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from typing import Tuple, List
import cv2
import numpy as np
import supervision as sv
import torch
from PIL import Image
from torchvision.ops import box_convert
import groundingdino.datasets.transforms as T
from groundingdino.models import build_model
from groundingdino.util.misc import clean_state_dict
from groundingdino.util.slconfig import SLConfig
from groundingdino.util.utils import get_phrases_from_posmap
def preprocess_caption(caption: str) -> str:
result = caption.lower().strip()
if result.endswith("."):
return result
return result + "."
def load_model(model_config_path: str, model_checkpoint_path: str, device='cuda'):
args = SLConfig.fromfile(model_config_path)
args.device = device
model = build_model(args)
checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
model.eval()
return model
def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:
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_source = Image.open(image_path).convert("RGB")
image = np.asarray(image_source)
image_transformed, _ = transform(image_source, None)
return image, image_transformed
def predict(
model,
image: torch.Tensor,
caption: str,
box_threshold: float,
text_threshold: float,
device='cuda',
) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:
caption = preprocess_caption(caption=caption)
model = model.to(device)
image = image.to(device)
with torch.no_grad():
outputs = model(image[None], captions=[caption])
prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0] # prediction_logits.shape = (nq, 256)
prediction_boxes = outputs["pred_boxes"].cpu()[0] # prediction_boxes.shape = (nq, 4)
mask = prediction_logits.max(dim=1)[0] > box_threshold
logits = prediction_logits[mask] # logits.shape = (n, 256)
boxes = prediction_boxes[mask] # boxes.shape = (n, 4)
tokenizer = model.tokenizer
tokenized = tokenizer(caption)
phrases = [
get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '')
for logit
in logits
]
return boxes, logits.max(dim=1)[0], phrases
def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str]) -> np.ndarray:
h, w, _ = image_source.shape
boxes = boxes * torch.Tensor([w, h, w, h])
xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
detections = sv.Detections(xyxy=xyxy)
labels = [
f"{phrase} {logit:.2f}"
for phrase, logit
in zip(phrases, logits)
]
box_annotator = sv.BoxAnnotator()
annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR)
annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
return annotated_frame
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