File size: 4,239 Bytes
f19c1db 0bfb07f f19c1db 0bfb07f f19c1db 0bfb07f |
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 |
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
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,
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}."))
|