File size: 4,328 Bytes
f19c1db
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5c28041
 
f19c1db
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0bfb07f
5c28041
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
131
132
133

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}."))