Merge pull request #148 from bertsky/v3-api
fix, merge, resolve conflicts, apply review, migrate sbb-binarizepull/130/head
Konstantin Baierer
1 week ago
committed by
GitHub
commit
85566c2186
@ -0,0 +1,6 @@
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tests
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dist
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build
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env*
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*.egg-info
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models_eynollah*
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@ -0,0 +1,44 @@
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name: CD
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on:
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push:
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branches: [ "master" ]
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workflow_dispatch: # run manually
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jobs:
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build:
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runs-on: ubuntu-latest
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permissions:
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packages: write
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contents: read
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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with:
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# we need tags for docker version tagging
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fetch-tags: true
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fetch-depth: 0
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- # Activate cache export feature to reduce build time of images
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name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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- name: Login to GitHub Container Registry
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uses: docker/login-action@v3
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with:
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registry: ghcr.io
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Log in to Docker Hub
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uses: docker/login-action@v3
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with:
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username: ${{ secrets.DOCKERIO_USERNAME }}
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password: ${{ secrets.DOCKERIO_PASSWORD }}
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- name: Build the Docker image
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# build both tags at the same time
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run: make docker DOCKER_TAG="docker.io/ocrd/eynollah -t ghcr.io/qurator-spk/eynollah"
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- name: Test the Docker image
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run: docker run --rm ocrd/eynollah ocrd-eynollah-segment -h
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- name: Push to Dockerhub
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run: docker push docker.io/ocrd/eynollah
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- name: Push to Github Container Registry
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run: docker push ghcr.io/qurator-spk/eynollah
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@ -1,8 +1,8 @@
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# ocrd includes opencv, numpy, shapely, click
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ocrd >= 3.0.0b4
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ocrd >= 3.3.0
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numpy <1.24.0
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scikit-learn >= 0.23.2
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tensorflow < 2.13
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imutils >= 0.5.3
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matplotlib
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setuptools >= 50
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numba <= 0.58.1
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loky
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,109 @@
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from typing import Optional
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from PIL import Image
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import numpy as np
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import cv2
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from click import command
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from ocrd import Processor, OcrdPageResult, OcrdPageResultImage
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from ocrd_models.ocrd_page import OcrdPage, AlternativeImageType
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from ocrd.decorators import ocrd_cli_options, ocrd_cli_wrap_processor
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from .sbb_binarize import SbbBinarizer
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def cv2pil(img):
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return Image.fromarray(img.astype('uint8'))
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def pil2cv(img):
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# from ocrd/workspace.py
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color_conversion = cv2.COLOR_GRAY2BGR if img.mode in ('1', 'L') else cv2.COLOR_RGB2BGR
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pil_as_np_array = np.array(img).astype('uint8') if img.mode == '1' else np.array(img)
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return cv2.cvtColor(pil_as_np_array, color_conversion)
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class SbbBinarizeProcessor(Processor):
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# already employs GPU (without singleton process atm)
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max_workers = 1
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@property
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def executable(self):
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return 'ocrd-sbb-binarize'
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def setup(self):
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"""
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Set up the model prior to processing.
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"""
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# resolve relative path via OCR-D ResourceManager
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model_path = self.resolve_resource(self.parameter['model'])
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self.binarizer = SbbBinarizer(model_dir=model_path, logger=self.logger)
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def process_page_pcgts(self, *input_pcgts: Optional[OcrdPage], page_id: Optional[str] = None) -> OcrdPageResult:
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"""
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Binarize images with sbb_binarization (based on selectional auto-encoders).
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For each page of the input file group, open and deserialize input PAGE-XML
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and its respective images. Then iterate over the element hierarchy down to
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the requested ``operation_level``.
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For each segment element, retrieve a raw (non-binarized) segment image
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according to the layout annotation (from an existing ``AlternativeImage``,
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or by cropping into the higher-level images, and deskewing when applicable).
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Pass the image to the binarizer (which runs in fixed-size windows/patches
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across the image and stitches the results together).
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Serialize the resulting bilevel image as PNG file and add it to the output
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file group (with file ID suffix ``.IMG-BIN``) along with the output PAGE-XML
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(referencing it as new ``AlternativeImage`` for the segment element).
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Produce a new PAGE output file by serialising the resulting hierarchy.
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"""
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assert input_pcgts
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assert input_pcgts[0]
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assert self.parameter
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oplevel = self.parameter['operation_level']
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pcgts = input_pcgts[0]
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result = OcrdPageResult(pcgts)
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page = pcgts.get_Page()
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page_image, page_xywh, _ = self.workspace.image_from_page(
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page, page_id, feature_filter='binarized')
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if oplevel == 'page':
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self.logger.info("Binarizing on 'page' level in page '%s'", page_id)
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page_image_bin = cv2pil(self.binarizer.run(image=pil2cv(page_image), use_patches=True))
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# update PAGE (reference the image file):
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page_image_ref = AlternativeImageType(comments=page_xywh['features'] + ',binarized,clipped')
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page.add_AlternativeImage(page_image_ref)
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result.images.append(OcrdPageResultImage(page_image_bin, '.IMG-BIN', page_image_ref))
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elif oplevel == 'region':
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regions = page.get_AllRegions(['Text', 'Table'], depth=1)
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if not regions:
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self.logger.warning("Page '%s' contains no text/table regions", page_id)
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for region in regions:
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region_image, region_xywh = self.workspace.image_from_segment(
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region, page_image, page_xywh, feature_filter='binarized')
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region_image_bin = cv2pil(self.binarizer.run(image=pil2cv(region_image), use_patches=True))
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# update PAGE (reference the image file):
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region_image_ref = AlternativeImageType(comments=region_xywh['features'] + ',binarized')
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region.add_AlternativeImage(region_image_ref)
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result.images.append(OcrdPageResultImage(region_image_bin, region.id + '.IMG-BIN', region_image_ref))
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elif oplevel == 'line':
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lines = page.get_AllTextLines()
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if not lines:
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self.logger.warning("Page '%s' contains no text lines", page_id)
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for line in lines:
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line_image, line_xywh = self.workspace.image_from_segment(line, page_image, page_xywh, feature_filter='binarized')
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line_image_bin = cv2pil(self.binarizer.run(image=pil2cv(line_image), use_patches=True))
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# update PAGE (reference the image file):
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line_image_ref = AlternativeImageType(comments=line_xywh['features'] + ',binarized')
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line.add_AlternativeImage(region_image_ref)
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result.images.append(OcrdPageResultImage(line_image_bin, line.id + '.IMG-BIN', line_image_ref))
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return result
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@command()
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@ocrd_cli_options
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def main(*args, **kwargs):
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return ocrd_cli_wrap_processor(SbbBinarizeProcessor, *args, **kwargs)
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"""
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Tool to load model and binarize a given image.
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"""
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import sys
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from glob import glob
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import os
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import logging
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import numpy as np
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from PIL import Image
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import cv2
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from ocrd_utils import tf_disable_interactive_logs
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tf_disable_interactive_logs()
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import tensorflow as tf
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from tensorflow.keras.models import load_model
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from tensorflow.python.keras import backend as tensorflow_backend
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def resize_image(img_in, input_height, input_width):
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return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
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class SbbBinarizer:
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def __init__(self, model_dir, logger=None):
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self.model_dir = model_dir
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self.log = logger if logger else logging.getLogger('SbbBinarizer')
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self.start_new_session()
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self.model_files = glob(self.model_dir+"/*/", recursive = True)
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self.models = []
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for model_file in self.model_files:
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self.models.append(self.load_model(model_file))
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def start_new_session(self):
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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self.session = tf.compat.v1.Session(config=config) # tf.InteractiveSession()
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tensorflow_backend.set_session(self.session)
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def end_session(self):
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tensorflow_backend.clear_session()
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self.session.close()
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del self.session
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def load_model(self, model_name):
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model = load_model(os.path.join(self.model_dir, model_name), compile=False)
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model_height = model.layers[len(model.layers)-1].output_shape[1]
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model_width = model.layers[len(model.layers)-1].output_shape[2]
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n_classes = model.layers[len(model.layers)-1].output_shape[3]
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return model, model_height, model_width, n_classes
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def predict(self, model_in, img, use_patches, n_batch_inference=5):
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tensorflow_backend.set_session(self.session)
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model, model_height, model_width, n_classes = model_in
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img_org_h = img.shape[0]
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img_org_w = img.shape[1]
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if img.shape[0] < model_height and img.shape[1] >= model_width:
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img_padded = np.zeros(( model_height, img.shape[1], img.shape[2] ))
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index_start_h = int( abs( img.shape[0] - model_height) /2.)
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index_start_w = 0
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img_padded [ index_start_h: index_start_h+img.shape[0], :, : ] = img[:,:,:]
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elif img.shape[0] >= model_height and img.shape[1] < model_width:
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img_padded = np.zeros(( img.shape[0], model_width, img.shape[2] ))
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index_start_h = 0
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index_start_w = int( abs( img.shape[1] - model_width) /2.)
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img_padded [ :, index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
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elif img.shape[0] < model_height and img.shape[1] < model_width:
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img_padded = np.zeros(( model_height, model_width, img.shape[2] ))
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index_start_h = int( abs( img.shape[0] - model_height) /2.)
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index_start_w = int( abs( img.shape[1] - model_width) /2.)
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img_padded [ index_start_h: index_start_h+img.shape[0], index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
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else:
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index_start_h = 0
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index_start_w = 0
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img_padded = np.copy(img)
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img = np.copy(img_padded)
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if use_patches:
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margin = int(0.1 * model_width)
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width_mid = model_width - 2 * margin
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height_mid = model_height - 2 * margin
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img = img / float(255.0)
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img_h = img.shape[0]
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img_w = img.shape[1]
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prediction_true = np.zeros((img_h, img_w, 3))
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mask_true = np.zeros((img_h, img_w))
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nxf = img_w / float(width_mid)
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nyf = img_h / float(height_mid)
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if nxf > int(nxf):
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nxf = int(nxf) + 1
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else:
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nxf = int(nxf)
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if nyf > int(nyf):
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nyf = int(nyf) + 1
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else:
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nyf = int(nyf)
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list_i_s = []
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list_j_s = []
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list_x_u = []
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list_x_d = []
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list_y_u = []
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list_y_d = []
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batch_indexer = 0
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img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
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for i in range(nxf):
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for j in range(nyf):
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if i == 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + model_width
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elif i > 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + model_width
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if j == 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + model_height
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elif j > 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + model_height
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if index_x_u > img_w:
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index_x_u = img_w
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index_x_d = img_w - model_width
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if index_y_u > img_h:
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index_y_u = img_h
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index_y_d = img_h - model_height
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list_i_s.append(i)
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list_j_s.append(j)
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list_x_u.append(index_x_u)
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list_x_d.append(index_x_d)
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list_y_d.append(index_y_d)
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list_y_u.append(index_y_u)
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img_patch[batch_indexer,:,:,:] = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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batch_indexer = batch_indexer + 1
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if batch_indexer == n_batch_inference:
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label_p_pred = model.predict(img_patch,verbose=0)
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seg = np.argmax(label_p_pred, axis=3)
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#print(seg.shape, len(seg), len(list_i_s))
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indexer_inside_batch = 0
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for i_batch, j_batch in zip(list_i_s, list_j_s):
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seg_in = seg[indexer_inside_batch,:,:]
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seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
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index_y_u_in = list_y_u[indexer_inside_batch]
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index_y_d_in = list_y_d[indexer_inside_batch]
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index_x_u_in = list_x_u[indexer_inside_batch]
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index_x_d_in = list_x_d[indexer_inside_batch]
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if i_batch == 0 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch == 0 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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else:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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indexer_inside_batch = indexer_inside_batch +1
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list_i_s = []
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list_j_s = []
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list_x_u = []
|
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list_x_d = []
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list_y_u = []
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list_y_d = []
|
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batch_indexer = 0
|
||||
|
||||
img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
|
||||
|
||||
elif i==(nxf-1) and j==(nyf-1):
|
||||
label_p_pred = model.predict(img_patch,verbose=0)
|
||||
|
||||
seg = np.argmax(label_p_pred, axis=3)
|
||||
|
||||
#print(seg.shape, len(seg), len(list_i_s))
|
||||
|
||||
indexer_inside_batch = 0
|
||||
for i_batch, j_batch in zip(list_i_s, list_j_s):
|
||||
seg_in = seg[indexer_inside_batch,:,:]
|
||||
seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
|
||||
|
||||
index_y_u_in = list_y_u[indexer_inside_batch]
|
||||
index_y_d_in = list_y_d[indexer_inside_batch]
|
||||
|
||||
index_x_u_in = list_x_u[indexer_inside_batch]
|
||||
index_x_d_in = list_x_d[indexer_inside_batch]
|
||||
|
||||
if i_batch == 0 and j_batch == 0:
|
||||
seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
|
||||
elif i_batch == nxf - 1 and j_batch == nyf - 1:
|
||||
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
|
||||
elif i_batch == 0 and j_batch == nyf - 1:
|
||||
seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
|
||||
elif i_batch == nxf - 1 and j_batch == 0:
|
||||
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
|
||||
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
|
||||
elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
|
||||
seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
|
||||
elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
|
||||
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
|
||||
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
|
||||
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
|
||||
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
|
||||
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
|
||||
else:
|
||||
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
|
||||
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
|
||||
|
||||
indexer_inside_batch = indexer_inside_batch +1
|
||||
|
||||
|
||||
list_i_s = []
|
||||
list_j_s = []
|
||||
list_x_u = []
|
||||
list_x_d = []
|
||||
list_y_u = []
|
||||
list_y_d = []
|
||||
|
||||
batch_indexer = 0
|
||||
|
||||
img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
|
||||
|
||||
|
||||
|
||||
prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:]
|
||||
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 = resize_image(img, model_height, model_width)
|
||||
|
||||
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 = resize_image(seg_color, img_h_page, img_w_page)
|
||||
prediction_true = prediction_true.astype(np.uint8)
|
||||
return prediction_true[:,:,0]
|
||||
|
||||
def run(self, image=None, image_path=None, save=None, use_patches=False, dir_in=None, dir_out=None):
|
||||
print(dir_in,'dir_in')
|
||||
if not dir_in:
|
||||
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_path)
|
||||
img_last = 0
|
||||
for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
|
||||
self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
|
||||
|
||||
res = self.predict(model, 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
|
||||
else:
|
||||
ls_imgs = os.listdir(dir_in)
|
||||
for image_name in ls_imgs:
|
||||
image_stem = image_name.split('.')[0]
|
||||
print(image_name,'image_name')
|
||||
image = cv2.imread(os.path.join(dir_in,image_name) )
|
||||
img_last = 0
|
||||
for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
|
||||
self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
|
||||
|
||||
res = self.predict(model, 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
|
||||
|
||||
cv2.imwrite(os.path.join(dir_out,image_stem+'.png'), img_last)
|
||||
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Load Diff
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Reference in New Issue