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import impact.core as core
from impact.config import MAX_RESOLUTION
import impact.segs_nodes as segs_nodes
import numpy as np
import impact.utils as utils
class SAMDetectorCombined:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"sam_model": ("SAM_MODEL", ),
"segs": ("SEGS", ),
"image": ("IMAGE", ),
"detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area",
"mask-points", "mask-point-bbox", "none"],),
"dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
"threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}),
"bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
"mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
"mask_hint_use_negative": (["False", "Small", "Outter"], )
}
}
RETURN_TYPES = ("MASK",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
def doit(self, sam_model, segs, image, detection_hint, dilation,
threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
return (core.make_sam_mask(sam_model, segs, image, detection_hint, dilation,
threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative), )
class SAMDetectorSegmented:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"sam_model": ("SAM_MODEL", ),
"segs": ("SEGS", ),
"image": ("IMAGE", ),
"detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area",
"mask-points", "mask-point-bbox", "none"],),
"dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
"threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}),
"bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
"mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
"mask_hint_use_negative": (["False", "Small", "Outter"], )
}
}
RETURN_TYPES = ("MASK", "MASK")
RETURN_NAMES = ("combined_mask", "batch_masks")
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
def doit(self, sam_model, segs, image, detection_hint, dilation,
threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
combined_mask, batch_masks = core.make_sam_mask_segmented(sam_model, segs, image, detection_hint, dilation,
threshold, bbox_expansion, mask_hint_threshold,
mask_hint_use_negative)
return (combined_mask, batch_masks, )
class BboxDetectorForEach:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bbox_detector": ("BBOX_DETECTOR", ),
"image": ("IMAGE", ),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}),
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
"labels": ("STRING", {"multiline": True, "default": "all", "placeholder": "List the types of segments to be allowed, separated by commas"}),
},
"optional": {"detailer_hook": ("DETAILER_HOOK",), }
}
RETURN_TYPES = ("SEGS", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
def doit(self, bbox_detector, image, threshold, dilation, crop_factor, drop_size, labels=None, detailer_hook=None):
segs = bbox_detector.detect(image, threshold, dilation, crop_factor, drop_size, detailer_hook)
if labels is not None and labels != '':
labels = labels.split(',')
if len(labels) > 0:
segs, _ = segs_nodes.SEGSLabelFilter.filter(segs, labels)
return (segs, )
class SegmDetectorForEach:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"segm_detector": ("SEGM_DETECTOR", ),
"image": ("IMAGE", ),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}),
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
"labels": ("STRING", {"multiline": True, "default": "all", "placeholder": "List the types of segments to be allowed, separated by commas"}),
},
"optional": {"detailer_hook": ("DETAILER_HOOK",), }
}
RETURN_TYPES = ("SEGS", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
def doit(self, segm_detector, image, threshold, dilation, crop_factor, drop_size, labels=None, detailer_hook=None):
segs = segm_detector.detect(image, threshold, dilation, crop_factor, drop_size, detailer_hook)
if labels is not None and labels != '':
labels = labels.split(',')
if len(labels) > 0:
segs, _ = segs_nodes.SEGSLabelFilter.filter(segs, labels)
return (segs, )
class SegmDetectorCombined:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"segm_detector": ("SEGM_DETECTOR", ),
"image": ("IMAGE", ),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
}
}
RETURN_TYPES = ("MASK",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
def doit(self, segm_detector, image, threshold, dilation):
mask = segm_detector.detect_combined(image, threshold, dilation)
return (mask,)
class BboxDetectorCombined(SegmDetectorCombined):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bbox_detector": ("BBOX_DETECTOR", ),
"image": ("IMAGE", ),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"dilation": ("INT", {"default": 4, "min": -512, "max": 512, "step": 1}),
}
}
def doit(self, bbox_detector, image, threshold, dilation):
mask = bbox_detector.detect_combined(image, threshold, dilation)
return (mask,)
class SimpleDetectorForEach:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bbox_detector": ("BBOX_DETECTOR", ),
"image": ("IMAGE", ),
"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"bbox_dilation": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
"sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"sub_dilation": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
"sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
"sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional": {
"sam_model_opt": ("SAM_MODEL", ),
"segm_detector_opt": ("SEGM_DETECTOR", ),
}
}
RETURN_TYPES = ("SEGS",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
@staticmethod
def detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
sub_threshold, sub_dilation, sub_bbox_expansion,
sam_mask_hint_threshold, sam_model_opt=None, segm_detector_opt=None):
segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, crop_factor, drop_size)
if sam_model_opt is not None:
mask = core.make_sam_mask(sam_model_opt, segs, image, "center-1", sub_dilation,
sub_threshold, sub_bbox_expansion, sam_mask_hint_threshold, False)
segs = core.segs_bitwise_and_mask(segs, mask)
elif segm_detector_opt is not None:
segm_segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size)
mask = core.segs_to_combined_mask(segm_segs)
segs = core.segs_bitwise_and_mask(segs, mask)
return (segs,)
def doit(self, bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
sub_threshold, sub_dilation, sub_bbox_expansion,
sam_mask_hint_threshold, sam_model_opt=None, segm_detector_opt=None):
return SimpleDetectorForEach.detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
sub_threshold, sub_dilation, sub_bbox_expansion,
sam_mask_hint_threshold, sam_model_opt, segm_detector_opt)
class SimpleDetectorForEachPipe:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"detailer_pipe": ("DETAILER_PIPE", ),
"image": ("IMAGE", ),
"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"bbox_dilation": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
"sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"sub_dilation": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
"sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
"sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
},
}
RETURN_TYPES = ("SEGS",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
def doit(self, detailer_pipe, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold):
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
return SimpleDetectorForEach.detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
sub_threshold, sub_dilation, sub_bbox_expansion,
sam_mask_hint_threshold, sam_model_opt, segm_detector_opt)
class SimpleDetectorForAnimateDiff:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bbox_detector": ("BBOX_DETECTOR", ),
"image_frames": ("IMAGE", ),
"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"bbox_dilation": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
"sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"sub_dilation": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
"sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
"sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional": {
"sam_model_opt": ("SAM_MODEL", ),
"segm_detector_opt": ("SEGM_DETECTOR", ),
}
}
RETURN_TYPES = ("SEGS",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detector"
@staticmethod
def detect(bbox_detector, image_frames, bbox_threshold, bbox_dilation, crop_factor, drop_size,
sub_threshold, sub_dilation, sub_bbox_expansion,
sam_mask_hint_threshold, sam_model_opt=None, segm_detector_opt=None):
# gather segs for all frames
all_segs = []
for image in image_frames:
image = image.unsqueeze(0)
segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, crop_factor, drop_size)
if sam_model_opt is not None:
mask = core.make_sam_mask(sam_model_opt, segs, image, "center-1", sub_dilation,
sub_threshold, sub_bbox_expansion, sam_mask_hint_threshold, False)
segs = core.segs_bitwise_and_mask(segs, mask)
elif segm_detector_opt is not None:
segm_segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size)
mask = core.segs_to_combined_mask(segm_segs)
segs = core.segs_bitwise_and_mask(segs, mask)
all_segs.append(segs)
# create merged masks
all_masks = []
for segs in all_segs:
all_masks += segs_nodes.SEGSToMaskList().doit(segs)[0]
result_mask = all_masks[0]
for mask in all_masks[1:]:
result_mask += mask
result_mask = utils.to_binary_mask(result_mask, 0.1)
return segs_nodes.MaskToSEGS().doit(result_mask, False, crop_factor, False, drop_size)
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, 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, sam_model_opt, segm_detector_opt)
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