|
import torch |
|
import gradio as gr |
|
from transformers import Owlv2Processor, Owlv2ForObjectDetection |
|
import spaces |
|
|
|
|
|
if torch.cuda.is_available(): |
|
device = torch.device("cuda") |
|
else: |
|
device = torch.device("cpu") |
|
|
|
model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble").to(device) |
|
processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble") |
|
|
|
def query_image(Upload_Image, Text, score_threshold): |
|
Text = Text |
|
Text = Text.split(",") |
|
|
|
size = max(Upload_Image.shape[:2]) |
|
target_sizes = torch.Tensor([[size, size]]) |
|
inputs = processor(text=Text, images=Upload_Image, return_tensors="pt").to(device) |
|
|
|
with torch.no_grad(): |
|
outputs = model(**inputs) |
|
|
|
outputs.logits = outputs.logits.cpu() |
|
outputs.pred_boxes = outputs.pred_boxes.cpu() |
|
results = processor.post_process_object_detection(outputs=outputs, 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 |
|
result_labels.append((box, Text[label.item()])) |
|
return Upload_Image, result_labels |
|
|
|
|
|
description = """ |
|
You can use AnyVision to query images with text descriptions of any object. |
|
To use it, simply upload an image and enter comma separated text descriptions of objects you want to query the image for. You |
|
can also use the score threshold slider to set a threshold to filter out low probability predictions. |
|
|
|
You can get better predictions by querying the image with text templates used in training the original model: e.g. *"photo of a star-spangled banner"*, |
|
*"image of a shoe"*. |
|
""" |
|
demo = gr.Interface( |
|
query_image, |
|
inputs=[gr.Image(), "text", gr.Slider(0, 1, value=0.1)], |
|
outputs="annotatedimage", |
|
title="AnyVision - Zero-Shot Object Detector with Owl2", |
|
description=description |
|
) |
|
demo.launch() |
|
|
|
|
