Merge branch 'release-v0.0.2' into release-v0.0.3

.gitignore

@@ -1,2 +1,4 @@
 *.egg-info
 __pycache__
+/build
+/dist

CHANGELOG.md

@@ -0,0 +1,23 @@
+Change Log
+==========
+Versioned according to [Semantic Versioning](http://semver.org/).
+
+## Unreleased
+
+## 0.0.2
+
+Changed:
+
+  * `SBB_BINARIZE_DATA` can replace `model` parameter, #6
+
+Fixed:
+
+  * AlternativeImage/comments now set on page level, #8, #11
+  * Only try to load `*.h5` model files, #7, #10
+
+## 0.0.1
+
+Initial release
+
+<!-- link-labels -->
+[0.0.2]: ../../compare/v0.0.1...v0.0.2

sbb_binarize/ocrd-tool.json

@@ -1,5 +1,5 @@
 {
-  "version": "0.0.1",
+  "version": "0.0.2",
   "git_url": "https://github.com/qurator-spk/sbb_binarization",
   "tools": {
     "ocrd-sbb-binarize": {
@@ -19,7 +19,7 @@
         "model": {
           "description": "models directory.",
           "type": "string",
-          "required": true
+          "required": false
         }
       }
     }

sbb_binarize/ocrd_cli.py

@@ -37,6 +37,14 @@ class SbbBinarizeProcessor(Processor):
     def __init__(self, *args, **kwargs):
         kwargs['ocrd_tool'] = OCRD_TOOL['tools'][TOOL]
         kwargs['version'] = OCRD_TOOL['version']
+        if not(kwargs.get('show_help', None) or kwargs.get('dump_json', None) or kwargs.get('show_version')):
+            if not 'parameter' in kwargs:
+                kwargs['parameter'] = {}
+            if not 'model' in kwargs['parameter']:
+                if 'SBB_BINARIZE_DATA' in os.environ:
+                    kwargs['parameter']['model'] = os.environ['SBB_BINARIZE_DATA']
+                else:
+                    raise ValueError("Must pass 'model' parameter or set SBB_BINARIZE_DATA environment variable")
         super().__init__(*args, **kwargs)
 
     def process(self):
@@ -49,7 +57,7 @@ class SbbBinarizeProcessor(Processor):
 
         oplevel = self.parameter['operation_level']
         model_path = self.parameter['model'] # pylint: disable=attribute-defined-outside-init
-        binarizer = SbbBinarizer(model_dir=model_path)
+        binarizer = SbbBinarizer(model_dir=model_path, logger=LOG)
 
         for n, input_file in enumerate(self.input_files):
             file_id = make_file_id(input_file, self.output_file_grp)
@@ -69,7 +77,7 @@ class SbbBinarizeProcessor(Processor):
                         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']))
+                page.add_AlternativeImage(AlternativeImageType(filename=bin_image_path, comments='%s,binarized' % page_xywh['features']))
 
             elif oplevel == 'region':
                 regions = page.get_AllRegions(['Text', 'Table'], depth=1)

sbb_binarize/sbb_binarize.py

@@ -3,7 +3,8 @@ Tool to load model and binarize a given image.
 """
 
 import sys
-from os import listdir, environ, devnull
+from glob import glob
+from os import environ, devnull
 from os.path import join
 from warnings import catch_warnings, simplefilter
 
@@ -17,13 +18,16 @@ from keras.models import load_model
 sys.stderr = stderr
 import tensorflow as tf
 
+import logging
+
 def resize_image(img_in, input_height, input_width):
     return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
 
 class SbbBinarizer:
 
-    def __init__(self, model_dir):
+    def __init__(self, model_dir, logger=None):
         self.model_dir = model_dir
+        self.log = logger if logger else logging.getLogger('SbbBinarizer')
 
     def start_new_session(self):
         config = tf.ConfigProto()
@@ -191,9 +195,10 @@ class SbbBinarizer:
         if image_path is not None:
             image = cv2.imread(image_path)
         self.start_new_session()
-        list_of_model_files = listdir(self.model_dir)
+        list_of_model_files = glob('%s/*.h5' % self.model_dir)
         img_last = 0
-        for model_in in list_of_model_files:
+        for n, model_in in enumerate(list_of_model_files):
+            self.log.info('Predicting with model %s [%s/%s]' % (model_in, n + 1, len(list_of_model_files)))
 
             res = self.predict(model_in, image, use_patches)