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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="OWLv2 ⚔ Grounding DINO",
description="Evaluate state-of-the-art [Grounding DINO](https://huggingface.co/IDEA-Research/grounding-dino-base) zero-shot object detection models. Simply enter an image and the objects you want to find with comma, or try one of the examples. Play with the threshold to filter out low confidence predictions in the model.",
examples=[["./warthog.jpg", "zebra, warthog", 0.16], ["./zebra.png", "zebra, lion", 0.16]]
)
demo.launch(debug=True) |