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import gradio as gr
from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image, ImageDraw
# โหลดโมเดลจาก Hugging Face
model_name = "facebook/detr-resnet-50" # ตัวอย่างโมเดล DETR
processor = DetrImageProcessor.from_pretrained(model_name)
model = DetrForObjectDetection.from_pretrained(model_name)
# ฟังก์ชันตรวจจับวัตถุ/อวัยวะ
def detect_objects(image):
# แปลงรูปเป็น tensor
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
# ดึงผลลัพธ์
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.5)[0]
draw = ImageDraw.Draw(image)
boxes_info = []
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
draw.rectangle(box, outline="red", width=3)
boxes_info.append({
"box": box,
"label": model.config.id2label[label.item()],
"score": round(score.item(), 3)
})
return image, boxes_info
# สร้าง UI ด้วย Gradio
with gr.Blocks() as demo:
img_input = gr.Image(type="pil")
output_img = gr.Image(type="pil")
output_info = gr.JSON()
btn = gr.Button("ตรวจจับอวัยวะ")
btn.click(detect_objects, inputs=img_input, outputs=[output_img, output_info])
demo.launch() |