File size: 10,785 Bytes
bc68240 | 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 | import argparse
import os
import sys
import time
import torch
import numpy as np
import json
from PIL import Image
from concurrent.futures import ThreadPoolExecutor
sys.path.append(os.path.join(os.getcwd(), "GroundingDINO"))
sys.path.append(os.path.join(os.getcwd(), "segment_anything"))
# Grounding DINO imports
import GroundingDINO.groundingdino.datasets.transforms as T
from GroundingDINO.groundingdino.models import build_model
from GroundingDINO.groundingdino.util.slconfig import SLConfig
from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
# Segment Anything imports
from segment_anything import sam_model_registry, sam_hq_model_registry, SamPredictor
import cv2
import matplotlib.pyplot as plt
def load_image(image_path):
image_pil = Image.open(image_path).convert("RGB")
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)
return image_pil, image
def load_model(model_config_path, model_checkpoint_path, device):
print("Loading model from...........", device)
args = SLConfig.fromfile(model_config_path)
args.device = device
model = build_model(args)
# Load the model checkpoint onto the specific GPU
checkpoint = torch.load(model_checkpoint_path, map_location=device)
model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
model.eval()
model.to(device)
return model
def get_grounding_output(model, image, caption, box_threshold, text_threshold, device="cpu"):
caption = caption.lower().strip()
if not caption.endswith("."):
caption += "."
model.to(device)
image = image.to(device)
with torch.no_grad():
outputs = model(image[None], captions=[caption])
logits = outputs["pred_logits"].sigmoid()[0] # Keep it on the device
boxes = outputs["pred_boxes"][0] # Keep it on the device
filt_mask = logits.max(dim=1)[0] > box_threshold
logits_filt = logits[filt_mask]
boxes_filt = boxes[filt_mask]
tokenlizer = model.tokenizer
tokenized = tokenlizer(caption)
pred_phrases = []
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]})")
return boxes_filt, pred_phrases
def process_image(image_path, model, predictor, output_dir, text_prompt, box_threshold, text_threshold, device):
# Load the image and move to GPU
image_pil, image = load_image(image_path)
# image_pil.save(os.path.join(output_dir, f"raw_image_{os.path.basename(image_path)}.jpg"))
# Run GroundingDINO model to get bounding boxes and labels
boxes_filt, pred_phrases = get_grounding_output(
model, image, text_prompt, box_threshold, text_threshold, device=device
)
# Load SAM model onto GPU
image_cv = cv2.imread(image_path)
image_cv = cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB)
predictor.set_image(image_cv)
# Convert boxes to original image size
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], device=device)
boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
boxes_filt[i][2:] += boxes_filt[i][:2]
# Transform boxes to be compatible with SAM
transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image_cv.shape[:2]).to(device)
# Get masks using SAM
masks, _, _ = predictor.predict_torch(
point_coords=None,
point_labels=None,
boxes=transformed_boxes.to(device),
multimask_output=False,
)
# Visualization and saving
plt.figure(figsize=(10, 10))
plt.imshow(image_cv)
# 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.cpu().numpy(), plt.gca(), label)
image_base_name = os.path.basename(image_path).split('.')[0]
plt.axis('off')
plt.savefig(
os.path.join(output_dir, f"grounded_sam_output_{image_base_name}.jpg"),
bbox_inches="tight", dpi=300, pad_inches=0.0
)
plt.close()
save_mask_data(output_dir, masks, boxes_filt, pred_phrases, image_base_name)
# Clear GPU memory
del image, transformed_boxes, masks # model, sam
# torch.cuda.empty_cache()
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:]
# print("mask.shape:", mask.shape)
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, mask_list, box_list, label_list, image_base_name=''):
value = 0 # 0 for background
mask_img = torch.zeros(mask_list.shape[-2:], device=mask_list.device)
for idx, mask in enumerate(mask_list):
mask_img[mask[0] == True] = value + idx + 1
plt.figure(figsize=(10, 10))
plt.imshow(mask_img.cpu().numpy())
plt.axis('off')
plt.savefig(os.path.join(output_dir, f'{image_base_name}.jpg'), bbox_inches="tight", dpi=300, pad_inches=0.0)
plt.close()
json_data = [{
'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.append({
'value': value,
'label': name,
'logit': float(logit),
'box': box.cpu().numpy().tolist(),
})
with open(os.path.join(output_dir, f'{image_base_name}.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_version", type=str, default="vit_h", required=False, help="SAM ViT version: vit_b / vit_l / vit_h")
parser.add_argument("--sam_checkpoint", type=str, required=False, help="path to sam checkpoint file")
parser.add_argument("--sam_hq_checkpoint", type=str, default=None, help="path to sam-hq checkpoint file")
parser.add_argument("--use_sam_hq", action="store_true", help="using sam-hq for prediction")
parser.add_argument("--input_path", type=str, required=True, help="path to directory containing image files")
parser.add_argument("--text_prompt", type=str, required=True, help="text prompt")
parser.add_argument("--output_dir", "-o", type=str, default="outputs", required=True, help="output directory")
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("--device", type=str, default="cuda", help="device to run the inference on, e.g., 'cuda' or 'cuda:0'")
args = parser.parse_args()
torch.backends.cudnn.enabled = False
torch.backends.cudnn.benchmark = True
start_time = time.time()
# Determine if we are using a single GPU or all available GPUs
if args.device == "cuda":
if torch.cuda.device_count() > 1:
device_list = [torch.device(f"cuda:{i}") for i in range(torch.cuda.device_count())] # Use all GPUs
else:
device_list = [torch.device("cuda:0")] # Default to first GPU
else:
device_list = [torch.device(args.device)]
print("device_list:", device_list)
# Get list of images
image_paths = [os.path.join(args.input_path, img) for img in os.listdir(args.input_path) if img.endswith(('.png', '.jpg', '.jpeg'))]
# Split images among available GPUs
image_batches = np.array_split(image_paths, len(device_list))
print("Processing images:", image_batches)
# Function to process a batch of images on the specified device
def process_batch(batch_images, model_config, model_checkpoint, sam_version, sam_checkpoint, sam_hq_checkpoint, use_sam_hq, device, output_dir):
# Load model onto GPU
torch.cuda.set_device(device)
model = load_model(model_config, model_checkpoint, device)
# Load SAM model onto GPU
if use_sam_hq:
sam = sam_hq_model_registry[sam_version](checkpoint=sam_hq_checkpoint).to(device)
else:
sam = sam_model_registry[sam_version](checkpoint=sam_checkpoint).to(device)
# Move model to the correct device
device = torch.device(device)
model.to(device)
sam.to(device)
predictor = SamPredictor(sam)
for image_path in batch_images:
# Process each image
print("Processing image:", image_path)
process_image(
image_path=image_path,
model=model,
predictor=predictor,
output_dir=output_dir,
text_prompt=args.text_prompt,
box_threshold=args.box_threshold,
text_threshold=args.text_threshold,
device=device
)
print("Image processing complete {}".format(image_path))
# Clear GPU memory after processing the batch
# del model, sam
torch.cuda.empty_cache()
# Use ThreadPoolExecutor to parallelize the processing across GPUs
with ThreadPoolExecutor(max_workers=len(device_list)*2) as executor:
futures = []
for i, device in enumerate(device_list):
print(f"Processing images on device {device}")
print("Image batches for each GPU:", len(image_batches[i]))
futures.append(executor.submit(
process_batch, image_batches[i], args.config, args.grounded_checkpoint, args.sam_version, args.sam_checkpoint, args.sam_hq_checkpoint, args.use_sam_hq, device, args.output_dir
))
# Wait for all threads to complete
for future in futures:
future.result()
print("Processing complete. Results saved to the output directory.")
print(f"Total time taken: {time.time() - start_time:.2f} seconds") |