diff --git a/.circleci/config.yml b/.circleci/config.yml
index a17560d..4174b62 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -1,6 +1,6 @@
version: 2.1
orbs:
- codecov: codecov/codecov@3.3.0
+ codecov: codecov/codecov@5.0.3
jobs:
test:
@@ -21,13 +21,21 @@ jobs:
paths:
- "~/.cache/pip"
- run: pip3 install --upgrade pip
- - run: make install deps-test-ubuntu PIP_INSTALL="pip3 install"
- - run: make coverage
- - codecov/upload
+ - run:
+ name: install dependencies and package
+ command: make deps-test-ubuntu install PIP_INSTALL="pip3 install"
- save_cache:
key: v01-pydeps-<< parameters.python-image >>-{{ checksum "requirements.txt" }}-{{ checksum "requirements-dev.txt" }}
paths:
- "~/.cache/pip"
+ - run:
+ name: run regression test and coverage test
+ command: make coverage
+ no_output_timeout: 30m
+ - codecov/upload
+ - store_artifacts:
+ path: htmlcov
+ resource_class: large
workflows:
build:
diff --git a/LICENSE b/LICENSE
index bc7973a..99fbb49 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,201 +1,676 @@
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+ (at your option) any later version.
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+ This is free software, and you are welcome to redistribute it
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+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+.
+
+ The GNU General Public License does not permit incorporating your program
+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library. If this is what you want to do, use the GNU Lesser General
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+.
diff --git a/Makefile b/Makefile
index cfa7cd8..356d426 100644
--- a/Makefile
+++ b/Makefile
@@ -2,8 +2,12 @@ export # export variables to subshells
PIP_INSTALL = pip3 install
GIT_CLONE = git clone
PYTHON = python3
-PYTEST_ARGS = -W 'ignore::DeprecationWarning' -W 'ignore::FutureWarning'
-MODEL = qurator-gt4histocr-1.0
+PYTEST_ARGS = -W 'ignore::DeprecationWarning' -W 'ignore::FutureWarning' -vv
+# not usable with Calamari 2 ATM - see Calamari#362
+#MODEL = qurator-gt4histocr-1.0 # cannot be migrated to Calamari 2
+#MODEL = deep3_fraktur19 # too large for CI
+MODEL = fraktur_19th_century
+export MODEL # needed for pytest model selection
EXAMPLE = actevedef_718448162.first-page+binarization+segmentation
# BEGIN-EVAL makefile-parser --make-help Makefile
@@ -82,12 +86,13 @@ assets-clean:
# Run unit tests
test: test/assets $(MODEL)
# declare -p HTTP_PROXY
- $(PYTHON) -m pytest --continue-on-collection-errors test $(PYTEST_ARGS)
+ $(PYTHON) -m pytest --continue-on-collection-errors --durations=0 test $(PYTEST_ARGS)
# Run unit tests and determine test coverage
coverage: test/assets $(MODEL)
coverage erase
make test PYTHON="coverage run"
+ coverage combine
coverage report
coverage html
diff --git a/ocrd_calamari/config.py b/ocrd_calamari/config.py
deleted file mode 100644
index 1729f8c..0000000
--- a/ocrd_calamari/config.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import json
-
-from pkg_resources import resource_string
-
-OCRD_TOOL = json.loads(resource_string(__name__, "ocrd-tool.json").decode("utf8"))
diff --git a/ocrd_calamari/fix_calamari1_model.py b/ocrd_calamari/fix_calamari1_model.py
deleted file mode 100644
index 4989594..0000000
--- a/ocrd_calamari/fix_calamari1_model.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import json
-import re
-from copy import deepcopy
-from glob import glob
-
-import click
-
-from ocrd_calamari.util import working_directory
-
-
-@click.command
-@click.argument("checkpoint_dir")
-def fix_calamari1_model(checkpoint_dir):
- """
- Fix old Calamari 1 models.
-
- This currently means fixing regexen in "replacements" to have their global flags
- in front of the rest of the regex.
- """
- with working_directory(checkpoint_dir):
- for fn in glob("*.json"):
- with open(fn, "r") as fp:
- j = json.load(fp)
- old_j = deepcopy(j)
-
- for v in j["model"].values():
- if not isinstance(v, dict):
- continue
- for child in v.get("children", []):
- for replacement in child.get("replacements", []):
- # Move global flags in front
- replacement["old"] = re.sub(
- r"^(.*)\(\?u\)$", r"(?u)\1", replacement["old"]
- )
-
- if j == old_j:
- print(f"{fn} unchanged.")
- else:
- with open(fn, "w") as fp:
- json.dump(j, fp, indent=2)
- print(f"{fn} fixed.")
diff --git a/ocrd_calamari/ocrd-tool.json b/ocrd_calamari/ocrd-tool.json
index 6bdb971..3a3a6b7 100644
--- a/ocrd_calamari/ocrd-tool.json
+++ b/ocrd_calamari/ocrd-tool.json
@@ -1,6 +1,6 @@
{
"git_url": "https://github.com/OCR-D/ocrd_calamari",
- "version": "1.0.6",
+ "version": "2.0.0",
"tools": {
"ocrd-calamari-recognize": {
"executable": "ocrd-calamari-recognize",
@@ -11,24 +11,28 @@
"recognition/text-recognition"
],
"description": "Recognize lines with Calamari",
- "input_file_grp": [
- "OCR-D-SEG-LINE"
- ],
- "output_file_grp": [
- "OCR-D-OCR-CALAMARI"
- ],
+ "input_file_grp_cardinality": 1,
+ "output_file_grp_cardinality": 1,
"parameters": {
+ "device": {
+ "description": "Select computing device for Tensorflow (-1 for CPU, 0 for first CUDA GPU etc.). Downgraded to CPU if not available.",
+ "type": "number",
+ "format": "integer",
+ "default": 0
+ },
"checkpoint_dir": {
- "description": "The directory containing calamari model files (*.ckpt.json). Uses all checkpoints in that directory",
+ "description": "The directory (name or path) containing Calamari model files (*.ckpt.json). Will use all checkpoints in that directory.",
"type": "string",
"format": "uri",
"content-type": "text/directory",
"cacheable": true,
- "default": "qurator-gt4histocr-1.0"
+ "required": true
},
"voter": {
"description": "The voting algorithm to use",
- "type": "string", "default": "confidence_voter_default_ctc"
+ "type": "string",
+ "default": "confidence_voter_default_ctc",
+ "enum": ["confidence_voter_default_ctc", "sequence_voter"]
},
"textequiv_level": {
"type": "string",
@@ -50,107 +54,161 @@
"name": "qurator-gt4histocr-1.0",
"description": "Calamari model trained with GT4HistOCR",
"size": 90275264,
- "version_range": ">= 1.0.0"
- },
- {
- "url": "https://github.com/Calamari-OCR/calamari_models_experimental/releases/download/v0.0.1-pre1/c1_fraktur19-1.tar.gz",
- "type": "archive",
- "name": "zpd-fraktur19",
- "description": "Model trained on 19th century german fraktur",
- "path_in_archive": "c1_fraktur19-1",
- "size": 86009886,
- "version_range": ">= 1.0.0"
+ "version_range": ">= 1.0.0, < 2.0.0"
},
{
- "url": "https://github.com/Calamari-OCR/calamari_models_experimental/releases/download/v0.0.1-pre1/c1_latin-script-hist-3.tar.gz",
- "type": "archive",
- "name": "zpd-latin-script-hist-3",
- "path_in_archive": "c1_latin-script-hist-3",
- "description": "Model trained on historical latin-script texts",
- "size": 88416863,
- "version_range": ">= 1.0.0"
- },
- {
- "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/1.1/antiqua_historical.zip",
+ "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/2.2/antiqua_historical.tar.gz",
"type": "archive",
"name": "antiqua_historical",
"path_in_archive": "antiqua_historical",
"description": "Antiqua parts of GT4HistOCR from Calamari-OCR/calamari_models (5-fold ensemble, normalized grayscale, NFC)",
- "size": 89615540,
- "version_range": ">= 1.0.0"
+ "size": 30633860,
+ "version_range": ">= 2.0.0"
},
{
- "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/1.1/antiqua_historical_ligs.zip",
+ "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/2.2/antiqua_historical_ligs.tar.gz",
"type": "archive",
"name": "antiqua_historical_ligs",
"path_in_archive": "antiqua_historical_ligs",
"description": "Antiqua parts of GT4HistOCR with enriched ligatures from Calamari-OCR/calamari_models (5-fold ensemble, normalized grayscale, NFC)",
- "size": 87540762,
- "version_range": ">= 1.0.0"
+ "size": 30368081,
+ "version_range": ">= 2.0.0"
},
{
- "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/1.1/fraktur_19th_century.zip",
+ "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/2.2/fraktur_19th_century.tar.gz",
"type": "archive",
"name": "fraktur_19th_century",
"path_in_archive": "fraktur_19th_century",
"description": "Fraktur 19th century parts of GT4HistOCR mixed with Fraktur data from Archiscribe and jze from Calamari-OCR/calamari_models (5-fold ensemble, normalized grayscale and nlbin, NFC)",
- "size": 83895140,
- "version_range": ">= 1.0.0"
+ "size": 30018408,
+ "version_range": ">= 2.0.0"
},
{
- "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/1.1/fraktur_historical.zip",
+ "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/2.2/fraktur_historical.tar.gz",
"type": "archive",
"name": "fraktur_historical",
"path_in_archive": "fraktur_historical",
"description": "Fraktur parts of GT4HistOCR from Calamari-OCR/calamari_models (5-fold ensemble, normalized grayscale, NFC)",
- "size": 87807639,
- "version_range": ">= 1.0.0"
+ "size": 30232783,
+ "version_range": ">= 2.0.0"
},
{
- "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/1.1/fraktur_historical_ligs.zip",
+ "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/2.2/fraktur_historical_ligs.tar.gz",
"type": "archive",
"name": "fraktur_historical_ligs",
"path_in_archive": "fraktur_historical_ligs",
"description": "Fraktur parts of GT4HistOCR with enriched ligatures from Calamari-OCR/calamari_models (5-fold ensemble, normalized grayscale, NFC)",
- "size": 88039551,
- "version_range": ">= 1.0.0"
+ "size": 30622320,
+ "version_range": ">= 2.0.0"
},
{
- "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/1.1/gt4histocr.zip",
+ "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/2.2/gt4histocr.tar.gz",
"type": "archive",
"name": "gt4histocr",
"path_in_archive": "gt4histocr",
"description": "GT4HistOCR from Calamari-OCR/calamari_models (5-fold ensemble, normalized grayscale, NFC)",
- "size": 90107851,
- "version_range": ">= 1.0.0"
+ "size": 31159925,
+ "version_range": ">= 2.0.0"
},
{
- "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/1.1/historical_french.zip",
+ "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/2.2/historical_french.tar.gz",
"type": "archive",
"name": "historical_french",
"path_in_archive": "historical_french",
"description": "17-19th century French prints from Calamari-OCR/calamari_models (5-fold ensemble, nlbin, NFC)",
- "size": 87335250,
- "version_range": ">= 1.0.0"
+ "size": 30257128,
+ "version_range": ">= 2.0.0"
},
{
- "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/1.1/idiotikon.zip",
+ "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/2.2/idiotikon.tar.gz",
"type": "archive",
"name": "idiotikon",
"path_in_archive": "idiotikon",
"description": "Antiqua UW3 finetuned on Antiqua Idiotikon dictionary with many diacritics from Calamari-OCR/calamari_models (5-fold ensemble, nlbin, NFD)",
- "size": 100807764,
- "version_range": ">= 1.0.0"
+ "size": 30474541,
+ "version_range": ">= 2.0.0"
},
{
- "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/1.1/uw3-modern-english.zip",
+ "url": "https://github.com/Calamari-OCR/calamari_models/releases/download/2.2/uw3-modern-english.tar.gz",
"type": "archive",
"name": "uw3-modern-english",
"path_in_archive": "uw3-modern-english",
"description": "Antiqua UW3 corpus from Calamari-OCR/calamari_models (5-fold ensemble, nlbin, NFC)",
- "size": 85413520,
- "version_range": ">= 1.0.0"
- }
+ "size": 29897592,
+ "version_range": ">= 2.0.0"
+ },
+ {
+ "url": "https://github.com/Calamari-OCR/calamari_models_experimental/releases/download/v0.0.3/deep3_fraktur19.tar.gz",
+ "type": "archive",
+ "name": "deep3_fraktur19",
+ "path_in_archive": "deep3_fraktur19",
+ "description": "Model trained on 19th century German Fraktur, like zpd-fraktur19 but deeper (5-fold ensemble, nlbin, NFC) - val_CER=0.5%",
+ "size": 92555871,
+ "version_range": ">= 2.0.0"
+ },
+ {
+ "url": "https://github.com/Calamari-OCR/calamari_models_experimental/releases/download/v0.0.3/deep3_fraktur-hist.tar.gz",
+ "type": "archive",
+ "name": "deep3_fraktur-hist",
+ "path_in_archive": "deep3_fraktur-hist",
+ "description": "Model trained on 16th to 19th century German Fraktur, like fraktur-historical but deeper (5-fold ensemble, nlbin, NFC) - val_CER=0.9%",
+ "size": 92504515,
+ "version_range": ">= 2.0.0"
+ },
+ {
+ "url": "https://github.com/Calamari-OCR/calamari_models_experimental/releases/download/v0.0.3/deep3_antiqua-hist.tar.gz",
+ "type": "archive",
+ "name": "deep3_antiqua-hist",
+ "path_in_archive": "deep3_antiqua-hist",
+ "description": "Model trained on 16th to 19th century Antiqua, like antiqua-historical but deeper (5-fold ensemble, nlbin, NFC) - val_CER=0.5%",
+ "size": 92614001,
+ "version_range": ">= 2.0.0"
+ },
+ {
+ "url": "https://github.com/Calamari-OCR/calamari_models_experimental/releases/download/v0.0.3/deep3_antiqua-15-16-cent.tar.gz",
+ "type": "archive",
+ "name": "deep3_antiqua-15-16-cent",
+ "path_in_archive": "deep3_antiqua-15-16-cent",
+ "description": "Model trained on 15th and 16th century Latin Antiqua, like zpd-latin-script-hist-3 but deeper (5-fold ensemble, nlbin, NFC) - val_CER=0.5%",
+ "size": 92627999,
+ "version_range": ">= 2.0.0"
+ },
+ {
+ "url": "https://github.com/Calamari-OCR/calamari_models_experimental/releases/download/v0.0.3/deep3_lsh4.tar.gz",
+ "type": "archive",
+ "name": "deep3_lsh4",
+ "path_in_archive": "deep3_lsh4",
+ "description": "Model trained on 15th and 19th century on German, Latin, French etc. in Antiqua and Fraktur, like gt4histocr but deeper (5-fold ensemble, nlbin, NFC) - val_CER=1.6%",
+ "size": 92579708,
+ "version_range": ">= 2.0.0"
+ },
+ {
+ "url": "https://github.com/Calamari-OCR/calamari_models_experimental/releases/download/v0.0.3/deep3_htr-bastard.tar.gz",
+ "type": "archive",
+ "name": "deep3_htr-bastard",
+ "path_in_archive": "deep3_htr-bastard",
+ "description": "Model trained on 13th to 16th century German Gothic Bastarda (5-fold ensemble, nlbin, NFC) - val_CER=6.7%",
+ "size": 91539649,
+ "version_range": ">= 2.0.0"
+ },
+ {
+ "url": "https://github.com/Calamari-OCR/calamari_models_experimental/releases/download/v0.0.3/deep3_htr-gothic.tar.gz",
+ "type": "archive",
+ "name": "deep3_htr-gothic",
+ "path_in_archive": "deep3_htr-gothic",
+ "description": "Model trained on 13th to 16th century German Gothic Cursiva (5-fold ensemble, nlbin, NFC) - val_CER=2.5%",
+ "size": 91499098,
+ "version_range": ">= 2.0.0"
+ },
+ {
+ "url": "https://github.com/Calamari-OCR/calamari_models_experimental/releases/download/v0.0.3/def_arabic.tar.gz",
+ "type": "archive",
+ "name": "def_arabic",
+ "path_in_archive": "def_arabic",
+ "description": "Model trained for Arabic on ? (5-fold ensemble, nlbin, NFC) - val_CER=1.2%",
+ "size": 30651139,
+ "version_range": ">= 2.0.0"
+ }
]
}
}
diff --git a/ocrd_calamari/recognize.py b/ocrd_calamari/recognize.py
index edf69b9..3f5199d 100644
--- a/ocrd_calamari/recognize.py
+++ b/ocrd_calamari/recognize.py
@@ -1,393 +1,699 @@
from __future__ import absolute_import
+from typing import Optional
+from functools import cached_property
import itertools
-import os
from glob import glob
+import queue
+import multiprocessing as mp
+from threading import Thread
+import logging
+import weakref
import numpy as np
-from ocrd import Processor
-from ocrd_modelfactory import page_from_file
+import cv2 as cv
+from ocrd import Processor, OcrdPage, OcrdPageResult
from ocrd_models.ocrd_page import (
CoordsType,
GlyphType,
TextEquivType,
WordType,
- to_xml,
)
from ocrd_utils import (
- MIMETYPE_PAGE,
- assert_file_grp_cardinality,
+ VERSION as OCRD_VERSION,
coordinates_for_segment,
- getLogger,
- make_file_id,
points_from_polygon,
polygon_from_x0y0x1y1,
tf_disable_interactive_logs,
+ initLogging,
+ config
)
-# Disable tensorflow/keras logging via print before importing calamari
-# (and disable ruff's import checks and sorting here)
-# ruff: noqa: E402
-# ruff: isort: off
-tf_disable_interactive_logs()
-
-from tensorflow import __version__ as tensorflow_version
-from calamari_ocr import __version__ as calamari_version
-from calamari_ocr.ocr import MultiPredictor
-from calamari_ocr.ocr.voting import voter_from_proto
-from calamari_ocr.proto import VoterParams
-from tensorflow import config as tensorflow_config
-
# ruff: isort: on
-from ocrd_calamari.config import OCRD_TOOL
-
-TOOL = "ocrd-calamari-recognize"
+# BATCH_SIZE = 96 # size at smallest bound
+# GROUP_BOUNDS = [100, 200, 400, 800, 1600, 3200, 6400]
+# # default tfaip bucket_batch_sizes is buggy (inverse quotient)
+# BATCH_GROUPS = [max(1, (min(GROUP_BOUNDS) * BATCH_SIZE) // length)
+# for length in GROUP_BOUNDS] + [1]
+# we cannot use bucket_by_sequence_length (variable batch size),
+# because that would require exhausting the iterator
+BATCH_SIZE = 12
-BATCH_SIZE = 64
-
-def batched_length_limited(iterable, n, limit=32000):
- # batched('ABCDEFG', 3) → ABC DEF G
- if n < 1:
- raise ValueError('n must be at least one')
- iterator = iter(iterable)
- while batch := tuple(itertools.islice(iterator, n)):
- # implement poor man's batch bucketing to avoid OOM:
- maxlen = max(image.shape[1] for image in batch)
- if maxlen * n > limit and n > 1:
- yield from batched_length_limited(batch, n//2)
- else:
- yield batch
class CalamariRecognize(Processor):
- def __init__(self, *args, **kwargs):
- kwargs["ocrd_tool"] = OCRD_TOOL["tools"][TOOL]
- kwargs["version"] = "%s (calamari %s, tensorflow %s)" % (
- OCRD_TOOL["version"],
- calamari_version,
- tensorflow_version,
+ @property
+ def executable(self):
+ return 'ocrd-calamari-recognize'
+
+ def show_version(self):
+ from tensorflow import __version__ as tensorflow_version
+ from calamari_ocr import __version__ as calamari_version
+ from tfaip import __version__ as tfaip_version
+ print(f"Version {self.version}, "
+ f"calamari {calamari_version}, "
+ f"tfaip {tfaip_version}, "
+ f"tensorflow {tensorflow_version}, "
+ f"ocrd/core {OCRD_VERSION}"
)
- super(CalamariRecognize, self).__init__(*args, **kwargs)
- if hasattr(self, "output_file_grp"):
- # processing context
- self.setup()
def setup(self):
"""
Set up the model prior to processing.
"""
- log = getLogger("processor.CalamariRecognize")
- devices = tensorflow_config.list_physical_devices("GPU")
- for device in devices:
- log.info("using GPU device %s", device)
- tensorflow_config.experimental.set_memory_growth(device, True)
- resolved = self.resolve_resource(self.parameter["checkpoint_dir"])
- checkpoints = glob("%s/*.ckpt.json" % resolved)
- self.predictor = MultiPredictor(checkpoints=checkpoints, batch_size=BATCH_SIZE)
- log.info("loaded model %s", resolved)
-
- self.network_input_channels = self.predictor.predictors[
- 0
- ].network.input_channels
-
# not used:
- # self.network_input_channels = \
- # self.predictor.predictors[0].network_params.channels
- # not used:
- # binarization = \
- # self.predictor.predictors[0].model_params\
- # .data_preprocessor.binarization
+ # binarization = any(isinstance(preproc, calamari_ocr.ocr.dataset.imageprocessors.center_normalizer.CenterNormalizerProcessorParams) for preproc in self.predictor.data.params.pre_proc.processors)
# self.features = ('' if self.network_input_channels != 1 else
# 'binarized' if binarization != 'GRAY' else
# 'grayscale_normalized')
self.features = ""
- voter_params = VoterParams()
- voter_params.type = VoterParams.Type.Value(self.parameter["voter"].upper())
- self.voter = voter_from_proto(voter_params)
-
- def process(self):
+ # Run in a background thread so GPU parts can be interleaved with CPU pre-/post-processing across pages.
+ # We cannot use a ProcessPoolExecutor (or even ThreadPoolExecutor) for this,
+ # because that relies on threads to set up IPC, but when process_workspace
+ # starts forking/spawning subprocesses, these threads will break.
+ # (And we cannot use multithreading for process_workspace either, because
+ # Python's GIL would not allow true multiscalar compuation in the first place.)
+ # So instead, here we setup our own subprocess+queueing solution.
+ self.predictor = CalamariPredictor(
+ self.parameter['device'],
+ self.parameter["voter"],
+ self.resolve_resource(self.parameter["checkpoint_dir"])
+ )
+ self.logger.debug("model's network_input_channels is %d", self.network_input_channels)
+
+ @cached_property
+ def network_input_channels(self):
+ # as a special case, this information from the model is needed prior to
+ # prediction, but must be retrieved from the background process as soon as
+ # the model is loaded, so this will block upon first invocation
+ input_channels = self.predictor.network_input_channels
+ return input_channels
+
+ def shutdown(self):
+ if getattr(self, 'predictor', None):
+ self.predictor.shutdown()
+ del self.predictor
+
+ def process_page_pcgts(self, *input_pcgts: Optional[OcrdPage], page_id: Optional[str] = None) -> OcrdPageResult:
"""
- Perform text recognition with Calamari on the workspace.
+ Perform text recognition with Calamari.
If ``texequiv_level`` is ``word`` or ``glyph``, then additionally create word /
glyph level segments by splitting at white space characters / glyph boundaries.
In the case of ``glyph``, add all alternative character hypotheses down to
``glyph_conf_cutoff`` confidence threshold.
"""
- log = getLogger("processor.CalamariRecognize")
-
- assert_file_grp_cardinality(self.input_file_grp, 1)
- assert_file_grp_cardinality(self.output_file_grp, 1)
-
- for n, input_file in enumerate(self.input_files):
- 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))
+ pcgts = input_pcgts[0]
+ page = pcgts.get_Page()
+ page_image, page_coords, page_image_info = self.workspace.image_from_page(
+ page, page_id, feature_selector=self.features
+ )
- page = pcgts.get_Page()
- page_image, page_coords, page_image_info = self.workspace.image_from_page(
- page, page_id, feature_selector=self.features
+ tasks = []
+ class TaskThread(Thread):
+ def run(self):
+ try:
+ super().run()
+ self.exc = None
+ except Exception as exc:
+ self.exc = exc
+ def join(self, timeout=None):
+ super().join(timeout=timeout)
+ if self.exc:
+ raise self.exc from None
+ maxw = 0
+ for region in page.get_AllRegions(classes=["Text"]):
+ region_image, region_coords = self.workspace.image_from_segment(
+ region, page_image, page_coords, feature_selector=self.features
)
- lines = []
- for region in page.get_AllRegions(classes=["Text"]):
- region_image, region_coords = self.workspace.image_from_segment(
- region, page_image, page_coords, feature_selector=self.features
+ textlines = region.get_TextLine()
+ self.logger.info(
+ "About to recognize %i lines of region '%s'",
+ len(textlines),
+ region.id,
+ )
+ for line in textlines:
+ self.logger.debug(
+ "Recognizing line '%s' in region '%s'", line.id, region.id
)
- textlines = region.get_TextLine()
- log.info(
- "About to recognize %i lines of region '%s'",
- len(textlines),
- region.id,
+ line_image, line_coords = self.workspace.image_from_segment(
+ line,
+ region_image,
+ region_coords,
+ feature_selector=self.features,
)
- for line in textlines:
- log.debug(
- "Recognizing line '%s' in region '%s'", line.id, region.id
+ if (
+ "binarized" not in line_coords["features"]
+ and "grayscale_normalized" not in line_coords["features"]
+ and self.network_input_channels == 1
+ ):
+ # We cannot use a feature selector for this since we don't
+ # know whether the model expects (has been trained on)
+ # binarized or grayscale images; but raw images are likely
+ # always inadequate:
+ self.logger.warning(
+ "Using raw image for line '%s' in region '%s'",
+ line.id,
+ region.id,
)
- line_image, line_coords = self.workspace.image_from_segment(
- line,
- region_image,
- region_coords,
- feature_selector=self.features,
- )
- if (
- "binarized" not in line_coords["features"]
- and "grayscale_normalized" not in line_coords["features"]
- and self.network_input_channels == 1
- ):
- # We cannot use a feature selector for this since we don't
- # know whether the model expects (has been trained on)
- # binarized or grayscale images; but raw images are likely
- # always inadequate:
- log.warning(
- "Using raw image for line '%s' in region '%s'",
- line.id,
- region.id,
- )
-
- if (
- not all(line_image.size)
- or line_image.height <= 8
- or line_image.width <= 8
- or "binarized" in line_coords["features"]
- and line_image.convert("1").getextrema()[0] == 255
- ):
- # empty size or too tiny or no foreground at all: skip
- log.warning(
- "Skipping empty line '%s' in region '%s'",
- line.id,
- region.id,
- )
- continue
- lines.append((line, line_coords, np.array(line_image, dtype=np.uint8)))
-
- if len(lines):
- lines, coords, images = zip(*lines)
- else:
- log.warning("No text lines on page '%s'", page_id)
- lines, coords, images = [], [], []
-
- # not exposed in MultiPredictor yet, cf. calamari#361:
- # raw_results_all = self.predictor.predict_raw(images, progress_bar=False, batch_size=BATCH_SIZE)
- # avoid too large a batch size (causing OOM on CPU or GPU)
- fun = lambda x: self.predictor.predict_raw(x, progress_bar=False)
- results = itertools.chain.from_iterable(
- map(fun, batched_length_limited(images, BATCH_SIZE)))
-
- for line, line_coords, raw_results in zip(lines, coords, results):
- for i, p in enumerate(raw_results):
- p.prediction.id = "fold_{}".format(i)
-
- prediction = self.voter.vote_prediction_result(raw_results)
- prediction.id = "voted"
-
- # Build line text on our own
- #
- # Calamari does whitespace post-processing on prediction.sentence,
- # while it does not do the same on prediction.positions. Do it on
- # our own to have consistency.
- #
- # XXX Check Calamari's built-in post-processing on
- # prediction.sentence
-
- def _sort_chars(p):
- """Filter and sort chars of prediction p"""
- chars = p.chars
- chars = [
- c for c in chars if c.char
- ] # XXX Note that omission probabilities are not normalized?!
- chars = [
- c
- for c in chars
- if c.probability >= self.parameter["glyph_conf_cutoff"]
- ]
- chars = sorted(chars, key=lambda k: k.probability, reverse=True)
- return chars
-
- def _drop_leading_spaces(positions):
- return list(
- itertools.dropwhile(
- lambda p: _sort_chars(p)[0].char == " ", positions
- )
+ line_img = load_image(
+ np.array(line_image, dtype=np.uint8),
+ self.network_input_channels
+ )
+ if (
+ not all(line_image.size)
+ or line_image.height <= 8
+ or line_image.width <= 8
+ or "binarized" in line_coords["features"]
+ and line_img.min() == 255
+ ):
+ # empty size or too tiny or no foreground at all: skip
+ self.logger.warning(
+ "Skipping empty line '%s' in region '%s'",
+ line.id,
+ region.id,
)
+ continue
+
+ tasks.append(TaskThread(target=self._process_line,
+ args=(line, line_coords, line_img, page_id),
+ name="LinePredictor-%s-%s" % (page_id, line.id)))
+ tasks[-1].start()
+
+ if not len(tasks):
+ self.logger.warning("No text lines on page '%s'", page_id)
+ return OcrdPageResult(pcgts)
+
+ # We cannot delegate to predictor.predict_raw directly...
+ # predictions = self.predictor.predict_raw(images)
+ # ...because for efficiency, all page tasks must be synchronised
+ # on a single GPU-bound subprocess (no more than 1 simulatneous call).
+ # Moreover, we also cannot use predict_raw indirectly...
+ # taskq.put((page_id, images))
+ # page_id, images = taskq.get()
+ # result = predictor.predict_raw(images)
+ # resultq.put((page_id, result))
+ # predictions = resultq.get(page_id)
+ # ...because this would create a new pipeline for each page,
+ # which is wildly inefficient.
+ # Moreover, predict_raw() uses predict_dataset(), which is peaky
+ # itself.
+ # Instead, we interleave and flow line imges from all pages into
+ # a pipeline based on predict_on_batch(), which gets set up only once.
+ # Each sample is annotated with page+line metadata for re-identification.
+ # All page workers (subprocesses) communicate with the single predictor worker
+ # (subprocess) via queues and a single lock that controls whether or not batches
+ # are filled up with dummy data (as long as workers are still waiting for results).
+ Thread(target=self.predictor.fill.acquire, name="PagePredictor-fillneededby-%s" % page_id).start()
+ for task in tasks:
+ task.join()
+ Thread(target=self.predictor.fill.release, name="PagePredictor-fillnotneededby-%s" % page_id).start()
+ self.logger.info("All lines completed for page '%s'", page_id)
+
+ _page_update_higher_textequiv_levels("line", pcgts)
+ return OcrdPageResult(pcgts)
+
+ def _process_line(self, line, line_coords, line_image, page_id):
+ self.logger.debug("Sending line image for page '%s' line '%s'", page_id, line.id)
+ result = self.predictor(line_image, line.id, page_id)
+ self.logger.debug("Received line result for page '%s' line '%s'", page_id, line.id)
+ self._post_process_line(line, line_image.shape[0], line_coords, result)
+
+ def _post_process_line(self, line, line_height, line_coords, result):
+ _, prediction = result
+
+ # Build line text on our own
+ #
+ # Calamari does whitespace post-processing on prediction.sentence,
+ # while it does not do the same on prediction.positions. Do it on
+ # our own to have consistency.
+ #
+ # XXX Check Calamari's built-in post-processing on
+ # prediction.sentence
+
+ def _sort_chars(p):
+ """Filter and sort chars of prediction p"""
+ chars = p.chars
+ chars = [
+ c for c in chars if c.char
+ ] # XXX Note that omission probabilities are not normalized?!
+ chars = [
+ c
+ for c in chars
+ if c.probability >= self.parameter["glyph_conf_cutoff"]
+ ]
+ chars = sorted(chars, key=lambda k: k.probability, reverse=True)
+ return chars
+
+ def _drop_leading_spaces(positions):
+ return list(
+ itertools.dropwhile(
+ lambda p: _sort_chars(p)[0].char == " ", positions
+ )
+ )
- def _drop_trailing_spaces(positions):
- return list(reversed(_drop_leading_spaces(reversed(positions))))
-
- def _drop_double_spaces(positions):
- def _drop_double_spaces_generator(positions):
+ def _drop_trailing_spaces(positions):
+ return list(reversed(_drop_leading_spaces(reversed(positions))))
+
+ def _drop_double_spaces(positions):
+ def _drop_double_spaces_generator(positions):
+ last_was_space = False
+ for p in positions:
+ if p.chars[0].char == " ":
+ if not last_was_space:
+ yield p
+ last_was_space = True
+ else:
+ yield p
last_was_space = False
- for p in positions:
- if p.chars[0].char == " ":
- if not last_was_space:
- yield p
- last_was_space = True
- else:
- yield p
- last_was_space = False
-
- return list(_drop_double_spaces_generator(positions))
-
- positions = prediction.positions
- positions = _drop_leading_spaces(positions)
- positions = _drop_trailing_spaces(positions)
- positions = _drop_double_spaces(positions)
- positions = list(positions)
-
- line_text = "".join(_sort_chars(p)[0].char for p in positions)
- if line_text != prediction.sentence:
- log.warning(
- f"Our own line text is not the same as Calamari's:"
- f"'{line_text}' != '{prediction.sentence}'"
- )
- # Delete existing results
- if line.get_TextEquiv():
- log.warning("Line '%s' already contained text results", line.id)
- line.set_TextEquiv([])
- if line.get_Word():
- log.warning(
- "Line '%s' already contained word segmentation", line.id
+ return list(_drop_double_spaces_generator(positions))
+
+ positions = prediction.positions
+ positions = _drop_leading_spaces(positions)
+ positions = _drop_trailing_spaces(positions)
+ positions = _drop_double_spaces(positions)
+ positions = list(positions)
+
+ line_text = "".join(_sort_chars(p)[0].char for p in positions)
+ if line_text != prediction.sentence:
+ self.logger.warning(
+ f"Our own line text is not the same as Calamari's:"
+ f"'{line_text}' != '{prediction.sentence}'"
+ )
+
+ # Delete existing results
+ if line.get_TextEquiv():
+ self.logger.warning("Line '%s' already contained text results", line.id)
+ line.set_TextEquiv([])
+ if line.get_Word():
+ self.logger.warning(
+ "Line '%s' already contained word segmentation", line.id
+ )
+ line.set_Word([])
+
+ # Save line results
+ line_conf = prediction.avg_char_probability
+ line.set_TextEquiv(
+ [TextEquivType(Unicode=line_text, conf=line_conf)]
+ )
+
+ # Save word results
+ #
+ # Calamari OCR does not provide word positions, so we infer word
+ # positions from a. text segmentation and b. the glyph positions.
+ # This is necessary because the PAGE XML format enforces a strict
+ # hierarchy of lines > words > glyphs.
+ #
+ # FIXME: use calamari#282 for this
+
+ def _words(s):
+ """Split words based on spaces and include spaces as 'words'"""
+ spaces = None
+ word = ""
+ for c in s:
+ if c == " " and spaces is True:
+ word += c
+ elif c != " " and spaces is False:
+ word += c
+ else:
+ if word:
+ yield word
+ word = c
+ spaces = c == " "
+ yield word
+
+ if self.parameter["textequiv_level"] in ["word", "glyph"]:
+ word_no = 0
+ i = 0
+
+ for word_text in _words(line_text):
+ word_length = len(word_text)
+ if not all(c == " " for c in word_text):
+ word_positions = positions[i : i + word_length]
+ word_start = word_positions[0].global_start
+ word_end = word_positions[-1].global_end
+
+ polygon = polygon_from_x0y0x1y1(
+ [word_start, 0, word_end, line_height]
)
- line.set_Word([])
+ points = points_from_polygon(
+ coordinates_for_segment(polygon, None, line_coords)
+ )
+ # XXX Crop to line polygon?
- # Save line results
- line_conf = prediction.avg_char_probability
- line.set_TextEquiv(
- [TextEquivType(Unicode=line_text, conf=line_conf)]
- )
+ word = WordType(
+ id="%s_word%04d" % (line.id, word_no),
+ Coords=CoordsType(points),
+ )
+ word.add_TextEquiv(TextEquivType(Unicode=word_text))
- # Save word results
- #
- # Calamari OCR does not provide word positions, so we infer word
- # positions from a. text segmentation and b. the glyph positions.
- # This is necessary because the PAGE XML format enforces a strict
- # hierarchy of lines > words > glyphs.
-
- def _words(s):
- """Split words based on spaces and include spaces as 'words'"""
- spaces = None
- word = ""
- for c in s:
- if c == " " and spaces is True:
- word += c
- elif c != " " and spaces is False:
- word += c
- else:
- if word:
- yield word
- word = c
- spaces = c == " "
- yield word
-
- if self.parameter["textequiv_level"] in ["word", "glyph"]:
- word_no = 0
- i = 0
-
- for word_text in _words(line_text):
- word_length = len(word_text)
- if not all(c == " " for c in word_text):
- word_positions = positions[i : i + word_length]
- word_start = word_positions[0].global_start
- word_end = word_positions[-1].global_end
+ if self.parameter["textequiv_level"] == "glyph":
+ for glyph_no, p in enumerate(word_positions):
+ glyph_start = p.global_start
+ glyph_end = p.global_end
polygon = polygon_from_x0y0x1y1(
- [word_start, 0, word_end, line_image.height]
+ [
+ glyph_start,
+ 0,
+ glyph_end,
+ line_height,
+ ]
)
points = points_from_polygon(
- coordinates_for_segment(polygon, None, line_coords)
+ coordinates_for_segment(
+ polygon, None, line_coords
+ )
)
- # XXX Crop to line polygon?
- word = WordType(
- id="%s_word%04d" % (line.id, word_no),
+ glyph = GlyphType(
+ id="%s_glyph%04d" % (word.id, glyph_no),
Coords=CoordsType(points),
)
- word.add_TextEquiv(TextEquivType(Unicode=word_text))
-
- if self.parameter["textequiv_level"] == "glyph":
- for glyph_no, p in enumerate(word_positions):
- glyph_start = p.global_start
- glyph_end = p.global_end
-
- polygon = polygon_from_x0y0x1y1(
- [
- glyph_start,
- 0,
- glyph_end,
- line_image.height,
- ]
- )
- points = points_from_polygon(
- coordinates_for_segment(
- polygon, None, line_coords
- )
- )
- glyph = GlyphType(
- id="%s_glyph%04d" % (word.id, glyph_no),
- Coords=CoordsType(points),
+ # Add predictions (= TextEquivs)
+ char_index_start = 1
+ # Index must start with 1, see
+ # https://ocr-d.github.io/page#multiple-textequivs
+ for char_index, char in enumerate(
+ _sort_chars(p), start=char_index_start
+ ):
+ glyph.add_TextEquiv(
+ TextEquivType(
+ Unicode=char.char,
+ index=char_index,
+ conf=char.probability,
)
+ )
- # Add predictions (= TextEquivs)
- char_index_start = 1
- # Index must start with 1, see
- # https://ocr-d.github.io/page#multiple-textequivs
- for char_index, char in enumerate(
- _sort_chars(p), start=char_index_start
- ):
- glyph.add_TextEquiv(
- TextEquivType(
- Unicode=char.char,
- index=char_index,
- conf=char.probability,
- )
- )
-
- word.add_Glyph(glyph)
-
- line.add_Word(word)
- word_no += 1
-
- i += word_length
-
- _page_update_higher_textequiv_levels("line", pcgts)
-
- # Add metadata about this operation and its runtime parameters:
- self.add_metadata(pcgts)
- file_id = make_file_id(input_file, self.output_file_grp)
- pcgts.set_pcGtsId(file_id)
- self.workspace.add_file(
- file_id=file_id,
- file_grp=self.output_file_grp,
- page_id=input_file.pageId,
- mimetype=MIMETYPE_PAGE,
- local_filename=os.path.join(self.output_file_grp, file_id + ".xml"),
- content=to_xml(pcgts),
+ word.add_Glyph(glyph)
+
+ line.add_Word(word)
+ word_no += 1
+
+ i += word_length
+
+class CalamariPredictor:
+ class PredictWorker(mp.Process):
+ def __init__(self, logger, device, voter, checkpoint_dir, taskq, resultq, terminate, fill):
+ self.logger = logger # FIXME: synchronize loggers, too
+ #self.logger.setLevel(logging.DEBUG)
+ self.device = device
+ self.voter = voter
+ self.checkpoint_dir = checkpoint_dir
+ self.taskq = taskq
+ self.resultq = resultq
+ self.terminate = terminate
+ self.fill = fill
+ super().__init__()
+ def put(self, result):
+ while not self.terminate.is_set():
+ try:
+ self.resultq.put(result, timeout=0.3)
+ return
+ except queue.Full:
+ continue
+ page_id = result[0]
+ if page_id != "none":
+ self.logger.warning("dropping result for page '%s'", page_id)
+ def run(self):
+ initLogging()
+ tf_disable_interactive_logs()
+ try:
+ predictor = self.setup_predictor()
+ generator = self.setup_pipelines(predictor)
+ generator = iter(generator())
+ self.put(("input_channels", predictor.data.params.input_channels))
+ except Exception as e:
+ self.logger.exception("setup failed")
+ self.put(("input_channels", e))
+ # unrecoverable
+ self.terminate.set()
+ while not self.terminate.is_set():
+ try:
+ prediction = next(generator)
+ page_id, line_id = prediction.meta["id"]
+ result = prediction.outputs
+ self.put((page_id, line_id, result))
+ self.logger.debug("sent result for page '%s' line '%s'", page_id, line_id)
+ except StopIteration:
+ self.logger.info("prediction exhausted generator")
+ # unrecoverable
+ self.terminate.set()
+ except KeyboardInterrupt:
+ self.terminate.set()
+ except Exception as e:
+ # full traceback gets shown when base Processor handles exception
+ self.logger.error("prediction failed: %s", e.__class__.__name__)
+ self.put(("", "", e)) # for which page/line??
+ # Not only would we have to re-initialize Tensorflow here,
+ # we cannot even discern which tasks/pages the error occurred on,
+ # so there will be some worker waiting for results inevitably...
+ self.terminate.set()
+ self.logger.debug("terminating predictor: closing result queue")
+ self.resultq.close()
+ self.resultq.cancel_join_thread()
+ def setup_predictor(self):
+ """
+ Set up the model prior to processing.
+ """
+ from calamari_ocr.ocr.predict.predictor import MultiPredictor, PredictorParams
+ from calamari_ocr.ocr.voting import VoterParams, VoterType
+ from tfaip.data.databaseparams import DataPipelineParams
+ from tfaip import DeviceConfigParams
+ from tfaip.device.device_config import DistributionStrategy
+ import tensorflow as tf
+ # unfortunately, tfaip device selector is mandatory and does not provide auto-detection
+ if self.device < 0:
+ gpus = []
+ self.logger.debug("running on CPU")
+ elif self.device < len(tf.config.list_physical_devices("GPU")):
+ gpus = [self.device]
+ self.logger.info("running on selected GPU device cuda:%d", self.device)
+ else:
+ gpus = []
+ self.logger.warning("running on CPU because selected GPU device cuda:%d is not available", self.device)
+ # load model
+ pred_params = PredictorParams(
+ silent=True,
+ progress_bar=False,
+ device=DeviceConfigParams(
+ gpus=gpus,
+ soft_device_placement=False,
+ #gpu_memory=7000, # limit to 7GB (logical, no dynamic growth)
+ #dist_strategy=DistributionStrategy.CENTRAL_STORAGE,
+ ),
+ pipeline=DataPipelineParams(
+ batch_size=BATCH_SIZE,
+ # Number of processes for data loading.
+ num_processes=4,
+ use_shared_memory=True,
+ # group lines with similar lengths to reduce need for padding
+ # and optimally utilise batch size;
+ # unfortunately, we cannot use this in an infinite generator
+ # setting, because TF's bucket_by_sequence_length sometimes
+ # wants to read ahead for optimal group allocation, which can
+ # result in deadlocks (because the page workers cannot finish
+ # unless the already sent batches are returned), so bucketing
+ # must be disabled:
+ #bucket_boundaries=GROUP_BOUNDS,
+ #bucket_batch_sizes=BATCH_GROUPS,
+ )
)
+ voter_params = VoterParams()
+ voter_params.type = VoterType(self.voter)
+ #
+ checkpoints = glob("%s/*.ckpt.json" % self.checkpoint_dir)
+ self.logger.info("loading %d checkpoints", len(checkpoints))
+ predictor = MultiPredictor.from_paths(
+ checkpoints,
+ voter_params=voter_params,
+ predictor_params=pred_params,
+ )
+ #predictor.data.params.pre_proc.run_parallel = False
+ #predictor.data.params.post_proc.run_parallel = False
+ def element_length_fn(x):
+ return x["img_len"]
+ predictor.data.element_length_fn=lambda: element_length_fn
+ # rewrap voter JoinedModel and compile (to avoid repeating for each page):
+ class WrappedModel(tf.keras.models.Model):
+ def call(self, inputs, training=None, mask=None):
+ inputs, meta = inputs
+ return inputs, predictor._keras_model(inputs), meta
+ predictor.model = WrappedModel()
+ # for preproc in predictor.data.params.pre_proc.processors:
+ # self.logger.info("preprocessor: %s", str(preproc))
+ predictor.voter = predictor.create_voter(predictor.data.params)
+ return predictor
+ def setup_pipelines(self, predictor):
+ # set up pipeline and generators (as infinite dataset)
+ from dataclasses import field, dataclass
+ from paiargparse import pai_dataclass
+ from tfaip import Sample
+ from tfaip.data.databaseparams import DataGeneratorParams
+ from tfaip.data.pipeline.datapipeline import DataPipeline
+ from tfaip.data.pipeline.datagenerator import DataGenerator
+ from tfaip.data.pipeline.runningdatapipeline import _wrap_dataset
+ @pai_dataclass
+ @dataclass
+ class QueueDataGeneratorParams(DataGeneratorParams):
+ terminate : mp.Event = field(default=None)
+ fill : mp.Lock = field(default=None)
+ taskq : mp.Queue = field(default=None)
+ @staticmethod
+ def cls():
+ return QueueDataGenerator
+ class QueueDataGenerator(DataGenerator[QueueDataGeneratorParams]):
+ def __len__(self):
+ raise NotImplementedError()
+ def generate(self):
+ while not self.params.terminate.is_set():
+ try:
+ page_id, line_id, image = self.params.taskq.get(timeout=1.1)
+ except queue.Empty:
+ # anyone currently awaiting results?
+ if self.params.fill.acquire(block=False):
+ self.params.fill.release() # not needed
+ else:
+ # stuff with empty images to prevent pipeline / batching stall
+ # width=2: will be padded to batch anyway
+ yield Sample(inputs=np.ones((48, 2, predictor.data.params.input_channels), dtype=np.uint8), meta={"id": ("none", "none")})
+ continue
+ #print(f"feeding another input page {page_id} line {line_id}")
+ yield Sample(inputs=image, meta={"id": (page_id, line_id)})
+ class QueueDataPipeline(DataPipeline):
+ def create_data_generator(self):
+ return QueueDataGenerator(mode=self.mode, params=self.generator_params)
+ def input_dataset(self, auto_repeat=None):
+ gen = self.generate_input_samples(auto_repeat=auto_repeat)
+ #return gen.as_dataset(self._create_tf_dataset_generator())
+ gen.running_pipeline = gen.processor_pipeline_params.create(gen.pipeline_params, gen.data_params)
+ def generator():
+ running_samples_generator = gen._generate_input_samples()
+ for sample in running_samples_generator:
+ #print(f"feeding another input {sample.meta} len={sample.inputs['img'].shape[0]}")
+ yield sample
+ running_samples_generator.close()
+ dataset = self._create_tf_dataset_generator().create(generator, False)
+ def print_fn(*x):
+ import tensorflow as tf
+ tf.print(tf.shape(x[0]["img"]))
+ return x
+ #dataset = dataset.map(print_fn)
+ dataset = _wrap_dataset(
+ self.mode, dataset, self.pipeline_params, self.data, False
+ )
+ #dataset = dataset.map(print_fn)
+ return dataset
+ self.logger.debug("setting up input pipeline")
+ input_pipeline = QueueDataPipeline(
+ predictor.params.pipeline, predictor._data,
+ QueueDataGeneratorParams(terminate=self.terminate, fill=self.fill, taskq=self.taskq))
+ from tfaip.predict.predictorbase import data_adapter
+ from tfaip.util.tftyping import sync_to_numpy_or_python_type
+ from tfaip.data.pipeline.processor.params import SequentialProcessorPipelineParams
+ from tfaip.predict.multimodelpostprocessor import MultiModelPostProcessorParams
+ self.logger.debug("instantiating input dataset")
+ tf_dataset = input_pipeline.input_dataset()
+ import tensorflow as tf
+ tf_dataset = tf_dataset.apply(
+ tf.data.experimental.ignore_errors(log_warning=True)
+ )
+ self.logger.debug("setting up output pipeline")
+ def predict_dataset(dataset):
+ for batch in dataset:
+ #ids = sync_to_numpy_or_python_type(batch[1]['meta'])
+ #ids = [json.loads(l[0])['id'][1] for l in ids]
+ #print(f"batch size: {batch[0]['img'].shape} {ids.count('none')/len(ids)*100}%")
+ r = predictor.model.predict_on_batch(batch)
+ inputs, outputs, meta = sync_to_numpy_or_python_type(r)
+ for sample in predictor._unwrap_batch(inputs, {}, outputs, meta):
+ #print(f"feeding another output {sample.meta}")
+ yield sample
+ post_processors = [
+ d.get_or_create_pipeline(predictor.params.pipeline, input_pipeline.generator_params).create_output_pipeline()
+ for d in predictor.datas
+ ]
+ post_proc_pipeline = SequentialProcessorPipelineParams(
+ processors=[MultiModelPostProcessorParams(voter=predictor.voter, post_processors=post_processors)],
+ run_parallel=predictor.data.params.post_proc.run_parallel,
+ num_threads=predictor.data.params.post_proc.num_threads,
+ max_tasks_per_process=predictor.data.params.post_proc.max_tasks_per_process,
+ ).create(input_pipeline.pipeline_params, predictor.data.params)
+ def output_generator():
+ for sample in post_proc_pipeline.apply(predict_dataset(tf_dataset)):
+ yield predictor.voter.finalize_sample(sample)
+ return output_generator
+
+ def __init__(self, device, voter, checkpoint_dir):
+ self.logger = logging.getLogger("ocrd.processor.CalamariPredictor")
+ #self.logger.setLevel(logging.DEBUG)
+ ctxt = mp.get_context('spawn') # not necessary to fork, and spawn is safer
+ self.taskq = ctxt.Queue(maxsize=3 + config.OCRD_MAX_PARALLEL_PAGES * 200) # 3 + npages * nlines
+ self.resultq = ctxt.Queue(maxsize=3 + config.OCRD_MAX_PARALLEL_PAGES * 200)
+ self.terminate = ctxt.Event() # will be shared across all page workers forked from this process
+ self.fill = ctxt.Lock() # to switch on/off filling up batches in the continuous generator
+ # spawn single Calamari subprocess prior to base Processor forking any page worker subprocesses
+ CalamariPredictor.PredictWorker(self.logger, device, voter, checkpoint_dir,
+ self.taskq, self.resultq, self.terminate, self.fill).start()
+ id_, self.network_input_channels = self.resultq.get() # block until initialized
+ assert id_ == "input_channels" # sole possible task during setup/init
+ if isinstance(self.network_input_channels, Exception):
+ raise self.network_input_channels
+ self.logger.info("Loaded model")
+ # ensure multiple CalamariPredictor instances sync communicating with the same PredictWorker:
+ mgr = mp.get_context("fork").Manager() # base.Processor will fork workers
+ self.results = mgr.dict() # {}
+ weakref.finalize(self, self.shutdown)
+
+ def __del__(self):
+ self.shutdown() # sets self.terminate (on exception or gc)
+
+ def __call__(self, image, line_id, page_id):
+ self.taskq.put((page_id, line_id, image))
+ self.logger.debug("sent image for page '%s' line '%s'", page_id, line_id)
+ result = self.get(page_id, line_id)
+ self.logger.debug("received result for page '%s' line '%s'", page_id, line_id)
+ return result
+
+ def get(self, page_id, line_id):
+ self.logger.debug("requested result for page '%s' line '%s'", page_id, line_id)
+ err = None
+ while not self.terminate.is_set():
+ if (page_id, line_id) in self.results:
+ result = self.results.pop((page_id, line_id))
+ # if isinstance(result, Exception):
+ # raise Exception(f"prediction failed for page {page_id}") from result
+ return result
+ #self.logger.debug("awaiting result for page '%s' line '%s'", page_id, line_id)
+ try:
+ page_id1, line_id1, result = self.resultq.get(timeout=0.7)
+ except queue.Empty:
+ continue
+ # FIXME what if page_id == line_id == "" and result is an exception??
+ self.logger.debug("storing results for page '%s' line '%s'", page_id1, line_id1)
+ self.results[(page_id1, line_id1)] = result
+ if page_id1 == '' and line_id1 == '':
+ err = result
+ for page_id, line_id in self.results.keys():
+ if page_id != 'none':
+ self.logger.warning("dropping results for page '%s'", page_id)
+ if page_id == '' and line_id == '':
+ err = self.results[(page_id, line_id)]
+ raise Exception("predictor terminated prematurely") from err
+
+ def shutdown(self):
+ self.terminate.set()
+ # while not self.taskq.empty():
+ # page_id, _, _ = self.taskq.get()
+ # self.logger.warning("dropped task for page %s", page_id)
+ self.taskq.close()
+ self.taskq.cancel_join_thread()
# TODO: This is a copy of ocrd_tesserocr's function, and should probably be moved to a
@@ -432,3 +738,41 @@ def _page_update_higher_textequiv_levels(level, pcgts):
for line in lines
)
region.set_TextEquiv([TextEquivType(Unicode=region_unicode)]) # remove old
+
+# from calamari_ocr.utils.image.ImageLoader (but for PIL.Image objects)
+# (Calamari2 does not tolerate wrong input shape anymore -
+# common preprocessors do not change last dimension)
+def load_image(img: np.ndarray, channels: int, to_gray_method : str = "cv") -> np.ndarray:
+ if len(img.shape) == 2:
+ img_channels = 1
+ elif len(img.shape) == 3:
+ img_channels = img.shape[-1]
+ else:
+ raise ValueError(f"Unknown image format. Must bei either WxH or WxHxC, but got {img.shape}.")
+
+ if img_channels == channels:
+ pass # good
+ elif img_channels == 2 and channels == 1:
+ img = img[:, :, 0]
+ elif img_channels == 3 and channels == 1:
+ if to_gray_method == "avg":
+ img = np.mean(img.astype("float32"), axis=-1).astype(dtype=img.dtype)
+ elif to_gray_method == "cv":
+ img = cv.cvtColor(img, cv.COLOR_RGB2GRAY)
+ else:
+ raise ValueError(f"Unsupported image conversion method {to_gray_method}")
+ elif img_channels == 4 and channels == 1:
+ if to_gray_method == "avg":
+ img = np.mean(img[:, :, :3].astype("float32"), axis=-1).astype(dtype=img.dtype)
+ elif to_gray_method == "cv":
+ img = cv.cvtColor(img, cv.COLOR_RGBA2GRAY)
+ else:
+ raise ValueError(f"Unsupported image conversion method {to_gray_method}")
+ elif img_channels == 1 and channels == 3:
+ img = np.stack([img] * 3, axis=-1)
+ else:
+ raise ValueError(
+ f"Unsupported image format. Trying to convert from {img_channels} channels to "
+ f"{channels} channels."
+ )
+ return img
diff --git a/ocrd_calamari/util.py b/ocrd_calamari/util.py
deleted file mode 100644
index 33d5297..0000000
--- a/ocrd_calamari/util.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import os
-
-
-class working_directory:
- """Context manager to temporarily change the working directory"""
-
- def __init__(self, wd):
- self.wd = wd
-
- def __enter__(self):
- self.old_wd = os.getcwd()
- os.chdir(self.wd)
-
- def __exit__(self, etype, value, traceback):
- os.chdir(self.old_wd)
diff --git a/pyproject.toml b/pyproject.toml
index 8646876..133190d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,7 @@ classifiers = [
"Environment :: Console",
"Intended Audience :: Science/Research",
"Intended Audience :: Other Audience",
- "License :: OSI Approved :: Apache Software License",
+ "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3 :: Only",
"Topic :: Text Processing",
@@ -76,6 +76,10 @@ branch = true
source = [
"ocrd_calamari"
]
+concurrency = [
+ "thread",
+ "multiprocessing"
+]
[tool.coverage.report]
exclude_also = [
diff --git a/requirements.txt b/requirements.txt
index 3a96004..f2d38b5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,5 @@
-tensorflow >= 2.5.0, < 2.16
numpy
-calamari-ocr == 1.0.*, >= 1.0.7
-setuptools >= 41.0.0 # tensorboard depends on this, but why do we get an error at runtime?
+calamari-ocr >= 2.3.1
+tensorflow < 2.16, != 2.12.0 # avoid Keras 3, avoid broken release
click
-ocrd >= 2.54.0
+ocrd >= 3.0.0b7
diff --git a/test/base.py b/test/base.py
deleted file mode 100644
index d2dc025..0000000
--- a/test/base.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from test.assets import assets
-
-from ocrd_utils import initLogging
-
-initLogging()
-
-__all__ = ["assets"]
diff --git a/test/conftest.py b/test/conftest.py
new file mode 100644
index 0000000..68175a7
--- /dev/null
+++ b/test/conftest.py
@@ -0,0 +1,69 @@
+from multiprocessing import Process
+from time import sleep
+import gc
+import pytest
+
+from ocrd import Resolver, Workspace, OcrdMetsServer
+from ocrd_utils import pushd_popd, initLogging, disableLogging, setOverrideLogLevel, getLogger, config
+
+from .assets import assets
+
+CONFIGS = ['', 'metsserver+metscache', 'pageparallel', 'pageparallel+metscache']
+
+@pytest.fixture(params=CONFIGS)
+def workspace(tmpdir, pytestconfig, request):
+ def _make_workspace(workspace_path):
+ initLogging()
+ if pytestconfig.getoption('verbose') > 0:
+ setOverrideLogLevel('DEBUG')
+ with pushd_popd(tmpdir):
+ directory = str(tmpdir)
+ resolver = Resolver()
+ workspace = resolver.workspace_from_url(workspace_path, dst_dir=directory, download=True)
+ config.OCRD_MISSING_OUTPUT = "ABORT"
+ if 'metscache' in request.param:
+ config.OCRD_METS_CACHING = True
+ print("enabled METS caching")
+ if 'pageparallel' in request.param:
+ config.OCRD_MAX_PARALLEL_PAGES = 4
+ print("enabled page-parallel processing")
+ if 'pageparallel' in request.param or 'metsserver' in request.param:
+ def _start_mets_server(*args, **kwargs):
+ print("running with METS server")
+ server = OcrdMetsServer(*args, **kwargs)
+ server.startup()
+ process = Process(target=_start_mets_server,
+ kwargs={'workspace': workspace, 'url': 'mets.sock'})
+ process.start()
+ sleep(1)
+ workspace = Workspace(resolver, directory, mets_server_url='mets.sock')
+ yield workspace
+ process.terminate()
+ process.join()
+ else:
+ yield workspace
+ disableLogging()
+ config.reset_defaults()
+ gc.collect()
+ return _make_workspace
+
+
+@pytest.fixture
+def workspace_manifesto(workspace):
+ yield from workspace(assets.path_to('communist_manifesto/data/mets.xml'))
+
+@pytest.fixture
+def workspace_aufklaerung(workspace):
+ yield from workspace(assets.path_to('kant_aufklaerung_1784/data/mets.xml'))
+
+@pytest.fixture
+def workspace_aufklaerung_binarized(workspace):
+ yield from workspace(assets.path_to('kant_aufklaerung_1784-binarized/data/mets.xml'))
+
+@pytest.fixture
+def workspace_aufklaerung_glyph(workspace):
+ yield from workspace(assets.path_to('kant_aufklaerung_1784-page-region-line-word_glyph/data/mets.xml'))
+
+@pytest.fixture
+def workspace_sbb(workspace):
+ yield from workspace(assets.url_of('SBB0000F29300010000/data/mets_one_file.xml'))
diff --git a/test/test_recognize.py b/test/test_recognize.py
index f4e3587..56d75c4 100644
--- a/test/test_recognize.py
+++ b/test/test_recognize.py
@@ -1,198 +1,140 @@
import logging
import os
import shutil
-import subprocess
-import tempfile
-import pytest
from lxml import etree
-from ocrd.resolver import Resolver
+from ocrd import run_processor
+from ocrd_utils import MIMETYPE_PAGE as PAGE
+from ocrd_models.constants import NAMESPACES as NS
+from ocrd_modelfactory import page_from_file
from ocrd_calamari import CalamariRecognize
-from .base import assets
-
-METS_KANT = assets.url_of(
- "kant_aufklaerung_1784-page-region-line-word_glyph/data/mets.xml"
-)
-WORKSPACE_DIR = tempfile.mkdtemp(prefix="test-ocrd-calamari-")
-CHECKPOINT_DIR = os.getenv("MODEL", "qurator-gt4histocr-1.0")
+CHECKPOINT_DIR = os.getenv("MODEL", "fraktur_19th_century")
DEBUG = os.getenv("DEBUG", False)
-def page_namespace(tree):
- """Return the PAGE content namespace used in the given ElementTree.
-
- This relies on the assumption that, in any given PAGE content file, the root element
- has the local name "PcGts". We do not check if the files uses any valid PAGE
- namespace.
- """
- root_name = etree.QName(tree.getroot().tag)
- if root_name.localname == "PcGts":
- return root_name.namespace
- else:
- raise ValueError("Not a PAGE tree")
-
-
-def assertFileContains(fn, text):
+def assertFileContains(fn, text, msg=""):
"""Assert that the given file contains a given string."""
with open(fn, "r", encoding="utf-8") as f:
- assert text in f.read()
+ assert text in f.read(), msg
-def assertFileDoesNotContain(fn, text):
+def assertFileDoesNotContain(fn, text, msg=""):
"""Assert that the given file does not contain given string."""
with open(fn, "r", encoding="utf-8") as f:
- assert text not in f.read()
-
-
-@pytest.fixture
-def workspace():
- if os.path.exists(WORKSPACE_DIR):
- shutil.rmtree(WORKSPACE_DIR)
- os.makedirs(WORKSPACE_DIR)
-
- resolver = Resolver()
- # due to core#809 this does not always work:
- # workspace = resolver.workspace_from_url(METS_KANT, dst_dir=WORKSPACE_DIR)
- # workaround:
- shutil.rmtree(WORKSPACE_DIR)
- shutil.copytree(os.path.dirname(METS_KANT), WORKSPACE_DIR)
- workspace = resolver.workspace_from_url(os.path.join(WORKSPACE_DIR, "mets.xml"))
+ assert text not in f.read(), msg
- # The binarization options I have are:
- #
- # a. ocrd_kraken which tries to install cltsm, whose installation is borken on my
- # machine (protobuf)
- # b. ocrd_olena which 1. I cannot fully install via pip and 2. whose dependency
- # olena doesn't compile on my machine
- # c. just fumble with the original files
- #
- # So I'm going for option c.
- for imgf in workspace.mets.find_files(fileGrp="OCR-D-IMG"):
- imgf = workspace.download_file(imgf)
- path = os.path.join(workspace.directory, imgf.local_filename)
- subprocess.call(["mogrify", "-threshold", "50%", path])
- # Remove GT Words and TextEquivs, to not accidently check GT text instead of the
- # OCR text
- # XXX Review data again
- for of in workspace.mets.find_files(fileGrp="OCR-D-GT-SEG-WORD-GLYPH"):
- workspace.download_file(of)
- path = os.path.join(workspace.directory, of.local_filename)
- tree = etree.parse(path)
- nsmap_gt = {"pc": page_namespace(tree)}
- for to_remove in ["//pc:Word", "//pc:TextEquiv"]:
- for e in tree.xpath(to_remove, namespaces=nsmap_gt):
- e.getparent().remove(e)
- tree.write(path, xml_declaration=True, encoding="utf-8")
- assertFileDoesNotContain(path, "TextEquiv")
-
- yield workspace
-
- if not DEBUG:
- shutil.rmtree(WORKSPACE_DIR)
-
-
-def test_recognize(workspace):
- CalamariRecognize(
- workspace,
- input_file_grp="OCR-D-GT-SEG-WORD-GLYPH",
+def test_recognize(workspace_aufklaerung_binarized, caplog):
+ caplog.set_level(logging.WARNING)
+ ws = workspace_aufklaerung_binarized
+ page1 = ws.mets.physical_pages[0]
+ file1 = list(ws.find_files(file_grp="OCR-D-GT-WORD", page_id=page1, mimetype=PAGE))[0]
+ text1 = page_from_file(file1).etree.xpath(
+ '//page:TextLine/page:TextEquiv[1]/page:Unicode/text()', namespaces=NS)
+ assert len(text1) > 10
+ assert "verſchuldeten" in "\n".join(text1)
+ run_processor(
+ CalamariRecognize,
+ input_file_grp="OCR-D-GT-WORD",
output_file_grp="OCR-D-OCR-CALAMARI",
parameter={
"checkpoint_dir": CHECKPOINT_DIR,
},
- ).process()
- workspace.save_mets()
-
- page1 = os.path.join(
- workspace.directory, "OCR-D-OCR-CALAMARI/OCR-D-OCR-CALAMARI_phys_0001.xml"
+ workspace=ws,
)
- assert os.path.exists(page1)
- assertFileContains(page1, "verſchuldeten")
-
-
-def test_recognize_should_warn_if_given_rgb_image_and_single_channel_model(
- workspace, caplog
-):
+ overwrite_text_log_messages = [t[2] for t in caplog.record_tuples
+ if "already contained text results" in t[2]]
+ assert len(overwrite_text_log_messages) > 10 # For every line!
+ overwrite_word_log_messages = [t[2] for t in caplog.record_tuples
+ if "already contained word segmentation" in t[2]]
+ assert len(overwrite_word_log_messages) > 10 # For every line!
+ ws.save_mets()
+ file1 = next(ws.find_files(file_grp="OCR-D-OCR-CALAMARI", page_id=page1, mimetype=PAGE), False)
+ assert file1, "result for first page not referenced in METS"
+ assert os.path.exists(file1.local_filename), "result for first page not found in filesystem"
+ text1_out = page_from_file(file1).etree.xpath(
+ '//page:TextLine/page:TextEquiv[1]/page:Unicode/text()', namespaces=NS)
+ assert len(text1_out) == len(text1), "not all lines have been recognized"
+ assert "verſchuldeten" in "\n".join(text1_out), "result for first page is inaccurate"
+ assert "\n".join(text1_out) != "\n".join(text1), "result is suspiciously identical to GT"
+
+
+def test_recognize_rgb(workspace_aufklaerung, caplog):
caplog.set_level(logging.WARNING)
- CalamariRecognize(
- workspace,
- input_file_grp="OCR-D-GT-SEG-WORD-GLYPH",
- output_file_grp="OCR-D-OCR-CALAMARI-BROKEN",
+ run_processor(
+ CalamariRecognize,
+ input_file_grp="OCR-D-GT-PAGE",
+ output_file_grp="OCR-D-OCR-CALAMARI",
parameter={"checkpoint_dir": CHECKPOINT_DIR},
- ).process()
-
- interesting_log_messages = [
- t[2] for t in caplog.record_tuples if "Using raw image" in t[2]
- ]
+ workspace=workspace_aufklaerung,
+ )
+ interesting_log_messages = [t[2] for t in caplog.record_tuples
+ if "Using raw image" in t[2]]
assert len(interesting_log_messages) > 10 # For every line!
-def test_word_segmentation(workspace):
- CalamariRecognize(
- workspace,
- input_file_grp="OCR-D-GT-SEG-WORD-GLYPH",
+def test_words(workspace_aufklaerung_binarized):
+ run_processor(
+ CalamariRecognize,
+ input_file_grp="OCR-D-GT-WORD",
output_file_grp="OCR-D-OCR-CALAMARI",
parameter={
"checkpoint_dir": CHECKPOINT_DIR,
- "textequiv_level": "word", # Note that we're going down to word level here
+ "textequiv_level": "word",
},
- ).process()
- workspace.save_mets()
-
- page1 = os.path.join(
- workspace.directory, "OCR-D-OCR-CALAMARI/OCR-D-OCR-CALAMARI_phys_0001.xml"
+ workspace=workspace_aufklaerung_binarized
)
- assert os.path.exists(page1)
- tree = etree.parse(page1)
- nsmap = {"pc": page_namespace(tree)}
-
- # The result should contain a TextLine that contains the text "December"
- line = tree.xpath(
- ".//pc:TextLine[pc:TextEquiv/pc:Unicode[contains(text(),'December')]]",
- namespaces=nsmap,
- )[0]
- assert line is not None
-
+ ws = workspace_aufklaerung_binarized
+ ws.save_mets()
+ page1 = ws.mets.physical_pages[0]
+ file1 = next(ws.find_files(file_grp="OCR-D-OCR-CALAMARI", page_id=page1, mimetype=PAGE), False)
+ assert file1, "result for first page not referenced in METS"
+ assert os.path.exists(file1.local_filename), "result for first page not found in filesystem"
+ tree1 = page_from_file(file1).etree
+ # The result should contain a TextLine that contains the text "Berliniſche"
+ line = tree1.xpath(
+ "//page:TextLine[page:TextEquiv/page:Unicode[contains(text(),'Berliniſche')]]",
+ namespaces=NS,
+ )
+ assert len(line) == 1, "result is inaccurate"
+ line = line[0]
# The textline should
# a. contain multiple words and
# b. these should concatenate fine to produce the same line text
- words = line.xpath(".//pc:Word", namespaces=nsmap)
- assert len(words) >= 2
+ words = line.xpath(".//page:Word", namespaces=NS)
+ assert len(words) >= 2, "result does not contain words"
words_text = " ".join(
- word.xpath("pc:TextEquiv/pc:Unicode", namespaces=nsmap)[0].text
+ word.xpath("page:TextEquiv[1]/page:Unicode/text()", namespaces=NS)[0]
for word in words
)
- line_text = line.xpath("pc:TextEquiv/pc:Unicode", namespaces=nsmap)[0].text
- assert words_text == line_text
-
+ line_text = line.xpath("page:TextEquiv[1]/page:Unicode/text()", namespaces=NS)[0]
+ assert words_text == line_text, "word-level text result does not concatenate to line-level text result"
# For extra measure, check that we're not seeing any glyphs, as we asked for
# textequiv_level == "word"
- glyphs = tree.xpath("//pc:Glyph", namespaces=nsmap)
- assert len(glyphs) == 0
+ glyphs = tree1.xpath("//page:Glyph", namespaces=NS)
+ assert len(glyphs) == 0, "result must not contain glyph-level segments"
-def test_glyphs(workspace):
- CalamariRecognize(
- workspace,
- input_file_grp="OCR-D-GT-SEG-WORD-GLYPH",
+def test_glyphs(workspace_aufklaerung_binarized):
+ run_processor(
+ CalamariRecognize,
+ input_file_grp="OCR-D-GT-WORD",
output_file_grp="OCR-D-OCR-CALAMARI",
parameter={
"checkpoint_dir": CHECKPOINT_DIR,
- # Note that we're going down to glyph level here
"textequiv_level": "glyph",
},
- ).process()
- workspace.save_mets()
-
- page1 = os.path.join(
- workspace.directory, "OCR-D-OCR-CALAMARI/OCR-D-OCR-CALAMARI_phys_0001.xml"
+ workspace=workspace_aufklaerung_binarized,
)
- assert os.path.exists(page1)
- tree = etree.parse(page1)
- nsmap = {"pc": page_namespace(tree)}
-
+ ws = workspace_aufklaerung_binarized
+ ws.save_mets()
+ page1 = ws.mets.physical_pages[0]
+ file1 = next(ws.find_files(file_grp="OCR-D-OCR-CALAMARI", page_id=page1, mimetype=PAGE), False)
+ assert file1, "result for first page not referenced in METS"
+ assert os.path.exists(file1.local_filename), "result for first page not found in filesystem"
+ tree1 = page_from_file(file1).etree
# The result should contain a lot of glyphs
- glyphs = tree.xpath("//pc:Glyph", namespaces=nsmap)
- assert len(glyphs) >= 100
+ glyphs = tree1.xpath("//page:Glyph", namespaces=NS)
+ assert len(glyphs) >= 100, "result must contain lots of glyphs"