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import spaces
from transformers import Owlv2Processor, Owlv2ForObjectDetection, AutoProcessor, AutoModelForZeroShotObjectDetection
import torch
import gradio as gr
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dino_processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-base")
dino_model = AutoModelForZeroShotObjectDetection.from_pretrained("IDEA-Research/grounding-dino-base").to("cuda")
@spaces.GPU
def infer(img, text_queries, score_threshold, model):
if model == "dino":
queries=""
for query in text_queries:
queries += f"{query}. "
width, height = img.shape[:2]
target_sizes=[(width, height)]
inputs = dino_processor(text=queries, images=img, return_tensors="pt").to(device)
with torch.no_grad():
outputs = dino_model(**inputs)
outputs.logits = outputs.logits.cpu()
outputs.pred_boxes = outputs.pred_boxes.cpu()
results = dino_processor.post_process_grounded_object_detection(outputs=outputs, input_ids=inputs.input_ids,
box_threshold=score_threshold,
target_sizes=target_sizes)
boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]
result_labels = []
for box, score, label in zip(boxes, scores, labels):
box = [int(i) for i in box.tolist()]
if score < score_threshold:
continue
if model == "dino":
if label != "":
result_labels.append((box, label))
return result_labels
def query_image(img, text_queries, dino_threshold):
text_queries = text_queries
text_queries = text_queries.split(",")
dino_output = infer(img, text_queries, dino_threshold, "dino")
return (img, dino_output)
dino_threshold = gr.Slider(0, 1, value=0.12, label="Grounding DINO Threshold")
dino_output = gr.AnnotatedImage(label="Grounding DINO Output")
demo = gr.Interface(
query_image,
inputs=[gr.Image(label="Input Image"), gr.Textbox(label="Candidate Labels"), dino_threshold],
outputs=[ dino_output],
title="Grounding DINO DSA2024",
description="DSA2024 Space to evaluate state-of-the-art [Grounding DINO](https://huggingface.co/IDEA-Research/grounding-dino-base) zero-shot object detection model. Simply upload an image and enter a list of the objects you want to detect with comma, or try one of the examples. Play with the threshold to filter out low confidence predictions in the model.",
examples=[["./deer.jpg", "zebra, deer, goat", 0.16], ["./zebra.jpg", "zebra, lion, deer", 0.16]]
)
demo.launch(debug=True)
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