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import cv2
import numpy as np
import supervision as sv
import argparse
import torch
import torchvision
from groundingdino.util.inference import Model
from segment_anything import SamPredictor
from MobileSAM.setup_mobile_sam import setup_model
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--MOBILE_SAM_CHECKPOINT_PATH", type=str, default="./EfficientSAM/mobile_sam.pt", help="model"
)
parser.add_argument(
"--SOURCE_IMAGE_PATH", type=str, default="./assets/demo2.jpg", help="path to image file"
)
parser.add_argument(
"--CAPTION", type=str, default="The running dog", help="text prompt for GroundingDINO"
)
parser.add_argument(
"--OUT_FILE_BOX", type=str, default="groundingdino_annotated_image.jpg", help="the output filename"
)
parser.add_argument(
"--OUT_FILE_SEG", type=str, default="grounded_mobile_sam_annotated_image.jpg", help="the output filename"
)
parser.add_argument(
"--OUT_FILE_BIN_MASK", type=str, default="grounded_mobile_sam_bin_mask.jpg", help="the output filename"
)
parser.add_argument("--BOX_THRESHOLD", type=float, default=0.25, help="")
parser.add_argument("--TEXT_THRESHOLD", type=float, default=0.25, help="")
parser.add_argument("--NMS_THRESHOLD", type=float, default=0.8, help="")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
parser.add_argument(
"--DEVICE", type=str, default=device, help="cuda:[0,1,2,3,4] or cpu"
)
return parser.parse_args()
def main(args):
DEVICE = args.DEVICE
# 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
MOBILE_SAM_CHECKPOINT_PATH = args.MOBILE_SAM_CHECKPOINT_PATH
checkpoint = torch.load(MOBILE_SAM_CHECKPOINT_PATH)
mobile_sam = setup_model()
mobile_sam.load_state_dict(checkpoint, strict=True)
mobile_sam.to(device=DEVICE)
sam_predictor = SamPredictor(mobile_sam)
# Predict classes and hyper-param for GroundingDINO
SOURCE_IMAGE_PATH = args.SOURCE_IMAGE_PATH
CLASSES = [args.CAPTION]
BOX_THRESHOLD = args.BOX_THRESHOLD
TEXT_THRESHOLD = args.TEXT_THRESHOLD
NMS_THRESHOLD = args.NMS_THRESHOLD
# 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(args.OUT_FILE_BOX, 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=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
)
binary_mask = detections.mask[0].astype(np.uint8)*255
cv2.imwrite(args.OUT_FILE_BIN_MASK, binary_mask)
# 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(args.OUT_FILE_SEG, annotated_image)
if __name__ == "__main__":
args = parse_args()
main(args)
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