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import warnings
warnings.filterwarnings('ignore')
import subprocess, io, os, sys, time
from loguru import logger
# os.system("pip install diffuser==0.6.0")
# os.system("pip install transformers==4.29.1")
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')
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
from utils import computer_info
# 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
from app import *
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 = 'cpu'
os.makedirs(output_dir, exist_ok=True)
groundingdino_model = None
sam_device = None
sam_model = None
sam_predictor = None
sam_mask_generator = None
sd_pipe = None
lama_cleaner_model= None
ram_model = None
kosmos_model = None
kosmos_processor = None
def get_args():
argparser = argparse.ArgumentParser()
argparser.add_argument("--input_image", "-i", type=str, default="", help="")
argparser.add_argument("--text", "-t", type=str, default="", help="")
argparser.add_argument("--output_image", "-o", type=str, default="", help="")
args = argparser.parse_args()
return args
# usage:
# python app_cli.py --input_image dog.png --text dog --output_image dog_remove.png
if __name__ == '__main__':
args = get_args()
logger.info(f'\nargs={args}\n')
logger.info(f'loading models ... ')
# set_device() # If you have enough GPUs, you can open this comment
groundingdino_model = load_groundingdino_model('cpu')
load_sam_model(device)
# load_sd_model(device)
load_lama_cleaner_model(device)
# load_ram_model(device)
input_image = Image.open(args.input_image)
output_images, _ = run_anything_task(input_image = input_image,
text_prompt = args.text,
task_type = 'remove',
inpaint_prompt = '',
box_threshold = 0.3,
text_threshold = 0.25,
iou_threshold = 0.8,
inpaint_mode = "merge",
mask_source_radio = "type what to detect below",
remove_mode = "rectangle", # ["segment", "rectangle"]
remove_mask_extend = "10",
num_relation = 5,
kosmos_input = None,
cleaner_size_limit = -1,
)
if len(output_images) > 0:
logger.info(f'save result to {args.output_image} ... ')
output_images[-1].save(args.output_image)
# count = 0
# for output_image in output_images:
# count += 1
# if isinstance(output_image, np.ndarray):
# output_image = PIL.Image.fromarray(output_image.astype(np.uint8))
# output_image.save(args.output_image.replace(".", f"_{count}."))
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