File size: 11,022 Bytes
483de47 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 |
import argparse
import os
from warnings import warn
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
import litellm
# 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
# segment anything
from segment_anything import build_sam, SamPredictor
import cv2
import numpy as np
import matplotlib.pyplot as plt
# diffusers
import PIL
import requests
import torch
from io import BytesIO
from diffusers import StableDiffusionInpaintPipeline
# whisper
import whisper
# ChatGPT
import openai
def load_image(image_path):
# load image
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="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 speech_recognition(speech_file, model):
# whisper
# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio(speech_file)
audio = whisper.pad_or_trim(audio)
# make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(audio).to(model.device)
# detect the spoken language
_, probs = model.detect_language(mel)
speech_language = max(probs, key=probs.get)
# decode the audio
options = whisper.DecodingOptions()
result = whisper.decode(model, mel, options)
# print the recognized text
speech_text = result.text
return speech_text, speech_language
def filter_prompts_with_chatgpt(caption, max_tokens=100, model="gpt-3.5-turbo"):
prompt = [
{
'role': 'system',
'content': f"Extract the main object to be replaced and marked it as 'main_object', " + \
f"Extract the remaining part as 'other prompt' " + \
f"Return (main_object, other prompt)" + \
f'Given caption: {caption}.'
}
]
response = litellm.completion(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens)
reply = response['choices'][0]['message']['content']
try:
det_prompt, inpaint_prompt = reply.split('\n')[0].split(':')[-1].strip(), reply.split('\n')[1].split(':')[-1].strip()
except:
warn(f"Failed to extract tags from caption") # use caption as det_prompt, inpaint_prompt
det_prompt, inpaint_prompt = caption, caption
return det_prompt, inpaint_prompt
if __name__ == "__main__":
parser = argparse.ArgumentParser("Grounded-Segment-Anything Demo", add_help=True)
parser.add_argument("--config", type=str, required=True, help="path to config file")
parser.add_argument(
"--grounded_checkpoint", type=str, required=True, help="path to checkpoint file"
)
parser.add_argument(
"--sam_checkpoint", type=str, required=True, help="path to checkpoint file"
)
parser.add_argument("--input_image", type=str, required=True, help="path to image file")
parser.add_argument(
"--output_dir", "-o", type=str, default="outputs", required=True, help="output directory"
)
parser.add_argument("--cache_dir", type=str, default=None, help="save your huggingface large model cache")
parser.add_argument("--det_speech_file", type=str, help="grounding speech file")
parser.add_argument("--inpaint_speech_file", type=str, help="inpaint speech file")
parser.add_argument("--prompt_speech_file", type=str, help="prompt speech file, no need to provide det_speech_file")
parser.add_argument("--enable_chatgpt", action="store_true", help="enable chatgpt")
parser.add_argument("--openai_key", type=str, help="key for chatgpt")
parser.add_argument("--openai_proxy", default=None, type=str, help="proxy for chatgpt")
parser.add_argument("--whisper_model", type=str, default="small", help="whisper model version: tiny, base, small, medium, large")
parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold")
parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold")
parser.add_argument("--inpaint_mode", type=str, default="first", help="inpaint mode")
parser.add_argument("--device", type=str, default="cpu", help="running on cpu only!, default=False")
parser.add_argument("--prompt_extra", type=str, default=" high resolution, real scene", help="extra prompt for inpaint")
args = parser.parse_args()
# cfg
config_file = args.config # change the path of the model config file
grounded_checkpoint = args.grounded_checkpoint # change the path of the model
sam_checkpoint = args.sam_checkpoint
image_path = args.input_image
output_dir = args.output_dir
cache_dir=args.cache_dir
# if not os.path.exists(cache_dir):
# print(f"create your cache dir:{cache_dir}")
# os.mkdir(cache_dir)
box_threshold = args.box_threshold
text_threshold = args.text_threshold
inpaint_mode = args.inpaint_mode
device = args.device
# make dir
os.makedirs(output_dir, exist_ok=True)
# load image
image_pil, image = load_image(image_path)
# load model
model = load_model(config_file, grounded_checkpoint, device=device)
# visualize raw image
image_pil.save(os.path.join(output_dir, "raw_image.jpg"))
# recognize speech
whisper_model = whisper.load_model(args.whisper_model)
if args.enable_chatgpt:
openai.api_key = args.openai_key
if args.openai_proxy:
openai.proxy = {"http": args.openai_proxy, "https": args.openai_proxy}
speech_text, _ = speech_recognition(args.prompt_speech_file, whisper_model)
det_prompt, inpaint_prompt = filter_prompts_with_chatgpt(speech_text)
inpaint_prompt += args.prompt_extra
print(f"det_prompt: {det_prompt}, inpaint_prompt: {inpaint_prompt}")
else:
det_prompt, det_speech_language = speech_recognition(args.det_speech_file, whisper_model)
inpaint_prompt, inpaint_speech_language = speech_recognition(args.inpaint_speech_file, whisper_model)
print(f"det_prompt: {det_prompt}, using language: {det_speech_language}")
print(f"inpaint_prompt: {inpaint_prompt}, using language: {inpaint_speech_language}")
# run grounding dino model
boxes_filt, pred_phrases = get_grounding_output(
model, image, det_prompt, box_threshold, text_threshold, device=device
)
# initialize SAM
predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device))
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
predictor.set_image(image)
size = image_pil.size
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 = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device)
masks, _, _ = predictor.predict_torch(
point_coords = None,
point_labels = None,
boxes = transformed_boxes.to(device),
multimask_output = False,
)
# masks: [1, 1, 512, 512]
# inpainting pipeline
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() # simply choose the first mask, which will be refine in the future release
mask_pil = Image.fromarray(mask)
image_pil = Image.fromarray(image)
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16,cache_dir=cache_dir
)
pipe = pipe.to("cuda")
# prompt = "A sofa, high quality, detailed"
image = pipe(prompt=inpaint_prompt, image=image_pil, mask_image=mask_pil).images[0]
image.save(os.path.join(output_dir, "grounded_sam_inpainting_output.jpg"))
# 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')
# plt.savefig(os.path.join(output_dir, "grounded_sam_output.jpg"), bbox_inches="tight")
|