| import cv2 | |
| import tensorflow as tf | |
| import numpy as np | |
| from fastapi import FastAPI, UploadFile, File | |
| from pydantic import BaseModel | |
| import uvicorn | |
| app = FastAPI() | |
| 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)) | |
| 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): | |
| model_image = filter(image) | |
| model_image = np.array([model_image], dtype=np.float32) / 255.0 | |
| infer = model.signatures["serving_default"] | |
| predictions = infer(tf.constant(model_image))[next(iter(infer.structured_outputs.keys()))].numpy() | |
| return predictions | |
| 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) | |
| result_json = { | |
| "class": class_labels[predictions_index], | |
| "probability": predictions[predictions_index] | |
| } | |
| return result_json | |
| # Model tanımlamaları | |
| models_names = ["ODIR-B-2K-5Class-LastTrain-Xception"] | |
| models_paths = [ | |
| "ODIR-B-2K-5Class-LastTrain-Xception" | |
| ] | |
| filters = [filter1, filter1, filter1, filter1] | |
| models = [] | |
| for model_path in models_paths: | |
| model = tf.saved_model.load(model_path) | |
| models.append(model) | |
| async def predict_endpoint(file: UploadFile = File(...)): | |
| contents = await file.read() | |
| nparr = np.fromstring(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 result_json |