Merge d14fe06352 into 36deef2c57

nodes.py

@@ -1665,39 +1665,42 @@ class LoadImage:
 
         excluded_formats = ['MPO']
 
-        for i in ImageSequence.Iterator(img):
-            i = node_helpers.pillow(ImageOps.exif_transpose, i)
+        try:
+            for i in ImageSequence.Iterator(img):
+                i = node_helpers.pillow(ImageOps.exif_transpose, i)
 
-            if i.mode == 'I':
-                i = i.point(lambda i: i * (1 / 255))
-            image = i.convert("RGB")
+                if i.mode == 'I':
+                    i = i.point(lambda i: i * (1 / 255))
+                image = i.convert("RGB")
 
-            if len(output_images) == 0:
-                w = image.size[0]
-                h = image.size[1]
+                if len(output_images) == 0:
+                    w = image.size[0]
+                    h = image.size[1]
 
-            if image.size[0] != w or image.size[1] != h:
-                continue
+                if image.size[0] != w or image.size[1] != h:
+                    continue
 
-            image = np.array(image).astype(np.float32) / 255.0
-            image = torch.from_numpy(image)[None,]
-            if 'A' in i.getbands():
-                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
-                mask = 1. - torch.from_numpy(mask)
-            elif i.mode == 'P' and 'transparency' in i.info:
-                mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
-                mask = 1. - torch.from_numpy(mask)
+                image = np.array(image).astype(np.float32) / 255.0
+                image = torch.from_numpy(image)[None,]
+                if 'A' in i.getbands():
+                    mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                    mask = 1. - torch.from_numpy(mask)
+                elif i.mode == 'P' and 'transparency' in i.info:
+                    mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
+                    mask = 1. - torch.from_numpy(mask)
+                else:
+                    mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+                output_images.append(image)
+                output_masks.append(mask.unsqueeze(0))
+
+            if len(output_images) > 1 and img.format not in excluded_formats:
+                output_image = torch.cat(output_images, dim=0)
+                output_mask = torch.cat(output_masks, dim=0)
             else:
-                mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
-            output_images.append(image)
-            output_masks.append(mask.unsqueeze(0))
-
-        if len(output_images) > 1 and img.format not in excluded_formats:
-            output_image = torch.cat(output_images, dim=0)
-            output_mask = torch.cat(output_masks, dim=0)
-        else:
-            output_image = output_images[0]
-            output_mask = output_masks[0]
+                output_image = output_images[0]
+                output_mask = output_masks[0]
+        finally:
+            img.close()
 
         return (output_image, output_mask)
 
@@ -1734,20 +1737,23 @@ class LoadImageMask:
     def load_image(self, image, channel):
         image_path = folder_paths.get_annotated_filepath(image)
         i = node_helpers.pillow(Image.open, image_path)
-        i = node_helpers.pillow(ImageOps.exif_transpose, i)
-        if i.getbands() != ("R", "G", "B", "A"):
-            if i.mode == 'I':
-                i = i.point(lambda i: i * (1 / 255))
-            i = i.convert("RGBA")
-        mask = None
-        c = channel[0].upper()
-        if c in i.getbands():
-            mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0
-            mask = torch.from_numpy(mask)
-            if c == 'A':
-                mask = 1. - mask
-        else:
-            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+        try:
+            i = node_helpers.pillow(ImageOps.exif_transpose, i)
+            if i.getbands() != ("R", "G", "B", "A"):
+                if i.mode == 'I':
+                    i = i.point(lambda i: i * (1 / 255))
+                i = i.convert("RGBA")
+            mask = None
+            c = channel[0].upper()
+            if c in i.getbands():
+                mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0
+                mask = torch.from_numpy(mask)
+                if c == 'A':
+                    mask = 1. - mask
+            else:
+                mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+        finally:
+            i.close()
         return (mask.unsqueeze(0),)
 
     @classmethod

tests-unit/test_load_image_file_handle.py

@@ -0,0 +1,91 @@
+"""Tests for LoadImage and LoadImageMask file handle management.
+
+Relates to issue #3477: close image file after loading
+"""
+
+import pytest
+import tempfile
+import os
+from PIL import Image
+
+
+class TestImageFileHandleRelease:
+    """Test that image files are properly closed after loading."""
+
+    def test_file_handle_released_after_close(self):
+        """Verify file handle is released after calling close()."""
+        # Create a temporary test image
+        with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as f:
+            temp_path = f.name
+
+        try:
+            # Create a test image
+            img = Image.new('RGB', (64, 64), color='red')
+            img.save(temp_path)
+
+            # Open and close the image
+            loaded_img = Image.open(temp_path)
+            loaded_img.load()  # Force load the image data
+            loaded_img.close()
+
+            # Try to delete the file - should succeed if handle is released
+            os.unlink(temp_path)
+            assert not os.path.exists(temp_path), "File should be deleted"
+        except Exception:
+            # Cleanup in case of failure
+            if os.path.exists(temp_path):
+                os.unlink(temp_path)
+            raise
+
+    def test_try_finally_pattern_releases_handle(self):
+        """Verify try/finally pattern properly releases file handle."""
+        with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as f:
+            temp_path = f.name
+
+        try:
+            # Create a test image
+            img = Image.new('RGBA', (64, 64), color='blue')
+            img.save(temp_path)
+
+            # Simulate the pattern used in LoadImage
+            loaded_img = Image.open(temp_path)
+            try:
+                # Process the image
+                _ = loaded_img.convert("RGB")
+            finally:
+                loaded_img.close()
+
+            # Verify file can be accessed/deleted
+            os.unlink(temp_path)
+            assert not os.path.exists(temp_path)
+        except Exception:
+            if os.path.exists(temp_path):
+                os.unlink(temp_path)
+            raise
+
+    def test_image_data_preserved_after_close(self):
+        """Verify image data is preserved after closing the file."""
+        with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as f:
+            temp_path = f.name
+
+        try:
+            # Create a test image with specific size
+            original_size = (128, 64)
+            img = Image.new('RGB', original_size, color='green')
+            img.save(temp_path)
+
+            # Load and process
+            loaded_img = Image.open(temp_path)
+            try:
+                loaded_img.load()
+                size = loaded_img.size
+                mode = loaded_img.mode
+            finally:
+                loaded_img.close()
+
+            # Data should still be valid after close
+            assert size == original_size
+            assert mode == 'RGB'
+        finally:
+            if os.path.exists(temp_path):
+                os.unlink(temp_path)