Merge branch 'master-upstream'

# Conflicts:
#	README.md
#	sbb_binarize/cli.py
#	sbb_binarize/sbb_binarize.py

CHANGELOG.md

@@ -4,6 +4,12 @@ Versioned according to [Semantic Versioning](http://semver.org/).
 
 ## Unreleased
 
+## [0.0.10] - 2022-10-24
+
+Added:
+
+  * Trained models listed in ocrd-tool.json for download with OCR-D resource manager, #53
+
 ## [0.0.10] - 2022-07-21
 
 Fixed:
@@ -71,6 +77,7 @@ Fixed:
 Initial release
 
 <!-- link-labels -->
+[0.0.11]: ../../compare/v0.0.11...v0.0.10
 [0.0.10]: ../../compare/v0.0.10...v0.0.9
 [0.0.9]: ../../compare/v0.0.9...v0.0.8
 [0.0.8]: ../../compare/v0.0.8...v0.0.7

README.md

@@ -8,7 +8,7 @@
 
 ## Introduction
 
-This tool performs document image binarization using trained models. The method is based on [Calvo-Zaragoza and Gallego, 2018](https://arxiv.org/abs/1706.10241).
+This tool performs document image binarization using a trained ResNet50-UNet model. 
 
 ## Installation
 
@@ -18,10 +18,14 @@ Clone the repository, enter it and run
 
 ### Models
 
-Pre-trained models can be downloaded from here:   
+Pre-trained models in  `HDF5` format can be downloaded from here:   
 
 https://qurator-data.de/sbb_binarization/
 
+We also provide a Tensorflow `saved_model` via Huggingface:
+
+https://huggingface.co/SBB/sbb_binarization
+
 ## Usage
 
 ```sh
@@ -31,7 +35,9 @@ sbb_binarize \
   <output image>
 ```
 
-Example
+Images containing a lot of border noise (black pixels) should be cropped beforehand to improve the quality of results.
+
+### Example
 
 ```sh
 sbb_binarize -m /path/to/models/ myimage.tif myimage-bin.tif

requirements.txt

@@ -1,6 +1,6 @@
 numpy
 setuptools >= 41
 opencv-python-headless
-ocrd >= 2.22.3
+ocrd >= 2.38.0
 tensorflow >= 2.4.0
 mpire

sbb_binarize/ocrd-tool.json

@@ -1,5 +1,5 @@
 {
-  "version": "0.0.10",
+  "version": "0.0.11",
   "git_url": "https://github.com/qurator-spk/sbb_binarization",
   "tools": {
     "ocrd-sbb-binarize": {
@@ -17,13 +17,31 @@
           "description": "PAGE XML hierarchy level to operate on"
         },
         "model": {
-          "description": "Directory containing HDF5 models. Can be an absolute path or a path relative to the current working directory or $SBB_BINARIZE_DATA environment variable (if set)",
+          "description": "Directory containing HDF5 or SavedModel/ProtoBuf models. Can be an absolute path or a path relative to the OCR-D resource location, the current working directory or the $SBB_BINARIZE_DATA environment variable (if set)",
           "type": "string",
           "format": "uri",
           "content-type": "text/directory",
           "required": true
         }
-      }
+      },
+      "resources": [
+        {
+          "url": "https://github.com/apacha/sbb_binarization/releases/download/pre-trained-models/model_2020_01_16.zip",
+          "name": "default",
+          "type": "archive",
+          "path_in_archive": "model_2020_01_16",
+          "size": 562917559,
+          "description": "default models provided by github.com/qurator-spk"
+        },
+        {
+          "url": "https://github.com/apacha/sbb_binarization/releases/download/pre-trained-models/model_2021_03_09.zip",
+          "name": "default-2021-03-09",
+          "type": "archive",
+          "path_in_archive": ".",
+          "size": 133693693,
+          "description": "updated default models provided by github.com/qurator-spk"
+        }
+      ]
     }
   }
 }

sbb_binarize/ocrd_cli.py

@@ -30,6 +30,10 @@ def cv2pil(img):
 
 def pil2cv(img):
     # from ocrd/workspace.py
+    if img.mode in ('LA', 'RGBA'):
+        newimg = Image.new(img.mode[:-1], img.size, 'white')
+        newimg.paste(img, mask=img.getchannel('A'))
+        img = newimg
     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)
@@ -106,7 +110,7 @@ class SbbBinarizeProcessor(Processor):
 
             if oplevel == 'page':
                 LOG.info("Binarizing on 'page' level in page '%s'", page_id)
-                bin_image = cv2pil(self.binarizer.run(image=pil2cv(page_image), use_patches=True))
+                bin_image = cv2pil(self.binarizer.run(image=pil2cv(page_image)))
                 # update METS (add the image file):
                 bin_image_path = self.workspace.save_image_file(bin_image,
                         file_id + '.IMG-BIN',
@@ -120,7 +124,7 @@ class SbbBinarizeProcessor(Processor):
                     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 = cv2pil(binarizer.run(image=pil2cv(region_image)))
                     region_image_bin_path = self.workspace.save_image_file(
                             region_image_bin,
                             "%s_%s.IMG-BIN" % (file_id, region.id),
@@ -135,7 +139,7 @@ class SbbBinarizeProcessor(Processor):
                     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 = cv2pil(binarizer.run(image=pil2cv(line_image)))
                     line_image_bin_path = self.workspace.save_image_file(
                             line_image_bin,
                             "%s_%s_%s.IMG-BIN" % (file_id, region_id, line.id),