add dhash prediction function

heroes/hero_comparison.py

@@ -1,9 +1,13 @@
 from __future__ import annotations
 from PIL.Image import Image
+from PIL import Image as ImageOpen, ImageFilter
+from functools import lru_cache
 import torch
 import importlib
 from pathlib import Path
 from typing import Any
+
+import imagehash
 model_cache: dict[Any, Any] = dict()
 class Heroes:
     def __init__(self):
@@ -138,7 +142,7 @@ def __init__(self):
         }
 
 
-    def predict_hero_name(self, image: Image, model_directory: Path) -> str:
+    def predict_hero_name_neural_network(self, image: Image, model_directory: Path) -> str:
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         NNModel = importlib.import_module(f"models.{model_directory.name}").FrozenNeuralNetworkModel
         transformer = importlib.import_module(f"models.{model_directory.name}").transformer
@@ -164,3 +168,21 @@ def predict_hero_name(self, image: Image, model_directory: Path) -> str:
             prediction = int(classes[int(torch.argmax(output, dim=1).item())])
 
         return self.hero_labels[prediction]
+    def predict_hero_name_dhash_comparison(self, image: Image, model_directory: Path) -> str:
+        input_hash = imagehash.dhash(transform(image))
+        hashes = []
+        heroes = []
+        for f in Path("./assets/heroes").iterdir():
+            hashes.append(get_image_hash(f))
+            heroes.append(f.name.replace(".png", "").replace("2", ""))
+
+        diffs: list[str] = [abs(input_hash - h) for h in hashes]
+        ret = sorted(zip(diffs, heroes), key=lambda x: x[0])
+        return ret[0][1]
+
+@lru_cache
+def get_image_hash(path: Path):
+    return imagehash.dhash(transform(ImageOpen.open(path)))
+
+def transform(img: Image):
+    return img

leaderboards/leaderboard_parser.py

@@ -109,7 +109,7 @@ def parse_leaderboard_to_leaderboard_entries(leaderboard_image: ImageType, regio
     for row in split_column_entries: # each record (10)
 
         results.append(LeaderboardEntry(
-            heroes=[hero_comp.predict_hero_name(hero_image, Path(f"./models/{model_name}")) for hero_image in row], 
+            heroes=[hero_comp.predict_hero_name_dhash_comparison(hero_image, Path(f"./models/{model_name}")) for hero_image in row], 
             role=role,
             region=region