EfficientSAM/grounded_efficient_sam.py

import cv2
import numpy as np
import supervision as sv

import torch
import torchvision
from torchvision.transforms import ToTensor

from groundingdino.util.inference import 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
EFFICIENT_SAM_CHECHPOINT_PATH = "./EfficientSAM/efficientsam_s_gpu.jit"
efficientsam = torch.jit.load(EFFICIENT_SAM_CHECHPOINT_PATH)


# 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")


def efficient_sam_box_prompt_segment(image, pts_sampled, model):
    bbox = torch.reshape(torch.tensor(pts_sampled), [1, 1, 2, 2])
    bbox_labels = torch.reshape(torch.tensor([2, 3]), [1, 1, 2])
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    img_tensor = ToTensor()(image)

    predicted_logits, predicted_iou = model(
        img_tensor[None, ...].cuda(),
        bbox.cuda(),
        bbox_labels.cuda(),
    )
    predicted_logits = predicted_logits.cpu()
    all_masks = torch.ge(torch.sigmoid(predicted_logits[0, 0, :, :, :]), 0.5).numpy()
    predicted_iou = predicted_iou[0, 0, ...].cpu().detach().numpy()

    max_predicted_iou = -1
    selected_mask_using_predicted_iou = None
    for m in range(all_masks.shape[0]):
        curr_predicted_iou = predicted_iou[m]
        if (
            curr_predicted_iou > max_predicted_iou
            or selected_mask_using_predicted_iou is None
        ):
            max_predicted_iou = curr_predicted_iou
            selected_mask_using_predicted_iou = all_masks[m]
    return selected_mask_using_predicted_iou


# collect segment results from EfficientSAM
result_masks = []
for box in detections.xyxy:
    mask = efficient_sam_box_prompt_segment(image, box, efficientsam)
    result_masks.append(mask)

detections.mask = np.array(result_masks)

# 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/gronded_efficient_sam_anontated_image.jpg", annotated_image)