ds

app.py

@@ -1,74 +1,18 @@
-from fastapi import FastAPI, File, UploadFile, HTTPException
-from fastapi.responses import JSONResponse
 from PIL import Image
-import numpy as np
+import requests
 from transformers import SamModel, SamProcessor
-import io
-import base64
-import torch
-import uvicorn
 
-app = FastAPI(title="SAM-ViT-Base API")
-
-# SAM modelini ve işlemciyi yükle
 model = SamModel.from_pretrained("facebook/sam-vit-base")
 processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
 
-@app.post("/segment/")
-async def segment_image(file: UploadFile = File(...)):
-    try:
-        # Görüntüyü oku
-        image_data = await file.read()
-        image = Image.open(io.BytesIO(image_data)).convert("RGB")
-        
-        # Görüntü boyutlarını al
-        original_width, original_height = image.size
-        if original_width < 64 or original_height < 64:
-            raise HTTPException(status_code=400, detail=f"Görüntü boyutu çok küçük: {original_width}x{original_height}. Minimum 64x64 piksel olmalı.")
-        
-        # Görüntüyü işlemciye hazırla
-        inputs = processor(image, return_tensors="pt", do_rescale=True, do_resize=True)
-        
-        # Model ile segmentasyon yap
-        with torch.no_grad():
-            outputs = model(**inputs)
-        
-        # Maskeleri al
-        masks = outputs.pred_masks.detach().cpu().numpy()  # Shape: (batch_size, num_masks, height, width)
-        if masks.shape[1] == 0:
-            raise HTTPException(status_code=500, detail="Hiç maske üretilmedi.")
-        
-        # En iyi maskeyi seç
-        iou_scores = outputs.iou_scores.detach().cpu().numpy()  # Shape: (batch_size, num_masks)
-        if iou_scores.shape[1] > 1:
-            best_mask_idx = np.argmax(iou_scores[0])  # En yüksek skora sahip maskeyi seç
-        else:
-            best_mask_idx = 0  # Tek maske varsa onu kullan
-        mask = masks[0][best_mask_idx]  # Shape: (height, width)
-        
-        # Maske şeklini kontrol et
-        if len(mask.shape) != 2:
-            raise HTTPException(status_code=500, detail=f"Hatalı maske şekli: {mask.shape}. 2D matris bekleniyor.")
-        
-        # Maskeyi binary hale getir
-        mask = (mask > 0).astype(np.uint8) * 255
-        
-        # Maskeyi orijinal görüntü boyutlarına yeniden boyutlandır
-        mask_image = Image.fromarray(mask).resize((original_width, original_height), Image.NEAREST)
-        
-        # Maskeyi PNG olarak kaydet
-        buffered = io.BytesIO()
-        mask_image.save(buffered, format="PNG")
-        mask_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
-        
-        return JSONResponse(content={"mask": f"data:image/png;base64,{mask_base64}"})
-    
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
+raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
+input_points = [[[450, 600]]] # 2D localization of a window
+
+inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to("cuda")
+outputs = model(**inputs)
+masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu())
+scores = outputs.iou_scores
 
-@app.get("/")
-async def root():
-    return {"message": "SAM-ViT-Base API çalışıyor. /segment endpoint'ine görüntü yükleyin."}
 
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+print(scores)

request.py

@@ -5,6 +5,7 @@ from io import BytesIO
 
 url = "https://sezer91-sam.hf.space/segment/"
 file_path = "img.jpeg"
+file_path = "input.jpg"
 
 try:
     # Görüntü dosyasını kontrol et