File size: 2,423 Bytes
fc741f1 7b52e04 fc741f1 c685cf7 cde77b5 fc741f1 cde77b5 fc741f1 c685cf7 fc741f1 c685cf7 fc741f1 cde77b5 fc741f1 26e6832 fc741f1 26e6832 fc741f1 f48495a cde77b5 fc741f1 26e6832 fc741f1 f48495a fc741f1 49a8630 fc741f1 7b52e04 fc741f1 7b52e04 fc741f1 c685cf7 fc741f1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import cv2
import tensorflow as tf
import numpy as np
from fastapi import FastAPI, UploadFile, File
from pydantic import BaseModel
from huggingface_hub import from_pretrained_keras
app = FastAPI()
def load_ben_color(image, sigmaX=10):
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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))
r, g, b = cv2.split(image)
r = clahe.apply(r)
g = clahe.apply(g)
b = clahe.apply(b)
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
predictions = model(tf.constant(model_image))
return predictions.numpy()
def result(predictions):
class_labels = ["Age related Macular Degeneration", "Cataract", "Diabetes", "Glaucoma", "Hypertension", "Normal", "Others", "Pathological Myopia"]
predictions = np.array(predictions)
predictions = predictions.tolist()[0]
predictions_index = np.argmax(predictions)
return class_labels[predictions_index]
# Model tanımlamaları
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
models = []
for model_path in model_paths:
model = from_pretrained_keras(model_path)
models.append(model)
class PredictionResponse(BaseModel):
predictions: dict
@app.post("/predict", response_model=PredictionResponse)
async def predict_endpoint(file: UploadFile = File(...)):
contents = await file.read()
nparr = np.frombuffer(contents, np.uint8)
image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
result_json = {}
for i in range(len(models)):
model = models[i]
prediction = predict(image, model, filters[i])
result_json[models_names[i]] = result(prediction)
return {"predictions": result_json}
|