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=TEXT_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)