diff --git a/.circleci/config.yml b/.circleci/config.yml new file mode 100644 index 0000000..ca93957 --- /dev/null +++ b/.circleci/config.yml @@ -0,0 +1,47 @@ +version: 2 + +jobs: + + build-python36: + docker: + - image: python:3.6 + steps: + - checkout + - restore_cache: + keys: + - model-cache + - run: make model + - save_cache: + key: model-cache + paths: + models.tar.gz + models + - run: make install + - run: git submodule update --init + - run: make test + + build-python37: + docker: + - image: python:3.7 + steps: + - checkout + - restore_cache: + keys: + - model-cache + - run: make model + - save_cache: + key: model-cache + paths: + models.tar.gz + models + - run: make install + - run: git submodule update --init + - run: make test + +workflows: + version: 2 + build: + jobs: + - build-python36 + - build-python37 + #- build-python38 # no tensorflow for python 3.8 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c14b1f1 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +*.egg-info +__pycache__ diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..5b24fbb --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "repo/assets"] + path = repo/assets + url = https://github.com/OCR-D/assets diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..95ddbfe --- /dev/null +++ b/Makefile @@ -0,0 +1,36 @@ +# Directory to store models +MODEL_DIR = $(PWD)/models + +# BEGIN-EVAL makefile-parser --make-help Makefile + +help: + @echo "" + @echo " Targets" + @echo "" + @echo " install Install with pip" + @echo " model Downloads the pre-trained models from qurator-data.de" + @echo " test Run tests" + @echo "" + @echo " Variables" + @echo "" + @echo " MODEL_DIR Directory to store models" + +# END-EVAL + +# Install with pip +install: + pip install . + +# Downloads the pre-trained models from qurator-data.de +model: $(MODEL_DIR)/model1_bin.h5 + +$(MODEL_DIR)/model1_bin.h5: models.tar.gz + tar xf models.tar.gz + +models.tar.gz: + wget 'https://qurator-data.de/sbb_binarization/models.tar.gz' + +# Run tests +test: model + cd repo/assets/data/kant_aufklaerung_1784/data; ocrd-sbb-binarize -I OCR-D-IMG -O BIN -P model $(MODEL_DIR) + cd repo/assets/data/kant_aufklaerung_1784-page-region/data; ocrd-sbb-binarize -I OCR-D-IMG -O BIN -P model $(MODEL_DIR) -P level-of-operation region diff --git a/README.md b/README.md index be80345..dfb4d5c 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,30 @@ # Binarization + > Binarization for document images ## Introduction -This tool performs document image binarization (i.e. transform colour/grayscale to black-and-white pixels) for OCR using multiple trained models. + +This tool performs document image binarization (i.e. transform colour/grayscale +to black-and-white pixels) for OCR using multiple trained models. ## Installation + Clone the repository, enter it and run -`./make` + +`pip install .` ### Models + Pre-trained models can be downloaded from here: + https://qurator-data.de/sbb_binarization/ ## Usage -`sbb_binarize -m -i --p --s ` + +```sh +sbb_binarize \ + -m \ + -i \ + -p \ + -s ` +``` diff --git a/ocrd-tool.json b/ocrd-tool.json new file mode 120000 index 0000000..3c8dc95 --- /dev/null +++ b/ocrd-tool.json @@ -0,0 +1 @@ +sbb_binarize/ocrd-tool.json \ No newline at end of file diff --git a/repo/assets b/repo/assets new file mode 160000 index 0000000..32fde9e --- /dev/null +++ b/repo/assets @@ -0,0 +1 @@ +Subproject commit 32fde9eb242c595a1986a193090c689f52eeb734 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..d6a9388 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +numpy >= 1.17.0, < 1.19.0 +setuptools >= 41 +opencv-python-headless +ocrd >= 2.18.0 +keras >= 2.3.1, < 2.4 +tensorflow >= 1.15, < 1.16 diff --git a/sbb_binarize/cli.py b/sbb_binarize/cli.py new file mode 100644 index 0000000..1ae1aa4 --- /dev/null +++ b/sbb_binarize/cli.py @@ -0,0 +1,16 @@ +""" +sbb_binarize CLI +""" + +from click import command, option, argument, version_option + +from .sbb_binarize import SbbBinarizer + +@command() +@version_option() +@option('--patches/--no-patches', default=True, help='by enabling this parameter you let the model to see the image in patches.') +@option('--model-dir', '-m', required=True, help='directory containing models for prediction') +@argument('input_image') +@argument('output_image') +def main(patches, model_dir, input_image, output_image): + SbbBinarizer(model_dir).run(image_path=input_image, use_patches=patches, save=output_image) diff --git a/sbb_binarize/ocrd-tool.json b/sbb_binarize/ocrd-tool.json new file mode 100644 index 0000000..3095eeb --- /dev/null +++ b/sbb_binarize/ocrd-tool.json @@ -0,0 +1,27 @@ +{ + "version": "0.0.1", + "git_url": "https://github.com/qurator-spk/sbb_binarization", + "tools": { + "ocrd-sbb-binarize": { + "executable": "ocrd-sbb-binarize", + "description": "Pixelwise binarization with selectional auto-encoders in Keras", + "categories": ["Image preprocessing"], + "steps": ["preprocessing/optimization/binarization"], + "input_file_grp": [], + "output_file_grp": [], + "parameters": { + "operation_level": { + "type": "string", + "enum": ["page", "region"], + "default": "page", + "description": "PAGE XML hierarchy level to operate on" + }, + "model": { + "description": "models directory.", + "type": "string", + "required": true + } + } + } + } +} diff --git a/sbb_binarize/ocrd_cli.py b/sbb_binarize/ocrd_cli.py new file mode 100644 index 0000000..df4daef --- /dev/null +++ b/sbb_binarize/ocrd_cli.py @@ -0,0 +1,115 @@ +import os.path +from pkg_resources import resource_string +from json import loads + +from PIL import Image +import numpy as np +import cv2 +from click import command + +from ocrd_utils import ( + getLogger, + assert_file_grp_cardinality, + make_file_id, + MIMETYPE_PAGE +) +from ocrd import Processor +from ocrd_modelfactory import page_from_file +from ocrd_models.ocrd_page import AlternativeImageType, to_xml +from ocrd.decorators import ocrd_cli_options, ocrd_cli_wrap_processor + +from .sbb_binarize import SbbBinarizer + +OCRD_TOOL = loads(resource_string(__name__, 'ocrd-tool.json').decode('utf8')) +TOOL = 'ocrd-sbb-binarize' + +def cv2pil(img): + return Image.fromarray(img.astype('uint8')) + +def pil2cv(img): + # from ocrd/workspace.py + color_conversion = cv2.COLOR_GRAY2BGR if img.mode in ('1', 'L') else cv2.COLOR_RGB2BGR + pil_as_np_array = np.array(img).astype('uint8') if img.mode == '1' else np.array(img) + return cv2.cvtColor(pil_as_np_array, color_conversion) + +class SbbBinarizeProcessor(Processor): + + def __init__(self, *args, **kwargs): + kwargs['ocrd_tool'] = OCRD_TOOL['tools'][TOOL] + kwargs['version'] = OCRD_TOOL['version'] + super().__init__(*args, **kwargs) + + def process(self): + """ + Binarize with sbb_binarization + """ + LOG = getLogger('processor.SbbBinarize') + assert_file_grp_cardinality(self.input_file_grp, 1) + assert_file_grp_cardinality(self.output_file_grp, 1) + + oplevel = self.parameter['operation_level'] + model_path = self.parameter['model'] # pylint: disable=attribute-defined-outside-init + binarizer = SbbBinarizer(model_dir=model_path) + + for n, input_file in enumerate(self.input_files): + file_id = make_file_id(input_file, self.output_file_grp) + page_id = input_file.pageId or input_file.ID + LOG.info("INPUT FILE %i / %s", n, page_id) + pcgts = page_from_file(self.workspace.download_file(input_file)) + self.add_metadata(pcgts) + pcgts.set_pcGtsId(file_id) + page = pcgts.get_Page() + + if oplevel == 'page': + LOG.info("Binarizing on 'page' level in page '%s'", page_id) + page_image, page_xywh, _ = self.workspace.image_from_page(page, page_id, feature_filter='binarized') + bin_image = cv2pil(binarizer.run(image=pil2cv(page_image), use_patches=True)) + # update METS (add the image file): + bin_image_path = self.workspace.save_image_file(bin_image, + file_id + '.IMG-BIN', + page_id=input_file.pageId, + file_grp=self.output_file_grp) + page.add_AlternativeImage(AlternativeImageType(filename=bin_image_path, comment='%s,binarized' % page_xywh['features'])) + + elif oplevel == 'region': + regions = page.get_AllRegions(['Text', 'Table'], depth=1) + if not regions: + LOG.warning("Page '%s' contains no text/table regions", page_id) + for region in regions: + region_image, region_xywh = self.workspace.image_from_segment(region, page_image, page_xywh, feature_filter='binarized') + region_image_bin = cv2pil(binarizer.run(image=pil2cv(region_image), use_patches=True)) + region_image_bin_path = self.workspace.save_image_file( + region_image_bin, + "%s_%s.IMG-BIN" % (file_id, region.id), + page_id=input_file.pageId, + file_grp=self.output_file_grp) + region.add_AlternativeImage( + AlternativeImageType(filename=region_image_bin_path, comments='%s,binarized' % region_xywh['features'])) + + elif oplevel == 'line': + region_line_tuples = [(r.id, r.get_TextLine()) for r in page.get_AllRegions(['Text'], depth=0)] + if not region_line_tuples: + LOG.warning("Page '%s' contains no text lines", page_id) + for region_id, line in region_line_tuples: + line_image, line_xywh = self.workspace.image_from_segment(line, page_image, page_xywh, feature_filter='binarized') + line_image_bin = cv2pil(binarizer.run(image=pil2cv(line_image), use_patches=True)) + line_image_bin_path = self.workspace.save_image_file( + line_image_bin, + "%s_%s_%s.IMG-BIN" % (file_id, region_id, line.id), + page_id=input_file.pageId, + file_grp=self.output_file_grp) + line.add_AlternativeImage( + AlternativeImageType(filename=line_image_bin_path, comments='%s,binarized' % line_xywh['features'])) + + self.workspace.add_file( + ID=file_id, + file_grp=self.output_file_grp, + pageId=input_file.pageId, + mimetype=MIMETYPE_PAGE, + local_filename=os.path.join(self.output_file_grp, file_id + '.xml'), + content=to_xml(pcgts)) + +@command() +@ocrd_cli_options +def cli(*args, **kwargs): + return ocrd_cli_wrap_processor(SbbBinarizeProcessor, *args, **kwargs) diff --git a/sbb_binarize/sbb_binarize.py b/sbb_binarize/sbb_binarize.py index f701a1c..a664d6d 100644 --- a/sbb_binarize/sbb_binarize.py +++ b/sbb_binarize/sbb_binarize.py @@ -1,67 +1,56 @@ -#! /usr/bin/env python3 - -__version__= '1.0' +""" +Tool to load model and binarize a given image. +""" -import argparse import sys -import os +from os import listdir, environ, devnull +from os.path import join +from warnings import catch_warnings, simplefilter + import numpy as np -import warnings +from PIL import Image import cv2 +environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +stderr = sys.stderr +sys.stderr = open(devnull, 'w') from keras.models import load_model +sys.stderr = stderr import tensorflow as tf +def resize_image(img_in, input_height, input_width): + return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST) +class SbbBinarizer: -with warnings.catch_warnings(): - warnings.simplefilter("ignore") - -__doc__=\ -""" -Tool to load model and binarize a given image. -""" + def __init__(self, model_dir): + self.model_dir = model_dir -class sbb_binarize: - def __init__(self,image,model, patches='false',save=None ): - self.image=image - self.patches=patches - self.save=save - self.model_dir=model - - def resize_image(self,img_in,input_height,input_width): - return cv2.resize( img_in, ( input_width,input_height) ,interpolation=cv2.INTER_NEAREST) - - def start_new_session_and_model(self): + def start_new_session(self): config = tf.ConfigProto() - config.gpu_options.allow_growth=True - - self.session =tf.Session(config=config)# tf.InteractiveSession() - def load_model(self,model_name): - self.model = load_model(self.model_dir+'/'+model_name , compile=False) - - - self.img_height=self.model.layers[len(self.model.layers)-1].output_shape[1] - self.img_width=self.model.layers[len(self.model.layers)-1].output_shape[2] - self.n_classes=self.model.layers[len(self.model.layers)-1].output_shape[3] + config.gpu_options.allow_growth = True + + self.session = tf.Session(config=config) # tf.InteractiveSession() def end_session(self): self.session.close() + del self.session + def load_model(self, model_name): + model = load_model(join(self.model_dir, model_name), compile=False) + model_height = model.layers[len(model.layers)-1].output_shape[1] + model_width = model.layers[len(model.layers)-1].output_shape[2] + n_classes = model.layers[len(model.layers)-1].output_shape[3] + return model, model_height, model_width, n_classes - del self.model - del self.session - def predict(self,model_name): - self.load_model(model_name) - img=cv2.imread(self.image) - img_width_model=self.img_width - img_height_model=self.img_height + def predict(self, model_name, img, use_patches): + model, model_height, model_width, n_classes = self.load_model(model_name) - if self.patches=='true' or self.patches=='True': + if use_patches: - margin = int(0.1 * img_width_model) + margin = int(0.1 * model_width) - width_mid = img_width_model - 2 * margin - height_mid = img_height_model - 2 * margin + width_mid = model_width - 2 * margin + height_mid = model_height - 2 * margin img = img / float(255.0) @@ -89,167 +78,140 @@ class sbb_binarize: if i == 0: index_x_d = i * width_mid - index_x_u = index_x_d + img_width_model + index_x_u = index_x_d + model_width elif i > 0: index_x_d = i * width_mid - index_x_u = index_x_d + img_width_model + index_x_u = index_x_d + model_width if j == 0: index_y_d = j * height_mid - index_y_u = index_y_d + img_height_model + index_y_u = index_y_d + model_height elif j > 0: index_y_d = j * height_mid - index_y_u = index_y_d + img_height_model + index_y_u = index_y_d + model_height if index_x_u > img_w: index_x_u = img_w - index_x_d = img_w - img_width_model + index_x_d = img_w - model_width if index_y_u > img_h: index_y_u = img_h - index_y_d = img_h - img_height_model - - + index_y_d = img_h - model_height img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] - label_p_pred = self.model.predict( - img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) + label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) seg = np.argmax(label_p_pred, axis=3)[0] seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) - if i==0 and j==0: + if i == 0 and j == 0: seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :] seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin] mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg - prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin, - :] = seg_color - - elif i==nxf-1 and j==nyf-1: + prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color + + elif i == nxf-1 and j == nyf-1: seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :] seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0] mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg - prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0, - :] = seg_color - - elif i==0 and j==nyf-1: + prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0, :] = seg_color + + elif i == 0 and j == nyf-1: seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :] seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin] mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg - prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin, - :] = seg_color - - elif i==nxf-1 and j==0: + prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin, :] = seg_color + + elif i == nxf-1 and j == 0: seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :] seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0] mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg - prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0, - :] = seg_color - - elif i==0 and j!=0 and j!=nyf-1: + prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color + + elif i == 0 and j != 0 and j != nyf-1: seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :] seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin] mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg - prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin, - :] = seg_color - - elif i==nxf-1 and j!=0 and j!=nyf-1: + prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color + + elif i == nxf-1 and j != 0 and j != nyf-1: seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :] seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0] mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg - prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0, - :] = seg_color - - elif i!=0 and i!=nxf-1 and j==0: + prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color + + elif i != 0 and i != nxf-1 and j == 0: seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :] seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin] mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg - prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin, - :] = seg_color - - elif i!=0 and i!=nxf-1 and j==nyf-1: + prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color + + elif i != 0 and i != nxf-1 and j == nyf-1: seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :] seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin] mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg - prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin, - :] = seg_color + prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin, :] = seg_color else: seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :] seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin] mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg - prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, - :] = seg_color + prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color prediction_true = prediction_true.astype(np.uint8) - + else: - img_h_page=img.shape[0] - img_w_page=img.shape[1] - img = img /float( 255.0) - img = self.resize_image(img, img_height_model, img_width_model) + img_h_page = img.shape[0] + img_w_page = img.shape[1] + img = img / float(255.0) + img = resize_image(img, model_height, model_width) - label_p_pred = self.model.predict( - img.reshape(1, img.shape[0], img.shape[1], img.shape[2])) + label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2])) seg = np.argmax(label_p_pred, axis=3)[0] - seg_color =np.repeat(seg[:, :, np.newaxis], 3, axis=2) - prediction_true = self.resize_image(seg_color, img_h_page, img_w_page) + seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) + prediction_true = resize_image(seg_color, img_h_page, img_w_page) prediction_true = prediction_true.astype(np.uint8) return prediction_true[:,:,0] - def run(self): - self.start_new_session_and_model() - models_n=os.listdir(self.model_dir) - img_last=0 - for model_in in models_n: - - res=self.predict(model_in) - - img_fin=np.zeros((res.shape[0],res.shape[1],3) ) - res[:,:][res[:,:]==0]=2 - res=res-1 - res=res*255 - img_fin[:,:,0]=res - img_fin[:,:,1]=res - img_fin[:,:,2]=res - - img_fin=img_fin.astype(np.uint8) - img_fin=(res[:,:]==0)*255 - img_last=img_last+img_fin - kernel = np.ones((5,5),np.uint8) - img_last[:,:][img_last[:,:]>0]=255 - img_last=(img_last[:,:]==0)*255 - if self.save is not None: - cv2.imwrite(self.save,img_last) -def main(): - parser=argparse.ArgumentParser() - - parser.add_argument('-i','--image', dest='inp1', default=None, help='image.') - parser.add_argument('-p','--patches', dest='inp3', default=False, help='by setting this parameter to true you let the model to see the image in patches.') - parser.add_argument('-s','--save', dest='inp4', default=False, help='save prediction with a given name here. The name and format should be given (outputname.tif).') - parser.add_argument('-m','--model', dest='inp2', default=None, help='models directory.') - - options=parser.parse_args() - - possibles=globals() - possibles.update(locals()) - x=sbb_binarize(options.inp1,options.inp2,options.inp3,options.inp4) - x.run() - -if __name__=="__main__": - main() - - - - + def run(self, image=None, image_path=None, save=None, use_patches=False): + if (image is not None and image_path is not None) or \ + (image is None and image_path is None): + raise ValueError("Must pass either a opencv2 image or an image_path") + if image_path is not None: + image = cv2.imread(image) + self.start_new_session() + list_of_model_files = listdir(self.model_dir) + img_last = 0 + for model_in in list_of_model_files: + + res = self.predict(model_in, image, use_patches) + + img_fin = np.zeros((res.shape[0], res.shape[1], 3)) + res[:, :][res[:, :] == 0] = 2 + res = res - 1 + res = res * 255 + img_fin[:, :, 0] = res + img_fin[:, :, 1] = res + img_fin[:, :, 2] = res + + img_fin = img_fin.astype(np.uint8) + img_fin = (res[:, :] == 0) * 255 + img_last = img_last + img_fin + + kernel = np.ones((5, 5), np.uint8) + img_last[:, :][img_last[:, :] > 0] = 255 + img_last = (img_last[:, :] == 0) * 255 + if save: + cv2.imwrite(save, img_last) + return img_last diff --git a/setup.py b/setup.py index ac55505..7ab6e02 100644 --- a/setup.py +++ b/setup.py @@ -1,6 +1,30 @@ #!/usr/bin/env python3 +# -*- coding: utf-8 -*- +from json import load +from setuptools import setup, find_packages -import setuptools -from numpy.distutils.core import Extension, setup +with open('./ocrd-tool.json', 'r') as f: + version = load(f)['version'] -setup(name='sbb_binarize',version=1.0,packages=['sbb_binarize']) +install_requires = open('requirements.txt').read().split('\n') + +setup( + name='sbb_binarization', + version=version, + description='Pixelwise binarization with selectional auto-encoders in Keras', + long_description=open('README.md').read(), + long_description_content_type='text/markdown', + author='Vahid Rezanezhad', + url='https://github.com/qurator-spk/sbb_binarization', + license='Apache License 2.0', + packages=find_packages(exclude=('tests', 'docs')), + include_package_data=True, + package_data={'': ['*.json', '*.yml', '*.yaml']}, + install_requires=install_requires, + entry_points={ + 'console_scripts': [ + 'sbb_binarize=sbb_binarize.cli:main', + 'ocrd-sbb-binarize=sbb_binarize.ocrd_cli:cli', + ] + }, +)