LoadImagesFromFolderKJ

__init__.py

@@ -63,6 +63,7 @@ NODE_CONFIG = {
     "ImageUpscaleWithModelBatched": {"class": ImageUpscaleWithModelBatched, "name": "Image Upscale With Model Batched"},
     "InsertImagesToBatchIndexed": {"class": InsertImagesToBatchIndexed, "name": "Insert Images To Batch Indexed"},
     "LoadAndResizeImage": {"class": LoadAndResizeImage, "name": "Load & Resize Image"},
+    "LoadImagesFromFolderKJ": {"class": LoadImagesFromFolderKJ, "name": "Load Images From Folder (KJ)"},
     "MergeImageChannels": {"class": MergeImageChannels, "name": "Merge Image Channels"},
     "PreviewAnimation": {"class": PreviewAnimation, "name": "Preview Animation"},
     "RemapImageRange": {"class": RemapImageRange, "name": "Remap Image Range"},

nodes/image_nodes.py

@@ -1682,3 +1682,100 @@ class LoadAndResizeImage:
             return "Invalid image file: {}".format(image)
 
         return True
+
+class LoadImagesFromFolderKJ:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "folder": ("STRING", {"default": ""}),
+            },
+            "optional": {
+                "image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK", "INT", "STRING",)
+    RETURN_NAMES = ("image", "mask", "count", "image_path",)
+    FUNCTION = "load_images"
+
+    CATEGORY = "image"
+
+    def load_images(self, folder, image_load_cap, start_index):
+        if not os.path.isdir(folder):
+            raise FileNotFoundError(f"Folder '{folder} cannot be found.'")
+        dir_files = os.listdir(folder)
+        if len(dir_files) == 0:
+            raise FileNotFoundError(f"No files in directory '{folder}'.")
+
+        # Filter files by extension
+        valid_extensions = ['.jpg', '.jpeg', '.png', '.webp']
+        dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)]
+
+        dir_files = sorted(dir_files)
+        dir_files = [os.path.join(folder, x) for x in dir_files]
+
+        # start at start_index
+        dir_files = dir_files[start_index:]
+
+        images = []
+        masks = []
+        image_path_list = []
+
+        limit_images = False
+        if image_load_cap > 0:
+            limit_images = True
+        image_count = 0
+
+        has_non_empty_mask = False
+
+        for image_path in dir_files:
+            if os.path.isdir(image_path) and os.path.ex:
+                continue
+            if limit_images and image_count >= image_load_cap:
+                break
+            i = Image.open(image_path)
+            i = ImageOps.exif_transpose(i)
+            image = i.convert("RGB")
+            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)
+                has_non_empty_mask = True
+            else:
+                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+            images.append(image)
+            masks.append(mask)
+            image_path_list.append(image_path)
+            image_count += 1
+
+        if len(images) == 1:
+            return (images[0], masks[0], 1)
+
+        elif len(images) > 1:
+            image1 = images[0]
+            mask1 = None
+
+            for image2 in images[1:]:
+                if image1.shape[1:] != image2.shape[1:]:
+                    image2 = common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1)
+                image1 = torch.cat((image1, image2), dim=0)
+
+            for mask2 in masks[1:]:
+                if has_non_empty_mask:
+                    if image1.shape[1:3] != mask2.shape:
+                        mask2 = torch.nn.functional.interpolate(mask2.unsqueeze(0).unsqueeze(0), size=(image1.shape[2], image1.shape[1]), mode='bilinear', align_corners=False)
+                        mask2 = mask2.squeeze(0)
+                    else:
+                        mask2 = mask2.unsqueeze(0)
+                else:
+                    mask2 = mask2.unsqueeze(0)
+
+                if mask1 is None:
+                    mask1 = mask2
+                else:
+                    mask1 = torch.cat((mask1, mask2), dim=0)
+
+            return (image1, mask1, len(images), image_path_list)