.dvc/config

@@ -5,4 +5,4 @@
     url = ssh://vector.iqser.com/research/image-prediction/
     port = 22
 ['remote "azure_remote"']
-    url = azure://image-classification-dvc/
+    url = azure://ic-sa-dvc/

.gitlab-ci.yml

@@ -1,51 +1,31 @@
 include:
   - project: "Gitlab/gitlab"
-    ref: main
-    file: "/ci-templates/research/dvc.gitlab-ci.yml"
-  - project: "Gitlab/gitlab"
-    ref: main
-    file: "/ci-templates/research/versioning-build-test-release.gitlab-ci.yml"
+    ref: 0.3.0
+    file: "/ci-templates/research/dvc-versioning-build-release.gitlab-ci.yml"
 
 variables:
   NEXUS_PROJECT_DIR: red
   IMAGENAME: "${CI_PROJECT_NAME}"
   INTEGRATION_TEST_FILE: "${CI_PROJECT_ID}.pdf"
-  FF_USE_FASTZIP: "true" # enable fastzip - a faster zip implementation that also supports level configuration.
-  ARTIFACT_COMPRESSION_LEVEL: default # can also be set to fastest, fast, slow and slowest. If just enabling fastzip is not enough try setting this to fastest or fast.
-  CACHE_COMPRESSION_LEVEL: default # same as above, but for caches
-  # TRANSFER_METER_FREQUENCY: 5s # will display transfer progress every 5 seconds for artifacts and remote caches. For debugging purposes.
   
-stages:
-  - data
-  - setup
-  - tests
-  - sonarqube
-  - versioning
-  - build
-  - integration-tests
-  - release
-
-docker-build:
-  extends: .docker-build
-  needs:
-    - job: dvc-pull
-      artifacts: true
-    - !reference [.needs-versioning, needs] # leave this line as is
-  
-###################
-# INTEGRATION TESTS
-trigger-integration-tests:
-  extends: .integration-tests
-  # ADD THE MODEL BUILD WHICH SHOULD TRIGGER THE INTEGRATION TESTS
-  # needs:
-  #   - job: docker-build::model_name
-  #     artifacts: true
+#################################
+# temp. disable integration tests, b/c they don't cover the CV analysis case yet
+trigger integration tests:
   rules:
     - when: never
 
-#########
-# RELEASE
-release:
-  extends: .release
+release build:
+  stage: release
   needs:
-    - !reference [.needs-versioning, needs] # leave this line as is
+  - job: set custom version
+    artifacts: true
+    optional: true
+  - job: calculate patch version
+    artifacts: true
+    optional: true
+  - job: calculate minor version
+    artifacts: true
+    optional: true
+  - job: build docker nexus
+    artifacts: true
+#################################

.python-version

@@ -1 +1 @@
-3.10
+3.10.12

Dockerfile

@@ -1,17 +1,11 @@
-FROM python:3.10-slim AS builder
-
-ARG GITLAB_USER
-ARG GITLAB_ACCESS_TOKEN
+FROM python:3.10
 
+ARG USERNAME
+ARG TOKEN
 ARG PYPI_REGISTRY_RESEARCH=https://gitlab.knecon.com/api/v4/groups/19/-/packages/pypi
 ARG POETRY_SOURCE_REF_RESEARCH=gitlab-research
-
 ARG PYPI_REGISTRY_RED=https://gitlab.knecon.com/api/v4/groups/12/-/packages/pypi
 ARG POETRY_SOURCE_REF_RED=gitlab-red
-
-ARG PYPI_REGISTRY_FFORESIGHT=https://gitlab.knecon.com/api/v4/groups/269/-/packages/pypi
-ARG POETRY_SOURCE_REF_FFORESIGHT=gitlab-fforesight
-
 ARG VERSION=dev
 
 LABEL maintainer="Research <research@knecon.com>"
@@ -19,55 +13,27 @@ LABEL version="${VERSION}"
 
 WORKDIR /app
 
-###########
-# ENV SETUP
-ENV PYTHONDONTWRITEBYTECODE=true
 ENV PYTHONUNBUFFERED=true
 ENV POETRY_HOME=/opt/poetry
 ENV PATH="$POETRY_HOME/bin:$PATH"
 
-RUN apt-get update && \
-    apt-get install -y curl git bash build-essential libffi-dev libssl-dev && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists/*
-
 RUN curl -sSL https://install.python-poetry.org | python3 -
-RUN poetry --version
 
-COPY pyproject.toml poetry.lock ./
+COPY ./data ./data
+COPY ./config ./config
+COPY ./src ./src
+COPY pyproject.toml poetry.lock banner.txt ./
 
-RUN poetry config virtualenvs.create true && \
-    poetry config virtualenvs.in-project true && \
+RUN poetry config virtualenvs.create false && \
     poetry config installer.max-workers 10 && \
     poetry config repositories.${POETRY_SOURCE_REF_RESEARCH} ${PYPI_REGISTRY_RESEARCH} && \
-    poetry config http-basic.${POETRY_SOURCE_REF_RESEARCH} ${GITLAB_USER} ${GITLAB_ACCESS_TOKEN} && \
+    poetry config http-basic.${POETRY_SOURCE_REF_RESEARCH} ${USERNAME} ${TOKEN} && \
     poetry config repositories.${POETRY_SOURCE_REF_RED} ${PYPI_REGISTRY_RED} && \
-    poetry config http-basic.${POETRY_SOURCE_REF_RED} ${GITLAB_USER} ${GITLAB_ACCESS_TOKEN} && \
-    poetry config repositories.${POETRY_SOURCE_REF_FFORESIGHT} ${PYPI_REGISTRY_FFORESIGHT} && \
-    poetry config http-basic.${POETRY_SOURCE_REF_FFORESIGHT} ${GITLAB_USER} ${GITLAB_ACCESS_TOKEN} && \
-    poetry install --without=dev -vv --no-interaction --no-root
-
-###############
-# WORKING IMAGE
-FROM python:3.10-slim
-
-WORKDIR /app
-
-# COPY SOURCE CODE FROM BUILDER IMAGE
-COPY --from=builder /app /app
-# COPY BILL OF MATERIALS (BOM)
-COPY bom.json /bom.json
-
-ENV PATH="/app/.venv/bin:$PATH"
-
-###################
-# COPY SOURCE CODE
-COPY ./src ./src
-COPY ./config ./config
-COPY ./data ./data
-COPY banner.txt ./
+    poetry config http-basic.${POETRY_SOURCE_REF_RED} ${USERNAME} ${TOKEN} && \
+    poetry install --without=dev -vv --no-interaction
 
 EXPOSE 5000
 EXPOSE 8080
 
+
 CMD [ "python", "src/serve.py"]

config/pyinfra.toml

@@ -1,20 +1,9 @@
-
-[asyncio]
-max_concurrent_tasks = 10
-
-[dynamic_tenant_queues]
-enabled = true
-
 [metrics.prometheus]
 enabled = true
 prefix = "redactmanager_image_service"
 
-[tracing]
-enabled = true
-# possible values "opentelemetry" | "azure_monitor" (Excpects APPLICATIONINSIGHTS_CONNECTION_STRING environment variable.)
-type = "azure_monitor" 
-
 [tracing.opentelemetry]
+enabled = true
 endpoint = "http://otel-collector-opentelemetry-collector.otel-collector:4318/v1/traces"
 service_name = "redactmanager_image_service"
 exporter = "otlp"
@@ -36,16 +25,6 @@ input_queue = "request_queue"
 output_queue = "response_queue"
 dead_letter_queue = "dead_letter_queue"
 
-tenant_event_queue_suffix = "_tenant_event_queue"
-tenant_event_dlq_suffix = "_tenant_events_dlq"
-tenant_exchange_name = "tenants-exchange"
-queue_expiration_time = 300000  # 5 minutes in milliseconds
-
-service_request_queue_prefix = "image_request_queue"
-service_request_exchange_name = "image_request_exchange"
-service_response_exchange_name = "image_response_exchange"
-service_dlq_name = "image_dlq"
-
 [storage]
 backend = "s3"
 
@@ -63,6 +42,3 @@ connection_string = ""
 [storage.tenant_server]
 public_key = ""
 endpoint =  "http://tenant-user-management:8081/internal-api/tenants"
-
-[kubernetes]
-pod_name = "test_pod"

config/settings.toml

@@ -4,39 +4,25 @@ level = "INFO"
 [service]
 # Print document processing progress to stdout
 verbose = false
-batch_size = 6
-image_stiching_tolerance = 1  # in pixels
+batch_size = 16
 mlflow_run_id = "fabfb1f192c745369b88cab34471aba7"
 
 # These variables control filters that are applied to either images, image metadata or service_estimator predictions.
 # The filter result values are reported in the service responses. For convenience the response to a request contains a
 # "filters.allPassed" field, which is set to false if any of the values returned by the filters did not meet its
 # specified required value.
-[filters.confidence]
+[filters]
 # Minimum permissible prediction confidence
-min = 0.5
+min_confidence = 0.5
 
 # Image size to page size ratio (ratio of geometric means of areas)
 [filters.image_to_page_quotient]
 min = 0.05
 max = 0.75
 
-[filters.is_scanned_page]
-# Minimum permissible image to page ratio tolerance for a page to be considered scanned.
-# This is only used for filtering small images on scanned pages and is applied before processing the image, therefore
-# superseding the image_to_page_quotient filter that only applies a tag to the image after processing.
-tolerance = 0
-
 # Image width to height ratio
 [filters.image_width_to_height_quotient]
 min = 0.1
 max = 10
 
-# put class specific filters here ['signature', 'formula', 'logo']
-[filters.overrides.signature.image_to_page_quotient]
-max = 0.4
-
-[filters.overrides.logo.image_to_page_quotient]
-min = 0.06
-
 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "image-classification-service"
-version = "2.17.0"
+version = "2.0.0"
 description = ""
 authors = ["Team Research <research@knecon.com>"]
 readme = "README.md"
@@ -8,10 +8,8 @@ packages = [{ include = "image_prediction", from = "src" }]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<3.11"
-# FIXME: This should be recent pyinfra, but the recent protobuf packages are not compatible with tensorflow 2.9.0, also
-#  see RED-9948.
-pyinfra = { version = "3.4.2", source = "gitlab-research" }
-kn-utils = { version = ">=0.4.0", source = "gitlab-research" }
+pyinfra = { version = "2.0.0", source = "gitlab-research" }
+kn-utils = { version = "0.2.7", source = "gitlab-research" }
 dvc = "^2.34.0"
 dvc-ssh = "^2.20.0"
 dvc-azure = "^2.21.2"
@@ -25,10 +23,7 @@ mlflow = "^1.24.0"
 numpy = "^1.22.3"
 tqdm = "^4.64.0"
 pandas = "^1.4.2"
-# FIXME: Our current model significantly changes the prediction behaviour when using newer tensorflow (/ protobuf)
-#  versions which is introduuced by pyinfra updates using newer protobuf versions, see RED-9948.
-tensorflow = "2.9.0"
-protobuf = "^3.20"
+tensorflow = "^2.8.0"
 pytest = "^7.1.0"
 funcy = "^2"
 PyMuPDF = "^1.19.6"
@@ -37,11 +32,11 @@ coverage = "^6.3.2"
 Pillow = "^9.1.0"
 pdf2image = "^1.16.0"
 frozendict = "^2.3.0"
+protobuf = "^3.20.0"
 fsspec = "^2022.11.0"
 PyMonad = "^2.4.0"
 pdfnetpython3 = "9.4.2"
 loguru = "^0.7.0"
-cyclonedx-bom = "^4.5.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.0.1"

scripts/debug/debug.py

@@ -1,46 +0,0 @@
-"""Script to debug RED-9948. The predictions unexpectedly changed for some images, and we need to understand why."""
-
-import json
-import random
-from pathlib import Path
-
-import numpy as np
-import tensorflow as tf
-from kn_utils.logging import logger
-
-from image_prediction.config import CONFIG
-from image_prediction.pipeline import load_pipeline
-
-
-def process_pdf(pipeline, pdf_path, page_range=None):
-    with open(pdf_path, "rb") as f:
-        logger.info(f"Processing {pdf_path}")
-        predictions = list(pipeline(f.read(), page_range=page_range))
-
-    return predictions
-
-
-def ensure_seeds():
-    seed = 42
-    np.random.seed(seed)
-    random.seed(seed)
-    tf.random.set_seed(seed)
-
-
-def debug_info():
-    devices = tf.config.list_physical_devices()
-    print("Available devices:", devices)
-
-
-if __name__ == "__main__":
-    # For in container debugging, copy the file and adjust the path.
-    debug_file_path = Path(__file__).parents[2] / "test" / "data" / "RED-9948" / "SYNGENTA_EFSA_sanitisation_GFL_v2"
-    ensure_seeds()
-    debug_info()
-
-    pipeline = load_pipeline(verbose=True, batch_size=CONFIG.service.batch_size)
-    predictions = process_pdf(pipeline, debug_file_path)
-    # This is the image that has the wrong prediction mentioned in RED-9948. The predictions should inconclusive, and
-    # the flag all passed should be false.
-    predictions = [x for x in predictions if x["representation"] == "FA30F080F0C031CE17E8CF237"]
-    print(json.dumps(predictions, indent=2))

scripts/docker_build_run.sh

@@ -1,6 +1,6 @@
-docker build -t --platform linux/amd64 image-clsasification-service:$(poetry version -s)-dev \                                                               
+docker build -t image-clsasification-service:$(poetry version -s)-dev \                                                               
     -f Dockerfile \
-    --build-arg GITLAB_USER=$GITLAB_USER \
-    --build-arg GITLAB_ACCESS_TOKEN=$GITLAB_ACCESS_TOKEN \
+    --build-arg USERNAME=$GITLAB_USER \
+    --build-arg TOKEN=$GITLAB_ACCESS_TOKEN \
     . && \
 docker run -it --rm image-clsasification-service:$(poetry version -s)-dev

scripts/run_pipeline.py

@@ -3,15 +3,12 @@ import json
 import os
 from glob import glob
 
-from image_prediction.config import CONFIG
 from image_prediction.pipeline import load_pipeline
 from image_prediction.utils import get_logger
 from image_prediction.utils.pdf_annotation import annotate_pdf
 
 logger = get_logger()
 
-logger.setLevel("DEBUG")
-
 
 def parse_args():
     parser = argparse.ArgumentParser()
@@ -38,7 +35,7 @@ def process_pdf(pipeline, pdf_path, page_range=None):
 
 
 def main(args):
-    pipeline = load_pipeline(verbose=CONFIG.service.verbose, batch_size=CONFIG.service.batch_size, tolerance=CONFIG.service.image_stiching_tolerance)
+    pipeline = load_pipeline(verbose=True, tolerance=3)
 
     if os.path.isfile(args.input):
         pdf_paths = [args.input]

src/image_prediction/encoder/encoders/hash_encoder.py

@@ -13,7 +13,7 @@ class HashEncoder(Encoder):
         yield from self.encode(images)
 
 
-def hash_image(image: Image.Image) -> str:
+def hash_image(image: Image.Image):
     """See: https://stackoverflow.com/a/49692185/3578468"""
     image = image.resize((10, 10), Image.ANTIALIAS)
     image = image.convert("L")
@@ -21,6 +21,4 @@ def hash_image(image: Image.Image) -> str:
     avg_pixel = sum(pixel_data) / len(pixel_data)
     bits = "".join(["1" if (px >= avg_pixel) else "0" for px in pixel_data])
     hex_representation = str(hex(int(bits, 2)))[2:][::-1].upper()
-    # Note: For each 4 leading zeros, the hex representation will be shorter by one character.
-    # To ensure that all hashes have the same length, we pad the hex representation with zeros (also see RED-3813).
-    return hex_representation.zfill(25)
+    return hex_representation

src/image_prediction/image_extractor/extractors/parsable.py

@@ -3,7 +3,7 @@ import json
 import traceback
 from _operator import itemgetter
 from functools import partial, lru_cache
-from itertools import chain, starmap, filterfalse, tee
+from itertools import chain, starmap, filterfalse
 from operator import itemgetter, truth
 from typing import Iterable, Iterator, List, Union
 
@@ -11,10 +11,9 @@ import fitz
 import numpy as np
 from PIL import Image
 from funcy import merge, pluck, compose, rcompose, remove, keep
-from scipy.stats import gmean
 
 from image_prediction.config import CONFIG
-from image_prediction.exceptions import InvalidBox
+from image_prediction.exceptions import InvalidBox, BadXref
 from image_prediction.formatter.formatters.enum import EnumFormatter
 from image_prediction.image_extractor.extractor import ImageExtractor, ImageMetadataPair
 from image_prediction.info import Info
@@ -35,7 +34,7 @@ class ParsablePDFImageExtractor(ImageExtractor):
             tolerance: The tolerance in pixels for the distance between images, beyond which they will not be stitched
                 together
         """
-        self.doc: fitz.Document = None
+        self.doc: fitz.fitz.Document = None
         self.verbose = verbose
         self.tolerance = tolerance
 
@@ -48,7 +47,7 @@ class ParsablePDFImageExtractor(ImageExtractor):
 
         yield from image_metadata_pairs
 
-    def __process_images_on_page(self, page: fitz.Page):
+    def __process_images_on_page(self, page: fitz.fitz.Page):
         metadata = extract_valid_metadata(self.doc, page)
         images = get_images_on_page(self.doc, metadata)
 
@@ -65,13 +64,9 @@ class ParsablePDFImageExtractor(ImageExtractor):
 
     @staticmethod
     def __filter_valid_images(image_metadata_pairs: Iterable[ImageMetadataPair]) -> Iterator[ImageMetadataPair]:
-        def validate_image_is_not_corrupt(image: Image.Image, metadata: dict):
-            """See RED-5148: Some images are corrupt and cannot be processed by the image classifier. This function
-            filters out such images by trying to resize and convert them to RGB. If this fails, the image is considered
-            corrupt and is dropped.
-            TODO: find cleaner solution
-            """
+        def validate(image: Image.Image, metadata: dict):
             try:
+                # TODO: stand-in heuristic for testing if image is valid => find cleaner solution (RED-5148)
                 image.resize((100, 100)).convert("RGB")
                 return ImageMetadataPair(image, metadata)
             except (OSError, Exception) as err:
@@ -79,41 +74,7 @@ class ParsablePDFImageExtractor(ImageExtractor):
                 logger.warning(f"Invalid image encountered. Image metadata:\n{metadata}\n\n{traceback.format_exc()}")
                 return None
 
-        def filter_small_images_on_scanned_pages(image_metadata_pairs) -> Iterable[ImageMetadataPair]:
-            """See RED-9746: Small images on scanned pages should be dropped, so they are not classified. This is a
-            heuristic to filter out images that are too small in relation to the page size if they are on a scanned page.
-
-            The ratio is computed as the geometric mean of the width and height of the image divided by the geometric mean
-            of the width and height of the page. If the ratio is below the threshold, the image is dropped.
-            """
-
-            def image_is_a_scanned_page(image_metadata_pair: ImageMetadataPair) -> bool:
-                tolerance = CONFIG.filters.is_scanned_page.tolerance
-                width_ratio = image_metadata_pair.metadata[Info.WIDTH] / image_metadata_pair.metadata[Info.PAGE_WIDTH]
-                height_ratio = (
-                    image_metadata_pair.metadata[Info.HEIGHT] / image_metadata_pair.metadata[Info.PAGE_HEIGHT]
-                )
-                return width_ratio >= 1 - tolerance and height_ratio >= 1 - tolerance
-
-            def image_fits_geometric_mean_ratio(image_metadata_pair: ImageMetadataPair) -> bool:
-                min_ratio = CONFIG.filters.image_to_page_quotient.min
-                metadatum = image_metadata_pair.metadata
-                image_gmean = gmean([metadatum[Info.WIDTH], metadatum[Info.HEIGHT]])
-                page_gmean = gmean([metadatum[Info.PAGE_WIDTH], metadatum[Info.PAGE_HEIGHT]])
-                ratio = image_gmean / page_gmean
-                return ratio >= min_ratio
-
-            pairs, pairs_copy = tee(image_metadata_pairs)
-
-            if any(map(image_is_a_scanned_page, pairs_copy)):
-                logger.debug("Scanned page detected, filtering out small images ...")
-                return filter(image_fits_geometric_mean_ratio, pairs)
-            else:
-                return pairs
-
-        image_metadata_pairs = filter_small_images_on_scanned_pages(image_metadata_pairs)
-
-        return filter(truth, starmap(validate_image_is_not_corrupt, image_metadata_pairs))
+        return filter(truth, starmap(validate, image_metadata_pairs))
 
 
 def extract_pages(doc, page_range):
@@ -130,7 +91,7 @@ def get_images_on_page(doc, metadata):
     yield from images
 
 
-def extract_valid_metadata(doc: fitz.Document, page: fitz.Page):
+def extract_valid_metadata(doc: fitz.fitz.Document, page: fitz.fitz.Page):
     metadata = get_metadata_for_images_on_page(page)
     metadata = filter_valid_metadata(metadata)
     metadata = add_alpha_channel_info(doc, metadata)
@@ -138,6 +99,7 @@ def extract_valid_metadata(doc: fitz.Document, page: fitz.Page):
     return list(metadata)
 
 
+
 def get_metadata_for_images_on_page(page: fitz.Page):
     metadata = map(get_image_metadata, get_image_infos(page))
     metadata = add_page_metadata(page, metadata)
@@ -191,7 +153,7 @@ def xref_to_image(doc, xref) -> Union[Image.Image, None]:
         return
 
 
-def convert_pixmap_to_array(pixmap: fitz.Pixmap):
+def convert_pixmap_to_array(pixmap: fitz.fitz.Pixmap):
     array = np.frombuffer(pixmap.samples, dtype=np.uint8).reshape(pixmap.h, pixmap.w, pixmap.n)
     array = _normalize_channels(array)
     return array
@@ -210,6 +172,7 @@ def _normalize_channels(array: np.ndarray):
 
 
 def get_image_metadata(image_info):
+
     xref, coords = itemgetter("xref", "bbox")(image_info)
     x1, y1, x2, y2 = map(rounder, coords)
 
@@ -265,6 +228,7 @@ def get_page_metadata(page):
 
 
 def has_alpha_channel(doc, xref):
+
     maybe_image = load_image_handle_from_xref(doc, xref)
     maybe_smask = maybe_image["smask"] if maybe_image else None
 

src/image_prediction/pipeline.py

@@ -1,10 +1,8 @@
 import os
 from functools import lru_cache, partial
 from itertools import chain, tee
-from typing import Iterable, Any
 
 from funcy import rcompose, first, compose, second, chunks, identity, rpartial
-from kn_utils.logging import logger
 from tqdm import tqdm
 
 from image_prediction.config import CONFIG
@@ -23,7 +21,6 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
 @lru_cache(maxsize=None)
 def load_pipeline(**kwargs):
-    logger.info(f"Loading pipeline with kwargs: {kwargs}")
     model_loader = get_mlflow_model_loader(MLRUNS_DIR)
     model_identifier = CONFIG.service.mlflow_run_id
 
@@ -55,7 +52,7 @@ class Pipeline:
         join = compose(starlift(lambda prd, rpr, mdt: {"classification": prd, **mdt, "representation": rpr}), star(zip))
 
         #                       />--classify--\
-        # --extract-->--split--+->--encode---->+--join-->reformat-->filter_duplicates
+        # --extract-->--split--+->--encode---->+--join-->reformat
         #                       \>--identity--/
 
         self.pipe = rcompose(
@@ -64,7 +61,6 @@ class Pipeline:
             pairwise_apply(classify, represent, identity),  # ... apply functions to the streams pairwise
             join,  # ... the streams by zipping
             reformat,  # ... the items
-            filter_duplicates,  # ... filter out duplicate images
         )
 
     def __call__(self, pdf: bytes, page_range: range = None):
@@ -74,32 +70,3 @@ class Pipeline:
             unit=" images",
             disable=not self.verbose,
         )
-
-
-def filter_duplicates(metadata: Iterable[dict[str, Any]]) -> Iterable[dict[str, Any]]:
-    """Filter out duplicate images from the `position` (image coordinates) and `page`, preferring the one with
-    `allPassed` set to True.
-    See RED-10765 (RM-241): Removed redactions reappear for why this is necessary.
-    """
-    keep = dict()
-    for image_meta in metadata:
-        key: tuple[int, int, int, int, int] = (
-            image_meta["position"]["x1"],
-            image_meta["position"]["x2"],
-            image_meta["position"]["y1"],
-            image_meta["position"]["y2"],
-            image_meta["position"]["pageNumber"],
-        )
-        if key in keep:
-            logger.warning(
-                f"Duplicate image found: x1={key[0]}, x2={key[1]}, y1={key[2]}, y2={key[3]}, pageNumber={key[4]}"
-            )
-            if image_meta["filters"]["allPassed"]:
-                logger.warning("Setting the image with allPassed flag set to True")
-                keep[key] = image_meta
-            else:
-                logger.warning("Keeping the previous image since the current image has allPassed flag set to False")
-        else:
-            keep[key] = image_meta
-
-    yield from keep.values()

src/image_prediction/transformer/transformers/response.py

@@ -1,8 +1,13 @@
+import json
 import math
-from dynaconf import Dynaconf
+import os
+from functools import lru_cache
 from operator import itemgetter
 
+from funcy import first
+
 from image_prediction.config import CONFIG
+from image_prediction.exceptions import ParsingError
 from image_prediction.transformer.transformer import Transformer
 from image_prediction.utils import get_logger
 
@@ -27,22 +32,21 @@ def build_image_info(data: dict) -> dict:
     geometric_quotient = round(compute_geometric_quotient(page_width, page_height, x2, x1, y2, y1), 4)
 
     min_image_to_page_quotient_breached = bool(
-        geometric_quotient < get_class_specific_filter_value(label, CONFIG, "image_to_page_quotient", "min")
+        geometric_quotient < get_class_specific_min_image_to_page_quotient(label)
     )
     max_image_to_page_quotient_breached = bool(
-        geometric_quotient > get_class_specific_filter_value(label, CONFIG, "image_to_page_quotient", "max")
+        geometric_quotient > get_class_specific_max_image_to_page_quotient(label)
     )
 
     min_image_width_to_height_quotient_breached = bool(
-        width / height < get_class_specific_filter_value(label, CONFIG, "image_width_to_height_quotient", "min")
+        width / height < get_class_specific_min_image_width_to_height_quotient(label)
     )
     max_image_width_to_height_quotient_breached = bool(
-        width / height > get_class_specific_filter_value(label, CONFIG, "image_width_to_height_quotient", "max")
+        width / height > get_class_specific_max_image_width_to_height_quotient(label)
     )
 
     min_confidence_breached = bool(
-        max(classification["probabilities"].values())
-        < get_class_specific_filter_value(label, CONFIG, "confidence", "min")
+        max(classification["probabilities"].values()) < get_class_specific_min_classification_confidence(label)
     )
 
     image_info = {
@@ -86,15 +90,65 @@ def compute_geometric_quotient(page_width, page_height, x2, x1, y2, y1):
     return image_area_sqrt / page_area_sqrt
 
 
-def get_class_specific_filter_value(label: str, settings: Dynaconf, filter_type: str, bound: str = None):
-    try:
-        value = (
-            settings.filters.overrides[label][filter_type][bound]
-            if bound
-            else settings.filters.overrides[label][filter_type]
-        )
-        logger.warning(f"Using {label=} specific {bound=} {filter_type=} {value=}.")
-    except KeyError:
-        value = settings.filters[filter_type][bound]
+def get_class_specific_min_image_to_page_quotient(label, table=None):
+    return get_class_specific_value(
+        "REL_IMAGE_SIZE", label, "min", CONFIG.filters.image_to_page_quotient.min, table=table
+    )
 
-    return value
+
+def get_class_specific_max_image_to_page_quotient(label, table=None):
+    return get_class_specific_value(
+        "REL_IMAGE_SIZE", label, "max", CONFIG.filters.image_to_page_quotient.max, table=table
+    )
+
+
+def get_class_specific_min_image_width_to_height_quotient(label, table=None):
+    return get_class_specific_value(
+        "IMAGE_FORMAT", label, "min", CONFIG.filters.image_width_to_height_quotient.min, table=table
+    )
+
+
+def get_class_specific_max_image_width_to_height_quotient(label, table=None):
+    return get_class_specific_value(
+        "IMAGE_FORMAT", label, "max", CONFIG.filters.image_width_to_height_quotient.max, table=table
+    )
+
+
+def get_class_specific_min_classification_confidence(label, table=None):
+    return get_class_specific_value("CONFIDENCE", label, "min", CONFIG.filters.min_confidence, table=table)
+
+
+def get_class_specific_value(prefix, label, bound, fallback_value, table=None):
+    def fallback():
+        return fallback_value
+
+    def success():
+        threshold_map = parse_env_var(prefix, table=table) or {}
+        value = threshold_map.get(label, {}).get(bound)
+        if value:
+            logger.debug(f"Using class '{label}' specific {bound} {prefix.lower().replace('_', '-')} value.")
+        return value
+
+    assert bound in ["min", "max"]
+
+    return success() or fallback()
+
+
+@lru_cache(maxsize=None)
+def parse_env_var(prefix, table=None):
+    table = table or os.environ
+    head = first(filter(lambda s: s == prefix, table))
+    if head:
+        try:
+            return parse_env_var_value(table[head])
+        except ParsingError as err:
+            logger.warning(err)
+    else:
+        return None
+
+
+def parse_env_var_value(env_var_value):
+    try:
+        return json.loads(env_var_value)
+    except Exception as err:
+        raise ParsingError(f"Failed to parse {env_var_value}") from err

src/image_prediction/utils/pdf_annotation.py

@@ -56,8 +56,7 @@ def annotate_image(doc, image_info):
 
 def init():
     PDFNet.Initialize(
-        # "Knecon AG(en.knecon.swiss):OEM:DDA-R::WL+:AMS(20211029):BECC974307DAB4F34B513BC9B2531B24496F6FCB83CD8AC574358A959730B622FABEF5C7"
-        "Knecon AG:OEM:DDA-R::WL+:AMS(20270129):EA5FDFB23C7F36B9C2AE606F4F0D9197DE1FB649119F9730B622FABEF5C7"
+        "Knecon AG(en.knecon.swiss):OEM:DDA-R::WL+:AMS(20211029):BECC974307DAB4F34B513BC9B2531B24496F6FCB83CD8AC574358A959730B622FABEF5C7"
     )
 
 

src/serve.py

@@ -9,7 +9,8 @@ from image_prediction.pipeline import load_pipeline
 from image_prediction.utils.banner import load_banner
 from image_prediction.utils.process_wrapping import wrap_in_process
 
-logger.reconfigure(sink=stdout, level=CONFIG.logging.level)
+logger.remove()
+logger.add(sink=stdout, level=CONFIG.logging.level)
 
 
 # A component of the processing pipeline (probably tensorflow) does not release allocated memory (see RED-4206).
@@ -18,7 +19,7 @@ logger.reconfigure(sink=stdout, level=CONFIG.logging.level)
 # FIXME: Find more fine-grained solution or if the problem occurs persistently for python services,
 @wrap_in_process
 def process_data(data: bytes, _message: dict) -> list:
-    pipeline = load_pipeline(verbose=CONFIG.service.verbose, batch_size=CONFIG.service.batch_size, tolerance=CONFIG.service.image_stiching_tolerance)
+    pipeline = load_pipeline(verbose=CONFIG.service.verbose, batch_size=CONFIG.service.batch_size)
     return list(pipeline(data))
 
 

test/data.dvc

@@ -1,5 +1,5 @@
 outs:
-- md5: 08bf8a63f04b3f19f859008556699708.dir
-  size: 7979836
-  nfiles: 7
+- md5: 4b0fec291ce0661b3efbbd8b80f4f514.dir
+  size: 107332
+  nfiles: 4
   path: data

test/regressions_tests/image_classification_test.py

@@ -1,21 +0,0 @@
-from pathlib import Path
-
-from funcy import first
-
-from image_prediction.config import CONFIG
-from image_prediction.pipeline import load_pipeline
-
-
-def test_image_classification_does_not_regress():
-    """See RED-9948: the predictions unexpectedly changed for some images. In the end the issue is the tensorflow
-    version. We ensure that the prediction of the image with the hash FA30F080F0C031CE17E8CF237 is inconclusive,
-    and that the flag all_passed is false."""
-    pdf_path = Path(__file__).parents[1] / "data" / "RED-9948" / "SYNGENTA_EFSA_sanitisation_GFL_v2.pdf"
-    pdf_bytes = pdf_path.read_bytes()
-
-    pipeline = load_pipeline(verbose=True, batch_size=CONFIG.service.batch_size)
-    predictions = list(pipeline(pdf_bytes))
-    predictions = first([x for x in predictions if x["representation"] == "FA30F080F0C031CE17E8CF237"])
-
-    assert predictions["filters"]["allPassed"] is False
-    assert predictions["filters"]["probability"]["unconfident"] is True

test/regressions_tests/image_deduplication_test.py

@@ -1,35 +0,0 @@
-from pathlib import Path
-
-from image_prediction.config import CONFIG
-from image_prediction.pipeline import load_pipeline
-
-
-def test_all_duplicate_images_are_filtered():
-    """See RED-10765 (RM-241): Removed redactions reappear."""
-    pdf_path = (
-        Path(__file__).parents[1]
-        / "data"
-        / "RED-10765"
-        / "RM-241-461c90d6d6dc0416ad5f0b05feef4dfc.UNTOUCHED_shortened.pdf"
-    )
-    pdf_bytes = pdf_path.read_bytes()
-
-    pipeline = load_pipeline(verbose=True, batch_size=CONFIG.service.batch_size)
-    predictions = list(pipeline(pdf_bytes))
-
-    seen = set()
-    for prediction in predictions:
-        key = (
-            prediction["position"]["x1"],
-            prediction["position"]["x2"],
-            prediction["position"]["y1"],
-            prediction["position"]["y2"],
-            prediction["position"]["pageNumber"],
-        )
-        assert key not in seen, f"Duplicate found: {key}"
-        seen.add(key)
-
-    all_passed = sum(1 for prediction in predictions if prediction["filters"]["allPassed"])
-    assert all_passed == 1, f"Expected 1 image with allPassed flag set to True, but got {all_passed}"
-
-    assert len(predictions) == 177, f"Expected 177 images, but got {len(predictions)}"

test/regressions_tests/image_hashing_test.py

@@ -1,18 +0,0 @@
-from pathlib import Path
-
-from image_prediction.encoder.encoders.hash_encoder import HashEncoder
-from image_prediction.image_extractor.extractors.parsable import ParsablePDFImageExtractor
-
-
-def test_all_hashes_have_length_of_twentyfive():
-    """See RED-3814: all hashes should have 25 characters."""
-    pdf_path = Path(__file__).parents[1] / "data" / "RED-3814" / "similarImages2.pdf"
-    pdf_bytes = pdf_path.read_bytes()
-    image_extractor = ParsablePDFImageExtractor()
-    image_metadata_pairs = list(image_extractor.extract(pdf_bytes))
-    images = [image for image, _ in image_metadata_pairs]
-
-    hash_encoder = HashEncoder()
-    hashes = list(hash_encoder.encode(images))
-
-    assert all(len(h) == 25 for h in hashes)

test/unit_tests/encoder_test.py

@@ -6,8 +6,7 @@ import pytest
 from PIL.Image import Image
 from funcy import compose, first
 
-from image_prediction.encoder.encoders.hash_encoder import HashEncoder
-from image_prediction.encoder.encoders.hash_encoder import hash_image
+from image_prediction.encoder.encoders.hash_encoder import HashEncoder, hash_image
 from image_prediction.utils.generic import lift
 
 

test/unit_tests/response_transformer_test.py

@@ -1,7 +1,15 @@
-import pytest
+import json
 
-from image_prediction.config import CONFIG
-from image_prediction.transformer.transformers.response import get_class_specific_filter_value
+import pytest
+from frozendict import frozendict
+
+from image_prediction.transformer.transformers.response import (
+    get_class_specific_min_image_to_page_quotient,
+    get_class_specific_max_image_to_page_quotient,
+    get_class_specific_max_image_width_to_height_quotient,
+    get_class_specific_min_image_width_to_height_quotient,
+    get_class_specific_min_classification_confidence,
+)
 
 
 @pytest.fixture
@@ -9,9 +17,20 @@ def label():
     return "signature"
 
 
-def test_read_environment_vars_for_thresholds(label):
-    assert get_class_specific_filter_value(label, CONFIG, "image_to_page_quotient", "min") == 0.05
-    assert get_class_specific_filter_value(label, CONFIG, "image_to_page_quotient", "max") == 0.4
-    assert get_class_specific_filter_value(label, CONFIG, "image_width_to_height_quotient", "min") == 0.1
-    assert get_class_specific_filter_value(label, CONFIG, "image_width_to_height_quotient", "max") == 10
-    assert get_class_specific_filter_value(label, CONFIG, "confidence", "min") == 0.5
+@pytest.fixture
+def page_quotient_threshold_map(label):
+    return frozendict(
+        {
+            "REL_IMAGE_SIZE": json.dumps({label: {"min": 0.1, "max": 0.2}}),
+            "IMAGE_FORMAT": json.dumps({label: {"min": 0.5, "max": 0.4}}),
+            "CONFIDENCE": json.dumps({label: {"min": 0.8}}),
+        }
+    )
+
+
+def test_read_environment_vars_for_thresholds(page_quotient_threshold_map, label):
+    assert get_class_specific_min_image_to_page_quotient(label, table=page_quotient_threshold_map) == 0.1
+    assert get_class_specific_max_image_to_page_quotient(label, table=page_quotient_threshold_map) == 0.2
+    assert get_class_specific_min_image_width_to_height_quotient(label, table=page_quotient_threshold_map) == 0.5
+    assert get_class_specific_max_image_width_to_height_quotient(label, table=page_quotient_threshold_map) == 0.4
+    assert get_class_specific_min_classification_confidence(label, table=page_quotient_threshold_map) == 0.8