|
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: str = "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: str = "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_boxes = outputs["pred_boxes"].cpu()[0] |
|
|
|
mask = prediction_logits.max(dim=1)[0] > box_threshold |
|
logits = prediction_logits[mask] |
|
boxes = prediction_boxes[mask] |
|
|
|
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 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class Model: |
|
|
|
def __init__( |
|
self, |
|
model_config_path: str, |
|
model_checkpoint_path: str, |
|
device: str = "cuda" |
|
): |
|
self.model = load_model( |
|
model_config_path=model_config_path, |
|
model_checkpoint_path=model_checkpoint_path, |
|
device=device |
|
).to(device) |
|
self.device = device |
|
|
|
def predict_with_caption( |
|
self, |
|
image: np.ndarray, |
|
caption: str, |
|
box_threshold: float = 0.35, |
|
text_threshold: float = 0.25 |
|
) -> Tuple[sv.Detections, List[str]]: |
|
""" |
|
import cv2 |
|
|
|
image = cv2.imread(IMAGE_PATH) |
|
|
|
model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH) |
|
detections, labels = model.predict_with_caption( |
|
image=image, |
|
caption=caption, |
|
box_threshold=BOX_THRESHOLD, |
|
text_threshold=TEXT_THRESHOLD |
|
) |
|
|
|
import supervision as sv |
|
|
|
box_annotator = sv.BoxAnnotator() |
|
annotated_image = box_annotator.annotate(scene=image, detections=detections, labels=labels) |
|
""" |
|
processed_image = Model.preprocess_image(image_bgr=image).to(self.device) |
|
boxes, logits, phrases = predict( |
|
model=self.model, |
|
image=processed_image, |
|
caption=caption, |
|
box_threshold=box_threshold, |
|
text_threshold=text_threshold) |
|
source_h, source_w, _ = image.shape |
|
detections = Model.post_process_result( |
|
source_h=source_h, |
|
source_w=source_w, |
|
boxes=boxes, |
|
logits=logits) |
|
return detections, phrases |
|
|
|
def predict_with_classes( |
|
self, |
|
image: np.ndarray, |
|
classes: List[str], |
|
box_threshold: float, |
|
text_threshold: float |
|
) -> sv.Detections: |
|
""" |
|
import cv2 |
|
|
|
image = cv2.imread(IMAGE_PATH) |
|
|
|
model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH) |
|
detections = model.predict_with_classes( |
|
image=image, |
|
classes=CLASSES, |
|
box_threshold=BOX_THRESHOLD, |
|
text_threshold=TEXT_THRESHOLD |
|
) |
|
|
|
|
|
import supervision as sv |
|
|
|
box_annotator = sv.BoxAnnotator() |
|
annotated_image = box_annotator.annotate(scene=image, detections=detections) |
|
""" |
|
caption = ", ".join(classes) |
|
processed_image = Model.preprocess_image(image_bgr=image).to(self.device) |
|
boxes, logits, phrases = predict( |
|
model=self.model, |
|
image=processed_image, |
|
caption=caption, |
|
box_threshold=box_threshold, |
|
text_threshold=text_threshold) |
|
source_h, source_w, _ = image.shape |
|
detections = Model.post_process_result( |
|
source_h=source_h, |
|
source_w=source_w, |
|
boxes=boxes, |
|
logits=logits) |
|
class_id = Model.phrases2classes(phrases=phrases, classes=classes) |
|
detections.class_id = class_id |
|
return detections |
|
|
|
@staticmethod |
|
def preprocess_image(image_bgr: np.ndarray) -> 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_pillow = Image.fromarray(cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)) |
|
image_transformed, _ = transform(image_pillow, None) |
|
return image_transformed |
|
|
|
@staticmethod |
|
def post_process_result( |
|
source_h: int, |
|
source_w: int, |
|
boxes: torch.Tensor, |
|
logits: torch.Tensor |
|
) -> sv.Detections: |
|
boxes = boxes * torch.Tensor([source_w, source_h, source_w, source_h]) |
|
xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy() |
|
confidence = logits.numpy() |
|
return sv.Detections(xyxy=xyxy, confidence=confidence) |
|
|
|
@staticmethod |
|
def phrases2classes(phrases: List[str], classes: List[str]) -> np.ndarray: |
|
class_ids = [] |
|
for phrase in phrases: |
|
try: |
|
class_ids.append(classes.index(phrase)) |
|
except ValueError: |
|
class_ids.append(None) |
|
return np.array(class_ids) |
|
|
