| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from PIL import Image | |
| from tensorflow.keras.models import load_model | |
| model = load_model('my_model.keras') | |
| model.summary() | |
| def predict(img_path): | |
| class_dict = {'glioma': 0, 'meningioma': 1, 'notumor': 2, 'pituitary': 3} | |
| label = list(class_dict.keys()) | |
| plt.figure(figsize=(12, 12)) | |
| img = Image.open(img_path) | |
| resized_img = img.resize((299, 299)) | |
| img = np.asarray(resized_img) | |
| img = np.expand_dims(img, axis=0) | |
| img = img / 255 | |
| predictions = model.predict(img) | |
| probs = list(predictions[0]) | |
| labels = label | |
| plt.subplot(2, 1, 1) | |
| plt.imshow(resized_img) | |
| plt.subplot(2, 1, 2) | |
| bars = plt.barh(labels, probs) | |
| plt.xlabel('Olasılık', fontsize=15) | |
| ax = plt.gca() | |
| ax.bar_label(bars, fmt = '%.2f') | |
| plt.show() | |
| predict("Testing/notumor/Te-no_0010.jpg") |