Spaces:
Running
Running
File size: 22,452 Bytes
613c9ab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 |
import impact.core as core
from impact.config import MAX_RESOLUTION
import impact.segs_nodes as segs_nodes
import impact.utils as utils
import torch
from impact.core import SEG
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):
if len(image) > 1:
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.')
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):
if len(image) > 1:
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.')
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": -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}),
"sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"sub_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "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": {
"post_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
"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, post_dilation=0, sam_model_opt=None, segm_detector_opt=None,
detailer_hook=None):
if len(image) > 1:
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.')
segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, crop_factor, drop_size, detailer_hook=detailer_hook)
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, detailer_hook=detailer_hook)
mask = core.segs_to_combined_mask(segm_segs)
segs = core.segs_bitwise_and_mask(segs, mask)
segs = core.dilate_segs(segs, post_dilation)
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, post_dilation=0, 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, post_dilation=post_dilation,
sam_model_opt=sam_model_opt, segm_detector_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": -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}),
"sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"sub_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "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": {
"post_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
}
}
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, post_dilation=0):
if len(image) > 1:
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.')
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, post_dilation=post_dilation, sam_model_opt=sam_model_opt, segm_detector_opt=segm_detector_opt,
detailer_hook=detailer_hook)
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": {
"masking_mode": (["Pivot SEGS", "Combine neighboring frames", "Don't combine"],),
"segs_pivot": (["Combined mask", "1st frame mask"],),
"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,
masking_mode="Pivot SEGS", segs_pivot="Combined mask", sam_model_opt=None, segm_detector_opt=None):
h = image_frames.shape[1]
w = image_frames.shape[2]
# gather segs for all frames
segs_by_frames = []
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)
segs_by_frames.append(segs)
def get_masked_frames():
masks_by_frame = []
for i, segs in enumerate(segs_by_frames):
masks_in_frame = segs_nodes.SEGSToMaskList().doit(segs)[0]
current_frame_mask = (masks_in_frame[0] * 255).to(torch.uint8)
for mask in masks_in_frame[1:]:
current_frame_mask |= (mask * 255).to(torch.uint8)
current_frame_mask = (current_frame_mask/255.0).to(torch.float32)
current_frame_mask = utils.to_binary_mask(current_frame_mask, 0.1)[0]
masks_by_frame.append(current_frame_mask)
return masks_by_frame
def get_empty_mask():
return torch.zeros((h, w), dtype=torch.float32, device="cpu")
def get_neighboring_mask_at(i, masks_by_frame):
prv = masks_by_frame[i-1] if i > 1 else get_empty_mask()
cur = masks_by_frame[i]
nxt = masks_by_frame[i-1] if i > 1 else get_empty_mask()
prv = prv if prv is not None else get_empty_mask()
cur = cur.clone() if cur is not None else get_empty_mask()
nxt = nxt if nxt is not None else get_empty_mask()
return prv, cur, nxt
def get_merged_neighboring_mask(masks_by_frame):
if len(masks_by_frame) <= 1:
return masks_by_frame
result = []
for i in range(0, len(masks_by_frame)):
prv, cur, nxt = get_neighboring_mask_at(i, masks_by_frame)
cur = (cur * 255).to(torch.uint8)
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_merged_neighboring_segs():
pivot_segs = get_pivot_segs()
masks_by_frame = get_masked_frames()
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
def get_separated_segs():
pivot_segs = get_pivot_segs()
masks_by_frame = get_masked_frames()
new_segs = []
for seg in pivot_segs[1]:
cropped_mask = torch.zeros(seg.cropped_mask.shape, dtype=torch.float32, device="cpu").unsqueeze(0)
x1, y1, x2, y2 = seg.crop_region
for mask in masks_by_frame:
cropped_mask_at_frame = mask[y1:y2, x1:x2]
cropped_mask = torch.cat((cropped_mask, cropped_mask_at_frame), dim=0)
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
# create result mask
if masking_mode == "Pivot SEGS":
return (get_pivot_segs(), )
elif masking_mode == "Combine neighboring frames":
return (get_merged_neighboring_segs(), )
else: # elif masking_mode == "Don't combine":
return (get_separated_segs(), )
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)
|