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147
ocr
147
ocr
@ -36,6 +36,12 @@ def parse_arguments():
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requirements: imagemagick, ocropus, pdftoppm, pdfunite, \
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poppler-utils, pyflow, python2.7, python3.5, tesseract'
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)
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parser.add_argument(
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'-i',
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dest='inputDirectory',
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help='The input directory.',
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required=True
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)
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parser.add_argument(
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'-l',
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dest='lang',
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@ -43,18 +49,10 @@ def parse_arguments():
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required=True
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)
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parser.add_argument(
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'--i',
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default=os.path.normpath('/files_for_ocr'),
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dest='inputDirectory',
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help='The input directory.',
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required=False
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)
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parser.add_argument(
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'--o',
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default=os.path.normpath('/files_from_ocr'),
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'-o',
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dest='outputDirectory',
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help='The output directory.',
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required=False
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required=True
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)
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parser.add_argument(
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'--skip-binarisation',
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@ -93,7 +91,9 @@ class OCRWorkflow(WorkflowRunner):
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def workflow(self):
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'''
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' Creating output directories...
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' ##################################################
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' # Create output directories #
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' ##################################################
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'''
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create_output_directories_jobs = []
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for index, job in enumerate(self.jobs):
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@ -119,7 +119,9 @@ class OCRWorkflow(WorkflowRunner):
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)
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'''
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' Splitting...
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' ##################################################
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' # Split #
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' ##################################################
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'''
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split_jobs = []
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split_job_nCores = min(
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@ -128,7 +130,7 @@ class OCRWorkflow(WorkflowRunner):
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)
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for index, job in enumerate(self.jobs):
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if job['filename'].endswith(('.tif', '.tiff')):
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cmd = 'convert "%s" -compress LZW -density 300 -scene 1 "%s"/page-%%d.tif' % (
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cmd = 'convert "%s" -compress LZW -density 300 -scene 1 "%s/page-%%d.tif"' % (
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job['path'],
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os.path.join(job['output_dir'], 'tmp')
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)
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@ -148,7 +150,15 @@ class OCRWorkflow(WorkflowRunner):
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if not self.skipBinarisation:
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'''
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' Binarising...
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' The binarisation_jobs are created based of the output files of
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' the split_jobs. So wait until they are finished.
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'''
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self.waitForTasks()
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'''
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' ##################################################
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' # Binarise #
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' ##################################################
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'''
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binarisation_jobs = []
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'''
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@ -158,10 +168,17 @@ class OCRWorkflow(WorkflowRunner):
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'''
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binarisation_job_nCores = min(4, self.nCores)
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for index, job in enumerate(self.jobs):
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cmd = 'ls --quoting-style=shell-escape -v "%s"/*.tif | xargs ocropus-nlbin --output "%s" --parallel "%i"' % (
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files = os.listdir(os.path.join(job['output_dir'], 'tmp'))
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files = filter(lambda x: x.endswith('.tif'), files)
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files.sort(key=lambda x: int(re.search(r'\d+', x).group(0)))
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files = map(
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lambda x: '"' + os.path.join(job['output_dir'], 'tmp', x) + '"',
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files
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)
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cmd = 'ocropus-nlbin --output "%s" --parallel "%i" %s' % (
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os.path.join(job['output_dir'], 'tmp'),
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os.path.join(job['output_dir'], 'tmp'),
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binarisation_job_nCores
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binarisation_job_nCores,
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' '.join(files)
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)
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binarisation_jobs.append(
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self.addTask(
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@ -173,25 +190,30 @@ class OCRWorkflow(WorkflowRunner):
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)
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'''
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' Normalising file names from binarisation...
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' The post_binarisation_jobs are created based of the output files
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' of the binarisation_jobs. So wait until they are finished.
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'''
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self.waitForTasks()
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'''
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' ##################################################
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' # Normalise file names from binarisation #
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' ##################################################
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'''
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post_binarisation_jobs = []
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for index, job in enumerate(self.jobs):
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number = 0
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files = os.listdir(os.path.join(job['output_dir'], 'tmp'))
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files = filter(lambda x: x.endswith('.bin.png'), files)
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files = sorted(
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files,
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key=lambda x: int(re.search(r'\d+', x).group(0))
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)
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files.sort()
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for file in files:
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cmd = 'mv "%s" "%s"' % (
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os.path.join(job['output_dir'], 'tmp', file),
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os.path.join(job['output_dir'], 'tmp', 'page-%i.%s' % (
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int(file.split('.', 1)[0]),
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file.split('.', 1)[1])
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),
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os.path.join(
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job['output_dir'],
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'tmp',
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'page-%i.bin.png' % (int(file.split('.', 1)[0]))
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)
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)
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post_binarisation_jobs.append(
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self.addTask(
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@ -206,9 +228,17 @@ class OCRWorkflow(WorkflowRunner):
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number += 1
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'''
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' Performing OCR...
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' The ocr_jobs are created based of the output files of either the
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' split_jobs or post_binarisation_jobs. So wait until they are
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' finished.
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'''
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self.waitForTasks()
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'''
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' ##################################################
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' # Optical Character Recognition #
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' ##################################################
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'''
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ocr_jobs = []
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'''
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' Tesseract runs fastest with four cores. So we run it with either four
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@ -223,19 +253,20 @@ class OCRWorkflow(WorkflowRunner):
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if self.lang == "deu_frak":
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ocr_job_nCores = 1
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for index, job in enumerate(self.jobs):
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number = 0
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files = os.listdir(os.path.join(job['output_dir'], 'tmp'))
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if self.skipBinarisation:
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files = filter(lambda x: x.endswith('.tif'), files)
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else:
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files = filter(lambda x: x.endswith('.bin.png'), files)
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files = sorted(
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files,
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key=lambda x: int(re.search(r'\d+', x).group(0))
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files.sort(key=lambda x: int(re.search(r'\d+', x).group(0)))
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files = map(
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lambda x: os.path.join(job['output_dir'], 'tmp', x),
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files
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)
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number = 0
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for file in files:
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cmd = 'tesseract "%s" "%s" -l "%s" hocr pdf txt' % (
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os.path.join(job['output_dir'], 'tmp', file),
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file,
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os.path.join(
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job['output_dir'],
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'tmp',
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@ -253,7 +284,6 @@ class OCRWorkflow(WorkflowRunner):
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),
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post_binarisation_jobs
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)
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print(ocr_job_dependencies)
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ocr_jobs.append(
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self.addTask(
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command=cmd,
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@ -265,7 +295,15 @@ class OCRWorkflow(WorkflowRunner):
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number += 1
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'''
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' Creating TEI P5 files...
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' The following jobs are created based of the output files of the
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' ocr_jobs. So wait until they are finished.
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'''
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self.waitForTasks()
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'''
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' ##################################################
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' # Create TEI P5 files #
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' ##################################################
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'''
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hocr_to_tei_jobs = []
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for index, job in enumerate(self.jobs):
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@ -273,7 +311,7 @@ class OCRWorkflow(WorkflowRunner):
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os.path.join(job['output_dir'], 'tmp'),
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os.path.join(
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job['output_dir'],
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job['filename'].rsplit('.', 1)[0] + '.xml'
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os.path.join(job['output_dir'], job['name'] + '.xml')
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)
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)
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hocr_to_tei_jobs.append(
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@ -288,15 +326,24 @@ class OCRWorkflow(WorkflowRunner):
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)
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'''
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' Merging PDF files...
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' ##################################################
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' # Merge PDF files #
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' ##################################################
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'''
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pdf_merge_jobs = []
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for index, job in enumerate(self.jobs):
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cmd = '(ls --quoting-style=shell-escape -v "%s"/*.pdf && echo "\'%s\'") | xargs pdfunite' % (
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os.path.join(job['output_dir'], 'tmp'),
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files = os.listdir(os.path.join(job['output_dir'], 'tmp'))
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files = filter(lambda x: x.endswith('.pdf'), files)
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files.sort(key=lambda x: int(re.search(r'\d+', x).group(0)))
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files = map(
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lambda x: '"' + os.path.join(job['output_dir'], 'tmp', x) + '"',
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files
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)
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cmd = 'pdfunite %s "%s"' % (
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' '.join(files),
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os.path.join(
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job['output_dir'],
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job['filename'].rsplit('.', 1)[0] + '.pdf'
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os.path.join(job['output_dir'], job['name'] + '.pdf')
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)
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)
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pdf_merge_jobs.append(
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@ -311,15 +358,24 @@ class OCRWorkflow(WorkflowRunner):
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)
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'''
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' Merging text files...
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' ##################################################
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' # Merge text files #
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' ##################################################
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'''
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txt_merge_jobs = []
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for index, job in enumerate(self.jobs):
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cmd = 'ls --quoting-style=shell-escape -v "%s"/*.txt | xargs cat > "%s"' % (
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os.path.join(job['output_dir'], 'tmp'),
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files = os.listdir(os.path.join(job['output_dir'], 'tmp'))
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files = filter(lambda x: x.endswith('.txt'), files)
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files.sort(key=lambda x: int(re.search(r'\d+', x).group(0)))
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files = map(
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lambda x: '"' + os.path.join(job['output_dir'], 'tmp', x) + '"',
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files
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)
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cmd = 'cat %s > "%s"' % (
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' '.join(files),
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os.path.join(
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job['output_dir'],
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job['filename'].rsplit('.', 1)[0] + '.txt'
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os.path.join(job['output_dir'], job['name'] + '.txt')
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)
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)
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txt_merge_jobs.append(
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@ -334,7 +390,9 @@ class OCRWorkflow(WorkflowRunner):
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)
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'''
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' Cleanup...
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' ##################################################
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' # Cleanup #
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' ##################################################
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'''
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cleanup_jobs = []
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if self.keepIntermediates:
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@ -407,6 +465,7 @@ def analyze_jobs(inputDirectory, outputDirectory):
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jobs.append(
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{
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'filename': file,
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'name': file.rsplit('.', 1)[0],
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'output_dir': os.path.join(outputDirectory, file),
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'path': os.path.join(inputDirectory, file)
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}
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