corrected image set comparison; refactoring

image_prediction/image_extractor/extractors/parsable.py

@@ -7,13 +7,14 @@ from typing import List
 
 import fitz
 from PIL import Image
-from funcy import rcompose, merge, zipdict
+from funcy import rcompose, merge, zipdict, pluck, curry, compose
 from tqdm import tqdm
 
 from image_prediction.image_extractor.extractor import ImageExtractor, ImageMetadataPair
 from image_prediction.info import Info
 from image_prediction.stitching.stitching import stitch_pairs
 from image_prediction.stitching.utils import validate_box_coords, validate_box_size
+from image_prediction.utils.generic import lift
 
 
 class ParsablePDFImageExtractor(ImageExtractor):
@@ -129,11 +130,14 @@ def add_page_metadata(page, metadata):
 
 
 def add_alpha_channel_info(doc, page, metadata):
-    xrefs = map(itemgetter("xref"), get_image_infos(page))
+
-    alpha = map(partial(has_alpha_channel, doc), xrefs)
+    page_to_xrefs = compose(curry(pluck)("xref"), get_image_infos)
-    alpha = ({Info.ALPHA: a} for a in alpha)
+    xref_to_alpha = partial(has_alpha_channel, doc)
-    # alpha = map(dict, zip(repeat(Info.ALPHA), alpha))
+    page_to_alpha_value_per_image = compose(lift(xref_to_alpha), page_to_xrefs)
-    metadata = starmap(merge, zip(alpha, metadata))
+    alpha_to_dict = compose(dict, lambda a: [(Info.ALPHA, a)])
+    page_to_alpha_mapping_per_image = compose(lift(alpha_to_dict), page_to_alpha_value_per_image)
+
+    metadata = starmap(merge, zip(page_to_alpha_mapping_per_image(page), metadata))
 
     return metadata
 

image_prediction/utils/generic.py

@@ -1,5 +1,9 @@
-from funcy import iterate, first
+from funcy import iterate, first, curry, map
 
 
 def until(cond, func, *args, **kwargs):
     return first(filter(cond, iterate(func, *args, **kwargs)))
+
+
+def lift(fn):
+    return curry(map)(fn)

test/utils/comparison.py

@@ -1,8 +1,10 @@
+from itertools import starmap, product, repeat
 from typing import Iterable
 
 import numpy as np
-from PIL import Image
+from PIL.Image import Image
 from frozendict import frozendict
+from funcy import ilen
 
 from image_prediction.estimator.preprocessor.utils import image_to_normalized_tensor
 
@@ -15,9 +17,24 @@ def images_equal(im1: Image, im2: Image, **kwargs):
     return np.allclose(image_to_normalized_tensor(im1), image_to_normalized_tensor(im2), **kwargs)
 
 
-def metadata_equal(mdat1: Iterable, mdat2: Iterable):
+def metadata_equal(mdat1: Iterable[dict], mdat2: Iterable[dict]):
     return set(map(frozendict, mdat1)) == set(map(frozendict, mdat2))
 
 
-def image_sets_equal(ims1, ims2):
+def image_sets_equal(ims1: Iterable[Image], ims2: Iterable[Image]):
-    return all(any(images_equal(im1, im2) for im2 in ims2) for im1 in ims1)
+    ims1, ims2 = map(lambda x: sorted(map(image_to_normalized_tensor, x), key=np.mean), (ims1, ims2))
+
+    n = len(ims1)
+    assert isinstance(ims1, list)
+    assert len(ims2) == n
+
+    used = set()
+    covered = set()
+
+    for im1i, im2i in product(*repeat(range(n), 2)):
+
+        if im1i not in covered and im2i not in used and images_equal(ims1[im1i], ims2[im2i]):
+            covered.add(im1i)
+            used.add(im2i)
+
+    return len(covered) == len(used) == n