tidy-up

image_prediction/default_objects.py

@@ -7,18 +7,16 @@ from image_prediction.classifier.image_classifier import ImageClassifier
 from image_prediction.compositor.compositor import TransformerCompositor
 from image_prediction.encoder.encoders.hash_encoder import HashEncoder
 from image_prediction.estimator.adapter.adapter import EstimatorAdapter
+from image_prediction.formatter.formatter import format_image_plus
 from image_prediction.formatter.formatters.camel_case import Snake2CamelCaseKeyFormatter
 from image_prediction.formatter.formatters.enum import EnumFormatter
-from image_prediction.image_extractor.extractor import ImageMetadataPair
 from image_prediction.image_extractor.extractors.parsable import ParsablePDFImageExtractor
-from image_prediction.info import Info
 from image_prediction.label_mapper.mappers.probability import ProbabilityMapper
 from image_prediction.model_loader.loader import ModelLoader
 from image_prediction.model_loader.loaders.mlflow import MlflowConnector
 from image_prediction.redai_adapter.mlflow import MlflowModelReader
 from image_prediction.transformer.transformers.coordinate.pdfnet import PDFNetCoordinateTransformer
 from image_prediction.transformer.transformers.response import ResponseTransformer
-from pdf2img.default_objects.image import ImagePlus
 from pdf2img.extraction import extract_images_via_metadata
 
 
@@ -32,10 +30,23 @@ def get_image_classifier(model_loader, model_identifier):
     return ImageClassifier(Classifier(EstimatorAdapter(model), ProbabilityMapper(classes)))
 
 
-def get_extractor(**kwargs):
+# def get_extractor(**kwargs):
+#     image_extractor = ParsablePDFImageExtractor(**kwargs)
+#
+#     return image_extractor
+
+
+def get_dispatched_extract(**kwargs):
     image_extractor = ParsablePDFImageExtractor(**kwargs)
 
-    return image_extractor
+    def extract(pdf: bytes, page_range: range = None, metadata_per_image: Iterable[dict] = None):
+        if metadata_per_image:
+            image_pluses = extract_images_via_metadata(pdf, metadata_per_image)
+            yield from map(format_image_plus, image_pluses)
+        else:
+            yield from image_extractor.extract(pdf, page_range)
+
+    return extract
 
 
 def get_formatter():
@@ -47,24 +58,3 @@ def get_formatter():
 
 def get_encoder():
     return HashEncoder()
-
-
-def extract_images_via_metadata_and_format_to_image_metadata_pair(pdf: bytes, metadata_per_image: Iterable[dict]):
-    image_pluses = extract_images_via_metadata(pdf, metadata_per_image)
-
-    def reformat(image: ImagePlus):
-        enum_metadata = {
-            Info.PAGE_WIDTH: image.info.pageInfo.width,
-            Info.PAGE_HEIGHT: image.info.pageInfo.height,
-            Info.PAGE_IDX: image.info.pageInfo.number,
-            Info.ALPHA: image.info.alpha,
-            Info.WIDTH: image.info.boundingBox.width,
-            Info.HEIGHT: image.info.boundingBox.height,
-            Info.X1: image.info.boundingBox.x0,
-            Info.X2: image.info.boundingBox.x1,
-            Info.Y1: image.info.boundingBox.y0,
-            Info.Y2: image.info.boundingBox.y1,
-        }
-        return ImageMetadataPair(image.aspil(), enum_metadata)
-
-    yield from map(reformat, image_pluses)

image_prediction/formatter/formatter.py

@@ -1,6 +1,10 @@
 import abc
 
+from image_prediction.image_extractor.extractor import ImageMetadataPair
+from image_prediction.info import Info
+
 from image_prediction.transformer.transformer import Transformer
+from pdf2img.default_objects.image import ImagePlus
 
 
 class Formatter(Transformer):
@@ -13,3 +17,19 @@ class Formatter(Transformer):
 
     def __call__(self, obj):
         return self.format(obj)
+
+
+def format_image_plus(image: ImagePlus) -> ImageMetadataPair:
+    enum_metadata = {
+        Info.PAGE_WIDTH: image.info.pageInfo.width,
+        Info.PAGE_HEIGHT: image.info.pageInfo.height,
+        Info.PAGE_IDX: image.info.pageInfo.number,
+        Info.ALPHA: image.info.alpha,
+        Info.WIDTH: image.info.boundingBox.width,
+        Info.HEIGHT: image.info.boundingBox.height,
+        Info.X1: image.info.boundingBox.x0,
+        Info.X2: image.info.boundingBox.x1,
+        Info.Y1: image.info.boundingBox.y0,
+        Info.Y2: image.info.boundingBox.y1,
+    }
+    return ImageMetadataPair(image.aspil(), enum_metadata)

image_prediction/pipeline.py

@@ -11,9 +11,8 @@ from image_prediction.default_objects import (
     get_formatter,
     get_mlflow_model_loader,
     get_image_classifier,
-    get_extractor,
     get_encoder,
-    extract_images_via_metadata_and_format_to_image_metadata_pair,
+    get_dispatched_extract,
 )
 from image_prediction.locations import MLRUNS_DIR
 from image_prediction.utils.generic import lift, starlift
@@ -42,7 +41,7 @@ class Pipeline:
     def __init__(self, model_loader, model_identifier, batch_size=16, verbose=True, **kwargs):
         self.verbose = verbose
 
-        extract = get_extractor(**kwargs)
+        extract = get_dispatched_extract(**kwargs)
         classifier = get_image_classifier(model_loader, model_identifier)
         reformat = get_formatter()
         represent = get_encoder()
@@ -63,25 +62,10 @@ class Pipeline:
             join,  # ... the streams by zipping
             reformat,  # ... the items
         )
-        self.pipe2 = rcompose(
-            extract_images_via_metadata_and_format_to_image_metadata_pair,
-            split,  # ... into an image stream and a metadata stream
-            pairwise_apply(classify, represent, identity),  # ... apply functions to the streams pairwise
-            join,  # ... the streams by zipping
-            reformat,  # ... the items
-        )
 
-    def __call__(self, pdf: bytes, page_range: range = None):
+    def __call__(self, pdf: bytes, page_range: range = None, metadata_per_image: Iterable[dict] = None):
         yield from tqdm(
-            self.pipe(pdf, page_range=page_range),
+            self.pipe(pdf, page_range=page_range, metadata_per_image=metadata_per_image),
-            desc="Processing images from document",
-            unit=" images",
-            disable=not self.verbose,
-        )
-
-    def extract_via_metadata(self, pdf: bytes, metadata_per_page: Iterable[dict]):
-        yield from tqdm(
-            self.pipe2(pdf, metadata_per_page),
             desc="Processing images from document",
             unit=" images",
             disable=not self.verbose,

src/serve.py

@@ -34,15 +34,12 @@ def process_request(request_message):
     try:  # TODO: add figure detection file target to request message to avoid this
         metadata_bytes = storage.get_object(PYINFRA_CONFIG.storage_bucket, f"{dossier_id}/{file_id}.FIGURE.json.gz")
         metadata_bytes = gzip.decompress(metadata_bytes)
-        metadata = json.load(io.BytesIO(metadata_bytes))
+        metadata_per_image = json.load(io.BytesIO(metadata_bytes))["data"]
-        logger.info("Metadata aquired")
+        logger.info("Metadata acquired")
     except:
-        metadata = None
+        metadata_per_image = None
 
-    if metadata:
+    classifications = list(pipeline(pdf=object_bytes, metadata_per_image=metadata_per_image))
-        classifications = list(pipeline.extract_via_metadata(object_bytes, metadata_per_page=metadata["data"]))
-    else:
-        classifications = list(pipeline(object_bytes))
 
     result = {**request_message, "data": classifications}