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https://git.datalinker.icu/kijai/ComfyUI-KJNodes.git
synced 2026-03-30 15:06:59 +08:00
Restructuring fixes
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kijai
parent
0c1aacac97
commit
11a62b6fc3
12
__init__.py
12
__init__.py
@ -40,11 +40,14 @@ NODE_CONFIG = {
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#images
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"AddLabel": {"class": AddLabel, "name": "Add Label"},
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"ColorMatch": {"class": ColorMatch, "name": "Color Match"},
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"CrossFadeImages": {"class": CrossFadeImages, "name": "Cross Fade Images"},
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"GetImageRangeFromBatch": {"class": GetImageRangeFromBatch, "name": "Get Image Range From Batch"},
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"GetImageSizeAndCount": {"class": GetImageSizeAndCount, "name": "Get Image Size & Count"},
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"ImageAndMaskPreview": {"class": ImageAndMaskPreview},
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"ImageBatchMulti": {"class": ImageBatchMulti, "name": "Image Batch Multi"},
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"ImageBatchRepeatInterleaving": {"class": ImageBatchRepeatInterleaving},
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"ImageBatchTestPattern": {"class": ImageBatchTestPattern, "name": "Image Batch Test Pattern"},
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"ImageConcanate": {"class": ImageConcanate, "name": "Image Concanate"},
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"ImageConcanate": {"class": ImageConcanate, "name": "Image Concatenate"},
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"ImageGrabPIL": {"class": ImageGrabPIL, "name": "Image Grab PIL"},
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"ImageGridComposite2x2": {"class": ImageGridComposite2x2, "name": "Image Grid Composite 2x2"},
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"ImageGridComposite3x3": {"class": ImageGridComposite3x3, "name": "Image Grid Composite 3x3"},
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@ -54,12 +57,11 @@ NODE_CONFIG = {
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"ImageUpscaleWithModelBatched": {"class": ImageUpscaleWithModelBatched, "name": "Image Upscale With Model Batched"},
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"InsertImagesToBatchIndexed": {"class": InsertImagesToBatchIndexed, "name": "Insert Images To Batch Indexed"},
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"MergeImageChannels": {"class": MergeImageChannels, "name": "Merge Image Channels"},
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"ReverseImageBatch": {"class": ReverseImageBatch, "name": "Reverse Image Batch"},
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"RemapImageRange": {"class": RemapImageRange, "name": "Remap Image Range"},
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"ReverseImageBatch": {"class": ReverseImageBatch, "name": "Reverse Image Batch"},
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"ReplaceImagesInBatch": {"class": ReplaceImagesInBatch, "name": "Replace Images In Batch"},
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"SaveImageWithAlpha": {"class": SaveImageWithAlpha, "name": "Save Image With Alpha"},
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"SplitImageChannels": {"class": SplitImageChannels, "name": "Split Image Channels"},
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"CrossFadeImages": {"class": CrossFadeImages, "name": "Cross Fade Images"},
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"GetImageRangeFromBatch": {"class": GetImageRangeFromBatch, "name": "Get Image Range From Batch"},
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"SplitImageChannels": {"class": SplitImageChannels, "name": "Split Image Channels"},
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#batch cropping
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"BatchCropFromMask": {"class": BatchCropFromMask, "name": "Batch Crop From Mask"},
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"BatchCropFromMaskAdvanced": {"class": BatchCropFromMaskAdvanced, "name": "Batch Crop From Mask Advanced"},
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@ -6,6 +6,23 @@ from torchvision.transforms import Resize, CenterCrop, InterpolationMode
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import math
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#based on nodes from mtb https://github.com/melMass/comfy_mtb
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def bbox_to_region(bbox, target_size=None):
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bbox = bbox_check(bbox, target_size)
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return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
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def bbox_check(bbox, target_size=None):
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if not target_size:
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return bbox
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new_bbox = (
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bbox[0],
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bbox[1],
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min(target_size[0] - bbox[0], bbox[2]),
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min(target_size[1] - bbox[1], bbox[3]),
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)
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return new_bbox
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class BatchCropFromMask:
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@classmethod
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@ -136,23 +153,6 @@ class BatchCropFromMask:
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return (original_images, cropped_out, bounding_boxes, self.max_bbox_width, self.max_bbox_height, )
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def bbox_to_region(bbox, target_size=None):
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bbox = bbox_check(bbox, target_size)
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return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
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def bbox_check(bbox, target_size=None):
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if not target_size:
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return bbox
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new_bbox = (
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bbox[0],
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bbox[1],
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min(target_size[0] - bbox[0], bbox[2]),
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min(target_size[1] - bbox[1], bbox[3]),
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)
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return new_bbox
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class BatchUncrop:
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@classmethod
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@ -532,22 +532,6 @@ Returns:
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return (images_after_insert, )
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def bbox_to_region(bbox, target_size=None):
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bbox = bbox_check(bbox, target_size)
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return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
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def bbox_check(bbox, target_size=None):
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if not target_size:
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return bbox
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new_bbox = (
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bbox[0],
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bbox[1],
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min(target_size[0] - bbox[0], bbox[2]),
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min(target_size[1] - bbox[1], bbox[3]),
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)
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return new_bbox
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class BatchUncropAdvanced:
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@classmethod
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@ -674,4 +658,80 @@ Splits the specified bbox list at the given index into two lists.
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bboxes_a = bboxes[:index] # Sub-list from the start of bboxes up to (but not including) the index
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bboxes_b = bboxes[index:] # Sub-list from the index to the end of bboxes
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return (bboxes_a, bboxes_b,)
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return (bboxes_a, bboxes_b,)
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class BboxToInt:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"bboxes": ("BBOX",),
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"index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}),
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},
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}
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RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
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RETURN_NAMES = ("x_min","y_min","width","height", "center_x","center_y",)
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FUNCTION = "bboxtoint"
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CATEGORY = "KJNodes/masking"
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DESCRIPTION = """
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Returns selected index from bounding box list as integers.
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"""
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def bboxtoint(self, bboxes, index):
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x_min, y_min, width, height = bboxes[index]
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center_x = int(x_min + width / 2)
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center_y = int(y_min + height / 2)
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return (x_min, y_min, width, height, center_x, center_y,)
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class BboxVisualize:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"bboxes": ("BBOX",),
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"line_width": ("INT", {"default": 1,"min": 1, "max": 10, "step": 1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "visualizebbox"
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DESCRIPTION = """
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Visualizes the specified bbox on the image.
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"""
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CATEGORY = "KJNodes/masking"
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def visualizebbox(self, bboxes, images, line_width):
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image_list = []
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for image, bbox in zip(images, bboxes):
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x_min, y_min, width, height = bbox
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image = image.permute(2, 0, 1)
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img_with_bbox = image.clone()
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# Define the color for the bbox, e.g., red
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color = torch.tensor([1, 0, 0], dtype=torch.float32)
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# Draw lines for each side of the bbox with the specified line width
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for lw in range(line_width):
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# Top horizontal line
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img_with_bbox[:, y_min + lw, x_min:x_min + width] = color[:, None]
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# Bottom horizontal line
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img_with_bbox[:, y_min + height - lw, x_min:x_min + width] = color[:, None]
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# Left vertical line
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img_with_bbox[:, y_min:y_min + height, x_min + lw] = color[:, None]
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# Right vertical line
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img_with_bbox[:, y_min:y_min + height, x_min + width - lw] = color[:, None]
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img_with_bbox = img_with_bbox.permute(1, 2, 0).unsqueeze(0)
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image_list.append(img_with_bbox)
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return (torch.cat(image_list, dim=0),)
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@ -730,82 +730,6 @@ To see node id's, enable node id display from Manager badge menu.
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raise NameError(f"Node not found: {id}")
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return (', '.join(results).strip(', '), )
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class BboxToInt:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"bboxes": ("BBOX",),
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"index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}),
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},
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}
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RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
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RETURN_NAMES = ("x_min","y_min","width","height", "center_x","center_y",)
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FUNCTION = "bboxtoint"
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CATEGORY = "KJNodes/masking"
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DESCRIPTION = """
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Returns selected index from bounding box list as integers.
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"""
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def bboxtoint(self, bboxes, index):
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x_min, y_min, width, height = bboxes[index]
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center_x = int(x_min + width / 2)
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center_y = int(y_min + height / 2)
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return (x_min, y_min, width, height, center_x, center_y,)
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class BboxVisualize:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"bboxes": ("BBOX",),
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"line_width": ("INT", {"default": 1,"min": 1, "max": 10, "step": 1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "visualizebbox"
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DESCRIPTION = """
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Visualizes the specified bbox on the image.
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"""
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CATEGORY = "KJNodes/masking"
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def visualizebbox(self, bboxes, images, line_width):
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image_list = []
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for image, bbox in zip(images, bboxes):
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x_min, y_min, width, height = bbox
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image = image.permute(2, 0, 1)
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img_with_bbox = image.clone()
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# Define the color for the bbox, e.g., red
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color = torch.tensor([1, 0, 0], dtype=torch.float32)
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# Draw lines for each side of the bbox with the specified line width
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for lw in range(line_width):
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# Top horizontal line
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img_with_bbox[:, y_min + lw, x_min:x_min + width] = color[:, None]
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# Bottom horizontal line
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img_with_bbox[:, y_min + height - lw, x_min:x_min + width] = color[:, None]
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# Left vertical line
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img_with_bbox[:, y_min:y_min + height, x_min + lw] = color[:, None]
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# Right vertical line
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img_with_bbox[:, y_min:y_min + height, x_min + width - lw] = color[:, None]
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img_with_bbox = img_with_bbox.permute(1, 2, 0).unsqueeze(0)
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image_list.append(img_with_bbox)
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return (torch.cat(image_list, dim=0),)
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class DummyLatentOut:
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@classmethod
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