type-checker issues

src/cleanvision/dataset/base_dataset.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from collections.abc import Sized
-from typing import List, Union
+from typing import List, Union, Optional
 
 from PIL import Image
 
@@ -20,7 +20,7 @@ def __len__(self) -> int:
         """Returns the number of examples in the dataset"""
         raise NotImplementedError
 
-    def __getitem__(self, item: Union[int, str]) -> Image.Image:
+    def __getitem__(self, item: Union[int, str]) -> Optional[Image.Image]:
         """Returns the image at a given index"""
         raise NotImplementedError
 

src/cleanvision/dataset/hf_dataset.py

@@ -29,7 +29,7 @@ def __len__(self) -> int:
 
     def __getitem__(self, item: Union[int, str]) -> Optional[Image.Image]:
         try:
-            image = self._data[item][self._image_key]
+            image: Image.Image = self._data[item][self._image_key]
             return image
         except Exception as e:
             print(f"Could not load image at index: {item}\n", e)

src/cleanvision/dataset/torch_dataset.py

@@ -26,7 +26,8 @@ def __len__(self) -> int:
         return len(self._data)
 
     def __getitem__(self, item: Union[int, str]) -> Image.Image:
-        return self._data[item][self._image_idx]
+        img: Image.Image = self._data[item][self._image_idx]
+        return img
 
     def get_name(self, index: Union[int, str]) -> str:
         return f"idx: {index}"

src/cleanvision/imagelab.py

@@ -7,7 +7,7 @@
 from __future__ import annotations
 
 import random
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, TypeVar
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, TypeVar, cast
 
 import numpy as np
 import pandas as pd
@@ -502,6 +502,7 @@ def _visualize(
             scores = sorted_df.head(num_images)[get_score_colname(issue_type)]
             indices = scores.index.tolist()
             images = [self._dataset[i] for i in indices]
+            images = cast(list[Image.Image], images)
 
             # construct title info
             title_info = {"scores": [f"score : {x:.4f}" for x in scores]}
@@ -526,6 +527,7 @@ def _visualize(
             image_sets = []
             for indices in image_sets_indices:
                 image_sets.append([self._dataset[index] for index in indices])
+            image_sets = cast(list[list[Image.Image]], image_sets)
 
             title_info_sets = []
             for s in image_sets_indices:
@@ -620,7 +622,7 @@ def visualize(
         elif image_files is not None:
             if len(image_files) == 0:
                 raise ValueError("image_files list is empty.")
-            images = [Image.open(path) for path in image_files]
+            images: List[Image.Image] = [Image.open(path) for path in image_files]
             title_info = {"path": [path.split("/")[-1] for path in image_files]}
             VizManager.individual_images(
                 images,
@@ -629,7 +631,7 @@ def visualize(
                 cell_size=cell_size,
             )
         elif indices:
-            images = [self._dataset[i] for i in indices]
+            images = [cast(Image.Image, self._dataset[i]) for i in indices]
             title_info = {"name": [self._dataset.get_name(i) for i in indices]}
             VizManager.individual_images(
                 images,
@@ -644,7 +646,7 @@ def visualize(
                 image_indices = random.sample(
                     self._dataset.index, min(num_images, len(self._dataset))
                 )
-                images = [self._dataset[i] for i in image_indices]
+                images = [cast(Image.Image, self._dataset[i]) for i in image_indices]
                 title_info = {
                     "name": [self._dataset.get_name(i) for i in image_indices]
                 }

src/cleanvision/issue_managers/duplicate_issue_manager.py

@@ -17,10 +17,20 @@
 
 def get_hash(image: Image.Image, params: Dict[str, Any]) -> str:
     hash_type, hash_size = params["hash_type"], params.get("hash_size", None)
+    supported_types = ["md5", "whash", "phash", "ahash", "dhash", "chash"]
+    if hash_type not in supported_types:
+        raise ValueError(
+            f"Hash type `{hash_type}` is not supported. Must be one of: {supported_types}"
+        )
+
     if hash_type == "md5":
         pixels = np.asarray(image)
         return hashlib.md5(pixels.tobytes()).hexdigest()
-    elif hash_type == "whash":
+
+    if not isinstance(hash_size, int):
+        raise ValueError("hash_size must be declared as a int in params")
+
+    if hash_type == "whash":
         return str(imagehash.whash(image, hash_size=hash_size))
     elif hash_type == "phash":
         return str(imagehash.phash(image, hash_size=hash_size))
@@ -31,7 +41,7 @@ def get_hash(image: Image.Image, params: Dict[str, Any]) -> str:
     elif hash_type == "chash":
         return str(imagehash.colorhash(image, binbits=hash_size))
     else:
-        raise ValueError("Hash type not supported")
+        raise ValueError("hash_type not supported")
 
 
 def compute_hash(

src/cleanvision/utils/viz_manager.py

@@ -1,4 +1,4 @@
-from typing import List, Tuple, Dict
+from typing import List, Tuple, Dict, Sequence
 
 import math
 import matplotlib.axes
@@ -9,7 +9,7 @@
 class VizManager:
     @staticmethod
     def individual_images(
-        images: List[Image.Image],
+        images: Sequence[Image.Image],
         title_info: Dict[str, List[str]],
         ncols: int,
         cell_size: Tuple[int, int],
@@ -86,7 +86,7 @@ def construct_titles(title_info: Dict[str, List[str]], cell_width: int) -> List[
 
 
 def plot_image_grid(
-    images: List[Image.Image],
+    images: Sequence[Image.Image],
     title_info: Dict[str, List[str]],
     ncols: int,
     cell_size: Tuple[int, int],