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 get_sam_vit_h_4b8939() load_groundingdino_model() load_sam_model() # load_sd_model() load_lama_cleaner_model() # load_ram_model() 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}."))