Spaces:
Sleeping
Sleeping
File size: 31,414 Bytes
760bde3 6e4c9f7 72fe59d 9546498 760bde3 434a891 72fe59d 40c7708 ed7763f 760bde3 5c0bd4c 72fe59d fd01170 6061d17 fd01170 4ba09fa 72fe59d 4ba09fa 6061d17 4ba09fa 72fe59d 4962329 4ba09fa 72fe59d 7a7f9d8 4ba09fa 7957dbb 4ba09fa 5d0da89 4ba09fa 72fe59d 4ba09fa c419c35 4ba09fa 72fe59d ca589ef 4ba09fa 5d0da89 4ba09fa 57ec633 91f6a3d c419c35 72fe59d c419c35 72fe59d e7ef33d 0d1ddf1 bf71fd5 c419c35 72fe59d c419c35 72fe59d f2bd037 72fe59d 4962329 72fe59d 7a7f9d8 fd3aa1a 7a7f9d8 4962329 7a7f9d8 4962329 7a7f9d8 4962329 7a7f9d8 af76788 7a7f9d8 4962329 7a7f9d8 648a7f1 6e3e561 648a7f1 7a7f9d8 6e3e561 7a7f9d8 6e3e561 7a7f9d8 6e3e561 7a7f9d8 6e3e561 7a7f9d8 72fe59d 7a7f9d8 b902809 91f6a3d 72fe59d 779c33a 7a7f9d8 4ba09fa 72fe59d 779c33a 4ba09fa ed7763f 9546498 72fe59d 4ba09fa 72fe59d 1d757ee 4ba09fa 72fe59d b902809 7a7f9d8 91f6a3d 72fe59d 4ba09fa 72fe59d 4ba09fa 7a7f9d8 72fe59d c419c35 4ba09fa c419c35 4ba09fa d60468e 4ba09fa 72fe59d 4ba09fa ed7763f 4ba09fa 72fe59d 7a7f9d8 72fe59d 7a7f9d8 b902809 72fe59d 7a7f9d8 72fe59d 6e4c9f7 72fe59d 7a7f9d8 72fe59d 4ba09fa ed7763f 72fe59d 6e4c9f7 72fe59d 6e4c9f7 72fe59d 6e4c9f7 72fe59d 7a7f9d8 72fe59d 1f8f331 4ba09fa ed7763f 1f8f331 4ba09fa ed7763f 7a7f9d8 72fe59d b902809 4ba09fa 72fe59d 7a7f9d8 b902809 72fe59d 11e651f 72fe59d 7a7f9d8 1c6fec7 11e651f ca589ef 72fe59d 7a7f9d8 1c6fec7 11e651f ca589ef 4ba09fa 72fe59d 7a7f9d8 72fe59d 7a7f9d8 b902809 72fe59d 7a7f9d8 1c6fec7 7a7f9d8 4ba09fa 5d0da89 4ba09fa 5d0da89 4ba09fa 72fe59d 5d0da89 72fe59d 4ba09fa d6ec0f9 648a7f1 d6ec0f9 291ae92 7a7f9d8 11e651f 1c6fec7 11e651f 4ba09fa e7ef33d 4962329 e5f7fa3 72fe59d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 |
import warnings
warnings.filterwarnings('ignore')
import subprocess, io, os, sys, time
from loguru import logger
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
if os.environ.get('IS_MY_DEBUG') is None:
result = subprocess.run(['pip', 'install', '-e', 'GroundingDINO'], check=True)
print(f'pip install GroundingDINO = {result}')
result = subprocess.run(['pip', 'list'], check=True)
print(f'pip list = {result}')
sys.path.insert(0, './GroundingDINO')
if not os.path.exists('./sam_vit_h_4b8939.pth'):
logger.info(f"get sam_vit_h_4b8939.pth...")
result = subprocess.run(['wget', 'https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth'], check=True)
print(f'wget sam_vit_h_4b8939.pth result = {result}')
import gradio as gr
import argparse
import copy
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont, ImageOps
# Grounding DINO
import GroundingDINO.groundingdino.datasets.transforms as T
from GroundingDINO.groundingdino.models import build_model
from GroundingDINO.groundingdino.util import box_ops
from GroundingDINO.groundingdino.util.slconfig import SLConfig
from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
import cv2
import numpy as np
import matplotlib.pyplot as plt
from lama_cleaner.model_manager import ModelManager
from lama_cleaner.schema import Config as lama_Config
# segment anything
from segment_anything import build_sam, SamPredictor, SamAutomaticMaskGenerator
# diffusers
import PIL
import requests
import torch
from io import BytesIO
from diffusers import StableDiffusionInpaintPipeline
from huggingface_hub import hf_hub_download
def load_model_hf(model_config_path, repo_id, filename, device='cpu'):
args = SLConfig.fromfile(model_config_path)
model = build_model(args)
args.device = device
cache_file = hf_hub_download(repo_id=repo_id, filename=filename)
checkpoint = torch.load(cache_file, map_location=device)
log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False)
print("Model loaded from {} \n => {}".format(cache_file, log))
_ = model.eval()
return model
def plot_boxes_to_image(image_pil, tgt):
H, W = tgt["size"]
boxes = tgt["boxes"]
labels = tgt["labels"]
assert len(boxes) == len(labels), "boxes and labels must have same length"
draw = ImageDraw.Draw(image_pil)
mask = Image.new("L", image_pil.size, 0)
mask_draw = ImageDraw.Draw(mask)
# draw boxes and masks
for box, label in zip(boxes, labels):
# from 0..1 to 0..W, 0..H
box = box * torch.Tensor([W, H, W, H])
# from xywh to xyxy
box[:2] -= box[2:] / 2
box[2:] += box[:2]
# random color
color = tuple(np.random.randint(0, 255, size=3).tolist())
# draw
x0, y0, x1, y1 = box
x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1)
draw.rectangle([x0, y0, x1, y1], outline=color, width=6)
# draw.text((x0, y0), str(label), fill=color)
font = ImageFont.load_default()
if hasattr(font, "getbbox"):
bbox = draw.textbbox((x0, y0), str(label), font)
else:
w, h = draw.textsize(str(label), font)
bbox = (x0, y0, w + x0, y0 + h)
# bbox = draw.textbbox((x0, y0), str(label))
draw.rectangle(bbox, fill=color)
font = os.path.join(cv2.__path__[0],'qt','fonts','DejaVuSans.ttf')
font_size = 36
new_font = ImageFont.truetype(font, font_size)
draw.text((x0+2, y0+2), str(label), font=new_font, fill="white")
mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6)
return image_pil, mask
def load_image(image_path):
# # load image
if isinstance(image_path, PIL.Image.Image):
image_pil = image_path
else:
image_pil = Image.open(image_path).convert("RGB") # load image
transform = T.Compose(
[
T.RandomResize([800], max_size=1333),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
image, _ = transform(image_pil, None) # 3, h, w
return image_pil, image
def load_model(model_config_path, model_checkpoint_path, device):
args = SLConfig.fromfile(model_config_path)
args.device = device
model = build_model(args)
checkpoint = torch.load(model_checkpoint_path, map_location=device) #"cpu")
load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
print(load_res)
_ = model.eval()
return model
def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, device="cpu"):
caption = caption.lower()
caption = caption.strip()
if not caption.endswith("."):
caption = caption + "."
model = model.to(device)
image = image.to(device)
with torch.no_grad():
outputs = model(image[None], captions=[caption])
logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256)
boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4)
logits.shape[0]
# filter output
logits_filt = logits.clone()
boxes_filt = boxes.clone()
filt_mask = logits_filt.max(dim=1)[0] > box_threshold
logits_filt = logits_filt[filt_mask] # num_filt, 256
boxes_filt = boxes_filt[filt_mask] # num_filt, 4
logits_filt.shape[0]
# get phrase
tokenlizer = model.tokenizer
tokenized = tokenlizer(caption)
# build pred
pred_phrases = []
for logit, box in zip(logits_filt, boxes_filt):
pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)
if with_logits:
pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
else:
pred_phrases.append(pred_phrase)
return boxes_filt, pred_phrases
def show_mask(mask, ax, random_color=False):
if random_color:
color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
else:
color = np.array([30/255, 144/255, 255/255, 0.6])
h, w = mask.shape[-2:]
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
ax.imshow(mask_image)
def show_box(box, ax, label):
x0, y0 = box[0], box[1]
w, h = box[2] - box[0], box[3] - box[1]
ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))
ax.text(x0, y0, label)
def xywh_to_xyxy(box, sizeW, sizeH):
if isinstance(box, list):
box = torch.Tensor(box)
box = box * torch.Tensor([sizeW, sizeH, sizeW, sizeH])
box[:2] -= box[2:] / 2
box[2:] += box[:2]
box = box.numpy()
return box
def mask_extend(img, box, extend_pixels=10, useRectangle=True):
box[0] = int(box[0])
box[1] = int(box[1])
box[2] = int(box[2])
box[3] = int(box[3])
region = img.crop(tuple(box))
new_width = box[2] - box[0] + 2*extend_pixels
new_height = box[3] - box[1] + 2*extend_pixels
region_BILINEAR = region.resize((int(new_width), int(new_height)))
if useRectangle:
region_draw = ImageDraw.Draw(region_BILINEAR)
region_draw.rectangle((0, 0, new_width, new_height), fill=(255, 255, 255))
img.paste(region_BILINEAR, (int(box[0]-extend_pixels), int(box[1]-extend_pixels)))
return img
def mix_masks(imgs):
re_img = 1 - np.asarray(imgs[0].convert("1"))
for i in range(len(imgs)-1):
re_img = np.multiply(re_img, 1 - np.asarray(imgs[i+1].convert("1")))
re_img = 1 - re_img
return Image.fromarray(np.uint8(255*re_img))
config_file = 'GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py'
ckpt_repo_id = "ShilongLiu/GroundingDINO"
ckpt_filenmae = "groundingdino_swint_ogc.pth"
sam_checkpoint = './sam_vit_h_4b8939.pth'
output_dir = "outputs"
device = evice = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'device={device}')
# make dir
os.makedirs(output_dir, exist_ok=True)
# initialize groundingdino model
logger.info(f"initialize groundingdino model...")
groundingdino_model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae)
# initialize SAM
logger.info(f"initialize SAM model...")
sam_model = build_sam(checkpoint=sam_checkpoint) # .to(device)
sam_predictor = SamPredictor(sam_model)
sam_mask_generator = SamAutomaticMaskGenerator(sam_model)
# initialize stable-diffusion-inpainting
logger.info(f"initialize stable-diffusion-inpainting...")
sd_pipe = None
if os.environ.get('IS_MY_DEBUG') is None:
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
torch_dtype=torch.float16
)
sd_pipe = sd_pipe.to(device)
# initialize lama_cleaner
logger.info(f"initialize lama_cleaner...")
from lama_cleaner.helper import (
load_img,
numpy_to_bytes,
resize_max_size,
)
lama_cleaner_model = ModelManager(
name='lama',
device='cpu', # device,
)
def lama_cleaner_process(image, mask):
ori_image = image
if mask.shape[0] == image.shape[1] and mask.shape[1] == image.shape[0] and mask.shape[0] != mask.shape[1]:
# rotate image
ori_image = np.transpose(image[::-1, ...][:, ::-1], axes=(1, 0, 2))[::-1, ...]
image = ori_image
original_shape = ori_image.shape
interpolation = cv2.INTER_CUBIC
size_limit = 1080
if size_limit == "Original":
size_limit = max(image.shape)
else:
size_limit = int(size_limit)
config = lama_Config(
ldm_steps=25,
ldm_sampler='plms',
zits_wireframe=True,
hd_strategy='Original',
hd_strategy_crop_margin=196,
hd_strategy_crop_trigger_size=1280,
hd_strategy_resize_limit=2048,
prompt='',
use_croper=False,
croper_x=0,
croper_y=0,
croper_height=512,
croper_width=512,
sd_mask_blur=5,
sd_strength=0.75,
sd_steps=50,
sd_guidance_scale=7.5,
sd_sampler='ddim',
sd_seed=42,
cv2_flag='INPAINT_NS',
cv2_radius=5,
)
if config.sd_seed == -1:
config.sd_seed = random.randint(1, 999999999)
# logger.info(f"Origin image shape_0_: {original_shape} / {size_limit}")
image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
# logger.info(f"Resized image shape_1_: {image.shape}")
# logger.info(f"mask image shape_0_: {mask.shape} / {type(mask)}")
mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
# logger.info(f"mask image shape_1_: {mask.shape} / {type(mask)}")
res_np_img = lama_cleaner_model(image, mask, config)
torch.cuda.empty_cache()
image = Image.open(io.BytesIO(numpy_to_bytes(res_np_img, 'png')))
return image
# relate anything
from ram_utils import iou, sort_and_deduplicate, relation_classes, MLP, show_anns, ram_show_mask
from ram_train_eval import RamModel,RamPredictor
from mmengine.config import Config as mmengine_Config
input_size = 512
hidden_size = 256
num_classes = 56
# load ram model
model_path = "./checkpoints/ram_epoch12.pth"
ram_config = dict(
model=dict(
pretrained_model_name_or_path='bert-base-uncased',
load_pretrained_weights=False,
num_transformer_layer=2,
input_feature_size=256,
output_feature_size=768,
cls_feature_size=512,
num_relation_classes=56,
pred_type='attention',
loss_type='multi_label_ce',
),
load_from=model_path,
)
ram_config = mmengine_Config(ram_config)
class Ram_Predictor(RamPredictor):
def __init__(self, config, device='cpu'):
self.config = config
self.device = torch.device(device)
self._build_model()
def _build_model(self):
self.model = RamModel(**self.config.model).to(self.device)
if self.config.load_from is not None:
self.model.load_state_dict(torch.load(self.config.load_from, map_location=self.device))
self.model.train()
ram_model = Ram_Predictor(ram_config, device)
# visualization
def draw_selected_mask(mask, draw):
color = (255, 0, 0, 153)
nonzero_coords = np.transpose(np.nonzero(mask))
for coord in nonzero_coords:
draw.point(coord[::-1], fill=color)
def draw_object_mask(mask, draw):
color = (0, 0, 255, 153)
nonzero_coords = np.transpose(np.nonzero(mask))
for coord in nonzero_coords:
draw.point(coord[::-1], fill=color)
def create_title_image(word1, word2, word3, width, font_path='./assets/OpenSans-Bold.ttf'):
# Define the colors to use for each word
color_red = (255, 0, 0)
color_black = (0, 0, 0)
color_blue = (0, 0, 255)
# Define the initial font size and spacing between words
font_size = 40
# Create a new image with the specified width and white background
image = Image.new('RGB', (width, 60), (255, 255, 255))
# Load the specified font
font = ImageFont.truetype(font_path, font_size)
# Keep increasing the font size until all words fit within the desired width
while True:
# Create a draw object for the image
draw = ImageDraw.Draw(image)
word_spacing = font_size / 2
# Draw each word in the appropriate color
x_offset = word_spacing
draw.text((x_offset, 0), word1, color_red, font=font)
x_offset += font.getsize(word1)[0] + word_spacing
draw.text((x_offset, 0), word2, color_black, font=font)
x_offset += font.getsize(word2)[0] + word_spacing
draw.text((x_offset, 0), word3, color_blue, font=font)
word_sizes = [font.getsize(word) for word in [word1, word2, word3]]
total_width = sum([size[0] for size in word_sizes]) + word_spacing * 3
# Stop increasing font size if the image is within the desired width
if total_width <= width:
break
# Increase font size and reset the draw object
font_size -= 1
image = Image.new('RGB', (width, 50), (255, 255, 255))
font = ImageFont.truetype(font_path, font_size)
draw = None
return image
def concatenate_images_vertical(image1, image2):
# Get the dimensions of the two images
width1, height1 = image1.size
width2, height2 = image2.size
# Create a new image with the combined height and the maximum width
new_image = Image.new('RGBA', (max(width1, width2), height1 + height2))
# Paste the first image at the top of the new image
new_image.paste(image1, (0, 0))
# Paste the second image below the first image
new_image.paste(image2, (0, height1))
return new_image
def relate_anything(input_image_mask, k):
input_image = input_image_mask['image']
logger.info(f'relate_anything_1_{input_image.size}_')
w, h = input_image.size
max_edge = 1500
if w > max_edge or h > max_edge:
ratio = max(w, h) / max_edge
new_size = (int(w / ratio), int(h / ratio))
input_image.thumbnail(new_size)
logger.info(f'relate_anything_2_')
# load image
pil_image = input_image.convert('RGBA')
image = np.array(input_image)
sam_masks = sam_mask_generator.generate(image)
filtered_masks = sort_and_deduplicate(sam_masks)
logger.info(f'relate_anything_3_')
feat_list = []
for fm in filtered_masks:
feat = torch.Tensor(fm['feat']).unsqueeze(0).unsqueeze(0).to(device)
feat_list.append(feat)
feat = torch.cat(feat_list, dim=1).to(device)
matrix_output, rel_triplets = ram_model.predict(feat)
logger.info(f'relate_anything_4_')
pil_image_list = []
for i, rel in enumerate(rel_triplets[:k]):
s,o,r = int(rel[0]),int(rel[1]),int(rel[2])
relation = relation_classes[r]
mask_image = Image.new('RGBA', pil_image.size, color=(0, 0, 0, 0))
mask_draw = ImageDraw.Draw(mask_image)
draw_selected_mask(filtered_masks[s]['segmentation'], mask_draw)
draw_object_mask(filtered_masks[o]['segmentation'], mask_draw)
current_pil_image = pil_image.copy()
current_pil_image.alpha_composite(mask_image)
title_image = create_title_image('Red', relation, 'Blue', current_pil_image.size[0])
concate_pil_image = concatenate_images_vertical(current_pil_image, title_image)
pil_image_list.append(concate_pil_image)
logger.info(f'relate_anything_5_')
yield pil_image_list
mask_source_draw = "draw a mask on input image"
mask_source_segment = "type what to detect below"
def run_anything_task(input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold,
iou_threshold, inpaint_mode, mask_source_radio, remove_mode, remove_mask_extend, num_relation):
text_prompt = text_prompt.strip()
if not ((task_type == 'inpainting' or task_type == 'remove') and mask_source_radio == mask_source_draw):
if text_prompt == '':
return [], gr.Gallery.update(label='Detection prompt is not found!ππππ')
if input_image is None:
return [], gr.Gallery.update(label='Please upload a image!ππππ')
file_temp = int(time.time())
logger.info(f'run_anything_task_[{file_temp}]_{task_type}/{inpaint_mode}/[{mask_source_radio}]/{remove_mode}/{remove_mask_extend}_[{text_prompt}]/[{inpaint_prompt}]___1_')
# load image
input_mask_pil = input_image['mask']
input_mask = np.array(input_mask_pil.convert("L"))
image_pil, image = load_image(input_image['image'].convert("RGB"))
# visualize raw image
# image_pil.save(os.path.join(output_dir, f"raw_image_{file_temp}.jpg"))
size = image_pil.size
output_images = []
# output_images.append(input_image['image'])
# run grounding dino model
if (task_type == 'inpainting' or task_type == 'remove') and mask_source_radio == mask_source_draw:
pass
else:
groundingdino_device = 'cpu'
if device != 'cpu':
try:
from groundingdino import _C
groundingdino_device = 'cuda:0'
except:
warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only in groundingdino!")
groundingdino_device = 'cpu'
boxes_filt, pred_phrases = get_grounding_output(
groundingdino_model, image, text_prompt, box_threshold, text_threshold, device=groundingdino_device
)
if boxes_filt.size(0) == 0:
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_[{text_prompt}]_1_[No objects detected, please try others.]_')
return [], gr.Gallery.update(label='No objects detected, please try others.ππππ')
boxes_filt_ori = copy.deepcopy(boxes_filt)
pred_dict = {
"boxes": boxes_filt,
"size": [size[1], size[0]], # H,W
"labels": pred_phrases,
}
image_with_box = plot_boxes_to_image(copy.deepcopy(image_pil), pred_dict)[0]
image_path = os.path.join(output_dir, f"grounding_dino_output_{file_temp}.jpg")
image_with_box.save(image_path)
detection_image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)
os.remove(image_path)
output_images.append(detection_image_result)
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_2_')
if task_type == 'segment' or ((task_type == 'inpainting' or task_type == 'remove') and mask_source_radio == mask_source_segment):
image = np.array(input_image['image'])
sam_predictor.set_image(image)
H, W = size[1], size[0]
for i in range(boxes_filt.size(0)):
boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])
boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
boxes_filt[i][2:] += boxes_filt[i][:2]
boxes_filt = boxes_filt.cpu()
transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2])
masks, _, _, _ = sam_predictor.predict_torch(
point_coords = None,
point_labels = None,
boxes = transformed_boxes,
multimask_output = False,
)
# masks: [9, 1, 512, 512]
assert sam_checkpoint, 'sam_checkpoint is not found!'
# draw output image
plt.figure(figsize=(10, 10))
plt.imshow(image)
for mask in masks:
show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)
for box, label in zip(boxes_filt, pred_phrases):
show_box(box.numpy(), plt.gca(), label)
plt.axis('off')
image_path = os.path.join(output_dir, f"grounding_seg_output_{file_temp}.jpg")
plt.savefig(image_path, bbox_inches="tight")
segment_image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)
os.remove(image_path)
output_images.append(segment_image_result)
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_3_')
if task_type == 'detection' or task_type == 'segment':
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_9_')
return output_images, gr.Gallery.update(label='result images')
elif task_type == 'inpainting' or task_type == 'remove':
if inpaint_prompt.strip() == '' and mask_source_radio == mask_source_segment:
task_type = 'remove'
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_4_')
if mask_source_radio == mask_source_draw:
mask_pil = input_mask_pil
mask = input_mask
else:
masks_ori = copy.deepcopy(masks)
if inpaint_mode == 'merge':
masks = torch.sum(masks, dim=0).unsqueeze(0)
masks = torch.where(masks > 0, True, False)
mask = masks[0][0].cpu().numpy()
mask_pil = Image.fromarray(mask)
image_path = os.path.join(output_dir, f"image_mask_{file_temp}.jpg")
# if reverse_mask:
# mask_pil = mask_pil.point(lambda _: 255-_)
mask_pil.convert("RGB").save(image_path)
image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)
os.remove(image_path)
output_images.append(image_result)
if task_type == 'inpainting':
# inpainting pipeline
image_source_for_inpaint = image_pil.resize((512, 512))
image_mask_for_inpaint = mask_pil.resize((512, 512))
image_inpainting = sd_pipe(prompt=inpaint_prompt, image=image_source_for_inpaint, mask_image=image_mask_for_inpaint).images[0]
else:
# remove from mask
if mask_source_radio == mask_source_segment:
mask_imgs = []
masks_shape = masks_ori.shape
boxes_filt_ori_array = boxes_filt_ori.numpy()
if inpaint_mode == 'merge':
extend_shape_0 = masks_shape[0]
extend_shape_1 = masks_shape[1]
else:
extend_shape_0 = 1
extend_shape_1 = 1
for i in range(extend_shape_0):
for j in range(extend_shape_1):
mask = masks_ori[i][j].cpu().numpy()
mask_pil = Image.fromarray(mask)
if remove_mode == 'segment':
useRectangle = False
else:
useRectangle = True
try:
remove_mask_extend = int(remove_mask_extend)
except:
remove_mask_extend = 10
mask_pil_exp = mask_extend(copy.deepcopy(mask_pil).convert("RGB"),
xywh_to_xyxy(torch.tensor(boxes_filt_ori_array[i]), size[0], size[1]),
extend_pixels=remove_mask_extend, useRectangle=useRectangle)
mask_imgs.append(mask_pil_exp)
mask_pil = mix_masks(mask_imgs)
image_path = os.path.join(output_dir, f"image_mask_{file_temp}.jpg")
# if reverse_mask:
# mask_pil = mask_pil.point(lambda _: 255-_)
mask_pil.convert("RGB").save(image_path)
image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)
os.remove(image_path)
output_images.append(image_result)
image_inpainting = lama_cleaner_process(np.array(image_pil), np.array(mask_pil.convert("L")))
image_inpainting = image_inpainting.resize((image_pil.size[0], image_pil.size[1]))
image_path = os.path.join(output_dir, f"grounded_sam_inpainting_output_{file_temp}.jpg")
image_inpainting.save(image_path)
image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)
os.remove(image_path)
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_9_')
output_images.append(image_result)
return output_images, gr.Gallery.update(label='result images')
else:
logger.info(f"task_type:{task_type} error!")
logger.info(f'run_anything_task_[{file_temp}]_9_9_')
return output_images, gr.Gallery.update(label='result images')
def change_radio_display(task_type, mask_source_radio):
text_prompt_visible = True
inpaint_prompt_visible = False
mask_source_radio_visible = False
num_relation_visible = False
run_button_visible = True
relate_all_button_visible = False
gsa_gallery_visible = True
ram_gallery_visible = False
if task_type == "inpainting":
inpaint_prompt_visible = True
if task_type == "inpainting" or task_type == "remove":
mask_source_radio_visible = True
if mask_source_radio == mask_source_draw:
text_prompt_visible = False
if task_type == "relate anything":
text_prompt_visible = False
num_relation_visible = True
run_button_visible = False
relate_all_button_visible = True
gsa_gallery_visible = False
ram_gallery_visible = True
return gr.Textbox.update(visible=text_prompt_visible), gr.Textbox.update(visible=inpaint_prompt_visible), gr.Radio.update(visible=mask_source_radio_visible), gr.Slider.update(visible=num_relation_visible), gr.Button.update(visible=run_button_visible), gr.Button.update(visible=relate_all_button_visible), gr.Gallery.update(visible=gsa_gallery_visible), gr.Gallery.update(visible=ram_gallery_visible)
if __name__ == "__main__":
parser = argparse.ArgumentParser("Grounded SAM demo", add_help=True)
parser.add_argument("--debug", action="store_true", help="using debug mode")
parser.add_argument("--share", action="store_true", help="share the app")
args = parser.parse_args()
print(f'args = {args}')
block = gr.Blocks().queue()
with block:
with gr.Row():
with gr.Column():
input_image = gr.Image(source='upload', elem_id="image_upload", tool='sketch', type='pil', label="Upload")
task_type = gr.Radio(["detection", "segment", "inpainting", "remove", "relate anything"], value="detection",
label='Task type', visible=True)
mask_source_radio = gr.Radio([mask_source_draw, mask_source_segment],
value=mask_source_segment, label="Mask from",
visible=False)
text_prompt = gr.Textbox(label="Detection Prompt[To detect multiple objects, seperating each name with '.', like this: cat . dog . chair ]", placeholder="Cannot be empty")
inpaint_prompt = gr.Textbox(label="Inpaint Prompt (if this is empty, then remove)", visible=False)
num_relation = gr.Slider(label="How many relations do you want to see", minimum=1, maximum=20, value=5, step=1, visible=False)
run_button = gr.Button(label="Run", visible=True)
relate_all_button = gr.Button(label="Run", visible=False)
with gr.Accordion("Advanced options", open=False) as advanced_options:
box_threshold = gr.Slider(
label="Box Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.001
)
text_threshold = gr.Slider(
label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
)
iou_threshold = gr.Slider(
label="IOU Threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.001
)
inpaint_mode = gr.Radio(["merge", "first"], value="merge", label="inpaint_mode")
with gr.Row():
with gr.Column(scale=1):
remove_mode = gr.Radio(["segment", "rectangle"], value="segment", label='remove mode')
with gr.Column(scale=1):
remove_mask_extend = gr.Textbox(label="remove_mask_extend", value='10')
with gr.Column():
gsa_gallery = gr.Gallery(label="result images", show_label=True, elem_id="gsa_allery", visible=True
).style(preview=True, grid=[2], full_width=True, full_height=True)
ram_gallery = gr.Gallery(label="Your Result", show_label=True, elem_id="ram_gallery", visible=False
).style(preview=True, columns=5, object_fit="scale-down")
run_button.click(fn=run_anything_task, inputs=[
input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold, iou_threshold, inpaint_mode, mask_source_radio, remove_mode, remove_mask_extend, num_relation], outputs=[gsa_gallery, gsa_gallery], show_progress=True, queue=True)
relate_all_button.click(fn=relate_anything, inputs=[input_image, num_relation], outputs=[ram_gallery], show_progress=True, queue=True)
task_type.change(fn=change_radio_display, inputs=[task_type, mask_source_radio], outputs=[text_prompt, inpaint_prompt, mask_source_radio, num_relation, run_button, relate_all_button, gsa_gallery, ram_gallery])
mask_source_radio.change(fn=change_radio_display, inputs=[task_type, mask_source_radio], outputs=[text_prompt, inpaint_prompt, mask_source_radio, num_relation, run_button, relate_all_button, gsa_gallery, ram_gallery])
DESCRIPTION = '### This demo from [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything). <br>'
DESCRIPTION += 'RAM from [RelateAnything](https://github.com/Luodian/RelateAnything). <br>'
DESCRIPTION += 'Thanks for their excellent work.'
DESCRIPTION += f'<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/yizhangliu/Grounded-Segment-Anything?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
gr.Markdown(DESCRIPTION)
block.launch(server_name='0.0.0.0', debug=args.debug, share=args.share)
|