Spaces:
Sleeping
Sleeping
File size: 11,380 Bytes
4ba09fa a71406a 4ba09fa a71406a 4ba09fa a71406a 4ba09fa a71406a 4ba09fa a71406a 4ba09fa a71406a 4ba09fa a71406a 4ba09fa |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 |
import argparse
import os
import copy
import numpy as np
import json
import torch
import torchvision
from PIL import Image, ImageDraw, ImageFont
# 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
# BLIP
from transformers import BlipProcessor, BlipForConditionalGeneration
# 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 generate_caption(raw_image, device):
# unconditional image captioning
if device == "cuda":
inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)
else:
inputs = processor(raw_image, return_tensors="pt")
out = blip_model.generate(**inputs)
caption = processor.decode(out[0], skip_special_tokens=True)
return caption
def generate_tags(caption, split=',', max_tokens=100, model="gpt-3.5-turbo"):
prompt = [
{
'role': 'system',
'content': 'Extract the unique nouns in the caption. Remove all the adjectives. ' + \
f'List the nouns in singular form. Split them by "{split} ". ' + \
f'Caption: {caption}.'
}
]
response = openai.ChatCompletion.create(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens)
reply = response['choices'][0]['message']['content']
# sometimes return with "noun: xxx, xxx, xxx"
tags = reply.split(':')[-1].strip()
return tags
def check_caption(caption, pred_phrases, max_tokens=100, model="gpt-3.5-turbo"):
object_list = [obj.split('(')[0] for obj in pred_phrases]
object_num = []
for obj in set(object_list):
object_num.append(f'{object_list.count(obj)} {obj}')
object_num = ', '.join(object_num)
print(f"Correct object number: {object_num}")
prompt = [
{
'role': 'system',
'content': 'Revise the number in the caption if it is wrong. ' + \
f'Caption: {caption}. ' + \
f'True object number: {object_num}. ' + \
'Only give the revised caption: '
}
]
response = openai.ChatCompletion.create(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens)
reply = response['choices'][0]['message']['content']
# sometimes return with "Caption: xxx, xxx, xxx"
caption = reply.split(':')[-1].strip()
return caption
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,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 = []
scores = []
for logit, box in zip(logits_filt, boxes_filt):
pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)
pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
scores.append(logit.max().item())
return boxes_filt, torch.Tensor(scores), 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 save_mask_data(output_dir, caption, mask_list, box_list, label_list):
value = 0 # 0 for background
mask_img = torch.zeros(mask_list.shape[-2:])
for idx, mask in enumerate(mask_list):
mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1
plt.figure(figsize=(10, 10))
plt.imshow(mask_img.numpy())
plt.axis('off')
plt.savefig(os.path.join(output_dir, 'mask.jpg'), bbox_inches="tight", dpi=300, pad_inches=0.0)
json_data = {
'caption': caption,
'mask':[{
'value': value,
'label': 'background'
}]
}
for label, box in zip(label_list, box_list):
value += 1
name, logit = label.split('(')
logit = logit[:-1] # the last is ')'
json_data['mask'].append({
'value': value,
'label': name,
'logit': float(logit),
'box': box.numpy().tolist(),
})
with open(os.path.join(output_dir, 'label.json'), 'w') as f:
json.dump(json_data, f)
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("--split", default=",", type=str, help="split for text prompt")
parser.add_argument("--openai_key", type=str, required=True, help="key for chatgpt")
parser.add_argument("--openai_proxy", default=None, type=str, help="proxy for chatgpt")
parser.add_argument(
"--output_dir", "-o", type=str, default="outputs", required=True, help="output directory"
)
parser.add_argument("--box_threshold", type=float, default=0.25, help="box threshold")
parser.add_argument("--text_threshold", type=float, default=0.2, help="text threshold")
parser.add_argument("--iou_threshold", type=float, default=0.5, help="iou threshold")
parser.add_argument("--device", type=str, default="cpu", help="running on cpu only!, default=False")
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
split = args.split
openai_key = args.openai_key
openai_proxy = args.openai_proxy
output_dir = args.output_dir
box_threshold = args.box_threshold
text_threshold = args.box_threshold
iou_threshold = args.iou_threshold
device = args.device
openai.api_key = openai_key
if openai_proxy:
openai.proxy = {"http": openai_proxy, "https": openai_proxy}
# 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"))
# generate caption and tags
# use Tag2Text can generate better captions
# https://huggingface.co/spaces/xinyu1205/Tag2Text
# but there are some bugs...
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
if device == "cuda":
blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda")
else:
blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
caption = generate_caption(image_pil, device=device)
# Currently ", " is better for detecting single tags
# while ". " is a little worse in some case
text_prompt = generate_tags(caption, split=split)
print(f"Caption: {caption}")
print(f"Tags: {text_prompt}")
# run grounding dino model
boxes_filt, scores, pred_phrases = get_grounding_output(
model, image, text_prompt, box_threshold, text_threshold, device=device
)
# initialize SAM
predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint))
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()
# use NMS to handle overlapped boxes
print(f"Before NMS: {boxes_filt.shape[0]} boxes")
nms_idx = torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist()
boxes_filt = boxes_filt[nms_idx]
pred_phrases = [pred_phrases[idx] for idx in nms_idx]
print(f"After NMS: {boxes_filt.shape[0]} boxes")
caption = check_caption(caption, pred_phrases)
print(f"Revise caption with number: {caption}")
transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2])
masks, _, _ = predictor.predict_torch(
point_coords = None,
point_labels = None,
boxes = transformed_boxes,
multimask_output = False,
)
# 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.title(caption)
plt.axis('off')
plt.savefig(
os.path.join(output_dir, "automatic_label_output.jpg"),
bbox_inches="tight", dpi=300, pad_inches=0.0
)
save_mask_data(output_dir, caption, masks, boxes_filt, pred_phrases)
|