From 775584f26923dec3fa9b6af34945504e8fff6069 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=B6rg=20Dietrich?= Date: Tue, 3 Jan 2023 21:06:56 +0100 Subject: [PATCH] Make matplotlib simulation and daltonization work again (#23) * Fixes to bugs introduced as part of the 0.1.x releases using gamma correction * Works around changes in recent matplotlib version, approx. >= 3.0.0 * Expand unit testing --- .gitignore | 3 +- README.md | 12 +- daltonize/daltonize.py | 78 ++++++------- .../test_daltonize/imshow_crayons_d.png | Bin 0 -> 76625 bytes .../imshow_crayons_d_daltonized.png | Bin 0 -> 74489 bytes .../imshow_scatter_with_colorbar_p.png | Bin 0 -> 6723 bytes ...how_scatter_with_colorbar_p_daltonized.png | Bin 0 -> 6628 bytes .../data/colored_crayons_daltonized_d.png | Bin 994697 -> 995879 bytes .../data/colored_crayons_daltonized_t.png | Bin 977073 -> 977139 bytes daltonize/tests/test_daltonize.py | 103 +++++++++++++----- .../Compute Transform Matrices.ipynb | 0 .../Ishihara_Plate_3-Deuteranopia.jpg | Bin 28725 -> 28751 bytes .../Ishihara_Plate_3-Protanopia.jpg | Bin 28977 -> 28850 bytes requirements.txt | 1 + setup.py | 8 +- tox.ini | 1 + 16 files changed, 125 insertions(+), 81 deletions(-) create mode 100644 daltonize/tests/baseline_images/test_daltonize/imshow_crayons_d.png create mode 100644 daltonize/tests/baseline_images/test_daltonize/imshow_crayons_d_daltonized.png create mode 100644 daltonize/tests/baseline_images/test_daltonize/imshow_scatter_with_colorbar_p.png create mode 100644 daltonize/tests/baseline_images/test_daltonize/imshow_scatter_with_colorbar_p_daltonized.png rename Compute Transform Matrices.ipynb => doc/Compute Transform Matrices.ipynb (100%) diff --git a/.gitignore b/.gitignore index 519b76e..bc45c1d 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,7 @@ dist .ipynb_checkpoints/ doc/colourmaps.pdf .vscode -.coverage +.coverage* .tox coverage.json +result-images/ diff --git a/README.md b/README.md index 01bb739..3eda06a 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ ![https://github.com/joergdietrich/daltonize/actions](https://img.shields.io/github/actions/workflow/status/joergdietrich/daltonize/main.yml) ![](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/joergdietrich/9deb619232c8098b5e15d259ef5ed534/raw/covbadge.json) -Daltonize simulates the three types of dichromatic color blindness and +Daltonize simulates the three types of dichromatic color blindness for images and matplotlib figures. Generalizing and omitting a lot of details these types are: @@ -10,7 +10,7 @@ details these types are: * Protanopia: red weakness * Tritanopia: blue weakness (extremely rare) -Daltonize can also adjust the color palette of an input image such +Daltonize can also adjust the color palette of an input image or matplotlib figure such that a color blind person can perceive the full information content. It can be used as a command line tool to convert pixel images but also as a Python module. If used as the latter, it provides an API @@ -19,6 +19,10 @@ to simulate and correct for color blindness in matplotlib figures. This allows to create color blind friendly vector graphics suitable for publication. +Color vision deficiencies are in fact very complex and can differ in intensity from person to person. +The algorithms used in here and in many other comparable software packages are based on often simplifying assumptions. [Nicolas Burrus](http://nicolas.burrus.name/) discusses these simplification and reviews daltonize and other software packages in this [blog post](https://daltonlens.org/opensource-cvd-simulation/). + + ## Installation ``` @@ -97,7 +101,7 @@ the `-s/--simulate` option and `-t/--type d` or `p` on these images. ### Deuteranopia ``` -python daltonize.py -s -t=d example_images/Ishihara_Plate_3.jpg example_images/Ishihara_Plate_3-Deuteranopia.jpg +daltonize -s -t=d example_images/Ishihara_Plate_3.jpg example_images/Ishihara_Plate_3-Deuteranopia.jpg ``` ![IshiharaPlate3](example_images/Ishihara_Plate_3-Deuteranopia.jpg) @@ -105,7 +109,7 @@ python daltonize.py -s -t=d example_images/Ishihara_Plate_3.jpg example_images/I ### Protanopia ``` -python daltonize.py -s -t=p example_images/Ishihara_Plate_3.jpg example_images/Ishihara_Plate_3-Protanopia.jpg +daltonize -s -t=p example_images/Ishihara_Plate_3.jpg example_images/Ishihara_Plate_3-Protanopia.jpg ``` ![IshiharaPlate3](example_images/Ishihara_Plate_3-Protanopia.jpg) diff --git a/daltonize/daltonize.py b/daltonize/daltonize.py index e66f03e..4d24796 100755 --- a/daltonize/daltonize.py +++ b/daltonize/daltonize.py @@ -21,6 +21,7 @@ except ImportError: _NO_MPL = True +COLOR_KEYS = ("color", "fc", "ec", "mec", "mfc", "mfcalt", "cmap", "array") def transform_colorspace(img, mat): """Transform image to a different color space. @@ -44,7 +45,7 @@ def simulate(rgb, color_deficit="d"): Arguments: ---------- rgb : array of shape (M, N, 3) - original image in RGB format + original image in RGB format, values must be [0, 1] bounded color_deficit : {"d", "p", "t"}, optional type of colorblindness, d for deuteronopia (default), p for protonapia, @@ -74,7 +75,7 @@ def simulate(rgb, color_deficit="d"): sim_lms = transform_colorspace(lms, cb_matrices[color_deficit]) # Transform back to RBG sim_rgb = transform_colorspace(sim_lms, lms2rgb) - return sim_rgb + return np.clip(sim_rgb, 0, 1) def daltonize(rgb, color_deficit='d'): @@ -84,7 +85,7 @@ def daltonize(rgb, color_deficit='d'): Arguments: ---------- rgb : array of shape (M, N, 3) - original image in RGB format + original image in RGB format, values must be [0, 1] bounded color_deficit : {"d", "p", "t"}, optional type of colorblindness, d for deuteronopia (default), p for protonapia, @@ -102,7 +103,7 @@ def daltonize(rgb, color_deficit='d'): # they can see. err = transform_colorspace(rgb - sim_rgb, err2mod) dtpn = err + rgb - return dtpn + return np.clip(dtpn, 0, 1) def array_to_img(arr, gamma=2.4): @@ -120,34 +121,12 @@ def array_to_img(arr, gamma=2.4): """ # clip values to lie in the range [0, 255] arr = inverse_gamma_correction(arr, gamma=gamma) - arr = clip_array(arr) + arr = np.clip(arr, 0, 255) arr = arr.astype('uint8') img = Image.fromarray(arr, mode='RGB') return img -def clip_array(arr, min_value=0, max_value=255): - """Ensure that all values in an array are between min and max values. - - Arguments: - ---------- - arr : array_like - min_value : float, optional - default 0 - max_value : float, optional - default 255 - - Returns: - -------- - arr : array_like - clipped such that all values are min_value <= arr <= max_value - """ - comp_arr = np.ones_like(arr) - arr = np.maximum(comp_arr * min_value, arr) - arr = np.minimum(comp_arr * max_value, arr) - return arr - - def get_child_colors(child, mpl_colors): """ Recursively enter all colors of a matplotlib objects and its @@ -232,16 +211,14 @@ def get_key_colors(mpl_colors, rgb, alpha): if _NO_MPL is True: raise ImportError("matplotlib not found, " "can only deal with pixel images") - cc = mpl.colors.ColorConverter() # pylint: disable=invalid-name - # Note that the order must match the insertion order in - # get_child_colors() - color_keys = ("color", "fc", "ec", "mec", "mfc", "mfcalt", "cmap", "array") - for color_key in color_keys: + for color_key in COLOR_KEYS: try: color = mpl_colors[color_key] # skip unset colors, otherwise they are turned into black. if isinstance(color, str) and color == 'none': continue + if isinstance(color, mpl.colors.ListedColormap): + continue if isinstance(color, mpl.colors.LinearSegmentedColormap): rgba = color(np.arange(color.N)) elif isinstance(color, np.ndarray) and color_key == "array": @@ -249,13 +226,13 @@ def get_key_colors(mpl_colors, rgb, alpha): a = np.zeros((color.shape[0], 1)) # pylint: disable=invalid-name rgba = np.hstack((color, a)) else: - rgba = cc.to_rgba_array(color) + rgba = mpl.colors.to_rgba_array(color) rgb = np.append(rgb, rgba[:, :3]) alpha = np.append(alpha, rgba[:, 3]) except KeyError: pass for key in mpl_colors.keys(): - if key in color_keys: + if key in COLOR_KEYS: continue rgb, alpha = get_key_colors(mpl_colors[key], rgb, alpha) return rgb, alpha @@ -293,10 +270,8 @@ def _set_colors_from_array(instance, mpl_colors, rgba, i=0): Set object instance colors to the modified ones in rgba. """ cc = mpl.colors.ColorConverter() # pylint: disable=invalid-name - # Note that the order must match the insertion order in - # get_child_colors() - color_keys = ("color", "fc", "ec", "mec", "mfc", "mfcalt", "cmap", "array") - for color_key in color_keys: + + for color_key in COLOR_KEYS: try: color = mpl_colors[color_key] if isinstance(color, mpl.colors.LinearSegmentedColormap): @@ -307,6 +282,8 @@ def _set_colors_from_array(instance, mpl_colors, rgba, i=0): # skip unset colors, otherwise they are turned into black. if isinstance(color, str) and color == 'none': continue + if isinstance(color, mpl.colors.ListedColormap): + continue color_shape = cc.to_rgba_array(color).shape j = color_shape[0] target_color = rgba[i: i + j, :] @@ -316,6 +293,10 @@ def _set_colors_from_array(instance, mpl_colors, rgba, i=0): if color_key == "color": instance.set_color(target_color) elif color_key == "fc": + # Circumvent https://github.com/matplotlib/matplotlib/issues/13131 by setting + # the cmap to None, which would otherwise take precedence over explicitly set colors. + if hasattr(instance, "set_array"): + instance.set_array(None) instance.set_facecolor(target_color) elif color_key == "ec": instance.set_edgecolor(target_color) @@ -371,7 +352,6 @@ def _join_rgb_alpha(rgb, alpha): """ Combine (m, n, 3) rgb and (m, n) alpha array into (m, n, 4) rgba. """ - rgb = clip_array(rgb, 0, 1) r, g, b = np.split(rgb, 3, 2) # pylint: disable=invalid-name, unbalanced-tuple-unpacking rgba = np.concatenate((r, g, b, alpha.reshape(alpha.size, 1, 1)), axis=2).reshape(-1, 4) @@ -397,13 +377,17 @@ def simulate_mpl(fig, color_deficit='d', copy=False): -------- fig : matplotlib.figure.Figure """ + # Ensure that everything that was specified is actually drawn + fig.canvas.draw() if copy: # mpl.transforms cannot be copy.deepcopy()ed. Thus we resort # to pickling. pfig = pickle.dumps(fig) fig = pickle.loads(pfig) rgb, alpha, mpl_colors = _prepare_for_transform(fig) - sim_rgb = simulate(array_to_img(rgb * 255), color_deficit) / 255 + rgb = inverse_gamma_correction(rgb) / 255 + sim_rgb = simulate(rgb, color_deficit) + sim_rgb = gamma_correction(sim_rgb * 255) rgba = _join_rgb_alpha(sim_rgb, alpha) set_mpl_colors(mpl_colors, rgba) fig.canvas.draw() @@ -429,13 +413,17 @@ def daltonize_mpl(fig, color_deficit='d', copy=False): -------- fig : matplotlib.figure.Figure """ + # Ensure that everything that was specified is actually drawn + fig.canvas.draw() if copy: # mpl.transforms cannot be copy.deepcopy()ed. Thus we resort # to pickling. pfig = pickle.dumps(fig) fig = pickle.loads(pfig) rgb, alpha, mpl_colors = _prepare_for_transform(fig) - dtpn = daltonize(array_to_img(rgb * 255), color_deficit) / 255 + rgb = inverse_gamma_correction(rgb) / 255 + dtpn = daltonize(rgb, color_deficit) + sim_rgb = gamma_correction(dtpn * 255) rgba = _join_rgb_alpha(dtpn, alpha) set_mpl_colors(mpl_colors, rgba) fig.canvas.draw() @@ -444,9 +432,9 @@ def daltonize_mpl(fig, color_deficit='d', copy=False): def gamma_correction(rgb, gamma=2.4): """ Apply sRGB gamma correction - :param rgb: + :param rgb: array of shape (M, N, 3) with sRGB values in the range [0, 255] :param gamma: - :return: linear_rgb + :return: linear_rgb, array of shape (M, N, 3) with linear sRGB values between in the range [0, 1] """ linear_rgb = np.zeros_like(rgb, dtype=np.float16) for i in range(3): @@ -461,7 +449,7 @@ def inverse_gamma_correction(linear_rgb, gamma=2.4): :param linear_rgb: array of shape (M, N, 3) with linear sRGB values between in the range [0, 1] :param gamma: float - :return: array of shape (M, N, 3) with inverse gamma correction applied + :return: array of shape (M, N, 3) with inverse gamma correction applied, values in the range [0, 255] """ rgb = np.zeros_like(linear_rgb, dtype=np.float16) for i in range(3): @@ -490,7 +478,7 @@ def main(): "(most common)") parser.add_argument("-g", "--gamma", type=float, default=2.4, help="value of gamma correction to be applied before transformation. 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zA@bW$%5UY&7jF7uTCIU8Iq?PbG4*aGnufguF9sf_;@|DNq?-=GWh!GJvZfOi7f5Q? zP_{ow9MQ2xdlc?F^+&PuZ27@L6uAoB>6(`O3LWU*6BaVQp#@fM3|3IX^?3eBi|2)( U|7QUsd4K@v@Vt$M{rCHS0jfjK#{d8T diff --git a/requirements.txt b/requirements.txt index f50b526..28d7f19 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,2 +1,3 @@ +matplotlib>=3.0.0 numpy>=1.9.0 Pillow \ No newline at end of file diff --git a/setup.py b/setup.py index fe32dd7..52b7ebd 100644 --- a/setup.py +++ b/setup.py @@ -22,10 +22,14 @@ ], python_requires='>=3.6', - install_requires=['numpy>=1.9', 'Pillow'], + install_requires=[ + 'numpy>=1.9', + 'Pillow', + ], extras_require={ - 'dev': ['pytest'], + 'test': ['matplotlib>=3.0.0', + 'pytest'], }, # To provide executable scripts, use entry points in preference to the diff --git a/tox.ini b/tox.ini index bd3f203..a4924fa 100644 --- a/tox.ini +++ b/tox.ini @@ -7,6 +7,7 @@ minversion = 4.0.16 deps = coverage pytest + matplotlib commands = python -m coverage run -p -m pytest