Split out frontend code to separate module

.bumpversion.cfg

+[bumpversion]
+current_version = 0.3.0
+commit = True
+tag = True
+
+[bumpversion:file:setup.py]

.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
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+
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+.cache
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+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+.static_storage/
+.media/
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
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+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# Vim
+*~
+
+# PyCharm
+.idea

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README.md

+# OCR4All Pixel Classifier
+
+## Requirements
+
+Python dependencies are specified in `requirements.txt` / `setup.py`.
+
+You must install the package via pip with either `ocr4all_pixel_classifier[tf_cpu]` to
+use CPU version of tensorflow or `ocr4all_pixel_classifier[tf_gpu]` to use GPU (CUDA)
+version of tensorflow. For the latter, your system should be set up with CUDA 9
+and CuDNN 7.
+
+## Usage
+
+### Pixel classifier
+
+#### Classification
+
+To run a model on some input images, use `ocr4all-pixel-classifier predict`:
+
+```sh
+ocr4all-pixel-classifier predict --load PATH_TO_MODEL \
+	--output OUTPUT_PATH \
+	--binary PATH_TO_BINARY_IMAGES \
+	--images PATH_TO_SOURCE_IMAGES \
+	--norm PATH_TO_NORMALIZATIONS
+```
+(`ocr4all-pixel-classifier` is an alias for `ocr4all-pixel-classifier predict`)
+
+This will create three folders at the output path:
+- `color`: the classification as color image, with pixel color corresponding to
+	the class for that pixel
+- `inverted`: inverted binary image with classification of foreground pixels
+	only (i.e. background is black, foreground is white or class color)
+- `overlay`: classification image layered transparently over the original image
+
+#### Training
+
+For training, you first have to create dataset files. A dataset file is a JSON
+file containing three arrays, for train, test and evaluation data (also
+called train/validation/test in other publications). The JSON file uses the
+following format:
+
+```json
+{
+	"train": [
+		//datasets here
+	],
+	"test": [
+		//datasets here
+	],
+	"eval": [
+		//datasets here
+	]
+}
+```
+
+A dataset describes a single input image and consists of several paths: the
+original image, a binarized version and the mask (pixel color corresponds to
+class). Furthermore, the line height of the page in pixels must be specified:
+```json
+{
+	"binary_path": "/path/to/image/binary/filename.bin.png",
+	"image_path":  "/path/to/image/color/filename.jpg",
+	"mask_path":  "/path/to/image/mask/filename_MASK.png",
+	"line_height_px": 18
+}
+```
+
+The generation of dataset files can be automated using `ocr4all-pixel-classifier
+create-dataset-file`. Refer to the command's `--help` output for further
+information.
+
+To start the training:
+
+```sh
+ocr4all-pixel-classifier train \
+    --train DATASET_FILE.json --test DATASET_FILE.json --eval DATASET_FILE.json \
+    --output MODEL_TARGET_PATH \
+    --n_iter 5000
+```
+The parameters `--train`, `--test` and `--eval` may be followed by any number of
+dataset files or patterns (shell globbing).
+
+Refer to `ocr4all-pixel-classifier train --help` for further parameters provided to
+affect the training procedure.
+
+You can combine several dataset files into a _split file_. The format of the
+split file is:
+
+```json
+{
+	"label": "name of split",
+	"train": [
+		"/path/to/dataset1.json",
+		"/path/to/dataset2.json",
+		...
+	],
+	"test": [
+		//dataset paths here
+	],
+	"eval": [
+		//dataset paths here
+	]
+}
+```
+To use a split file, add the `--split_file` parameter.
+
+### Examples
+
+See the examples for [dataset generation](examples/dataset-creation-example.sh) and [training](examples/model-training-example.sh)
+
+### `ocr4all-pixel-classifier compute-image-normalizations` / `ocrd_compute_normalizations`
+
+Calculate image normalizations, i.e. scaling factors based on average line
+height.
+
+Required arguments:
+
+- `--input_dir`: location of images
+- `--output_dir`: target location of norm files
+
+Optional arguments:
+- `--average_all`: Average height over all images
+- `--inverse`

examples/dataset-creation-example.sh

+#!/bin/sh
+
+# Example of creating a dataset for ocr4all-pixel-classifier
+#
+# This assumes your folder structure looks like this:
+# base dir
+# ├── book1
+# │   ├── binary  <- Binarized version of image
+# │   ├── jpg     <- Color version of image
+# │   └── page    <- PageXML
+# ├── ...
+# └── bookN
+#    ├── binary
+#    ├── jpg
+#    └── page
+
+for book in book*; do
+
+	# Generate the masks for training
+	# --setting defines, which region types will be used:
+	#   - all_types: one color for each region type in PageXML
+	#   - text_nontext: red for text, green for non_text
+	#   - baseline: draw baselines of text lines
+	#   - textline: draw polygons of text lines
+	# we set --image_map_dir to the current directory, which will overwrite the
+	# output in each loop, but the image map generated is constant for each
+	# setting and only one file is needed for training
+	ocr4all-pixel-classifier gen-masks \
+		--input-dir $book/page \
+		--output-dir $book/masks \
+		--threads $(nproc) \
+		--setting text_nontext \
+		--image-map_dir ./
+
+	# Estimate the xheight for all pages based on connected components in binary
+	# image.
+	# When running on images with different dpi/fontsize, don't use --average_all
+	ocr4all-pixel-classifier compute-image-normalizations \
+		--input-dir $book/binary
+		--average-all \
+		--output-dir $book/norms 
+
+	# create a json file usable as input for the pixel classifier's train command
+	# --n_train and --n_test determine the size of the training and validation set
+	# (note that validation set is called test set in the pixel classifier for
+	# historical reasons)
+	ocr4all-pixel-classifier create-dataset-file \
+		--images-dir jpg \
+		--binary-dir binary \
+		--masks-dir masks \ 
+		--normalizations-dir norms \
+		--output-file $book/dataset.json \
+		--n-train 0.8 --n-test 0.2
+		--dataset-path $(realpath $book/)
+done

examples/model-training-example.sh

+# Example call for training a model
+
+# This trains a model based on the data in the given json files (see
+# dataset-creation-example.sh)
+#
+# Note that the dataset files already differentiate between train,
+# test/validation and evaluation dataset, so even if you specify datasets for
+# both train and test, only the image in the relevant section in the JSON will
+# be used for each group
+#
+# --n-epoch / -E specifies the number of epochs for training, i.e. the training
+# duration. We also specify the number of iterations a worse performance is
+# allowed, when the model does not improve for that long the training stops
+# (only possible with test dataset). Of course we keep the best model, not the
+# one after the performance drops.
+#
+# --output specifies the folder where model.h5 and and logs will be placed.
+ocr4all-pixel-classifier train \
+	--train dataset1.json dataset2.json dataset3.json \
+	--test dataset1.json dataset2.json dataset3.json \
+	--n-epoch 100 \
+	--early-stopping-max-performance-drops 30 \
+	--output my-model \
+	--color_map image_map.json
+
+# if using a split file:
+ocr4all-pixel-classifier train \
+	--split-file splits.json \
+	-E 100 \
+	-S 30 \
+	--output my-model \
+	--color_map image_map.json
+
+
+# you can also use --load to specify an existing model on which to continue
+# training. There are some minor tuning options available, see the help output
+# for the "train" command for those:
+ocr4all-pixel-classifier train --help

ocr4all_pixel_classifier/scripts/compute_image_normalizations.py

+import argparse
+import json
+import multiprocessing
+import os
+import sys
+from functools import partial
+from math import isnan
+
+import numpy as np
+import tqdm
+
+from ocr4all_pixel_classifier.lib.image_ops import compute_char_height
+
+
+def compute_normalizations(input_dir, output_dir=None, inverse=False, average_all=True):
+    if not os.path.exists(input_dir):
+        raise Exception("Cannot open {}".format(input_dir))
+
+    if not os.path.isdir(input_dir):
+        files = [input_dir]
+    else:
+        files = [os.path.join(input_dir, f) for f in os.listdir(input_dir)]
+
+    # files = files[:10]
+
+    with multiprocessing.Pool(processes=12) as p:
+        char_heights = [v for v in
+                        tqdm.tqdm(p.imap(partial(compute_char_height, inverse=inverse), files), total=len(files))
+                        ]
+
+    av_height = np.mean([c for c in char_heights if c])
+    if isnan(av_height):
+        raise Exception("No chars found in dataset")
+    if average_all:
+        char_heights = [av_height] * len(char_heights)
+
+    if output_dir:
+        os.makedirs(output_dir, exist_ok=True)
+
+    for file, height in zip(files, char_heights):
+        filename, file_extension = os.path.splitext(os.path.basename(file))
+        if height is None:
+            height = av_height
+        if output_dir:
+            output_file = os.path.join(output_dir, filename + ".norm")
+            with open(output_file, 'w') as f:
+                json.dump(
+                    {"file": file, "char_height": int(height)},
+                    f,
+                    indent=4
+                )
+
+
+def main():
+    parser = argparse.ArgumentParser(add_help=False)
+    paths_args = parser.add_argument_group("Paths")
+    paths_args.add_argument("-I", "--input-dir", type=str, required=True,
+                            help="Image directory to process")
+    paths_args.add_argument("-O", "--output-dir", type=str, required=True,
+                            help="The output dir for the normalization data")
+
+    opt_args = parser.add_argument_group("optional arguments")
+    opt_args.add_argument("-h", "--help", action="help", help="show this help message and exit")
+    opt_args.add_argument("--average-all", "--average_all", action="store_true",
+                          help="Use average height over all images.")
+    opt_args.add_argument("--inverse", action="store_true",
+                          help="use if white is foreground")
+    opt_args.add_argument("--debug", action="store_true")
+
+    args = parser.parse_args()
+
+    if args.debug:
+        compute_normalizations(args.input_dir, args.output_dir, args.inverse, args.average_all)
+    else:
+        try:
+            import warnings
+            warnings.filterwarnings('ignore')
+            compute_normalizations(args.input_dir, args.output_dir, args.inverse, args.average_all)
+        except Exception:
+            print("Error:", sys.exc_info()[1])
+
+
+if __name__ == '__main__':
+    main()

ocr4all_pixel_classifier/scripts/create_dataset_file.py

+import argparse
+
+from ocr4all_pixel_classifier.lib.dataset import list_dataset, single_split, create_splits
+from random import seed
+import json
+
+
+def main():
+    parser = argparse.ArgumentParser(add_help=False)
+
+    paths_args = parser.add_argument_group("Main paths")
+    paths_args.add_argument("-D", "--dataset-path", type=str, required=True,
+                            help="base path of dataset")
+    paths_args.add_argument("-O", "--output-file", type=str, required=True,
+                            help="output location for dataset JSON. for generating multiple splits, add {} where the number should be.")
+