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import impact.core as core
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from nodes import MAX_RESOLUTION
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import impact.segs_nodes as segs_nodes
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import impact.utils as utils
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import torch
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from impact.core import SEG
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class SAMDetectorCombined:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"sam_model": ("SAM_MODEL", ),
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"segs": ("SEGS", ),
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"image": ("IMAGE", ),
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"detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area",
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"mask-points", "mask-point-bbox", "none"],),
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"dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
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"threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}),
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"bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
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"mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
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"mask_hint_use_negative": (["False", "Small", "Outter"], )
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}
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detector"
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def doit(self, sam_model, segs, image, detection_hint, dilation,
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threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
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return (core.make_sam_mask(sam_model, segs, image, detection_hint, dilation,
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threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative), )
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class SAMDetectorSegmented:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"sam_model": ("SAM_MODEL", ),
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"segs": ("SEGS", ),
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"image": ("IMAGE", ),
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"detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area",
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"mask-points", "mask-point-bbox", "none"],),
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"dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
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"threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}),
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"bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
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"mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
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"mask_hint_use_negative": (["False", "Small", "Outter"], )
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}
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}
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RETURN_TYPES = ("MASK", "MASK")
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RETURN_NAMES = ("combined_mask", "batch_masks")
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detector"
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def doit(self, sam_model, segs, image, detection_hint, dilation,
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threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
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combined_mask, batch_masks = core.make_sam_mask_segmented(sam_model, segs, image, detection_hint, dilation,
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threshold, bbox_expansion, mask_hint_threshold,
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mask_hint_use_negative)
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return (combined_mask, batch_masks, )
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class BboxDetectorForEach:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"bbox_detector": ("BBOX_DETECTOR", ),
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"image": ("IMAGE", ),
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"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}),
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"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
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"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
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"labels": ("STRING", {"multiline": True, "default": "all", "placeholder": "List the types of segments to be allowed, separated by commas"}),
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},
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"optional": {"detailer_hook": ("DETAILER_HOOK",), }
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}
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RETURN_TYPES = ("SEGS", )
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detector"
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def doit(self, bbox_detector, image, threshold, dilation, crop_factor, drop_size, labels=None, detailer_hook=None):
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if len(image) > 1:
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raise Exception('[Impact Pack] ERROR: BboxDetectorForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
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segs = bbox_detector.detect(image, threshold, dilation, crop_factor, drop_size, detailer_hook)
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if labels is not None and labels != '':
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labels = labels.split(',')
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if len(labels) > 0:
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segs, _ = segs_nodes.SEGSLabelFilter.filter(segs, labels)
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return (segs, )
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class SegmDetectorForEach:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"segm_detector": ("SEGM_DETECTOR", ),
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"image": ("IMAGE", ),
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"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}),
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"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
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"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
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"labels": ("STRING", {"multiline": True, "default": "all", "placeholder": "List the types of segments to be allowed, separated by commas"}),
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},
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"optional": {"detailer_hook": ("DETAILER_HOOK",), }
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}
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RETURN_TYPES = ("SEGS", )
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detector"
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def doit(self, segm_detector, image, threshold, dilation, crop_factor, drop_size, labels=None, detailer_hook=None):
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if len(image) > 1:
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raise Exception('[Impact Pack] ERROR: SegmDetectorForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
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segs = segm_detector.detect(image, threshold, dilation, crop_factor, drop_size, detailer_hook)
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if labels is not None and labels != '':
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labels = labels.split(',')
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if len(labels) > 0:
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segs, _ = segs_nodes.SEGSLabelFilter.filter(segs, labels)
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return (segs, )
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class SegmDetectorCombined:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"segm_detector": ("SEGM_DETECTOR", ),
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"image": ("IMAGE", ),
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"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
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}
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detector"
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def doit(self, segm_detector, image, threshold, dilation):
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mask = segm_detector.detect_combined(image, threshold, dilation)
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if mask is None:
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mask = torch.zeros((image.shape[2], image.shape[1]), dtype=torch.float32, device="cpu")
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return (mask.unsqueeze(0),)
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class BboxDetectorCombined(SegmDetectorCombined):
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"bbox_detector": ("BBOX_DETECTOR", ),
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"image": ("IMAGE", ),
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"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"dilation": ("INT", {"default": 4, "min": -512, "max": 512, "step": 1}),
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}
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}
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def doit(self, bbox_detector, image, threshold, dilation):
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mask = bbox_detector.detect_combined(image, threshold, dilation)
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if mask is None:
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mask = torch.zeros((image.shape[2], image.shape[1]), dtype=torch.float32, device="cpu")
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return (mask.unsqueeze(0),)
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class SimpleDetectorForEach:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"bbox_detector": ("BBOX_DETECTOR", ),
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"image": ("IMAGE", ),
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"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"bbox_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
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"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
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"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
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"sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"sub_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
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"sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
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"sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
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},
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"optional": {
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"post_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
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"sam_model_opt": ("SAM_MODEL", ),
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"segm_detector_opt": ("SEGM_DETECTOR", ),
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}
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}
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RETURN_TYPES = ("SEGS",)
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detector"
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@staticmethod
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def detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
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sub_threshold, sub_dilation, sub_bbox_expansion,
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sam_mask_hint_threshold, post_dilation=0, sam_model_opt=None, segm_detector_opt=None,
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detailer_hook=None):
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if len(image) > 1:
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raise Exception('[Impact Pack] ERROR: SimpleDetectorForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
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if segm_detector_opt is not None and hasattr(segm_detector_opt, 'bbox_detector') and segm_detector_opt.bbox_detector == bbox_detector:
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segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size, detailer_hook=detailer_hook)
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else:
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segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, crop_factor, drop_size, detailer_hook=detailer_hook)
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if sam_model_opt is not None:
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mask = core.make_sam_mask(sam_model_opt, segs, image, "center-1", sub_dilation,
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sub_threshold, sub_bbox_expansion, sam_mask_hint_threshold, False)
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segs = core.segs_bitwise_and_mask(segs, mask)
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elif segm_detector_opt is not None:
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segm_segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size, detailer_hook=detailer_hook)
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mask = core.segs_to_combined_mask(segm_segs)
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segs = core.segs_bitwise_and_mask(segs, mask)
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segs = core.dilate_segs(segs, post_dilation)
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return (segs,)
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def doit(self, bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
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sub_threshold, sub_dilation, sub_bbox_expansion,
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sam_mask_hint_threshold, post_dilation=0, sam_model_opt=None, segm_detector_opt=None):
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return SimpleDetectorForEach.detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
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sub_threshold, sub_dilation, sub_bbox_expansion,
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sam_mask_hint_threshold, post_dilation=post_dilation,
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sam_model_opt=sam_model_opt, segm_detector_opt=segm_detector_opt)
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class SimpleDetectorForEachPipe:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"detailer_pipe": ("DETAILER_PIPE", ),
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"image": ("IMAGE", ),
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"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"bbox_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
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"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
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"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
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"sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"sub_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
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"sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
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"sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
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},
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"optional": {
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"post_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
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}
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}
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RETURN_TYPES = ("SEGS",)
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detector"
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def doit(self, detailer_pipe, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
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sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold, post_dilation=0):
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if len(image) > 1:
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raise Exception('[Impact Pack] ERROR: SimpleDetectorForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
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model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, refiner_model, refiner_clip, refiner_positive, refiner_negative = detailer_pipe
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return SimpleDetectorForEach.detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
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sub_threshold, sub_dilation, sub_bbox_expansion,
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sam_mask_hint_threshold, post_dilation=post_dilation, sam_model_opt=sam_model_opt, segm_detector_opt=segm_detector_opt,
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detailer_hook=detailer_hook)
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class SimpleDetectorForAnimateDiff:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"bbox_detector": ("BBOX_DETECTOR", ),
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"image_frames": ("IMAGE", ),
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"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"bbox_dilation": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
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"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
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"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
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"sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"sub_dilation": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
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"sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
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"sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
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},
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"optional": {
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"masking_mode": (["Pivot SEGS", "Combine neighboring frames", "Don't combine"],),
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"segs_pivot": (["Combined mask", "1st frame mask"],),
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"sam_model_opt": ("SAM_MODEL", ),
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"segm_detector_opt": ("SEGM_DETECTOR", ),
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}
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}
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RETURN_TYPES = ("SEGS",)
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detector"
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@staticmethod
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def detect(bbox_detector, image_frames, bbox_threshold, bbox_dilation, crop_factor, drop_size,
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sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold,
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masking_mode="Pivot SEGS", segs_pivot="Combined mask", sam_model_opt=None, segm_detector_opt=None):
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h = image_frames.shape[1]
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w = image_frames.shape[2]
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segs_by_frames = []
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for image in image_frames:
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image = image.unsqueeze(0)
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segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, crop_factor, drop_size)
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if sam_model_opt is not None:
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mask = core.make_sam_mask(sam_model_opt, segs, image, "center-1", sub_dilation,
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sub_threshold, sub_bbox_expansion, sam_mask_hint_threshold, False)
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segs = core.segs_bitwise_and_mask(segs, mask)
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elif segm_detector_opt is not None:
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segm_segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size)
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mask = core.segs_to_combined_mask(segm_segs)
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segs = core.segs_bitwise_and_mask(segs, mask)
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segs_by_frames.append(segs)
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def get_masked_frames():
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masks_by_frame = []
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for i, segs in enumerate(segs_by_frames):
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masks_in_frame = segs_nodes.SEGSToMaskList().doit(segs)[0]
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current_frame_mask = (masks_in_frame[0] * 255).to(torch.uint8)
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for mask in masks_in_frame[1:]:
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current_frame_mask |= (mask * 255).to(torch.uint8)
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current_frame_mask = (current_frame_mask/255.0).to(torch.float32)
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current_frame_mask = utils.to_binary_mask(current_frame_mask, 0.1)[0]
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masks_by_frame.append(current_frame_mask)
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return masks_by_frame
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def get_empty_mask():
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return torch.zeros((h, w), dtype=torch.float32, device="cpu")
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def get_neighboring_mask_at(i, masks_by_frame):
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prv = masks_by_frame[i-1] if i > 1 else get_empty_mask()
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cur = masks_by_frame[i]
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nxt = masks_by_frame[i-1] if i > 1 else get_empty_mask()
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prv = prv if prv is not None else get_empty_mask()
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cur = cur.clone() if cur is not None else get_empty_mask()
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nxt = nxt if nxt is not None else get_empty_mask()
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return prv, cur, nxt
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def get_merged_neighboring_mask(masks_by_frame):
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if len(masks_by_frame) <= 1:
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return masks_by_frame
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result = []
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for i in range(0, len(masks_by_frame)):
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prv, cur, nxt = get_neighboring_mask_at(i, masks_by_frame)
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cur = (cur * 255).to(torch.uint8)
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cur |= (prv * 255).to(torch.uint8)
|
|
cur |= (nxt * 255).to(torch.uint8)
|
|
cur = (cur / 255.0).to(torch.float32)
|
|
cur = utils.to_binary_mask(cur, 0.1)[0]
|
|
result.append(cur)
|
|
|
|
return result
|
|
|
|
def get_whole_merged_mask():
|
|
all_masks = []
|
|
for segs in segs_by_frames:
|
|
all_masks += segs_nodes.SEGSToMaskList().doit(segs)[0]
|
|
|
|
merged_mask = (all_masks[0] * 255).to(torch.uint8)
|
|
for mask in all_masks[1:]:
|
|
merged_mask |= (mask * 255).to(torch.uint8)
|
|
|
|
merged_mask = (merged_mask / 255.0).to(torch.float32)
|
|
merged_mask = utils.to_binary_mask(merged_mask, 0.1)[0]
|
|
return merged_mask
|
|
|
|
def get_pivot_segs():
|
|
if segs_pivot == "1st frame mask":
|
|
return segs_by_frames[0][1]
|
|
else:
|
|
merged_mask = get_whole_merged_mask()
|
|
return segs_nodes.MaskToSEGS.doit(merged_mask, False, crop_factor, False, drop_size, contour_fill=True)[0]
|
|
|
|
def get_segs(merged_neighboring=False):
|
|
pivot_segs = get_pivot_segs()
|
|
|
|
masks_by_frame = get_masked_frames()
|
|
if merged_neighboring:
|
|
masks_by_frame = get_merged_neighboring_mask(masks_by_frame)
|
|
|
|
new_segs = []
|
|
for seg in pivot_segs[1]:
|
|
cropped_mask = torch.zeros(seg.cropped_mask.shape, dtype=torch.float32, device="cpu").unsqueeze(0)
|
|
pivot_mask = torch.from_numpy(seg.cropped_mask)
|
|
x1, y1, x2, y2 = seg.crop_region
|
|
for mask in masks_by_frame:
|
|
cropped_mask_at_frame = (mask[y1:y2, x1:x2] * pivot_mask).unsqueeze(0)
|
|
cropped_mask = torch.cat((cropped_mask, cropped_mask_at_frame), dim=0)
|
|
|
|
if len(cropped_mask) > 1:
|
|
cropped_mask = cropped_mask[1:]
|
|
|
|
new_seg = SEG(seg.cropped_image, cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper)
|
|
new_segs.append(new_seg)
|
|
|
|
return pivot_segs[0], new_segs
|
|
|
|
|
|
if masking_mode == "Pivot SEGS":
|
|
return (get_pivot_segs(), )
|
|
|
|
elif masking_mode == "Combine neighboring frames":
|
|
return (get_segs(merged_neighboring=True), )
|
|
|
|
else:
|
|
return (get_segs(merged_neighboring=False), )
|
|
|
|
def doit(self, bbox_detector, image_frames, bbox_threshold, bbox_dilation, crop_factor, drop_size,
|
|
sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold,
|
|
masking_mode="Pivot SEGS", segs_pivot="Combined mask", sam_model_opt=None, segm_detector_opt=None):
|
|
|
|
return SimpleDetectorForAnimateDiff.detect(bbox_detector, image_frames, bbox_threshold, bbox_dilation, crop_factor, drop_size,
|
|
sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold,
|
|
masking_mode, segs_pivot, sam_model_opt, segm_detector_opt)
|
|
|
