app.py

import streamlit as st
import torch
from transformers import Owlv2Processor, Owlv2ForObjectDetection
from PIL import Image, ImageDraw, ImageFont
import numpy as np
import random

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")

st.title("Zero-Shot Object Detection with OWLv2")

uploaded_image = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
text_queries = st.text_input("Enter text queries (comma-separated):")
score_threshold = st.slider("Score Threshold", min_value=0.0, max_value=1.0, value=0.1, step=0.01)


def query_image(img, text_queries, score_threshold):
  try:
    img = Image.open(img).convert("RGB")
    img_np = np.array(img)
    text_queries = text_queries.split(",")

    size = max(img_np.shape[:2])
    target_sizes = torch.Tensor([[size, size]])
    inputs = processor(text=text_queries, images=img_np, 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_queries[label.item()]))

    return img, result_labels

  except Exception as e:
    st.error(f"Error performing object detection: {e}")


if uploaded_image is not None:
  annotated_image, detected_objects = query_image(uploaded_image, text_queries, score_threshold)
  if annotated_image:
    draw = ImageDraw.Draw(annotated_image)
    font = ImageFont.load_default()
    for box, label in detected_objects:
      color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
      draw.rectangle(box, outline=color, width=3)
      draw.text((box[0], box[1]), label, fill="black", font=font)
    st.image(annotated_image, caption="Annotated Image", use_column_width=True)