Filtering boudning boxes in image annotation using SAM and grounding DINO

[sivaji123256]

Hi @hansent @tonylampada @yeldarby @RobertoNovelo ,
Thanks for the great work on image annotation. I was trying to filter out the bounding boxes by area on the output of the detections which is supervisior detection format. I was able to convert that into an numpy array. But, how to convert that numpy array back into supervisor detection class? Following is the small code :

# detect objects
detections = grounding_dino_model.predict_with_classes(
    image=image,
    classes=enhance_class_name(class_names=CLASSES),
    box_threshold=BOX_TRESHOLD,
    text_threshold=TEXT_TRESHOLD
)

#print(type(detections))

detections = np.array(detections)
det1 = []
for k in range(len(detections)):
    x1,y1,x2,y2 = detections[k][0][0],detections[k][0][1],detections[k][0][2],detections[k][0][3]
    x_diff = abs(x2-x1)
    y_diff = abs(y2-y1)
    if x_diff<250 and y_diff<150 :
        det1.append(detections[k])

detections = np.array(det1) 

# annotate image with detections
box_annotator = sv.BoxAnnotator()
labels = [
    #f"{CLASSES[class_id]} {confidence:0.2f}"
    f"{CLASSES[class_id]} {confidence:0.2f}" if class_id is not None else f"{'other'} {confidence:0.2f}"
    for _, _, confidence, class_id, _ 
    in detections]
annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)

Any suggestions would be highly useful.

[github-actions]

👋 Hello @sivaji123256, thank you for leaving an issue on Roboflow Notebooks.

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[SkalskiP]

Hi, @sivaji123256 👋🏻!

I think you can do it much easier. You have two options.

1. If you want to filter by area. Here is the docs link.

detections = detections[detections.area > AREA_TRESHOLD]

2. If you want to filter by box dimensions, the solution is a bit more hacky but quite concise.

w = detections.xyxy[:, 2] - detections.xyxy[:, 0]
h = detections.xyxy[:, 3] - detections.xyxy[:, 1]
detections = detections[(w > WIDTH_TRESHOLD) & (h > HEIGHT_TRESHOLD)]

You put that filtering part under grounding_dino_model.predict_with_classes call.

detections = grounding_dino_model.predict_with_classes(
    image=image,
    classes=enhance_class_name(class_names=CLASSES),
    box_threshold=BOX_TRESHOLD,
    text_threshold=TEXT_TRESHOLD
)

<<HERE>>

box_annotator = sv.BoxAnnotator()
labels = [
    f"{CLASSES[class_id]} {confidence:0.2f}" if class_id is not None else f"{'other'} {confidence:0.2f}"
    for _, _, confidence, class_id, _ 
    in detections]
annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)

[SkalskiP]

I'm closing the issue. Feel free to reopen in case you'll have more questions.