From ac0fc3aba8950ef43206562577d0194eb894b067 Mon Sep 17 00:00:00 2001 From: Kyle Mandli Date: Mon, 19 Sep 2011 21:45:55 -0500 Subject: [PATCH 1/5] Fix display of coverage bar --- doc/source/coverage_generator.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) mode change 100644 => 100755 doc/source/coverage_generator.py diff --git a/doc/source/coverage_generator.py b/doc/source/coverage_generator.py old mode 100644 new mode 100755 index 25ab388a..83fd5d86 --- a/doc/source/coverage_generator.py +++ b/doc/source/coverage_generator.py @@ -40,16 +40,16 @@ def calculate_coverage(reader): total_items += 1 if ":done:" in row[2] or ":done:" in row[3]: done_items += 1 - elif ":partial:" in row[2] or ":partial:" in row[3]: + if ":partial:" in row[2] or ":partial:" in row[3]: partial_items += 1 - elif ":na:" in row[2] or ":na:" in row[3]: + if ":na:" in row[2] or ":na:" in row[3]: na_items += 1 counts = (done_items, partial_items, total_items - (partial_items + done_items + na_items), na_items) - + return list(i / total_items for i in counts) def read_table_titles(reader): @@ -235,7 +235,7 @@ Coverage Bar for item, style in enumerate(('done-bar', 'partial-bar', 'missing-bar', 'na-bar')): - stream.write('XX' % \ + stream.write(' ' % \ (item_counts[item] * 100, style)) stream.write("\n\n") @@ -269,6 +269,6 @@ if __name__ == "__main__": generate_page(csv_path,output) output.close() - print "Generated %s from %s." % (output_path,csv_path) + print("Generated %s from %s." % (output_path,csv_path)) From cce3d7bcc5f285b17e2accce5661cdf8c0c84e92 Mon Sep 17 00:00:00 2001 From: Emmanuelle Gouillart Date: Fri, 23 Sep 2011 23:53:47 +0200 Subject: [PATCH 2/5] Example generation from scripts, taken from scikit learn (ext/gen_rst.py) --- doc/examples/README.txt | 6 + doc/examples/plot_lena_tv_denoise.py | 51 +++++ doc/ext/gen_rst.py | 303 +++++++++++++++++++++++++++ doc/source/conf.py | 8 +- doc/source/index.txt | 5 + examples/plot_lena_tv_denoise.py | 51 +++++ 6 files changed, 423 insertions(+), 1 deletion(-) create mode 100644 doc/examples/README.txt create mode 100644 doc/examples/plot_lena_tv_denoise.py create mode 100644 doc/ext/gen_rst.py create mode 100644 examples/plot_lena_tv_denoise.py diff --git a/doc/examples/README.txt b/doc/examples/README.txt new file mode 100644 index 00000000..8a6c6bd5 --- /dev/null +++ b/doc/examples/README.txt @@ -0,0 +1,6 @@ + +General examples +------------------- + +General-purpose and introductory examples for the scikit. + diff --git a/doc/examples/plot_lena_tv_denoise.py b/doc/examples/plot_lena_tv_denoise.py new file mode 100644 index 00000000..c4000d38 --- /dev/null +++ b/doc/examples/plot_lena_tv_denoise.py @@ -0,0 +1,51 @@ +""" +==================================================== +Denoising the picture of Lena using total variation +==================================================== + +In this example, we denoise a noisy version of the picture of Lena using the +total variation denoising filter. The result of this filter is an image that +has a minimal total variation norm, while being as close to the initial image +as possible. The total variation is the L1 norm of the gradient of the image, +and minimizing the total variation typically produces "posterized" images with +flat domains separated by sharp edges. + +It is possible to change the degree of posterization by controlling the +tradeoff between denoising and faithfulness to the original image. + +""" + +import numpy as np +import scipy +from scipy import ndimage +import matplotlib.pyplot as plt +from scikits.image.filter import tv_denoise + +l = scipy.lena() +l = l[230:290, 220:320] + +noisy = l + 0.4*l.std()*np.random.random(l.shape) + +tv_denoised = tv_denoise(noisy, weight=10) + + +plt.figure(figsize=(12,2.8)) + +plt.subplot(131) +plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.axis('off') +plt.title('noisy', fontsize=20) +plt.subplot(132) +plt.imshow(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.axis('off') +plt.title('TV denoising', fontsize=20) + +tv_denoised = tv_denoise(noisy, weight=50) +plt.subplot(133) +plt.imshow(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.axis('off') +plt.title('(more) TV denoising', fontsize=20) + +plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0, + right=1) + diff --git a/doc/ext/gen_rst.py b/doc/ext/gen_rst.py new file mode 100644 index 00000000..cb71a605 --- /dev/null +++ b/doc/ext/gen_rst.py @@ -0,0 +1,303 @@ +""" +Example generation for the scikit learn + +Generate the rst files for the examples by iterating over the python +example files. + +Files that generate images should start with 'plot' + +""" +import os +import shutil +import traceback +import glob + +import matplotlib +matplotlib.use('Agg') + +import token, tokenize + +rst_template = """ + +.. _example_%(short_fname)s: + +%(docstring)s + +**Python source code:** :download:`%(fname)s <%(fname)s>` + +.. literalinclude:: %(fname)s + :lines: %(end_row)s- + """ + +plot_rst_template = """ + +.. _example_%(short_fname)s: + +%(docstring)s + +%(image_list)s + +**Python source code:** :download:`%(fname)s <%(fname)s>` + +.. literalinclude:: %(fname)s + :lines: %(end_row)s- + """ + +# The following strings are used when we have several pictures: we use +# an html div tag that our CSS uses to turn the lists into horizontal +# lists. +HLIST_HEADER = """ +.. rst-class:: horizontal + +""" + +HLIST_IMAGE_TEMPLATE = """ + * + + .. image:: images/%s + :scale: 50 +""" + +SINGLE_IMAGE = """ +.. image:: images/%s + :align: center +""" + +def extract_docstring(filename): + """ Extract a module-level docstring, if any + """ + lines = file(filename).readlines() + start_row = 0 + if lines[0].startswith('#!'): + lines.pop(0) + start_row = 1 + + docstring = '' + first_par = '' + tokens = tokenize.generate_tokens(lines.__iter__().next) + for tok_type, tok_content, _, (erow, _), _ in tokens: + tok_type = token.tok_name[tok_type] + if tok_type in ('NEWLINE', 'COMMENT', 'NL', 'INDENT', 'DEDENT'): + continue + elif tok_type == 'STRING': + docstring = eval(tok_content) + # If the docstring is formatted with several paragraphs, extract + # the first one: + paragraphs = '\n'.join(line.rstrip() + for line in docstring.split('\n')).split('\n\n') + if len(paragraphs) > 0: + first_par = paragraphs[0] + break + return docstring, first_par, erow+1+start_row + + +def generate_example_rst(app): + """ Generate the list of examples, as well as the contents of + examples. + """ + root_dir = os.path.join(app.builder.srcdir, 'auto_examples') + example_dir = os.path.abspath(app.builder.srcdir + '/../' + 'examples') + try: + plot_gallery = eval(app.builder.config.plot_gallery) + except TypeError: + plot_gallery = bool(app.builder.config.plot_gallery) + if not os.path.exists(example_dir): + os.makedirs(example_dir) + if not os.path.exists(root_dir): + os.makedirs(root_dir) + + # we create an index.rst with all examples + fhindex = file(os.path.join(root_dir, 'index.txt'), 'w') + fhindex.write("""\ + +.. raw:: html + + + +Examples +======== + +.. _examples-index: +""") + # Here we don't use an os.walk, but we recurse only twice: flat is + # better than nested. + generate_dir_rst('.', fhindex, example_dir, root_dir, plot_gallery) + for dir in sorted(os.listdir(example_dir)): + if os.path.isdir(os.path.join(example_dir, dir)): + generate_dir_rst(dir, fhindex, example_dir, root_dir, plot_gallery) + fhindex.flush() + + +def generate_dir_rst(dir, fhindex, example_dir, root_dir, plot_gallery): + """ Generate the rst file for an example directory. + """ + if not dir == '.': + target_dir = os.path.join(root_dir, dir) + src_dir = os.path.join(example_dir, dir) + else: + target_dir = root_dir + src_dir = example_dir + if not os.path.exists(os.path.join(src_dir, 'README.txt')): + print 80*'_' + print ('Example directory %s does not have a README.txt file' + % src_dir) + print 'Skipping this directory' + print 80*'_' + return + fhindex.write(""" + + +%s + + +""" % file(os.path.join(src_dir, 'README.txt')).read()) + if not os.path.exists(target_dir): + os.makedirs(target_dir) + + def sort_key(a): + # put last elements without a plot + if not a.startswith('plot') and a.endswith('.py'): + return 'zz' + a + return a + for fname in sorted(os.listdir(src_dir), key=sort_key): + if fname.endswith('py'): + generate_file_rst(fname, target_dir, src_dir, plot_gallery) + thumb = os.path.join(dir, 'images', 'thumb', fname[:-3] + '.png') + link_name = os.path.join(dir, fname).replace(os.path.sep, '_') + fhindex.write('.. figure:: %s\n' % thumb) + if link_name.startswith('._'): + link_name = link_name[2:] + if dir != '.': + fhindex.write(' :target: ./%s/%s.html\n\n' % (dir, fname[:-3])) + else: + fhindex.write(' :target: ./%s.html\n\n' % link_name[:-3]) + fhindex.write(' :ref:`example_%s`\n\n' % link_name) + fhindex.write(""" +.. raw:: html + +
+ """) # clear at the end of the section + + +def generate_file_rst(fname, target_dir, src_dir, plot_gallery): + """ Generate the rst file for a given example. + """ + base_image_name = os.path.splitext(fname)[0] + image_fname = '%s_%%s.png' % base_image_name + + this_template = rst_template + last_dir = os.path.split(src_dir)[-1] + # to avoid leading . in file names, and wrong names in links + if last_dir == '.' or last_dir == 'examples': + last_dir = '' + else: + last_dir += '_' + short_fname = last_dir + fname + src_file = os.path.join(src_dir, fname) + example_file = os.path.join(target_dir, fname) + shutil.copyfile(src_file, example_file) + + # The following is a list containing all the figure names + figure_list = [] + + image_dir = os.path.join(target_dir, 'images') + thumb_dir = os.path.join(image_dir, 'thumb') + if not os.path.exists(image_dir): + os.makedirs(image_dir) + if not os.path.exists(thumb_dir): + os.makedirs(thumb_dir) + image_path = os.path.join(image_dir, image_fname) + thumb_file = os.path.join(thumb_dir, fname[:-3] + '.png') + if plot_gallery and fname.startswith('plot'): + # generate the plot as png image if file name + # starts with plot and if it is more recent than an + # existing image. + first_image_file = image_path % 1 + + if (not os.path.exists(first_image_file) or + os.stat(first_image_file).st_mtime <= + os.stat(src_file).st_mtime): + # We need to execute the code + print 'plotting %s' % fname + import matplotlib.pyplot as plt + plt.close('all') + cwd = os.getcwd() + try: + # First CD in the original example dir, so that any file created + # by the example get created in this directory + os.chdir(os.path.dirname(src_file)) + execfile(os.path.basename(src_file), {'pl' : plt}) + os.chdir(cwd) + + # In order to save every figure we have two solutions : + # * iterate from 1 to infinity and call plt.fignum_exists(n) + # (this requires the figures to be numbered + # incrementally: 1, 2, 3 and not 1, 2, 5) + # * iterate over [fig_mngr.num for fig_mngr in + # matplotlib._pylab_helpers.Gcf.get_all_fig_managers()] + for fig_num in (fig_mngr.num for fig_mngr in + matplotlib._pylab_helpers.Gcf.get_all_fig_managers()): + # Set the fig_num figure as the current figure as we can't + # save a figure that's not the current figure. + plt.figure(fig_num) + plt.savefig(image_path % fig_num) + figure_list.append(image_fname % fig_num) + except: + print 80*'_' + print '%s is not compiling:' % fname + traceback.print_exc() + print 80*'_' + finally: + os.chdir(cwd) + else: + figure_list = [f[len(image_dir):] + for f in glob.glob(image_path % '[1-9]')] + #for f in glob.glob(image_path % '*')] + + # generate thumb file + this_template = plot_rst_template + from matplotlib import image + if os.path.exists(first_image_file): + image.thumbnail(first_image_file, thumb_file, 0.2) + + if not os.path.exists(thumb_file): + # create something not to replace the thumbnail + shutil.copy('images/blank_image.png', thumb_file) + + docstring, short_desc, end_row = extract_docstring(example_file) + + # Depending on whether we have one or more figures, we're using a + # horizontal list or a single rst call to 'image'. + if len(figure_list) == 1: + figure_name = figure_list[0] + image_list = SINGLE_IMAGE % figure_name.lstrip('/') + else: + image_list = HLIST_HEADER + for figure_name in figure_list: + image_list += HLIST_IMAGE_TEMPLATE % figure_name.lstrip('/') + + f = open(os.path.join(target_dir, fname[:-2] + 'txt'),'w') + f.write(this_template % locals()) + f.flush() + + +def setup(app): + app.connect('builder-inited', generate_example_rst) + app.add_config_value('plot_gallery', True, 'html') diff --git a/doc/source/conf.py b/doc/source/conf.py index c9959dd4..d9e20793 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -23,11 +23,17 @@ sys.path.append(os.path.join(curpath, '..', 'ext')) # -- General configuration ----------------------------------------------------- +try: + import gen_rst +except: + pass + + # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.pngmath', 'numpydoc', 'sphinx.ext.autosummary', 'sphinx.ext.inheritance_diagram', - 'plot_directive'] + 'plot_directive', 'gen_rst'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] diff --git a/doc/source/index.txt b/doc/source/index.txt index 15b5b57e..2ec428ac 100644 --- a/doc/source/index.txt +++ b/doc/source/index.txt @@ -39,6 +39,11 @@ Sections Conditions on the use and redistribution of this package. + +`Examples `_ + +Introductory examples + Indices and Tables ================== diff --git a/examples/plot_lena_tv_denoise.py b/examples/plot_lena_tv_denoise.py new file mode 100644 index 00000000..c4000d38 --- /dev/null +++ b/examples/plot_lena_tv_denoise.py @@ -0,0 +1,51 @@ +""" +==================================================== +Denoising the picture of Lena using total variation +==================================================== + +In this example, we denoise a noisy version of the picture of Lena using the +total variation denoising filter. The result of this filter is an image that +has a minimal total variation norm, while being as close to the initial image +as possible. The total variation is the L1 norm of the gradient of the image, +and minimizing the total variation typically produces "posterized" images with +flat domains separated by sharp edges. + +It is possible to change the degree of posterization by controlling the +tradeoff between denoising and faithfulness to the original image. + +""" + +import numpy as np +import scipy +from scipy import ndimage +import matplotlib.pyplot as plt +from scikits.image.filter import tv_denoise + +l = scipy.lena() +l = l[230:290, 220:320] + +noisy = l + 0.4*l.std()*np.random.random(l.shape) + +tv_denoised = tv_denoise(noisy, weight=10) + + +plt.figure(figsize=(12,2.8)) + +plt.subplot(131) +plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.axis('off') +plt.title('noisy', fontsize=20) +plt.subplot(132) +plt.imshow(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.axis('off') +plt.title('TV denoising', fontsize=20) + +tv_denoised = tv_denoise(noisy, weight=50) +plt.subplot(133) +plt.imshow(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) +plt.axis('off') +plt.title('(more) TV denoising', fontsize=20) + +plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0, + right=1) + From 4174d5fef00e5f1b0cbf2c99c44954e12dd1b0e4 Mon Sep 17 00:00:00 2001 From: Emmanuelle Gouillart Date: Sat, 24 Sep 2011 15:05:16 +0200 Subject: [PATCH 3/5] A few fixes: give credit to scikit-learn guys, fix examples... --- CONTRIBUTORS.txt | 6 ++- doc/examples/plot_lena_tv_denoise.py | 4 +- doc/ext/LICENSE.txt | 43 +++++++++++++++ doc/ext/gen_rst.py | 6 ++- .../auto_examples/images/blank_image.png | Bin 0 -> 1508 bytes examples/plot_lena_tv_denoise.py | 51 ------------------ 6 files changed, 54 insertions(+), 56 deletions(-) create mode 100644 doc/source/auto_examples/images/blank_image.png delete mode 100644 examples/plot_lena_tv_denoise.py diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index 8b2ff9ff..548f37c5 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -44,7 +44,8 @@ Incorporating CellProfiler's Sobel edge detector, build and bug fixes. - Emmanuelle Guillart - Total variation noise filtering + Total variation noise filtering, integration of CellProfiler's + mathematical morphology tools. - Maël Primet Total variation noise filtering @@ -60,3 +61,6 @@ - Kyle Mandli CSV to ReST code for feature comparison table. + +- The Scikit Learn team + From whom we borrowed the example generation tools. diff --git a/doc/examples/plot_lena_tv_denoise.py b/doc/examples/plot_lena_tv_denoise.py index c4000d38..4797aaed 100644 --- a/doc/examples/plot_lena_tv_denoise.py +++ b/doc/examples/plot_lena_tv_denoise.py @@ -21,7 +21,7 @@ from scipy import ndimage import matplotlib.pyplot as plt from scikits.image.filter import tv_denoise -l = scipy.lena() +l = scipy.misc.lena() l = l[230:290, 220:320] noisy = l + 0.4*l.std()*np.random.random(l.shape) @@ -48,4 +48,4 @@ plt.title('(more) TV denoising', fontsize=20) plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0, right=1) - +plt.show() diff --git a/doc/ext/LICENSE.txt b/doc/ext/LICENSE.txt index c23b6bfc..3ebab893 100644 --- a/doc/ext/LICENSE.txt +++ b/doc/ext/LICENSE.txt @@ -66,3 +66,46 @@ products or services of Licensee, or any third party. 8. By copying, installing or otherwise using matplotlib 0.98.3, Licensee agrees to be bound by the terms and conditions of this License Agreement. + +The file + +- gen_rst.py + +was taken from the scikit-learn (http://scikit-learn.sourceforge.net), which +has the following license: + +New BSD License + +Copyright (c) 2007 - 2011 The scikit-learn developers. +All rights reserved. + + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of the Scikit-learn Developers nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT +LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY +OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH +DAMAGE. + + + + diff --git a/doc/ext/gen_rst.py b/doc/ext/gen_rst.py index cb71a605..0cf8cbd2 100644 --- a/doc/ext/gen_rst.py +++ b/doc/ext/gen_rst.py @@ -1,10 +1,12 @@ """ -Example generation for the scikit learn +Example generation for the scikit image. Generate the rst files for the examples by iterating over the python example files. -Files that generate images should start with 'plot' +Files that generate images should start with 'plot'. + +This code was taken from the scikit-learn. """ import os diff --git a/doc/source/auto_examples/images/blank_image.png b/doc/source/auto_examples/images/blank_image.png new file mode 100644 index 0000000000000000000000000000000000000000..4913b99b19fe0e6f91c068f27ac80831ea473bf2 GIT binary patch literal 1508 zcmVPx#24YJ`L;(K){{a7>y{D4^000SaNLh0L01ejw01ejxLMWSf00007bV*G`2ipk( z2{tlaE*H!I00m@8L_t(|+U?!HZ(CIu#_{J|w@K^*{UHP$2ryOZKm_7PtVp0r-9Ux^ zqNE6kKLCb`g^>kXrvnl~NbC_iYAHzzLmgPi)Z#Eunl^FeYrAsp%V5WG9AC$doNFKR z{Zy)|*ij?<$&>e-b7M;Y00000000000000000000001y>f!q1_=9Tk2An@PxsR_ON z%1OQUVy5}A@YkHA8tQTel66* z;}a_9vP$b{y{)g#jr`Z2;weO{YRz;DyQWlg zPA&*k$z?S)e_NGYmO#S~`9evH`A53@pt5)2<(w)DSxwC^E4(&Kpy5YLFWz~m>#N1i zcQB{&LROiJw-iML8XmZ}Ue%4&;;xq7$+OAHDa>W{Qfs88veBOV*XLv0Gbgl%}gKD)X|`=sU?$B zEmKnZ!(|#8ek>68ZsWBKAFh6ySp9lV;+(wUX|w zSL5$w8p@G4fyRNX6vCLr;dWKK<`sGb8Y8dU>0y2JFqtuOz_VF;Wi5=WXgNvYwX7<+ zSpp3|T6(2WirK=eU*{1-($xGd0uBEchfc1(jVH-2pCiy1gT-Ov>S=fRCjt#m#*Vcx zcI=Qzl>{$VBG4E>OPAtXx_;FXfrdZwg_3@c-R($r?~XuYAdADh>($t!a5<^6FiW7} z(=1(y-R&?rMG0L8N1!p7KtHH<*}gNSEF#eGsiET!S>$t>=-BaBw`-&cG<+L7I&R-N0u5h79oF^)+LYX)@CZ)<25F;Q(`I@1 z?-M#RIc8{#%fiqaFTqPgV=NYiF2U0JN_-!G%>%#$bs?j$KPS%|0jXQp@_`% z?alj&zB$K(0soypdz=RW}C_8=d0W~QMr z63uO9U0d1AdI~v#Mk0ZY3uC8`ZwGw8j}d4LeYEK1_FmhVKx3#QMW+yVYT|=@QxIq* z9_n6$+i7dB*Hc6Gp^->C_p<-oDd^juOG9I1+qs_^xl@?a=`;<%YYl8Fm5Occ=6!hBk@U!M(EIi>Pt(vyJjih)MBE`uw|SZw z8Y3I(UKWu11)7FNqT&J>1i`*X-o0$y(a=bwp Date: Sat, 24 Sep 2011 13:21:44 -0700 Subject: [PATCH 4/5] ENH: Expose data module. Currently just provides scipy's lena in a more convenient location. --- scikits/image/data/__init__.py | 4 ++++ scikits/image/setup.py | 1 + 2 files changed, 5 insertions(+) create mode 100644 scikits/image/data/__init__.py diff --git a/scikits/image/data/__init__.py b/scikits/image/data/__init__.py new file mode 100644 index 00000000..c46a2e37 --- /dev/null +++ b/scikits/image/data/__init__.py @@ -0,0 +1,4 @@ +import scipy.misc as _m + +lena = _m.lena + diff --git a/scikits/image/setup.py b/scikits/image/setup.py index b1831d4e..2d24389d 100644 --- a/scikits/image/setup.py +++ b/scikits/image/setup.py @@ -11,6 +11,7 @@ def configuration(parent_package='', top_path=None): config.add_subpackage('morphology') config.add_subpackage('filter') config.add_subpackage('transform') + config.add_subpackage('data') def add_test_directories(arg, dirname, fnames): if dirname.split(os.path.sep)[-1] == 'tests': From 707c4c801a0c35a1503575a6bd8c82fed6c589b6 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Sat, 24 Sep 2011 14:45:33 -0700 Subject: [PATCH 5/5] DOC: Update tv example to use data module. Rewrap some text. --- doc/examples/plot_lena_tv_denoise.py | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/doc/examples/plot_lena_tv_denoise.py b/doc/examples/plot_lena_tv_denoise.py index 4797aaed..55f167e1 100644 --- a/doc/examples/plot_lena_tv_denoise.py +++ b/doc/examples/plot_lena_tv_denoise.py @@ -3,25 +3,26 @@ Denoising the picture of Lena using total variation ==================================================== -In this example, we denoise a noisy version of the picture of Lena using the -total variation denoising filter. The result of this filter is an image that -has a minimal total variation norm, while being as close to the initial image -as possible. The total variation is the L1 norm of the gradient of the image, -and minimizing the total variation typically produces "posterized" images with -flat domains separated by sharp edges. +In this example, we denoise a noisy version of the picture of Lena +using the total variation denoising filter. The result of this filter +is an image that has a minimal total variation norm, while being as +close to the initial image as possible. The total variation is the L1 +norm of the gradient of the image, and minimizing the total variation +typically produces "posterized" images with flat domains separated by +sharp edges. -It is possible to change the degree of posterization by controlling the -tradeoff between denoising and faithfulness to the original image. +It is possible to change the degree of posterization by controlling +the tradeoff between denoising and faithfulness to the original image. """ import numpy as np -import scipy -from scipy import ndimage import matplotlib.pyplot as plt + +from scikits.image import data from scikits.image.filter import tv_denoise -l = scipy.misc.lena() +l = data.lena() l = l[230:290, 220:320] noisy = l + 0.4*l.std()*np.random.random(l.shape) @@ -46,6 +47,6 @@ plt.imshow(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220) plt.axis('off') plt.title('(more) TV denoising', fontsize=20) -plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0, - right=1) +plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0, + right=1) plt.show()