def generateMdFile(cardInfo):
now = datetime.now()
filename = now.strftime('%Y-%m-%d-%H-%M-%S')
reportname = '%s-report' % (filename)
print('Generating %s...' % (reportname))
mdFile = MdUtils(file_name=reportname, title='Today\'s Trello Report')
# Intro text
mdFile.write(
'Hey tech-team! Here’s a snapshot of what we’re working on based on our current Trello! '
)
mdFile.write(
'If you have updates you want to give or are interested in following up with/joining some of this work,'
)
mdFile.write(
'either ask here, message the folks on the task, or get in the Trello yourself and see what’s going on!'
)
mdFile.new_line()
mdFile.new_line()
for card in cardInfo:
# Card Title
print('Creating entry for %s...' % (card['name']))
mdFile.write('**%s**' % (card['name']))
# Card Tags
if (len(card['labels']) > 0):
mdFile.write(' (')
for i in range(len(card['labels'])):
mdFile.write('%s' % (card['labels'][i]))
if (i != len(card['labels']) - 1):
mdFile.write(', ')
mdFile.write(')')
# Names on the card
if (len(card['members']) > 0):
mdFile.write(' [')
for i in range(len(card['members'])):
mdFile.write('%s' % (card['members'][i]))
if (i != len(card['members']) - 1):
mdFile.write(', ')
mdFile.write(']')
# Recent comment
if (len(card['recentComment']) > 0):
mdFile.write(': ')
mdFile.write('%s' % card['recentComment'])
else:
mdFile.write(
': No further information (someone should add some comments/users)!'
)
# Lines between files
mdFile.new_line()
mdFile.new_line()
# Create file
mdFile.create_md_file()
print('Done!')
def format_entry(md_file: MdUtils, entry: Dict):
assert entry.get('title')
formatted = FORMAT.format(TITLE=entry.get('title'),
AUTHOR=entry.get('author',
'').replace(' and', ','),
YEAR=entry.get('year', ''),
JOURNAL=entry.get('journal', ''))
md_file.new_line(formatted)
def generate_report():
report = MdUtils(file_name=f'./report.md')
report.new_header(level=1, title='Контрастирование')
report.new_line(text='Выполнил Ахманов Алексей Б18-514')
for im in IMAGES:
image_path = f'{helpers.folder_helper.IMAGES_FOLDER_PATH}/{im}'
processed_image_folder_path = f'{image_path}_processed'
os.makedirs(processed_image_folder_path, exist_ok=True)
grayscaled_image_path = f'{processed_image_folder_path}/{im}_grayscaled.png'
linear_contrasted_image_path = f'{processed_image_folder_path}/{im}_linear_contrasted.png'
power_transformed_image_path = f'{processed_image_folder_path}/{im}_power_transformed_gamma=#.png'
image = Image.open(fp=image_path).convert(constants.RGB_MODE)
grayscaled = grayscale.mean_grayscale(image)
grayscaled.save(grayscaled_image_path)
# Contrasting
enhancement.contrasting.linear_contrasting(grayscaled).save(
linear_contrasted_image_path)
for gamma in numpy.arange(START_GAMMA, STOP_GAMMA, STEP_GAMMA):
gamma = round(gamma, GAMMA_ROUND_SIGNS)
enhancement.contrasting.power_transformation(grayscaled, gamma=gamma)\
.save(power_transformed_image_path.replace('#', str(gamma)))
# Report
report.new_header(level=2, title=f'{im}')
report.new_header(level=3, title='Исходная картинка')
report.new_line(
report.new_inline_image(text='Исходная картинка', path=image_path))
report.new_header(level=3, title='Оттенки серого')
report.new_line(
report.new_inline_image(text='Оттенки серого',
path=grayscaled_image_path))
report.new_header(level=3, title='Линейное контрастирование')
report.new_line(
report.new_inline_image(text='Линейное контрастирование',
path=linear_contrasted_image_path))
report.new_header(level=3, title='Степенное преобразование')
for gamma in numpy.arange(START_GAMMA, STOP_GAMMA, STEP_GAMMA):
gamma = round(gamma, GAMMA_ROUND_SIGNS)
report.new_header(level=4, title=f'Gamma = {gamma}')
report.new_line(
report.new_inline_image(
text='Степенное преобразование',
path=power_transformed_image_path.replace('#',
str(gamma))))
report.create_md_file()
def write_table(parts, keys, md: mdutils.MdUtils, sort_by='Comment'):
sorted_parts = sorted(parts, key=lambda i: i[sort_by])
tabulated = list(keys)
for part in sorted_parts:
tabulated.extend([part.get(key) for key in keys])
#print(part)
#print(len(keys), len(sorted_parts)+1, len(tabulated))
md.new_line()
md.new_table(columns=len(keys), rows=len(sorted_parts) + 1, text=tabulated)
def write_to_markdown(self, filename: str):
mdFile = MdUtils(
file_name=self.OUTPUT_DIR + filename,
title='Mycroft.ai commands (autogenerated from po files)')
for plugin_name in self.translations:
mdFile.new_header(level=1, title=plugin_name)
mdFile.new_line()
self.add_table(mdFile, self.translations[plugin_name])
mdFile.create_md_file()
def create_markdown(github, owner):
""" create markdown for repos in github dict
"""
filename = 'prepbadge'
print(f'Creating markdown file for {owner} repos in {filename}.md')
md = MdUtils(file_name=filename, title='EdgeXFoundry Repo Badges Preview')
for repo in github[0]['result']:
md.new_header(level=1, title=md.new_inline_link(link=repo['github_url'], text=repo['name']))
for badge in repo['badges']:
md.write(f'{badge} ')
md.new_line('')
md.create_md_file()
def to_markdown(self):
"""Generate markdown file."""
params = self._construct()
markdown = MdUtils(file_name=params._name)
markdown.new_header(level=1, title=str(params._name).capitalize())
markdown.new_header(level=2, title="Description")
markdown.new_paragraph(params._desc_feature_set)
markdown.new_line()
markdown.new_header(level=2, title="Feature Set Pipeline")
markdown.new_header(level=3, title="Source")
source = ["Reader", "Location"]
for r, l in params._source:
source.extend([r, l])
count_rows = len(source) // 2
markdown.new_table(columns=2,
rows=count_rows,
text=source,
text_align="center")
markdown.new_header(level=3, title="Sink")
sink = ["Writer"]
for w in params._sink:
sink.extend([w])
count_rows = len(sink)
markdown.new_table(columns=1,
rows=count_rows,
text=sink,
text_align="center")
markdown.new_header(level=3, title="Features")
features = ["Column name", "Data type", "Description"]
for c, desc in params._features:
features.extend([c["column_name"], str(c["type"]), desc])
count_rows = len(features) // 3
markdown.new_table(columns=3,
rows=count_rows,
text=features,
text_align="center")
if self.save:
markdown.create_md_file()
else:
return markdown.file_data_text
def md_file_create(self, research_topic, filename, ordered_by='time'):
'''
Creates a markdown file of data in the research database given
a research topic.
Returns the file name
'''
Record = Query()
records = self.research_db.search(
Record.research_topic == research_topic)
mdfile = MdUtils(file_name=filename, title=research_topic)
for record in records:
mdfile.new_line('- ' + mdfile.new_inline_link(
link=record["url"], text=record["page_title"]))
mdfile.create_md_file()
return
def write_designators(parts, keys, md: mdutils.MdUtils, sort_by='designator'):
parts = sorted(parts, key=lambda i: i[sort_by])
groups = groupby(parts, lambda i: i[sort_by])
for designator, group in groups:
#print('Designator {}'.format(designator))
md.new_line()
md.new_header(level=2, title=designator)
sorted_parts = sorted(group, key=lambda i: i['id'])
tabulated = list(keys)
for part in sorted_parts:
tabulated.extend([part.get(key) for key in keys])
md.new_line()
md.new_table(columns=len(keys),
rows=len(sorted_parts) + 1,
text=tabulated)
def report(fails: Dict[str, List[Fail]]):
print('Start writing report')
start_time = time.time()
mdFile = MdUtils(file_name='report', title='OCR Recognition Report')
for fail_typ in fails.keys():
mdFile.new_header(level=2,
title=fail_typ.title,
add_table_of_contents='n')
mdFile.new_header(level=3,
title="Explanation",
add_table_of_contents='n')
mdFile.new_paragraph(fail_typ.explanation)
mdFile.new_line(
f'There were in total {len(fails[fail_typ])} of {fail_typ.title}')
mdFile.new_header(level=3, title="Fails", add_table_of_contents='n')
mdFile.new_line()
if fail_typ == LenFail:
md_text: List[str] = [
'Font name - size', 'Words in PDF', 'Recognized words',
'Levenshtein distance of total text'
]
for len_fail in fails[fail_typ]:
md_text.extend([
f'{len_fail.font_name} - {len_fail.font_size}',
str(len_fail.pdf_len),
str(len_fail.ocr_len),
str(len_fail.total_levenshtein)
])
mdFile.new_table(columns=4,
rows=int(len(md_text) / 4),
text=md_text,
text_align='center')
else:
for fail in fails[fail_typ]:
mdFile = fail.to_md(mdFile)
mdFile.create_md_file()
print(f'Finished reporting after {time.time() - start_time} sec')
def generate_report():
report = MdUtils(file_name=f'./report.md')
report.new_header(level=1, title='Текстурный анализ')
report.new_line(text='Выполнил Ахманов Алексей Б18-514')
for im in IMAGES:
image_path = f'{helpers.folder_helper.IMAGES_FOLDER_PATH}/{im}'
processed_image_folder_path = f'{image_path}_processed'
os.makedirs(processed_image_folder_path, exist_ok=True)
series_length_matrix_image_path = f'{processed_image_folder_path}/{im}_series_length_matrix.png'
image = Image.open(image_path).convert(constants.constants.RGB_MODE)
grayscaled = grayscale.grayscale.mean_grayscale(image)
# Series length matrix
draw.draw.draw_series_length_matrix(grayscale.grayscale.mean_grayscale(image)).save(series_length_matrix_image_path)
# SRE
sre = texturing.texturing.sre_coefficient(grayscaled)
# Report
report.new_header(level=2, title=f'{im} (SRE = {sre})')
report.new_header(level=3, title='Исходная картинка')
report.new_line(report.new_inline_image(text='Исходная картинка', path=image_path))
report.new_header(level=3, title='Матрица длин серий')
report.new_line(report.new_inline_image(text='Матрица длин серий', path=series_length_matrix_image_path))
report.create_md_file()
def episodeToMarkdown(data, podInfo, auth):
""" Take in a podcast episode data from PodcastIndex and return Markdowns """
fileName = str(data['id']) + '.md'
published = time.strftime("%a, %d %b %Y %H:%M:%S +0000",
time.gmtime(data['datePublished']))
mf = MdUtils(file_name=fileName, title=data['title'])
showTxt = data['title'] + ' - ' + data['enclosureType']
mf.new_line(mf.new_inline_link(link=data['enclosureUrl'], text=showTxt))
mf.new_line(mf.new_inline_link(link=data['link'], text='Show Notes Link'))
mf.new_line(f'Published: {published}')
if 'image' in data:
mf.new_paragraph(Html.image(path=data['image']))
elif 'feedImage':
mf.new_paragraph(Html.image(path=data['feedImage']))
showTitle = "Podcast: " + podInfo['feed']['title']
mf.new_header(1, showTitle)
feedUrl = podInfo['feed']['url']
podLink = f'[Original RSS Feed]({feedUrl})'
mf.new_paragraph(podLink)
mf.new_paragraph(data['description'])
mf.new_paragraph('PodcastIndex.org ID: ' + str(data['id']))
mf.read_md_file('postfooter.md')
mf.file_data_text = mf.file_data_text.replace('somethingtoreplacehere777',
auth)
print(f'Working on {showTxt}')
return mf, fileName
def generate_report():
report = MdUtils(file_name='./report.md')
report.new_header(level=1, title='Фильтры и морфология')
report.new_line(text='Выполнил Ахманов Алексей Б18-514')
for im in IMAGES:
image_path = f'{folder_helper.IMAGES_FOLDER_PATH}/{im}'
processed_image_folder_path = f'{image_path}_processed'
os.makedirs(processed_image_folder_path, exist_ok=True)
image = Image.open(image_path).convert(constants.RGB_MODE)
# Smoothing
spatial_smoothed_image_path = f'{processed_image_folder_path}/{im}_spatial_smoothed.png'
spatial_smoothed_difference_image_path = f'{processed_image_folder_path}/{im}_spatial_smoothed_difference.png'
grayscaled = grayscale.mean_grayscale(image)
filtration.spatial_smoothing(grayscaled).save(
spatial_smoothed_image_path)
filtration.spatial_smoothing_difference(grayscaled).save(
spatial_smoothed_difference_image_path)
# Report
report.new_header(level=2, title=f'{im}')
report.new_header(level=3, title='Исходная картинка')
report.new_line(
report.new_inline_image(text='Исходная картинка', path=image_path))
report.new_header(level=3, title='Пространственное сглаживание')
report.new_line(
report.new_inline_image(text='Пространственное сглаживание',
path=spatial_smoothed_image_path))
report.new_header(level=3, title='Разница сглаженной и исходной')
report.new_line(
report.new_inline_image(
text='Разница сглаженной и исходной',
path=spatial_smoothed_difference_image_path))
report.create_md_file()
def to_md(self, mdFile: MdUtils) -> MdUtils:
mdFile.new_line(f'{self.font_name} - {self.font_size}:',
bold_italics_code='b')
mdFile.new_line(
f'There were in total {len(self.error_words)} mismatches.')
mdFile.new_line(
f'The levenshtein distance of the complete text is {self.total_levenshtein}'
)
mdFile.new_line()
md_text: List[str] = [
'Word in PDF', 'Recognized Word', 'Levenshtein Distance'
]
for word_fail in self.error_words:
md_text.extend([
word_fail.pdf_word, word_fail.ocr_word,
str(word_fail.error_value)
])
mdFile.new_table(columns=3,
rows=len(self.error_words) + 1,
text=md_text,
text_align='center')
return mdFile
def run(self):
Responder.run(self)
caseNumber = self.get_param('data.caseId') #Friendly case number
caseId = self.get_param('data.id') #Raw case number
case_observables = self.api.get_case_observables(caseId).json()
title = self.get_param('data.title', None, 'title is missing')
description = self.get_param('data.description', None,
'description is missing')
tags = self.get_param('data.tags')
data = self.get_param('data')
tlp = self.getTLP(data['tlp'])
# Title
#mdFile = MdUtils(file_name=str(caseNumber),title=tlp[0] + ' Case #' + str(caseNumber) + ': ' + title)
mdFile = MdUtils(file_name=str(self.tmpPath) + str(caseNumber),
title=tlp[0] + ' Case #' + str(caseNumber) + ': ' +
title)
# Case Summary
caseSummary = self.getCaseSummary(data)
mdFile.new_header(level=1, title='Case Summary')
mdFile.new_line(str(tlp[1]))
mdFile.new_table(columns=2,
rows=int(caseSummary.__len__() / 2),
text=caseSummary,
text_align='left')
# Case Description
mdFile.new_line('<div style="page-break-after: always;"></div>')
mdFile.new_line(' ')
mdFile.new_header(level=1, title='Case Description')
mdFile.new_line(str(data['description']))
mdFile.new_line(' ')
# Task Log
allTaskIds = self.getCaseTasks(caseId)
allTaskIds_sorted = sorted(allTaskIds.items(),
key=lambda x: x[1]['createdAt'])
mdFile.new_header(level=1, title='Task Log Entries')
for task in allTaskIds_sorted:
title = str(task[1]['taskGroup'] + ' \: ' + task[1]['taskTitle'])
createdAt = time.strftime(
'%Y-%m-%dT%H:%M:%SZ',
time.localtime(
task[1]['createdAt'] /
1000)) #Convert epoch ms to sec then human readable
mdFile.new_header(level=2, title=title)
mdFile.new_line(str('**Created At:** ') + str(createdAt))
mdFile.new_line(
str('**Created By:** ') + str(task[1]['createdBy']))
mdFile.new_line(str('**Assigned To:** ') + str(task[1]['owner']))
mdFile.new_line(str('**Case Status:** ') + str(task[1]['status']))
mdFile.new_line(' ')
mdFile.new_line(str('**Description:** '))
mdFile.new_line(str(task[1]['description']))
mdFile.new_line(' ')
caseTaskLog = self.getCaseTaskLog(task[0])
caseTaskLogEntries = (json.loads(caseTaskLog))
caseTaskLogEntries_sorted = sorted(caseTaskLogEntries,
key=lambda k: k['createdAt'])
for caseTaskLogEntry in caseTaskLogEntries_sorted:
createdAt = time.strftime(
'%Y-%m-%dT%H:%M:%SZ',
time.localtime(
caseTaskLogEntry['createdAt'] /
1000)) #Convert epoch ms to sec then human readable
mdFile.new_line(
str(createdAt) + ' : ' + str(caseTaskLogEntry['message']))
# Case Observables
mdFile.new_header(level=1, title='Case Observables')
caseObservables = self.getCaseObservables(case_observables)
mdFile.new_table(columns=6,
rows=int(caseObservables.__len__() / 6),
text=caseObservables,
text_align='left')
# TLP Protocol description
mdFile.new_line('<div style="page-break-after: always;"></div>')
mdFile.new_line(' ')
mdFile.new_header(
level=1,
title='Traffic Light Protocol (TLP) Definitions and Usage')
tlpFooter = self.getTlpFooter()
mdFile.new_table(columns=3, rows=5, text=tlpFooter, text_align='left')
# Build TOC
mdFile.new_table_of_contents(table_title='Table of Contents', depth=2)
# Compile the report
mdFile.create_md_file()
# Add the report to the case
addTask = json.loads(self.addTask(caseId))
taskId = addTask['_id']
# Add the MD file to the task
addTaskLog = json.loads(
self.addTaskLog(taskId,
str(self.tmpPath) + str(caseNumber) + '.md'))
# Cleanup the MD file
os.remove(str(self.tmpPath) + str(caseNumber) + '.md')
self.report({'report': 'created'})
def generate_report():
report = MdUtils(file_name='./report.md')
report.new_header(level=1, title='Бинаризация')
report.new_line(text='Выполнил Ахманов Алексей Б18-514')
for im in IMAGES:
image_path = f'{folder_helper.IMAGES_FOLDER_PATH}/{im}'
processed_image_folder_path = f'{image_path}_processed'
os.makedirs(processed_image_folder_path, exist_ok=True)
image = Image.open(image_path).convert(constants.RGB_MODE)
# Sampling
upsampled_integer_number_of_times_image_path = f'{processed_image_folder_path}/{im}_upsampled_m.png'
downsampled_integer_number_of_times_image_path = f'{processed_image_folder_path}/{im}_downsampled_n.png'
oversampled_two_pass_image_path = f'{processed_image_folder_path}/{im}_oversampled_two_pass.png'
oversampled_one_pass_image_path = f'{processed_image_folder_path}/{im}_oversampled_one_pass.png'
sampling.bilinear_interpolation_upsampling(
image,
UPSAMPLE_FACTOR).save(upsampled_integer_number_of_times_image_path)
sampling.decimation_downsampling(image, DOWNSAMPLE_FACTOR).save(
downsampled_integer_number_of_times_image_path)
sampling.decimation_downsampling(
sampling.bilinear_interpolation_upsampling(image, UPSAMPLE_FACTOR),
DOWNSAMPLE_FACTOR).save(oversampled_two_pass_image_path)
sampling.one_pass_resampling(
image, UPSAMPLE_FACTOR,
DOWNSAMPLE_FACTOR).save(oversampled_one_pass_image_path)
# Grayscale
mean_grayscaled_image_path = f'{processed_image_folder_path}/{im}_mean_grayscaled.png'
photoshop_grayscaled_image_path = f'{processed_image_folder_path}/{im}_photoshop_grayscaled.png'
grayscale.mean_grayscale(image).save(mean_grayscaled_image_path)
grayscale.photoshop_grayscale(image).save(
photoshop_grayscaled_image_path)
# Threshold
balansed_hist_thresholded_image_path = f'{processed_image_folder_path}/{im}_balansed_hist_thresholded.png'
thresholding.balansed_histogram_method(grayscale.mean_grayscale(
image)).save(balansed_hist_thresholded_image_path)
# Report
report.new_header(level=2, title=f'{im}')
report.new_header(level=3, title='Исходная картинка')
report.new_line(
report.new_inline_image(text='Исходная картинка', path=image_path))
report.new_header(
level=3, title=f'Интерполяция с коэффициентом {UPSAMPLE_FACTOR}')
report.new_line(
report.new_inline_image(
text=f'Интерполяция с коэффициентом {UPSAMPLE_FACTOR}',
path=upsampled_integer_number_of_times_image_path))
report.new_header(
level=3, title=f'Децимация с коэффициентом {DOWNSAMPLE_FACTOR}')
report.new_line(
report.new_inline_image(
text=f'Децимация с коэффициентом {DOWNSAMPLE_FACTOR}',
path=downsampled_integer_number_of_times_image_path))
report.new_header(
level=3,
title=
f'Двухпроходная передескритизация с коэффициентом {UPSAMPLE_FACTOR}/{DOWNSAMPLE_FACTOR}'
)
report.new_line(
report.new_inline_image(
text=
f'Двухпроходная передескритизация с коэффициентом {UPSAMPLE_FACTOR}/{DOWNSAMPLE_FACTOR}',
path=oversampled_two_pass_image_path))
report.new_header(
level=3,
title=
f'Однопроходная передескритизация с коэффициентом {UPSAMPLE_FACTOR}/{DOWNSAMPLE_FACTOR}'
)
report.new_line(
report.new_inline_image(
text=
f'Однопроходная передескритизация с коэффициентом {UPSAMPLE_FACTOR}/{DOWNSAMPLE_FACTOR}',
path=oversampled_two_pass_image_path))
report.new_header(level=3, title=f'Оттенки серого')
report.new_line(
report.new_inline_image(text=f'Оттенки серого',
path=mean_grayscaled_image_path))
report.new_header(level=3, title=f'Оттенки серого (как в Photoshop)')
report.new_line(
report.new_inline_image(text=f'Оттенки серого (как в Photoshop)',
path=photoshop_grayscaled_image_path))
report.new_header(level=3,
title=f'Бинаризация (балансировка гистограммы)')
report.new_line(
report.new_inline_image(
text=f'Бинаризация (балансировка гистограммы)',
path=balansed_hist_thresholded_image_path))
report.create_md_file()
def generate_report():
report = MdUtils(file_name='./report.md')
report.new_header(level=1, title='Контуры')
report.new_line(text='Выполнил Ахманов Алексей Б18-514')
for im in IMAGES:
image_path = f'{folder_helper.IMAGES_FOLDER_PATH}/{im}'
processed_image_folder_path = f'{image_path}_processed'
os.makedirs(processed_image_folder_path, exist_ok=True)
image = Image.open(image_path).convert(constants.RGB_MODE)
# Outline
smoothed_image_path = f'{processed_image_folder_path}/{im}_grayscaled.png'
roberts_cross_x_image_path = f'{processed_image_folder_path}/{im}_roberts_cross_x.png'
roberts_cross_y_image_path = f'{processed_image_folder_path}/{im}_roberts_cross_y.png'
roberts_cross_image_path = f'{processed_image_folder_path}/{im}_roberts_cross.png'
roberts_cross_normalized_image_path = f'{processed_image_folder_path}/{im}_roberts_cross_normalized_#.png'
grayscaled = grayscale.mean_grayscale(image)
smoothed = filtration.spatial_smoothing(grayscaled)
smoothed.save(smoothed_image_path)
filtration.roberts_cross_x(smoothed).save(roberts_cross_x_image_path)
filtration.roberts_cross_y(smoothed).save(roberts_cross_y_image_path)
filtration.roberts_cross(smoothed).save(roberts_cross_image_path)
for i in range(10, 31):
filtration.roberts_cross_threshold(smoothed, i).save(
roberts_cross_normalized_image_path.replace('#', f'{i}'))
# Report
report.new_header(level=2, title=f'{im}')
report.new_header(level=3, title='Исходная картинка')
report.new_line(
report.new_inline_image(text='Исходная картинка', path=image_path))
report.new_header(level=3, title='Сглаженные оттенки серого')
report.new_line(
report.new_inline_image(text='Сглаженные оттенки серого',
path=smoothed_image_path))
report.new_header(level=3, title='Оператор Робертса 2x2 (x)')
report.new_line(
report.new_inline_image(text='Оператор Робертса 2x2 (x)',
path=roberts_cross_x_image_path))
report.new_header(level=3, title='Оператор Робертса 2x2 (y)')
report.new_line(
report.new_inline_image(text='Оператор Робертса 2x2 (y)',
path=roberts_cross_y_image_path))
report.new_header(level=3, title='Оператор Робертса 2x2')
report.new_line(
report.new_inline_image(text='Оператор Робертса 2x2',
path=roberts_cross_image_path))
report.new_header(level=3,
title='Оператор Робертса 2x2 (нормализованная)')
for i in range(10, 31):
report.new_header(level=4, title=f'Порог {i}')
report.new_line(
report.new_inline_image(
text=f'Порог {i}',
path=roberts_cross_normalized_image_path.replace(
'#', f'{i}')))
report.create_md_file()
def makeMarkdown(data, path):
# Creates the README file
global base_url
mdf = MdUtils(file_name=path + 'README', title='RBA TechRadar for Azure')
adopt_list = list()
trial_list = list()
assess_list = list()
hold_list = list()
reject_list = list()
# Create categories on status
for key in data:
status = data[key].get("status")
if status == "ADOPT": adopt_list.append(key)
if status == "TRIAL": trial_list.append(key)
if status == "ASSESS": assess_list.append(key)
if status == "HOLD": hold_list.append(key)
if status == "REJECT": reject_list.append(key)
mdf.new_header(level=1, title='Overview')
mdf.new_header(level=2, title='What is the purpose?')
mdf.new_paragraph(
"The RBA TechRadar for Azure is a tool to inspire and "
"support engineering teams at Risk & Business Analytics to pick the best "
"technologies for new projects; it provides a platform to share knowledge "
"and experience in technologies, to reflect on technology decisions and "
"continuously evolve our technology landscape. Based on the pioneering "
"work at Thought Works, our radar sets out the changes in technologies "
"that are interesting in cloud development - changes that we think our "
"engineering teams should pay attention to and consider using in their "
"projects.")
mdf.new_header(level=2, title='How do we maintain it?')
mdf.new_paragraph(
"The RBA TechRadar for Azure is maintained by the Cloud "
"Center of Excellence - an open group of senior RBA technologists committed "
"to devote time to this purpose. The CCoE self organizes to maintain these "
"documents, including this version. Assignment of technologies to rings is "
"the outcome of status change proposals, which are discussed and voted on "
"in CCoE meetings. The radar depends on active participation and input from "
"all engineering teams at RBA.")
mdf.new_header(level=2, title='What are the current ring assignments?')
mdf.new_paragraph(
"The RBA TechRadar for Azure is a list of technologies, "
"complemented by an assesment result, called ring assignment. We use five "
"rings with the following semantics:")
# Handle the Adopt Section
mdf.new_header(level=3, title='Adopt')
mdf.new_paragraph(
"Technologies we have high confidence in to serve our "
"purpose, also at large scale. Technologies with a usage culture in the "
"RBA production environment, low risk, automated policy enforcement and "
"are recommended to be widely used.")
adopt_tbl = [
"<sub>Resource</sub>", "<sub>Description</sub>", "<sub>Type</sub>",
"<sub>Status</sub>"
]
adopt_cnt = len(adopt_list) + 1
for key in adopt_list:
resourceName = key
resourceDesc = data[key].get("description", "")
resourcePath = data[key].get("path", "")
resourceType = data[key].get("architecture review", "").get("type", "")
resourceUrl = data[key].get("url", "")
resourceStatus = data[key].get("status", "")
resourceName = "[" + resourceName + "](" + base_url + '/' + resourceUrl + ")"
adopt_tbl.extend([
'<sub>' + resourceName + '</sub>',
'<sub>' + resourceDesc + '</sub>',
'<sub>' + resourceType + '</sub>',
'<sub>' + resourceStatus + '</sub>'
])
if adopt_cnt == 1:
mdf.new_line("")
mdf.new_line("There are currently no resources at this ring level.",
bold_italics_code='bi',
color='red')
else:
mdf.new_line("")
mdf.new_table(columns=4, rows=adopt_cnt, text=adopt_tbl)
# Handle the Trial Section
mdf.new_header(level=3, title='Trial')
mdf.new_paragraph(
"Technologies that we have seen work with success in projects "
"to solve real problems; first serious usage experience that confirm benefits "
"and uncover limitations. TRIAL technologies are slightly more risky; some "
"engineers in our organization walked this path and will share knowledge and "
"experiences. This area can contain services that have been architecture and "
"security reviewed but do not contain automated policy managmeent.")
trial_tbl = [
"<sub>Resource</sub>", "<sub>Description</sub>", "<sub>Type</sub>",
"<sub>Status</sub>"
]
trial_cnt = len(trial_list) + 1
for key in trial_list:
resourceName = key
resourceDesc = data[key].get("description", "")
resourcePath = data[key].get("path", "")
resourceType = data[key].get("architecture review", "").get("type", "")
resourceUrl = data[key].get("url", "")
resourceStatus = data[key].get("status", "")
resourceName = "[" + resourceName + "](" + base_url + '/' + resourceUrl + ")"
trial_tbl.extend([
'<sub>' + resourceName + '</sub>',
'<sub>' + resourceDesc + '</sub>',
'<sub>' + resourceType + '</sub>',
'<sub>' + resourceStatus + '</sub>'
])
if trial_cnt == 1:
mdf.new_line("")
mdf.new_line("There are currently no resources at this ring level.",
bold_italics_code='bi',
color='red')
else:
mdf.new_line("")
mdf.new_table(columns=4, rows=trial_cnt, text=trial_tbl)
# Handle the Assess Section
mdf.new_header(level=3, title='Assess')
mdf.new_paragraph(
"Technologies that are promising and have clear potential "
"value-add for us; technologies worth investing some research and "
"prototyping efforts to see if it has impact. ASSESS technologies have "
"higher risks; they are often new to our organization and highly unproven "
"within RBA. You will find some engineers that have knowledge in the "
"technology and promote it, you may even find teams that have started "
"a prototyping effort. These technologies can also include services that "
"are currently in architecture or security review.")
assess_tbl = [
"<sub>Resource</sub>", "<sub>Description</sub>", "<sub>Type</sub>",
"<sub>Status</sub>"
]
assess_cnt = len(assess_list) + 1
for key in assess_list:
resourceName = key
resourceDesc = data[key].get("description", "")
resourcePath = data[key].get("path", "")
resourceType = data[key].get("architecture review", "").get("type", "")
resourceUrl = data[key].get("url", "")
resourceStatus = data[key].get("status", "")
resourceName = "[" + resourceName + "](" + base_url + '/' + resourceUrl + ")"
assess_tbl.extend([
'<sub>' + resourceName + '</sub>',
'<sub>' + resourceDesc + '</sub>',
'<sub>' + resourceType + '</sub>',
'<sub>' + resourceStatus + '</sub>'
])
if assess_cnt == 1:
mdf.new_line("")
mdf.new_line("There are currently no resources at this ring level.",
bold_italics_code='bi',
color='red')
else:
mdf.new_line("")
mdf.new_table(columns=4, rows=assess_cnt, text=assess_tbl)
# Handle the Hold Section
mdf.new_header(level=3, title='Hold')
mdf.new_paragraph(
"Technologies not recommended to be used for new projects. "
"Technologies that we think are not (yet) worth to (further) invest in. "
"HOLD technologies should not be used for new projects, but usually can be "
"continued for existing projects. These technologies may include services "
"that have yet to be evaluated by architecture and security due to a lack "
"of interest, time, or need.")
hold_tbl = [
"<sub>Resource</sub>", "<sub>Description</sub>", "<sub>Type</sub>",
"<sub>Status</sub>"
]
hold_cnt = len(hold_list) + 1
for key in hold_list:
resourceName = key
resourceDesc = data[key].get("description", "")
resourcePath = data[key].get("path", "")
resourceType = data[key].get("architecture review", "").get("type", "")
resourceUrl = data[key].get("url", "")
resourceStatus = data[key].get("status", "")
#resourceName = "["+resourceName+"]("+resourceUrl+")"
hold_tbl.extend([
'<sub>' + resourceName + '</sub>',
'<sub>' + resourceDesc + '</sub>',
'<sub>' + resourceType + '</sub>',
'<sub>' + resourceStatus + '</sub>'
])
if hold_cnt == 1:
mdf.new_line("")
mdf.new_line("There are currently no resources at this ring level.",
bold_italics_code='bi',
color='red')
else:
mdf.new_line("")
mdf.new_table(columns=4, rows=hold_cnt, text=hold_tbl)
# Handle the Reject Section
mdf.new_header(level=3, title='Reject')
mdf.new_paragraph(
"Technologies not recommended to be used for any projects. "
"Technologies that have undergone architecture and security review but do "
"not meet company standards for use. REJECT technologies should never be "
"used on any project and should be considered deprecated for existing "
"projects.")
reject_tbl = [
"<sub>Resource</sub>", "<sub>Description</sub>", "<sub>Type</sub>",
"<sub>Status</sub>"
]
reject_cnt = len(reject_list) + 1
for key in reject_list:
resourceName = key
resourceDesc = data[key].get("description", "")
resourcePath = data[key].get("path", "")
resourceType = data[key].get("architecture review", "").get("type", "")
resourceUrl = data[key].get("url", "")
resourceStatus = data[key].get("status", "")
#resourceName = "["+resourceName+"]("+resourceUrl+")"
reject_tbl.extend([
'<sub>' + resourceName + '</sub>',
'<sub>' + resourceDesc + '</sub>',
'<sub>' + resourceType + '</sub>',
'<sub>' + resourceStatus + '</sub>'
])
if reject_cnt == 1:
mdf.new_line("")
mdf.new_line("There are currently no resources at this ring level.",
bold_italics_code='bi',
color='red')
else:
mdf.new_line("")
mdf.new_table(columns=4, rows=reject_cnt, text=reject_tbl)
mdf.create_md_file()
measured_metrics,
table_format='per_sample')
file_name = 'results_{}.md'.format(sample_type)
mdfile = MdUtils(file_name=file_name)
write_table_to_md_file(mdfile, table_rows)
mdfile.create_md_file()
# Somehow the MDUtils outputs 4 empty lines prior to the actual table in the target file.
delete_four_head_lines(file_name)
# Output the overview table for the top-level README file
overview_file_name = 'results_overview.md'
mdfile = MdUtils(file_name=overview_file_name)
# Compose a mini-TOC
mdfile.new_line("Quick jump to sample type:")
mdfile.new_line("==========================")
for sample_type in sota_checkpoints_eval:
header_name = SAMPLE_TYPE_TO_SAMPLE_DISPLAY_NAME_DICT[sample_type]
mdfile.new_line("[{}]({})\n".format(header_name, header_name_to_link(header_name)))
mdfile.new_line()
for sample_type in sota_checkpoints_eval:
mdfile.new_header(level=4, title=SAMPLE_TYPE_TO_SAMPLE_DISPLAY_NAME_DICT[sample_type])
table_rows = get_results_table_rows(sota_checkpoints_eval[sample_type],
measured_metrics,
table_format='overview')
write_table_to_md_file(mdfile, table_rows)
mdfile.create_md_file()
delete_four_head_lines(overview_file_name)
class generateMarkdown():
def __repr__(self):
return "<generateMarkdown(mdFile={0})>".format(self.mdFile)
def __init__(self, repo):
self.mdFile = MdUtils(file_name='CHANGELOG', title='CHANGELOG')
self.mdFile.new_line(
"## **Release** " +
"[{0}](https://github.com/nanocurrency/nano-node/tree/{0})".format(
repo.end))
self.mdFile.new_line("[Full Changelog](https://github.com/nanocurrency"
"/nano-node/compare/{0}...{1})".format(
repo.start, repo.end))
sort = self.pull_to_section(repo.commits)
for section, prs in sort.items():
self.write_header_PR(section)
for pr in prs:
self.write_PR(pr, repo.commits[pr[0]])
if repo.other_commits:
self.write_header_no_PR()
for sha, message in repo.other_commits:
self.write_no_PR(repo, sha, message)
self.mdFile.create_md_file()
def write_header_PR(self, section):
self.mdFile.new_line("---")
self.mdFile.new_header(level=3, title=section)
self.mdFile.new_line("|Pull Request|Title")
self.mdFile.new_line("|:-:|:--")
def write_header_no_PR(self):
self.mdFile.new_line("---")
self.mdFile.new_header(level=3, title="Other Updates")
self.mdFile.new_line("|Commit|Title")
self.mdFile.new_line("|:-:|:--")
def write_PR(self, pr, info):
imp = ""
if pr[1]:
imp = "**BREAKING** "
self.mdFile.new_line("|[#{0}]({1})|{2}{3}".format(
pr[0], info['Url'], imp, info['Title']))
def write_no_PR(self, repo, sha, message):
url = "https://github.com/{0}/commit/{1}".format(repo.name, sha)
self.mdFile.new_line("|[{0}]({1})|{2}".format(sha[:8], url, message))
def handle_labels(self, labels):
for section, values in SECTIONS.items():
for label in labels:
if label in values:
if any(string in labels for string in [
'breaking',
]):
return section, True
else:
return section, False
return 'Other', False
def pull_to_section(self, commits):
sect = copy.deepcopy(SECTIONS)
result = {}
for a in sect:
sect[a] = []
for pull, info in commits.items():
section, important = self.handle_labels(info['labels'])
if important:
sect[section].insert(0, [pull, important])
else:
sect[section].append([pull, important])
for a in sect:
if len(sect[a]) > 0:
result[a] = sect[a]
return result
def create_report(res_files: [], output: str, title="", description=""):
res = []
for f in res_files:
with open(f) as json_file:
res.append(json.load(json_file))
mdFile = MdUtils(file_name=output, title=title)
if description:
mdFile.new_paragraph(description, bold_italics_code='b')
mdFile.new_header(level=1, title='Platform')
platform = res[0]['platform']
list_of_strings = ["System", "Information"]
rows = 1
for key in platform.keys():
list_of_strings.extend([key, str(platform[key])])
rows += 1
mdFile.new_line()
mdFile.new_table(columns=2,
rows=rows,
text=list_of_strings,
text_align='left')
mdFile.new_header(level=1, title='Test results')
tests = res[0]['tests']
for test_key in tests.keys():
mdFile.new_header(level=2, title=test_key)
mdFile.new_line('~~~')
mdFile.new_line(tests[test_key]['command'])
mdFile.new_line('~~~')
test_res = tests[test_key]['result']
list_of_strings = ['Test'] + list(test_res.keys())
cols = len(list_of_strings)
rows = 1
avg = {}
avgc = {}
for r in res:
list_of_strings.extend(
[r['name'] + " (" + r['iteration'] + ")"] +
list(r['tests'][test_key]['result'].values()))
rows += 1
if r['name'] not in avg.keys():
avg[r['name']] = list(r['tests'][test_key]['result'].values())
avgc[r['name']] = 1
else:
avg[r['name']] = [
str(float(x) + float(y)) for x, y in zip(
avg[r['name']],
list(r['tests'][test_key]['result'].values()))
]
avgc[r['name']] += 1
for avg_key in avg.keys():
avg[avg_key] = [
str(round(float(x) / avgc[avg_key], 3)) for x in avg[avg_key]
]
list_of_strings.extend([avg_key + " (avg)"] + avg[avg_key])
rows += 1
mdFile.new_table(columns=cols,
rows=rows,
text=list_of_strings,
text_align='left')
mdFile.new_line()
mdFile.create_md_file()
print("{}.md succesfully created".format(output))
GROUPBY = md.metadata.get('groupBy')
SORTBY = md.metadata.get('sortBy')
# %%
entries = bib_database.entries
entries.sort(key=lambda x: x.get(GROUPBY, ''), reverse=True)
#%%
md_file = MdUtils(file_name=OUTPUT_PATH.stem, title=OUTPUT_PATH.stem)
# %%
def format_entry(md_file: MdUtils, entry: Dict):
assert entry.get('title')
formatted = FORMAT.format(TITLE=entry.get('title'),
AUTHOR=entry.get('author',
'').replace(' and', ','),
YEAR=entry.get('year', ''),
JOURNAL=entry.get('journal', ''))
md_file.new_line(formatted)
#%%
for key, group in groupby(entries, lambda x: x.get(GROUPBY, '')):
md_file.new_header(level=1, title=f'{key}')
for entry in sorted(group, key=lambda x: x.get(SORTBY, '')):
format_entry(md_file=md_file, entry=entry)
md_file.new_line()
# %%
md_file.create_md_file()
# %%
os.rename(f'./{OUTPUT_PATH.stem}.md', OUTPUT_PATH)
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument(
'--results',
'-r',
help='A metrics.json file from a latest checkpoint evaluation run')
parser.add_argument(
'--config',
'-c',
help=
'A .json file with definitions of tested checkpoints (sota_checkpoints_eval.json)'
)
parser.add_argument(
'--output',
'-o',
help=
"If specified, will output a config file specified in -c with target metric values updated "
"to what was measured in the metrics.json file supplied via -r")
args = parser.parse_args(args=argv)
results = args.results
config = args.config
output = args.output
measured_metrics = json.load(open(results, 'r'))
sota_checkpoints_eval = json.load(open(config),
object_pairs_hook=OrderedDict)
# Output tables for per-sample README files
for sample_type in sota_checkpoints_eval:
table_rows = get_results_table_rows(sota_checkpoints_eval[sample_type],
measured_metrics,
sample_type,
table_format='per_sample')
file_name = 'results_{}.md'.format(sample_type)
mdfile = MdUtils(file_name=file_name)
write_table_to_md_file(mdfile, table_rows)
mdfile.create_md_file()
# Somehow the MDUtils outputs 4 empty lines prior to the actual table in the target file.
delete_four_head_lines(file_name)
# Output the overview table for the top-level README file
overview_file_name = 'results_overview.md'
mdfile = MdUtils(file_name=overview_file_name)
# Compose a mini-TOC
mdfile.new_line('### PyTorch models')
mdfile.new_line()
for sample_type in sota_checkpoints_eval:
mdfile.new_line('<a name = "pytorch_object_detection" > </a>')
mdfile.new_line()
mdfile.new_line(
f'#### {SAMPLE_TYPE_TO_SAMPLE_DISPLAY_NAME_DICT[sample_type]}')
mdfile.new_line()
table_rows = get_results_table_rows(sota_checkpoints_eval[sample_type],
measured_metrics,
sample_type,
table_format='overview')
write_table_to_md_file(mdfile, table_rows)
mdfile.create_md_file()
delete_four_head_lines(overview_file_name)
if args.output is not None:
update_target_metrics_and_thresholds(sota_checkpoints_eval,
measured_metrics)
with open(output, "w") as write_file:
json.dump(sota_checkpoints_eval, write_file, indent=4)
def generate_report():
report = MdUtils(file_name=f'./report.md')
report.new_header(level=1, title='Сегментация текста')
report.new_line(text='Выполнил Ахманов Алексей Б18-514')
report.new_line(text=f'Алфавит - {ALPHABET}')
report.new_line(text=f'Исходная фраза - {PHRASE}')
# Phrase
phrase_image_path = f'{folder_helper.IMAGES_FOLDER_PATH}/phrase.png'
phrase_projections_image_path = f'{folder_helper.IMAGES_FOLDER_PATH}/phrase_projections.png'
phrase_segments_image_path = f'{folder_helper.IMAGES_FOLDER_PATH}/phrase_segments.png'
phrase_result_image_path = f'{folder_helper.IMAGES_FOLDER_PATH}/phrase_result.png'
phrase_image = Image.new(mode=GRAYSCALE_MODE,
size=(1200, FONT_SIZE),
color=WHITE)
result = ImageDraw.Draw(im=phrase_image, mode=GRAYSCALE_MODE)
result.text(xy=(0, 0), text=PHRASE, font=FONT, fill=0, anchor='lt')
phrase_image = cut_empty_rows_and_cols(phrase_image)
phrase_image.save(phrase_image_path)
phrase_projections_image = draw.draw_projections(phrase_image)
phrase_projections_image.save(phrase_projections_image_path)
phrase_segments_image = draw.draw_symbol_segments(phrase_image,
SYMBOLS_DIFF_THRESHOLD)
phrase_segments_image.save(phrase_segments_image_path)
phrase_result_image = phrase_projections_image.copy()
phrase_result_image.paste(im=phrase_segments_image, box=(30, 0))
phrase_result_image.save(phrase_result_image_path)
# Report
report.new_header(level=2, title='Картинка с фразой')
report.new_line(
report.new_inline_image(text='Картинка с фразой',
path=phrase_image_path))
report.new_header(level=2, title='Картинка с профилями')
report.new_line(
report.new_inline_image(text='Картинка с профилями',
path=phrase_projections_image_path))
report.new_header(level=2, title='Картинка с сегментами символов')
report.new_line(
report.new_inline_image(text='Картинка с сегментами символов',
path=phrase_segments_image_path))
report.new_header(level=2, title='Картинка с результатом')
report.new_line(
report.new_inline_image(text='Картинка с результатом',
path=phrase_result_image_path))
report.create_md_file()
def to_md(self, mdFile: MdUtils) -> MdUtils:
mdFile.new_line(str(self))
return mdFile
def generate_report():
def generate_phrase_image(width: int, font: ImageFont) -> Image:
result = Image.new(mode=GRAYSCALE_MODE,
size=(width, font.size),
color=WHITE)
result_draw = ImageDraw.Draw(im=result, mode=GRAYSCALE_MODE)
result_draw.text(xy=(0, 0),
text=PHRASE,
font=font,
fill=0,
anchor='lt')
return cut_empty_rows_and_cols(result)
def p_ass_for_table(p_ass):
result = []
for row in p_ass:
for element in row:
result.append(str(element))
return result
report = MdUtils(file_name=f'./report.md')
report.new_header(level=1, title='Классификация')
report.new_line(text='Выполнил Ахманов Алексей Б18-514')
report.new_line(text=f'Алфавит - {ALPHABET}')
report.new_line(text=f'Исходная фраза - {PHRASE}')
report.new_line(text=f'Размер шрифта - {FONT_SIZE}')
# Phrase
phrase_image = generate_phrase_image(
1200, ImageFont.truetype(font=FONT_PATH, size=FONT_SIZE))
phrase_image_small_font = generate_phrase_image(
1200, ImageFont.truetype(font=FONT_PATH, size=SMALL_FONT_SIZE))
# Proximity assessment
p_assessment = proximity_assessment(image=phrase_image,
diff_threshold=SYMBOLS_DIFF_THRESHOLD,
phrase=PHRASE)
p_assessment_small_font = proximity_assessment(
image=phrase_image_small_font,
diff_threshold=SYMBOLS_DIFF_THRESHOLD,
phrase=PHRASE)
p_assessment_for_table = p_ass_for_table(p_assessment)
p_assessment_small_font_for_table = p_ass_for_table(
p_assessment_small_font)
# Report
report.new_header(level=2,
title=f'Оценка близости для размера шрифта {FONT_SIZE}')
rows = len(p_assessment)
columns = len(p_assessment[0]) if len(p_assessment) > 0 else 0
report.new_table(columns=columns, rows=rows, text=p_assessment_for_table)
report.new_header(
level=2, title=f'Оценка близости для размера шрифта {SMALL_FONT_SIZE}')
rows = len(p_assessment_small_font)
columns = len(
p_assessment_small_font[0]) if len(p_assessment_small_font) > 0 else 0
report.new_table(columns=columns,
rows=rows,
text=p_assessment_small_font_for_table)
report.new_line(
text=
'Так как были использованы нормализованные параметры для оценки близости символов, размер шрифта не влияет на результат'
)
report.create_md_file()