Update api.py

api.py

@@ -1,20 +1,51 @@
-import cv2
-import tensorflow as tf
+from fastapi import FastAPI, File, UploadFile
 import numpy as np
-from fastapi import FastAPI, UploadFile, File
+import tensorflow as tf
+from fastapi.middleware.cors import CORSMiddleware
+import cv2
 from pydantic import BaseModel
 from huggingface_hub import from_pretrained_keras
 
 app = FastAPI()
 
+origins = ["*"]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+#filtre kısmı
+def crop_image_from_gray(img, tol=7):
+    if img.ndim == 2:
+        mask = img > tol
+        return img[np.ix_(mask.any(1), mask.any(0))]
+    elif img.ndim == 3:
+        gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+        mask = gray_img > tol
+        check_shape = img[:, :, 0][np.ix_(mask.any(1), mask.any(0))].shape[0]
+        if check_shape == 0:
+            return img
+        else:
+            img1 = img[:, :, 0][np.ix_(mask.any(1), mask.any(0))]
+            img2 = img[:, :, 1][np.ix_(mask.any(1), mask.any(0))]
+            img3 = img[:, :, 2][np.ix_(mask.any(1), mask.any(0))]
+            img = np.stack([img1, img2, img3], axis=-1)
+        return img
+
+
 def load_ben_color(image, sigmaX=10):
     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image = crop_image_from_gray(image)
     image = cv2.resize(image, (224, 224))
     image = cv2.addWeighted(image, 4, cv2.GaussianBlur(image, (0, 0), sigmaX), -4, 128)
     return image
 
+
 def clahe(image):
-    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
     r, g, b = cv2.split(image)
     r = clahe.apply(r)
     g = clahe.apply(g)
@@ -22,15 +53,18 @@ def clahe(image):
     result = cv2.merge((r, g, b))
     return result
 
+
 def filter1(image):
     image = load_ben_color(image)
     return image
 
+
 def filter2(image):
     image = clahe(image)
     image = cv2.resize(image, (224, 224))
     return image
 
+
 def predict(image, model, filter_func):
     model_image = filter_func(image)
     model_image = np.array([model_image], dtype=np.float32) / 255.0
@@ -48,7 +82,7 @@ def result(predictions):
 models_names = ["ODIR-B-2K-5Class-LastTrain-Xception", "ODIR-B-2K-6Class-LastTrain-Xception"]
 model_paths = ["Bitirme/odirmodel/ODIR-B-2K-5Class-LastTrain-Xception", "Bitirme/odirmodel/ODIR-B-2K-6Class-LastTrain-Xception"]
 
-filters = [filter1, filter2]  # İhtiyacınıza göre filtre fonksiyonları burada tanımlandı
+filters = [filter1, filter2]  tanımlandı
 
 models = []
 for model_path in model_paths:
@@ -70,4 +104,4 @@ async def predict_endpoint(file: UploadFile = File(...)):
         prediction = predict(image, model, filters[i])
         result_json[models_names[i]] = result(prediction)
 
-    return {"predictions": result_json}
+    return {"predictions": result_json}