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, 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):
logger.info(f'relate_anything_1_')
input_image = input_image_mask['image']
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!😂😂😂😂')
if input_image is None:
return [], gr.Gallery.update(label='Please upload a image!😂😂😂😂')
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.😂😂😂😂')
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, num_relation, run_button, relate_all_button):
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
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
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)
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():
gallery = gr.Gallery(label="result images", show_label=True, elem_id="gallery"
).style(preview=True, grid=2, 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=[gallery, gallery], show_progress=True, queue=True)
relate_all_button.click(fn=relate_anything, inputs=[input_image, num_relation], outputs=[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])
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])
DESCRIPTION = '### This demo from [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything).
'
DESCRIPTION += 'RAM from [RelateAnything](https://github.com/Luodian/RelateAnything).
'
DESCRIPTION += 'Thanks for their excellent work.'
DESCRIPTION += f'For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. 
'
gr.Markdown(DESCRIPTION)
block.launch(server_name='0.0.0.0', debug=args.debug, share=args.share)