v1.0
The Impact Subpack is a custom node that exists independently from the Impact Pack.
This commit is contained in:
Dr.Lt.Data
@@ -1,11 +1,18 @@
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# ComfyUI-Impact-Subpack
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This extension serves as a complement to the Impact Pack, offering features that are not deemed suitable for inclusion by default in the ComfyUI Impact Pack.
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This node pack provides nodes that complement the [ComfyUI Impact Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack), such as the UltralyticsDetectorProvider.
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The nodes in this repository cannot be used standalone and depend on [ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack).
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## Nodes
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* UltralyticsDetectorProvider - This node provides an object detection detector based on Ultralystics.
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* By using this Detector Provider, you can replace the existing mmdet-based detector.
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* `UltralyticsDetectorProvider` - Loads the Ultralystics model to provide SEGM_DETECTOR, BBOX_DETECTOR.
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- Unlike `MMDetDetectorProvider`, for segm models, `BBOX_DETECTOR` is also provided.
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- The various models available in UltralyticsDetectorProvider can be downloaded through **ComfyUI-Manager**.
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## Ultralytics models
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* When using ultralytics models, save them separately in `models/ultralytics/bbox` and `models/ultralytics/segm` depending on the type of model. Many models can be downloaded by searching for `ultralytics` in the Model Manager of ComfyUI-Manager.
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* huggingface.co/Bingsu/[adetailer](https://huggingface.co/Bingsu/adetailer/tree/main) - You can download face, people detection models, and clothing detection models.
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* ultralytics/[assets](https://github.com/ultralytics/assets/releases/) - You can download various types of detection models other than faces or people.
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* civitai/[adetailer](https://civitai.com/search/models?sortBy=models_v5&query=adetailer) - You can download various types detection models....Many models are associated with NSFW content.
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## Credits
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+37
@@ -0,0 +1,37 @@
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"""
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@author: Dr.Lt.Data
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@title: Impact Subpack
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@nickname: Impact Subpack
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@description: This extension provides UltralyticsDetectorProvider node
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"""
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import importlib
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version_code = [1, 0]
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version_str = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
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print(f"### Loading: ComfyUI-Impact-Subpack ({version_str})")
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node_list = [
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"subpack_nodes",
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]
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NODE_CLASS_MAPPINGS = {}
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NODE_DISPLAY_NAME_MAPPINGS = {}
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for module_name in node_list:
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imported_module = importlib.import_module(".modules.{}".format(module_name), __name__)
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NODE_CLASS_MAPPINGS = {**NODE_CLASS_MAPPINGS, **imported_module.NODE_CLASS_MAPPINGS}
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NODE_DISPLAY_NAME_MAPPINGS = {**NODE_DISPLAY_NAME_MAPPINGS, **imported_module.NODE_DISPLAY_NAME_MAPPINGS}
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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try:
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import cm_global
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cm_global.register_extension('ComfyUI-Impact-Subpack',
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{'version': version_code,
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'name': 'Impact Subpack',
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'nodes': set(NODE_CLASS_MAPPINGS.keys()),
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'description': 'This extension provides UltralyticsDetectorProvider node.', })
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except:
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pass
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+6
-5
@@ -6,20 +6,21 @@ subpack_path = os.path.join(os.path.dirname(__file__))
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comfy_path = os.environ.get('COMFYUI_PATH')
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if comfy_path is None:
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print(f"\n[bold yellow]WARN: The `COMFYUI_PATH` environment variable is not set. Assuming `{os.path.dirname(__file__)}/../../../` as the ComfyUI path.[/bold yellow]", file=sys.stderr)
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comfy_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', '..'))
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comfy_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
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print(f"\nWARN: The `COMFYUI_PATH` environment variable is not set. Assuming `{comfy_path}` as the ComfyUI path.", file=sys.stderr)
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sys.path.append(comfy_path)
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model_path = os.environ.get('COMFYUI_MODEL_PATH')
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if model_path is None:
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print(f"\n[bold yellow]WARN: The `COMFYUI_MODEL_PATH` environment variable is not set. Assuming `{model_path}` as the ComfyUI path.[/bold yellow]", file=sys.stderr)
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model_path = os.path.abspath(os.path.join(comfy_path, 'models'))
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print(f"\nWARN: The `COMFYUI_MODEL_PATH` environment variable is not set. Assuming `{model_path}` as the ComfyUI path.", file=sys.stderr)
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ultralytics_bbox_path = os.path.join(model_path, "ultralytics", "bbox")
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ultralytics_segm_path = os.path.join(model_path, "ultralytics", "segm")
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if not os.path.exists(os.path.join(subpack_path, '..', '..', 'skip_download_model')):
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if not os.path.exists(os.path.join(subpack_path, '..', 'skip_download_model')):
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if not os.path.exists(ultralytics_bbox_path):
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os.makedirs(ultralytics_bbox_path)
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@@ -1,14 +1,42 @@
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from pathlib import Path
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from PIL import Image
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import impact.core as core
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import cv2
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import numpy as np
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from torchvision.transforms.functional import to_pil_image
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import torch
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from collections import namedtuple
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from . import utils
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orig_torch_load = torch.load
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SEG = namedtuple("SEG",
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['cropped_image', 'cropped_mask', 'confidence', 'crop_region', 'bbox', 'label', 'control_net_wrapper'],
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defaults=[None])
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class NO_BBOX_DETECTOR:
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pass
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class NO_SEGM_DETECTOR:
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pass
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def create_segmasks(results):
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bboxs = results[1]
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segms = results[2]
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confidence = results[3]
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results = []
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for i in range(len(segms)):
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item = (bboxs[i], segms[i].astype(np.float32), confidence[i])
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results.append(item)
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return results
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try:
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from ultralytics import YOLO
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except Exception as e:
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@@ -134,11 +162,11 @@ class UltraBBoxDetector:
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def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
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drop_size = max(drop_size, 1)
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detected_results = inference_bbox(self.bbox_model, core.tensor2pil(image), threshold)
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segmasks = core.create_segmasks(detected_results)
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detected_results = inference_bbox(self.bbox_model, utils.tensor2pil(image), threshold)
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segmasks = create_segmasks(detected_results)
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if dilation > 0:
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segmasks = core.dilate_masks(segmasks, dilation)
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segmasks = utils.dilate_masks(segmasks, dilation)
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items = []
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h = image.shape[1]
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@@ -151,17 +179,17 @@ class UltraBBoxDetector:
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y1, x1, y2, x2 = item_bbox
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if x2 - x1 > drop_size and y2 - y1 > drop_size: # minimum dimension must be (2,2) to avoid squeeze issue
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crop_region = core.make_crop_region(w, h, item_bbox, crop_factor)
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crop_region = utils.make_crop_region(w, h, item_bbox, crop_factor)
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if detailer_hook is not None:
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crop_region = detailer_hook.post_crop_region(w, h, item_bbox, crop_region)
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cropped_image = core.crop_image(image, crop_region)
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cropped_mask = core.crop_ndarray2(item_mask, crop_region)
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cropped_image = utils.crop_image(image, crop_region)
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cropped_mask = utils.crop_ndarray2(item_mask, crop_region)
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confidence = x[2]
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# bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h)
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item = core.SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, label, None)
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item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, label, None)
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items.append(item)
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@@ -174,12 +202,12 @@ class UltraBBoxDetector:
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return segs
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def detect_combined(self, image, threshold, dilation):
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detected_results = inference_bbox(self.bbox_model, core.tensor2pil(image), threshold)
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segmasks = core.create_segmasks(detected_results)
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detected_results = inference_bbox(self.bbox_model, utils.tensor2pil(image), threshold)
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segmasks = create_segmasks(detected_results)
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if dilation > 0:
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segmasks = core.dilate_masks(segmasks, dilation)
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segmasks = utils.dilate_masks(segmasks, dilation)
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return core.combine_masks(segmasks)
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return utils.combine_masks(segmasks)
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def setAux(self, x):
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pass
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@@ -193,11 +221,11 @@ class UltraSegmDetector:
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def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
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drop_size = max(drop_size, 1)
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detected_results = inference_segm(self.bbox_model, core.tensor2pil(image), threshold)
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segmasks = core.create_segmasks(detected_results)
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detected_results = inference_segm(self.bbox_model, utils.tensor2pil(image), threshold)
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segmasks = create_segmasks(detected_results)
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if dilation > 0:
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segmasks = core.dilate_masks(segmasks, dilation)
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segmasks = utils.dilate_masks(segmasks, dilation)
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items = []
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h = image.shape[1]
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@@ -210,17 +238,17 @@ class UltraSegmDetector:
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y1, x1, y2, x2 = item_bbox
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if x2 - x1 > drop_size and y2 - y1 > drop_size: # minimum dimension must be (2,2) to avoid squeeze issue
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crop_region = core.make_crop_region(w, h, item_bbox, crop_factor)
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crop_region = utils.make_crop_region(w, h, item_bbox, crop_factor)
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if detailer_hook is not None:
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crop_region = detailer_hook.post_crop_region(w, h, item_bbox, crop_region)
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cropped_image = core.crop_image(image, crop_region)
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cropped_mask = core.crop_ndarray2(item_mask, crop_region)
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cropped_image = utils.crop_image(image, crop_region)
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cropped_mask = utils.crop_ndarray2(item_mask, crop_region)
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confidence = x[2]
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# bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h)
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item = core.SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, label, None)
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item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, label, None)
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items.append(item)
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@@ -233,12 +261,12 @@ class UltraSegmDetector:
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return segs
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def detect_combined(self, image, threshold, dilation):
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detected_results = inference_segm(self.bbox_model, core.tensor2pil(image), threshold)
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segmasks = core.create_segmasks(detected_results)
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detected_results = inference_segm(self.bbox_model, utils.tensor2pil(image), threshold)
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segmasks = create_segmasks(detected_results)
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if dilation > 0:
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segmasks = core.dilate_masks(segmasks, dilation)
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segmasks = utils.dilate_masks(segmasks, dilation)
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return core.combine_masks(segmasks)
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return utils.combine_masks(segmasks)
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def setAux(self, x):
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pass
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@@ -1,17 +1,12 @@
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import os
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import folder_paths
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import impact.core as core
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import impact.subcore as subcore
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from impact.utils import add_folder_path_and_extensions
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version_code = 24
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print(f"### Loading: ComfyUI-Impact-Pack (Subpack: V0.8)")
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from . import subcore
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from . import utils
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model_path = folder_paths.models_dir
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add_folder_path_and_extensions("ultralytics_bbox", [os.path.join(model_path, "ultralytics", "bbox")], folder_paths.supported_pt_extensions)
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add_folder_path_and_extensions("ultralytics_segm", [os.path.join(model_path, "ultralytics", "segm")], folder_paths.supported_pt_extensions)
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add_folder_path_and_extensions("ultralytics", [os.path.join(model_path, "ultralytics")], folder_paths.supported_pt_extensions)
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utils.add_folder_path_and_extensions("ultralytics_bbox", [os.path.join(model_path, "ultralytics", "bbox")], folder_paths.supported_pt_extensions)
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utils.add_folder_path_and_extensions("ultralytics_segm", [os.path.join(model_path, "ultralytics", "segm")], folder_paths.supported_pt_extensions)
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utils.add_folder_path_and_extensions("ultralytics", [os.path.join(model_path, "ultralytics")], folder_paths.supported_pt_extensions)
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class UltralyticsDetectorProvider:
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@@ -30,7 +25,7 @@ class UltralyticsDetectorProvider:
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model = subcore.load_yolo(model_path)
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if model_name.startswith("bbox"):
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return subcore.UltraBBoxDetector(model), core.NO_SEGM_DETECTOR()
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return subcore.UltraBBoxDetector(model), subcore.NO_SEGM_DETECTOR()
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else:
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return subcore.UltraBBoxDetector(model), subcore.UltraSegmDetector(model)
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@@ -0,0 +1,151 @@
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import folder_paths
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from PIL import Image
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import numpy as np
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import cv2
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def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions):
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# Iterate over the list of full folder paths
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for full_folder_path in full_folder_paths:
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# Use the provided function to add each model folder path
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folder_paths.add_model_folder_path(folder_name, full_folder_path)
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# Now handle the extensions. If the folder name already exists, update the extensions
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if folder_name in folder_paths.folder_names_and_paths:
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# Unpack the current paths and extensions
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current_paths, current_extensions = folder_paths.folder_names_and_paths[folder_name]
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# Update the extensions set with the new extensions
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updated_extensions = current_extensions | extensions
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# Reassign the updated tuple back to the dictionary
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folder_paths.folder_names_and_paths[folder_name] = (current_paths, updated_extensions)
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else:
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# If the folder name was not present, add_model_folder_path would have added it with the last path
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# Now we just need to update the set of extensions as it would be an empty set
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# Also ensure that all paths are included (since add_model_folder_path adds only one path at a time)
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folder_paths.folder_names_and_paths[folder_name] = (full_folder_paths, extensions)
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def normalize_region(limit, startp, size):
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if startp < 0:
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new_endp = min(limit, size)
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new_startp = 0
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elif startp + size > limit:
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new_startp = max(0, limit - size)
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new_endp = limit
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else:
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new_startp = startp
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new_endp = min(limit, startp+size)
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return int(new_startp), int(new_endp)
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def _tensor_check_image(image):
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if image.ndim != 4:
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raise ValueError(f"Expected NHWC tensor, but found {image.ndim} dimensions")
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if image.shape[-1] not in (1, 3, 4):
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raise ValueError(f"Expected 1, 3 or 4 channels for image, but found {image.shape[-1]} channels")
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return
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def tensor2pil(image):
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_tensor_check_image(image)
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return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(0), 0, 255).astype(np.uint8))
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def dilate_masks(segmasks, dilation_factor, iter=1):
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if dilation_factor == 0:
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return segmasks
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dilated_masks = []
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kernel = np.ones((abs(dilation_factor), abs(dilation_factor)), np.uint8)
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for i in range(len(segmasks)):
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cv2_mask = segmasks[i][1]
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if dilation_factor > 0:
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dilated_mask = cv2.dilate(cv2_mask, kernel, iter)
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else:
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dilated_mask = cv2.erode(cv2_mask, kernel, iter)
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item = (segmasks[i][0], dilated_mask, segmasks[i][2])
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dilated_masks.append(item)
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return dilated_masks
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def combine_masks(masks):
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if len(masks) == 0:
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return None
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else:
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initial_cv2_mask = np.array(masks[0][1])
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combined_cv2_mask = initial_cv2_mask
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for i in range(1, len(masks)):
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cv2_mask = np.array(masks[i][1])
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if combined_cv2_mask.shape == cv2_mask.shape:
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combined_cv2_mask = cv2.bitwise_or(combined_cv2_mask, cv2_mask)
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else:
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# do nothing - incompatible mask
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pass
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mask = torch.from_numpy(combined_cv2_mask)
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return mask
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def make_crop_region(w, h, bbox, crop_factor, crop_min_size=None):
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x1 = bbox[0]
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y1 = bbox[1]
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x2 = bbox[2]
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y2 = bbox[3]
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bbox_w = x2 - x1
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bbox_h = y2 - y1
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crop_w = bbox_w * crop_factor
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crop_h = bbox_h * crop_factor
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if crop_min_size is not None:
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crop_w = max(crop_min_size, crop_w)
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crop_h = max(crop_min_size, crop_h)
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kernel_x = x1 + bbox_w / 2
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kernel_y = y1 + bbox_h / 2
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new_x1 = int(kernel_x - crop_w / 2)
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new_y1 = int(kernel_y - crop_h / 2)
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# make sure position in (w,h)
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new_x1, new_x2 = normalize_region(w, new_x1, crop_w)
|
||||
new_y1, new_y2 = normalize_region(h, new_y1, crop_h)
|
||||
|
||||
return [new_x1, new_y1, new_x2, new_y2]
|
||||
|
||||
|
||||
def crop_ndarray2(npimg, crop_region):
|
||||
x1 = crop_region[0]
|
||||
y1 = crop_region[1]
|
||||
x2 = crop_region[2]
|
||||
y2 = crop_region[3]
|
||||
|
||||
cropped = npimg[y1:y2, x1:x2]
|
||||
|
||||
return cropped
|
||||
|
||||
|
||||
def crop_ndarray4(npimg, crop_region):
|
||||
x1 = crop_region[0]
|
||||
y1 = crop_region[1]
|
||||
x2 = crop_region[2]
|
||||
y2 = crop_region[3]
|
||||
|
||||
cropped = npimg[:, y1:y2, x1:x2, :]
|
||||
|
||||
return cropped
|
||||
|
||||
|
||||
crop_tensor4 = crop_ndarray4
|
||||
|
||||
|
||||
def crop_image(image, crop_region):
|
||||
return crop_tensor4(image, crop_region)
|
||||
@@ -0,0 +1,15 @@
|
||||
[project]
|
||||
name = "comfyui-impact-subpack"
|
||||
description = "This node pack provides nodes that complement the Impact Pack, such as the UltralyticsDetectorProvider."
|
||||
version = "1.0"
|
||||
license = { file = "LICENSE.txt" }
|
||||
dependencies = ["matplotlib", "ultralytics==8.3.40"]
|
||||
|
||||
[project.urls]
|
||||
Repository = "https://github.com/ltdrdata/ComfyUI-Impact-Subpack"
|
||||
# Used by Comfy Registry https://comfyregistry.org
|
||||
|
||||
[tool.comfy]
|
||||
PublisherId = "drltdata"
|
||||
DisplayName = "ComfyUI Impact Subpack"
|
||||
Icon = ""
|
||||
@@ -1,2 +1,4 @@
|
||||
matplotlib
|
||||
ultralytics==8.3.40
|
||||
numpy<2
|
||||
opencv-python-headless
|
||||
Reference in New Issue
Block a user