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import cv2
import numpy as np
import supervision as sv
import torch
import torchvision
from groundingdino.util.inference import Model
from segment_anything import SamPredictor
from LightHQSAM.setup_light_hqsam import setup_model
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# GroundingDINO config and checkpoint
GROUNDING_DINO_CONFIG_PATH = "GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py"
GROUNDING_DINO_CHECKPOINT_PATH = "./groundingdino_swint_ogc.pth"
# Building GroundingDINO inference model
grounding_dino_model = Model(model_config_path=GROUNDING_DINO_CONFIG_PATH, model_checkpoint_path=GROUNDING_DINO_CHECKPOINT_PATH)
# Building MobileSAM predictor
HQSAM_CHECKPOINT_PATH = "./EfficientSAM/sam_hq_vit_tiny.pth"
checkpoint = torch.load(HQSAM_CHECKPOINT_PATH)
light_hqsam = setup_model()
light_hqsam.load_state_dict(checkpoint, strict=True)
light_hqsam.to(device=DEVICE)
sam_predictor = SamPredictor(light_hqsam)
# Predict classes and hyper-param for GroundingDINO
SOURCE_IMAGE_PATH = "./EfficientSAM/LightHQSAM/example_light_hqsam.png"
CLASSES = ["bench"]
BOX_THRESHOLD = 0.25
TEXT_THRESHOLD = 0.25
NMS_THRESHOLD = 0.8
# load image
image = cv2.imread(SOURCE_IMAGE_PATH)
# detect objects
detections = grounding_dino_model.predict_with_classes(
image=image,
classes=CLASSES,
box_threshold=BOX_THRESHOLD,
text_threshold=BOX_THRESHOLD
)
# annotate image with detections
box_annotator = sv.BoxAnnotator()
labels = [
f"{CLASSES[class_id]} {confidence:0.2f}"
for _, _, confidence, class_id, _
in detections]
annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)
# save the annotated grounding dino image
cv2.imwrite("EfficientSAM/LightHQSAM/groundingdino_annotated_image.jpg", annotated_frame)
# NMS post process
print(f"Before NMS: {len(detections.xyxy)} boxes")
nms_idx = torchvision.ops.nms(
torch.from_numpy(detections.xyxy),
torch.from_numpy(detections.confidence),
NMS_THRESHOLD
).numpy().tolist()
detections.xyxy = detections.xyxy[nms_idx]
detections.confidence = detections.confidence[nms_idx]
detections.class_id = detections.class_id[nms_idx]
print(f"After NMS: {len(detections.xyxy)} boxes")
# Prompting SAM with detected boxes
def segment(sam_predictor: SamPredictor, image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:
sam_predictor.set_image(image)
result_masks = []
for box in xyxy:
masks, scores, logits = sam_predictor.predict(
box=box,
multimask_output=False,
hq_token_only=True,
)
index = np.argmax(scores)
result_masks.append(masks[index])
return np.array(result_masks)
# convert detections to masks
detections.mask = segment(
sam_predictor=sam_predictor,
image=cv2.cvtColor(image, cv2.COLOR_BGR2RGB),
xyxy=detections.xyxy
)
# annotate image with detections
box_annotator = sv.BoxAnnotator()
mask_annotator = sv.MaskAnnotator()
labels = [
f"{CLASSES[class_id]} {confidence:0.2f}"
for _, _, confidence, class_id, _
in detections]
annotated_image = mask_annotator.annotate(scene=image.copy(), detections=detections)
annotated_image = box_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)
# save the annotated grounded-sam image
cv2.imwrite("EfficientSAM/LightHQSAM/grounded_light_hqsam_annotated_image.jpg", annotated_image)
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