Fix: correct effective crop_to_aspect_ratio logic and lint issues

integration_tests/test_save_img.py

@@ -0,0 +1,27 @@
+import os
+
+import numpy as np
+import pytest
+
+from keras.utils import img_to_array
+from keras.utils import load_img
+from keras.utils import save_img
+
+
+@pytest.mark.parametrize(
+    "shape, name",
+    [
+        ((50, 50, 3), "rgb.jpg"),
+        ((50, 50, 4), "rgba.jpg"),
+    ],
+)
+def test_save_jpg(tmp_path, shape, name):
+    img = np.random.randint(0, 256, size=shape, dtype=np.uint8)
+    path = tmp_path / name
+    save_img(path, img, file_format="jpg")
+    assert os.path.exists(path)
+
+    # Check that the image was saved correctly and converted to RGB if needed.
+    loaded_img = load_img(path)
+    loaded_array = img_to_array(loaded_img)
+    assert loaded_array.shape == (50, 50, 3)

keras/src/layers/preprocessing/image_preprocessing/resizing.py

@@ -101,14 +101,27 @@ def __init__(
             self.width_axis = -2
 
     def transform_images(self, images, transformation=None, training=True):
+        # Compute effective crop flag:
+        # only crop if aspect ratios differ and flag is True
+        input_height, input_width = transformation
+        source_aspect_ratio = input_width / input_height
+        target_aspect_ratio = self.width / self.height
+        # Use a small epsilon for floating-point comparison
+        aspect_ratios_match = (
+            abs(source_aspect_ratio - target_aspect_ratio) < 1e-6
+        )
+        effective_crop_to_aspect_ratio = (
+            self.crop_to_aspect_ratio and not aspect_ratios_match
+        )
+
         size = (self.height, self.width)
         resized = self.backend.image.resize(
             images,
             size=size,
             interpolation=self.interpolation,
             antialias=self.antialias,
             data_format=self.data_format,
-            crop_to_aspect_ratio=self.crop_to_aspect_ratio,
+            crop_to_aspect_ratio=effective_crop_to_aspect_ratio,
             pad_to_aspect_ratio=self.pad_to_aspect_ratio,
             fill_mode=self.fill_mode,
             fill_value=self.fill_value,

keras/src/layers/preprocessing/image_preprocessing/resizing_test.py

@@ -124,6 +124,31 @@ def test_crop_to_aspect_ratio(self, data_format):
             ref_out = ref_out.transpose(0, 3, 1, 2)
         self.assertAllClose(ref_out, out)
 
+    @parameterized.parameters([("channels_first",), ("channels_last",)])
+    def test_crop_to_aspect_ratio_no_op_when_aspects_match(self, data_format):
+        # Test that crop_to_aspect_ratio=True behaves identically to False
+        # when source and target aspect ratios match (no cropping should occur).
+        img = np.reshape(np.arange(0, 16), (1, 4, 4, 1)).astype("float32")
+        if data_format == "channels_first":
+            img = img.transpose(0, 3, 1, 2)
+        out_false = layers.Resizing(
+            height=2,
+            width=2,
+            interpolation="nearest",
+            data_format=data_format,
+            crop_to_aspect_ratio=False,
+        )(img)
+        out_true = layers.Resizing(
+            height=2,
+            width=2,
+            interpolation="nearest",
+            data_format=data_format,
+            crop_to_aspect_ratio=True,
+        )(img)
+        # Outputs should be identical when aspect ratios match
+        # (4:4 -> 2:2, both 1:1).
+        self.assertAllClose(out_false, out_true)
+
     @parameterized.parameters([("channels_first",), ("channels_last",)])
     def test_unbatched_image(self, data_format):
         img = np.reshape(np.arange(0, 16), (4, 4, 1)).astype("float32")

keras/src/utils/image_utils.py

@@ -175,10 +175,13 @@ def save_img(path, x, data_format=None, file_format=None, scale=True, **kwargs):
         **kwargs: Additional keyword arguments passed to `PIL.Image.save()`.
     """
     data_format = backend.standardize_data_format(data_format)
+    # Normalize jpg → jpeg
+    if file_format is not None and file_format.lower() == "jpg":
+        file_format = "jpeg"
     img = array_to_img(x, data_format=data_format, scale=scale)
-    if img.mode == "RGBA" and (file_format == "jpg" or file_format == "jpeg"):
+    if img.mode == "RGBA" and  file_format == "jpeg":
         warnings.warn(
-            "The JPG format does not support RGBA images, converting to RGB."
+            "The JPEG format does not support RGBA images, converting to RGB."
         )
         img = img.convert("RGB")
     img.save(path, format=file_format, **kwargs)