chore: try to fix custom build by pushing again

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "image-classification-service"
-version = "2.15.0"
+version = "2.15.1"
 description = ""
 authors = ["Team Research <research@knecon.com>"]
 readme = "README.md"

src/image_prediction/pipeline.py

@@ -1,6 +1,7 @@
 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
@@ -54,7 +55,7 @@ class Pipeline:
         join = compose(starlift(lambda prd, rpr, mdt: {"classification": prd, **mdt, "representation": rpr}), star(zip))
 
         #                       />--classify--\
-        # --extract-->--split--+->--encode---->+--join-->reformat
+        # --extract-->--split--+->--encode---->+--join-->reformat-->filter_duplicates
         #                       \>--identity--/
 
         self.pipe = rcompose(
@@ -63,6 +64,7 @@ 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):
@@ -72,3 +74,33 @@ 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), `page` and `representation` (perceptual hash).
+    See RED-10765 (RM-241): Removed redactions reappear for why this is necessary.
+
+    Args:
+        metadata: Iterable of image metadata dicts.
+
+    Returns:
+        Iterable of image metadata dicts with duplicates removed.
+    """
+    seen = set()
+    for item in metadata:
+        key = (
+            item["representation"],
+            item["position"]["x1"],
+            item["position"]["x2"],
+            item["position"]["y1"],
+            item["position"]["y2"],
+            item["position"]["pageNumber"],
+        )
+        if key not in seen:
+            seen.add(key)
+            yield item
+        else:
+            logger.warning(
+                f"Duplicate image found: representation={key[0]}, x1={key[1]}, x2={key[2]}, y1={key[3]}, y2={key[4]}, pageNumber={key[5]}"
+            )
+            continue

test/data.dvc

@@ -1,5 +1,5 @@
 outs:
-- md5: ab352d3b2c62ce2293cafb57c1b41b01.dir
+- md5: 08bf8a63f04b3f19f859008556699708.dir
-  size: 7469082
+  size: 7979836
-  nfiles: 6
+  nfiles: 7
   path: data

test/regressions_tests/image_deduplication_test.py

@@ -0,0 +1,20 @@
+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['representation'], 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)
+