Dr.Lt.Data
16a90e0a1a
improve: FaceDetailerPipe - support erosion(up to -512) and modify maximum value up to 255
238 lines
11 KiB
Python
238 lines
11 KiB
Python
import impact.core as core
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from impact.config import MAX_RESOLUTION
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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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}
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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):
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segs = bbox_detector.detect(image, threshold, dilation, crop_factor, drop_size)
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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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}
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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):
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segs = segm_detector.detect(image, threshold, dilation, crop_factor, drop_size)
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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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return (mask,)
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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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return (mask,)
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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": -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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"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, sam_model_opt=None, segm_detector_opt=None):
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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, bbox_threshold, bbox_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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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, 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, sam_model_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": 0, "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": 0, "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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}
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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):
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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, sam_model_opt, segm_detector_opt)
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