi=100)
buff_dpi1.seek(0)
arr_dpi1 = plt.imread(buff_dpi1, format=fmt)
buff_dpi100.seek(0)
arr_dpi100 = plt.imread(buff_dpi100, format=fmt)
assert arr_dpi1.shape == (1856, 2, 3 + has_alpha)
assert arr_dpi100.shape == (1856, 2, 3 + has_alpha)
assert_array_equal(arr_dpi1, arr_dpi100)
@pytest.mark.parametrize("fmt", ["png", "pdf", "ps", "eps", "svg"])
def test_imsave_fspath(fmt):
plt.imsave(Path(os.devnull), np.array([[0, 1]]), format=fmt)
def test_imsave_color_alpha():
# Test that imsave accept arrays with ndim=3 where the third dimension is
# color and alpha without raising any exceptions, and that the data is
# acceptably preserved through a save/read roundtrip.
np.random.seed(1)
for origin in ['lower', 'upper']:
data = np.random.rand(16, 16, 4)
buff = io.BytesIO()
plt.imsave(buff, data, origin=origin, format="png")
buff.seek(0)
arr_buf = plt.imread(buff)
# Recreate the float -> uint8 conversion of the data
# We can only expect to be the same with 8 bits of precision,
# since that's what the PNG file used.
data = (255*data).astype('uint8')
if origin == 'lower':
data = data[::-1]
arr_buf = (255*arr_buf).astype('uint8')
assert_array_equal(data, arr_buf)
def test_imsave_pil_kwargs_png():
from PIL.PngImagePlugin import PngInfo
buf = io.BytesIO()
pnginfo = PngInfo()
pnginfo.add_text("Software", "test")
plt.imsave(buf, [[0, 1], [2, 3]],
format="png", pil_kwargs={"pnginfo": pnginfo})
im = Image.open(buf)
assert im.info["Software"] == "test"
def test_imsave_pil_kwargs_tiff():
from PIL.TiffTags import TAGS_V2 as TAGS
buf = io.BytesIO()
pil_kwargs = {"description": "test image"}
plt.imsave(buf, [[0, 1], [2, 3]], format="tiff", pil_kwargs=pil_kwargs)
im = Image.open(buf)
tags = {TAGS[k].name: v for k, v in im.tag_v2.items()}
assert tags["ImageDescription"] == "test image"
@image_comparison(['image_alpha'], remove_text=True)
def test_image_alpha():
plt.figure()
np.random.seed(0)
Z = np.random.rand(6, 6)
plt.subplot(131)
plt.imshow(Z, alpha=1.0, interpolation='none')
plt.subplot(132)
plt.imshow(Z, alpha=0.5, interpolation='none')
plt.subplot(133)
plt.imshow(Z, alpha=0.5, interpolation='nearest')
def test_cursor_data():
from matplotlib.backend_bases import MouseEvent
fig, ax = plt.subplots()
im = ax.imshow(np.arange(100).reshape(10, 10), origin='upper')
x, y = 4, 4
xdisp, ydisp = ax.transData.transform([x, y])
event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
assert im.get_cursor_data(event) == 44
# Now try for a point outside the image
# Tests issue #4957
x, y = 10.1, 4
xdisp, ydisp = ax.transData.transform([x, y])
event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
assert im.get_cursor_data(event) is None
# Hmm, something is wrong here... I get 0, not None...
# But, this works further down in the tests with extents flipped
#x, y = 0.1, -0.1
#xdisp, ydisp = ax.transData.transform([x, y])
#event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
#z = im.get_cursor_data(event)
#assert z is None, "Did not get None, got %d" % z
ax.clear()
# Now try with the extents flipped.
im = ax.imshow(np.arange(100).reshape(10, 10), origin='lower')
x, y = 4, 4
xdisp, ydisp = ax.transData.transform([x, y])
event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
assert im.get_cursor_data(event) == 44
fig, ax = plt.subplots()
im = ax.imshow(np.arange(100).reshape(10, 10), extent=[0, 0.5, 0, 0.5])
x, y = 0.25, 0.25
xdisp, ydisp = ax.transData.transform([x, y])
event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
assert im.get_cursor_data(event) == 55
# Now try for a point outside the image
# Tests issue #4957
x, y = 0.75, 0.25
xdisp, ydisp = ax.transData.transform([x, y])
event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
assert im.get_cursor_data(event) is None
x, y = 0.01, -0.01
xdisp, ydisp = ax.transData.transform([x, y])
event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
assert im.get_cursor_data(event) is None
@pytest.mark.parametrize(
"data, text_without_colorbar, text_with_colorbar", [
([[10001, 10000]], "[1e+04]", "[10001]"),
([[.123, .987]], "[0.123]", "[0.123]"),
])
def test_format_cursor_data(data, text_without_colorbar, text_with_colorbar):
from matplotlib.backend_bases import MouseEvent
fig, ax = plt.subplots()
im = ax.imshow(data)
xdisp, ydisp = ax.transData.transform([0, 0])
event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
assert im.get_cursor_data(event) == data[0][0]
assert im.format_cursor_data(im.get_cursor_data(event)) \
== text_without_colorbar
fig.colorbar(im)
fig.canvas.draw() # This is necessary to set up the colorbar formatter.
assert im.get_cursor_data(event) == data[0][0]
assert im.format_cursor_data(im.get_cursor_data(event)) \
== text_with_colorbar
@image_comparison(['image_clip'], style='mpl20')
def test_image_clip():
d = [[1, 2], [3, 4]]
fig, ax = plt.subplots()
im = ax.imshow(d)
patch = patches.Circle((0, 0), radius=1, transform=ax.transData)
im.set_clip_path(patch)
@image_comparison(['image_cliprect'], style='mpl20')
def test_image_cliprect():
fig, ax = plt.subplots()
d = [[1, 2], [3, 4]]
im = ax.imshow(d, extent=(0, 5, 0, 5))
rect = patches.Rectangle(
xy=(1, 1), width=2, height=2, transform=im.axes.transData)
im.set_clip_path(rect)
@image_comparison(['imshow'], remove_text=True, style='mpl20')
def test_imshow():
fig, ax = plt.subplots()
arr = np.arange(100).reshape((10, 10))
ax.imshow(arr, interpolation="bilinear", extent=(1, 2, 1, 2))
ax.set_xlim(0, 3)
ax.set_ylim(0, 3)
@check_figures_equal(extensions=['png'])
def test_imshow_10_10_1(fig_test, fig_ref):
# 10x10x1 should be the same as 10x10
arr = np.arange(100).reshape((10, 10, 1))
ax = fig_ref.subplots()
ax.imshow(arr[:, :, 0], interpolation="bilinear", extent=(1, 2, 1, 2))
ax.set_xlim(0, 3)
ax.set_ylim(0, 3)
ax = fig_test.subplots()
ax.imshow(arr, interpolation="bilinear", extent=(1, 2, 1, 2))
ax.set_xlim(0, 3)
ax.set_ylim(0, 3)
def test_imshow_10_10_2():
fig, ax = plt.subplots()
arr = np.arange(200).reshape((10, 10, 2))
with pytest.raises(TypeError):
ax.imshow(arr)
def test_imshow_10_10_5():
fig, ax = plt.subplots()
arr = np.arange(500).reshape((10, 10, 5))
with pytest.raises(TypeError):
ax.imshow(arr)
@image_comparison(['no_interpolation_origin'], remove_text=True)
def test_no_interpolation_origin():
fig, axs = plt.subplots(2)
axs[0].imshow(np.arange(100).reshape((2, 50)), origin="lower",
interpolation='none')
axs[1].imshow(np.arange(100).reshape((2, 50)), interpolation='none')
@image_comparison(['image_shift'], remove_text=True, extensions=['pdf', 'svg'])
def test_image_shift():
imgData = [[1 / x + 1 / y for x in range(1, 100)] for y in range(1, 100)]
tMin = 734717.945208
tMax = 734717.946366
fig, ax = plt.subplots()
ax.imshow(imgData, norm=colors.LogNorm(), interpolation='none',
extent=(tMin, tMax, 1, 100))
ax.set_aspect('auto')
def test_image_edges():
fig = plt.figure(figsize=[1, 1])
ax = fig.add_axes([0, 0, 1, 1], frameon=False)
data = np.tile(np.arange(12), 15).reshape(20, 9)
im = ax.imshow(data, origin='upper', extent=[-10, 10, -10, 10],
interpolation='none', cmap='gray')
x = y = 2
ax.set_xlim([-x, x])
ax.set_ylim([-y, y])
ax.set_xticks([])
ax.set_yticks([])
buf = io.BytesIO()
fig.savefig(buf, facecolor=(0, 1, 0))
buf.seek(0)
im = plt.imread(buf)
r, g, b, a = sum(im[:, 0])
r, g, b, a = sum(im[:, -1])
assert g != 100, 'Expected a non-green edge - but sadly, it was.'
@image_comparison(['image_composite_background'],
remove_text=True, style='mpl20')
def test_image_composite_background():
fig, ax = plt.subplots()
arr = np.arange(12).reshape(4, 3)
ax.imshow(arr, extent=[0, 2, 15, 0])
ax.imshow(arr, extent=[4, 6, 15, 0])
ax.set_facecolor((1, 0, 0, 0.5))
ax.set_xlim([0, 12])
@image_comparison(['image_composite_alpha'], remove_text=True)
def test_image_composite_alpha():
"""
Tests that the alpha value is recognized and correctly applied in the
process of compositing images together.
"""
fig, ax = plt.subplots()
arr = np.zeros((11, 21, 4))
arr[:, :, 0] = 1
arr[:, :, 3] = np.concatenate(
(np.arange(0, 1.1, 0.1), np.arange(0, 1, 0.1)[::-1]))
arr2 = np.zeros((21, 11, 4))
arr2[:, :, 0] = 1
arr2[:, :, 1] = 1
arr2[:, :, 3] = np.concatenate(
(np.arange(0, 1.1, 0.1), np.arange(0, 1, 0.1)[::-1]))[:, np.newaxis]
ax.imshow(arr, extent=[1, 2, 5, 0], alpha=0.3)
ax.imshow(arr, extent=[2, 3, 5, 0], alpha=0.6)
ax.imshow(arr, extent=[3, 4, 5, 0])
ax.imshow(arr2, extent=[0, 5, 1, 2])
ax.imshow(arr2, extent=[0, 5, 2, 3], alpha=0.6)
ax.imshow(arr2, extent=[0, 5, 3, 4], alpha=0.3)
ax.set_facecolor((0, 0.5, 0, 1))
ax.set_xlim([0, 5])
ax.set_ylim([5, 0])
@image_comparison(['rasterize_10dpi'],
extensions=['pdf', 'svg'], remove_text=True, style='mpl20')
def test_rasterize_dpi():
# This test should check rasterized rendering with high output resolution.
# It plots a rasterized line and a normal image with imshow. So it will
# catch when images end up in the wrong place in case of non-standard dpi
# setting. Instead of high-res rasterization I use low-res. Therefore
# the fact that the resolution is non-standard is easily checked by
# image_comparison.
img = np.asarray([[1, 2], [3, 4]])
fig, axs = plt.subplots(1, 3, figsize=(3, 1))
axs[0].imshow(img)
axs[1].plot([0, 1], [0, 1], linewidth=20., rasterized=True)
axs[1].set(xlim=(0, 1), ylim=(-1, 2))
axs[2].plot([0, 1], [0, 1], linewidth=20.)
axs[2].set(xlim=(0, 1), ylim=(-1, 2))
# Low-dpi PDF rasterization errors prevent proper image comparison tests.
# Hide detailed structures like the axes spines.
for ax in axs:
ax.set_xticks([])
ax.set_yticks([])
for spine in ax.spines.values():
spine.set_visible(False)
rcParams['savefig.dpi'] = 10
@image_comparison(['bbox_image_inverted'], remove_text=True, style='mpl20')
def test_bbox_image_inverted():
# This is just used to produce an image to feed to BboxImage
image = np.arange(100).reshape((10, 10))
fig, ax = plt.subplots()
bbox_im = BboxImage(
TransformedBbox(Bbox([[100, 100], [0, 0]]), ax.transData),
interpolation='nearest')
bbox_im.set_data(image)
bbox_im.set_clip_on(False)
ax.set_xlim(0, 100)
ax.set_ylim(0, 100)
ax.add_artist(bbox_im)
image = np.identity(10)
bbox_im = BboxImage(TransformedBbox(Bbox([[0.1, 0.2], [0.3, 0.25]]),
ax.figure.transFigure),
interpolation='nearest')
bbox_im.set_data(image)
bbox_im.set_clip_on(False)
ax.add_artist(bbox_im)
def test_get_window_extent_for_AxisImage():
# Create a figure of known size (1000x1000 pixels), place an image
# object at a given location and check that get_window_extent()
# returns the correct bounding box values (in pixels).
im = np.array([[0.25, 0.75, 1.0, 0.75], [0.1, 0.65, 0.5, 0.4],
[0.6, 0.3, 0.0, 0.2], [0.7, 0.9, 0.4, 0.6]])
fig, ax = plt.subplots(figsize=(10, 10), dpi=100)
ax.set_position([0, 0, 1, 1])
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
im_obj = ax.imshow(
im, extent=[0.4, 0.7, 0.2, 0.9], interpolation='nearest')
fig.canvas.draw()
renderer = fig.canvas.renderer
im_bbox = im_obj.get_window_extent(renderer)
assert_array_equal(im_bbox.get_points(), [[400, 200], [700, 900]])
@image_comparison(['zoom_and_clip_upper_origin.png'],
remove_text=True, style='mpl20')
def test_zoom_and_clip_upper_origin():
image = np.arange(100)
image = image.reshape((10, 10))
fig, ax = plt.subplots()
ax.imshow(image)
ax.set_ylim(2.0, -0.5)
ax.set_xlim(-0.5, 2.0)
def test_nonuniformimage_setcmap():
ax = plt.gca()
im = NonUniformImage(ax)
im.set_cmap('Blues')
def test_nonuniformimage_setnorm():
ax = plt.gca()
im = NonUniformImage(ax)
im.set_norm(plt.Normalize())
def test_jpeg_2d():
# smoke test that mode-L pillow images work.
imd = np.ones((10, 10), dtype='uint8')
for i in range(10):
imd[i, :] = np.linspace(0.0, 1.0, 10) * 255
im = Image.new('L', (10, 10))
im.putdata(imd.flatten())
fig, ax = plt.subplots()
ax.imshow(im)
def test_jpeg_alpha():
plt.figure(figsize=(1, 1), dpi=300)
# Create an image that is all black, with a gradient from 0-1 in
# the alpha channel from left to right.
im = np.zeros((300, 300, 4), dtype=float)
im[..., 3] = np.linspace(0.0, 1.0, 300)
plt.figimage(im)
buff = io.BytesIO()
plt.savefig(buff, facecolor="red", format='jpg', dpi=300)
buff.seek(0)
image = Image.open(buff)
# If this fails, there will be only one color (all black). If this
# is working, we should have all 256 shades of grey represented.
num_colors = len(image.getcolors(256))
assert 175 <= num_colors <= 185
# The fully transparent part should be red.
corner_pixel = image.getpixel((0, 0))
assert corner_pixel == (254, 0, 0)
def test_axesimage_setdata():
ax = plt.gca()
im = AxesImage(ax)
z = np.arange(12, dtype=float).reshape((4, 3))
im.set_data(z)
z[0, 0] = 9.9
assert im._A[0, 0] == 0, 'value changed'
def test_figureimage_setdata():
fig = plt.gcf()
im = FigureImage(fig)
z = np.arange(12, dtype=float).reshape((4, 3))
im.set_data(z)
z[0, 0] = 9.9
assert im._A[0, 0] == 0, 'value changed'
@pytest.mark.parametrize(
"image_cls,x,y,a", [
(NonUniformImage,
np.arange(3.), np.arange(4.), np.arange(12.).reshape((4, 3))),
(PcolorImage,
np.arange(3.), np.arange(4.), np.arange(6.).reshape((3, 2))),
])
def test_setdata_xya(image_cls, x, y, a):
ax = plt.gca()
im = image_cls(ax)
im.set_data(x, y, a)
x[0] = y[0] = a[0, 0] = 9.9
assert im._A[0, 0] == im._Ax[0] == im._Ay[0] == 0, 'value changed'
im.set_data(x, y, a.reshape((*a.shape, -1))) # Just a smoketest.
def test_minimized_rasterized():
# This ensures that the rasterized content in the colorbars is
# only as thick as the colorbar, and doesn't extend to other parts
# of the image. See #5814. While the original bug exists only
# in Postscript, the best way to detect it is to generate SVG
# and then parse the output to make sure the two colorbar images
# are the same size.
from xml.etree import ElementTree
np.random.seed(0)
data = np.random.rand(10, 10)
fig, ax = plt.subplots(1, 2)
p1 = ax[0].pcolormesh(data)
p2 = ax[1].pcolormesh(data)
plt.colorbar(p1, ax=ax[0])
plt.colorbar(p2, ax=ax[1])
buff = io.BytesIO()
plt.savefig(buff, format='svg')
buff = io.BytesIO(buff.getvalue())
tree = ElementTree.parse(buff)
width = None
for image in tree.iter('image'):
if width is None:
width = image['width']
else:
if image['width'] != width:
assert False
def test_load_from_url():
path = Path(__file__).parent / "baseline_images/test_image/imshow.png"
url = ('file:'
+ ('///' if sys.platform == 'win32' else '')
+ path.resolve().as_posix())
plt.imread(url)
plt.imread(urllib.request.urlopen(url))
@image_comparison(['log_scale_image'], remove_text=True)
def test_log_scale_image():
Z = np.zeros((10, 10))
Z[::2] = 1
fig, ax = plt.subplots()
ax.imshow(Z, extent=[1, 100, 1, 100], cmap='viridis', vmax=1, vmin=-1,
aspect='auto')
ax.set(yscale='log')
@image_comparison(['rotate_image'], remove_text=True)
def test_rotate_image():
delta = 0.25
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi)
Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) /
(2 * np.pi * 0.5 * 1.5))
Z = Z2 - Z1 # difference of Gaussians
fig, ax1 = plt.subplots(1, 1)
im1 = ax1.imshow(Z, interpolation='none', cmap='viridis',
origin='lower',
extent=[-2, 4, -3, 2], clip_on=True)
trans_data2 = Affine2D().rotate_deg(30) + ax1.transData
im1.set_transform(trans_data2)
# display intended extent of the image
x1, x2, y1, y2 = im1.get_extent()
ax1.plot([x1, x2, x2, x1, x1], [y1, y1, y2, y2, y1], "r--", lw=3,
transform=trans_data2)
ax1.set_xlim(2, 5)
ax1.set_ylim(0, 4)
def test_image_preserve_size():
buff = io.BytesIO()
im = np.zeros((481, 321))
plt.imsave(buff, im, format="png")
buff.seek(0)
img = plt.imread(buff)
assert img.shape[:2] == im.shape
def test_image_preserve_size2():
n = 7
data = np.identity(n, float)
fig = plt.figure(figsize=(n, n), frameon=False)
ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(data, interpolation='nearest', origin='lower', aspect='auto')
buff = io.BytesIO()
fig.savefig(buff, dpi=1)
buff.seek(0)
img = plt.imread(buff)
assert img.shape == (7, 7, 4)
assert_array_equal(np.asarray(img[:, :, 0], bool),
np.identity(n, bool)[::-1])
@image_comparison(['mask_image_over_under.png'], remove_text=True)
def test_mask_image_over_under():
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi)
Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) /
(2 * np.pi * 0.5 * 1.5))
Z = 10*(Z2 - Z1) # difference of Gaussians
palette = copy(plt.cm.gray)
palette.set_over('r', 1.0)
palette.set_under('g', 1.0)
palette.set_bad('b', 1.0)
Zm = np.ma.masked_where(Z > 1.2, Z)
fig, (ax1, ax2) = plt.subplots(1, 2)
im = ax1.imshow(Zm, interpolation='bilinear',
cmap=palette,
norm=colors.Normalize(vmin=-1.0, vmax=1.0, clip=False),
origin='lower', extent=[-3, 3, -3, 3])
ax1.set_title('Green=low, Red=high, Blue=bad')
fig.colorbar(im, extend='both', orientation='horizontal',
ax=ax1, aspect=10)
im = ax2.imshow(Zm, interpolation='nearest',
cmap=palette,
norm=colors.BoundaryNorm([-1, -0.5, -0.2, 0, 0.2, 0.5, 1],
ncolors=256, clip=False),
origin='lower', extent=[-3, 3, -3, 3])
ax2.set_title('With BoundaryNorm')
fig.colorbar(im, extend='both', spacing='proportional',
orientation='horizontal', ax=ax2, aspect=10)
@image_comparison(['mask_image'], remove_text=True)
def test_mask_image():
# Test mask image two ways: Using nans and using a masked array.
fig, (ax1, ax2) = plt.subplots(1, 2)
A = np.ones((5, 5))
A[1:2, 1:2] = np.nan
ax1.imshow(A, interpolation='nearest')
A = np.zeros((5, 5), dtype=bool)
A[1:2, 1:2] = True
A = np.ma.masked_array(np.ones((5, 5), dtype=np.uint16), A)
ax2.imshow(A, interpolation='nearest')
@image_comparison(['imshow_endianess.png'], remove_text=True)
def test_imshow_endianess():
x = np.arange(10)
X, Y = np.meshgrid(x, x)
Z = np.hypot(X - 5, Y - 5)
fig, (ax1, ax2) = plt.subplots(1, 2)
kwargs = dict(origin="lower", interpolation='nearest', cmap='viridis')
ax1.imshow(Z.astype('<f8'), **kwargs)
ax2.imshow(Z.astype('>f8'), **kwargs)
@image_comparison(['imshow_masked_interpolation'],
tol=0 if platform.machine() == 'x86_64' else 0.01,
remove_text=True, style='mpl20')
def test_imshow_masked_interpolation():
cm = copy(plt.get_cmap('viridis'))
cm.set_over('r')
cm.set_under('b')
cm.set_bad('k')
N = 20
n = colors.Normalize(vmin=0, vmax=N*N-1)
data = np.arange(N*N, dtype=float).reshape(N, N)
data[5, 5] = -1
# This will cause crazy ringing for the higher-order
# interpolations
data[15, 5] = 1e5
# data[3, 3] = np.nan
data[15, 15] = np.inf
mask = np.zeros_like(data).astype('bool')
mask[5, 15] = True
data = np.ma.masked_array(data, mask)
fig, ax_grid = plt.subplots(3, 6)
interps = sorted(mimage._interpd_)
interps.remove('antialiased')
for interp, ax in zip(interps, ax_grid.ravel()):
ax.set_title(interp)
ax.imshow(data, norm=n, cmap=cm, interpolation=interp)
ax.axis('off')
def test_imshow_no_warn_invalid():
plt.imshow([[1, 2], [3, np.nan]]) # Check that no warning is emitted.
@pytest.mark.parametrize(
'dtype', [np.dtype(s) for s in 'u2 u4 i2 i4 i8 f4 f8'.split()])
def test_imshow_clips_rgb_to_valid_range(dtype):
arr = np.arange(300, dtype=dtype).reshape((10, 10, 3))
if dtype.kind != 'u':
arr -= 10
too_low = arr < 0
too_high = arr > 255
if dtype.kind == 'f':
arr = arr / 255
_, ax = plt.subplots()
out = ax.imshow(arr).get_array()
assert (out[too_low] == 0).all()
if dtype.kind == 'f':
assert (out[too_high] == 1).all()
assert out.dtype.kind == 'f'
else:
assert (out[too_high] == 255).all()
assert out.dtype == np.uint8
@image_comparison(['imshow_flatfield.png'], remove_text=True, style='mpl20')
def test_imshow_flatfield():
fig, ax = plt.subplots()
im = ax.imshow(np.ones((5, 5)), interpolation='nearest')
im.set_clim(.5, 1.5)
@image_comparison(['imshow_bignumbers.png'], remove_text=True, style='mpl20')
def test_imshow_bignumbers():
rcParams['image.interpolation'] = 'nearest'
# putting a big number in an array of integers shouldn't
# ruin the dynamic range of the resolved bits.
fig, ax = plt.subplots()
img = np.array([[1, 2, 1e12], [3, 1, 4]], dtype=np.uint64)
pc = ax.imshow(img)
pc.set_clim(0, 5)
@image_comparison(['imshow_bignumbers_real.png'],
remove_text=True, style='mpl20')
def test_imshow_bignumbers_real():
rcParams['image.interpolation'] = 'nearest'
# putting a big number in an array of integers shouldn't
# ruin the dynamic range of the resolved bits.
fig, ax = plt.subplots()
img = np.array([[2., 1., 1.e22], [4., 1., 3.]])
pc = ax.imshow(img)
pc.set_clim(0, 5)
@pytest.mark.parametrize(
"make_norm",
[colors.Normalize,
colors.LogNorm,
lambda: colors.SymLogNorm(1),
lambda: colors.PowerNorm(1)])
def test_empty_imshow(make_norm):
fig, ax = plt.subplots()
with pytest.warns(UserWarning,
match="Attempting to set identical left == right"):
im = ax.imshow([[]], norm=make_norm())
im.set_extent([-5, 5, -5, 5])
fig.canvas.draw()
with pytest.raises(RuntimeError):
im.make_image(fig._cachedRenderer)
def test_imshow_float128():
fig, ax = plt.subplots()
ax.imshow(np.zeros((3, 3), dtype=np.longdouble))
with (ExitStack() if np.can_cast(np.longdouble, np.float64, "equiv")
else pytest.warns(UserWarning)):
# Ensure that drawing doesn't cause crash.
fig.canvas.draw()
def test_imshow_bool():
fig, ax = plt.subplots()
ax.imshow(np.array([[True, False], [False, True]], dtype=bool))
def test_full_invalid():
fig, ax = plt.subplots()
ax.imshow(np.full((10, 10), np.nan))
with pytest.warns(UserWarning):
fig.canvas.draw()
@pytest.mark.parametrize("fmt,counted",
[("ps", b" colorimage"), ("svg", b"<image")])
@pytest.mark.parametrize("composite_image,count", [(True, 1), (False, 2)])
def test_composite(fmt, counted, composite_image, count):
# Test that figures can be saved with and without combining multiple images
# (on a single set of axes) into a single composite image.
X, Y = np.meshgrid(np.arange(-5, 5, 1), np.arange(-5, 5, 1))
Z = np.sin(Y ** 2)
fig, ax = plt.subplots()
ax.set_xlim(0, 3)
ax.imshow(Z, extent=[0, 1, 0, 1])
ax.imshow(Z[::-1], extent=[2, 3, 0, 1])
plt.rcParams['image.composite_image'] = composite_image
buf = io.BytesIO()
fig.savefig(buf, format=fmt)
assert buf.getvalue().count(counted) == count
def test_relim():
fig, ax = plt.subplots()
ax.imshow([[0]], extent=(0, 1, 0, 1))
ax.relim()
ax.autoscale()
assert ax.get_xlim() == ax.get_ylim() == (0, 1)
def test_unclipped():
fig, ax = plt.subplots()
ax.set_axis_off()
im = ax.imshow([[0, 0], [0, 0]], aspect="auto", extent=(-10, 10, -10, 10),
cmap='gray', clip_on=False)
ax.set(xlim=(0, 1), ylim=(0, 1))
fig.canvas.draw()
# The unclipped image should fill the *entire* figure and be black.
# Ignore alpha for this comparison.
assert (np.array(fig.canvas.buffer_rgba())[..., :3] == 0).all()
def test_respects_bbox():
fig, axs = plt.subplots(2)
for ax in axs:
ax.set_axis_off()
im = axs[1].imshow([[0, 1], [2, 3]], aspect="auto", extent=(0, 1, 0, 1))
im.set_clip_path(None)
# Make the image invisible in axs[1], but visible in axs[0] if we pan
# axs[1] up.
im.set_clip_box(axs[0].bbox)
buf_before = io.BytesIO()
fig.savefig(buf_before, format="rgba")
assert {*buf_before.getvalue()} == {0xff} # All white.
axs[1].set(ylim=(-1, 0))
buf_after = io.BytesIO()
fig.savefig(buf_after, format="rgba")
assert buf_before.getvalue() != buf_after.getvalue() # Not all white.
def test_image_cursor_formatting():
fig, ax = plt.subplots()
# Create a dummy image to be able to call format_cursor_data
im = ax.imshow(np.zeros((4, 4)))
data = np.ma.masked_array([0], mask=[True])
assert im.format_cursor_data(data) == '[]'
data = np.ma.masked_array([0], mask=[False])
assert im.format_cursor_data(data) == '[0]'
data = np.nan
assert im.format_cursor_data(data) == '[nan]'
@check_figures_equal()
def test_image_array_alpha(fig_test, fig_ref):
"""Per-pixel alpha channel test."""
x = np.linspace(0, 1)
xx, yy = np.meshgrid(x, x)
zz = np.exp(- 3 * ((xx - 0.5) ** 2) + (yy - 0.7 ** 2))
alpha = zz / zz.max()
cmap = plt.get_cmap('viridis')
ax = fig_test.add_subplot(111)
ax.imshow(zz, alpha=alpha, cmap=cmap, interpolation='nearest')
ax = fig_ref.add_subplot(111)
rgba = cmap(colors.Normalize()(zz))
rgba[..., -1] = alpha
ax.imshow(rgba, interpolation='nearest')
@pytest.mark.style('mpl20')
def test_exact_vmin():
cmap = copy(plt.cm.get_cmap("autumn_r"))
cmap.set_under(color="lightgrey")
# make the image exactly 190 pixels wide
fig = plt.figure(figsize=(1.9, 0.1), dpi=100)
ax = fig.add_axes([0, 0, 1, 1])
data = np.array(
[[-1, -1, -1, 0, 0, 0, 0, 43, 79, 95, 66, 1, -1, -1, -1, 0, 0, 0, 34]],
dtype=float,
)
im = ax.imshow(data, aspect="auto", cmap=cmap, vmin=0, vmax=100)
ax.axis("off")
fig.canvas.draw()
# get the RGBA slice from the image
from_image = im.make_image(fig.canvas.renderer)[0][0]
# expand the input to be 190 long and run through norm / cmap
direct_computation = (
im.cmap(im.norm((data * ([[1]] * 10)).T.ravel())) * 255
).astype(int)
# check than the RBGA values are the same
assert np.all(from_image == direct_computation)
@pytest.mark.network
@pytest.mark.flaky
def test_https_imread_smoketest():
v = mimage.imread('https://matplotlib.org/1.5.0/_static/logo2.png')
@check_figures_equal(extensions=['png'])
def test_huge_range_log(fig_test, fig_ref):
data = np.full((5, 5), -1, dtype=np.float64)
data[0:2, :] = 1E20
ax = fig_test.subplots()
im = ax.imshow(data, norm=colors.LogNorm(vmin=100, vmax=data.max()),
interpolation='nearest', cmap='viridis')
data = np.full((5, 5), -1, dtype=np.float64)
data[0:2, :] = 1000
cm = copy(plt.get_cmap('viridis'))
cm.set_under('w')
ax = fig_ref.subplots()
im = ax.imshow(data, norm=colors.Normalize(vmin=100, vmax=data.max()),
interpolation='nearest', cmap=cm)
Reference in a new issue Copy permalink