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from fastapi import FastAPI, HTTPException, Response
from fastapi.responses import HTMLResponse
from transformers import pipeline, YolosForObjectDetection, YolosImageProcessor
from PIL import Image, ImageDraw
import torch
import requests
import io
import base64
# Create a new FastAPI app instance
app = FastAPI()
# Initialize the Yolos model and image processor
yolos_model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')
yolos_image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny")
@app.get("/detect-objects", response_class=HTMLResponse)
def detect_objects(url: str):
try:
# Download the image from the specified URL
image = Image.open(requests.get(url, stream=True).raw)
# Preprocess the image using the Yolos image processor
inputs = yolos_image_processor(images=image, return_tensors="pt")
# Run the Yolos model on the preprocessed image
outputs = yolos_model(**inputs)
# model predicts bounding boxes and corresponding COCO classes
logits = outputs.logits
pred_boxes = outputs.pred_boxes
# Post-process the object detection results
target_sizes = torch.tensor([image.size[::-1]])
results = yolos_image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
# Draw bounding boxes on the image
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
image_draw = ImageDraw.Draw(image)
image_draw.rectangle(box.tolist(), outline="red", width=2)
image_draw.text((box[0], box[1]), f"{yolos_model.config.id2label[label.item()]}: {round(score.item(), 3)}", fill="red")
# Save the modified image to a byte stream
image_byte_array = io.BytesIO()
image.save(image_byte_array, format="PNG")
# Return the image as a Response with content type "image/png"
return Response(content=image_byte_array.getvalue(), media_type="image/png")
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
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