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| import cv2 | |
| import numpy as np | |
| import streamlit as st | |
| import tensorflow as tf | |
| from PIL import Image | |
| from tensorflow.keras.preprocessing import image | |
| from keras.preprocessing.image import img_to_array | |
| from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2,preprocess_input as mobilenet_v2_preprocess_input | |
| cnn = tf.keras.models.load_model("saved_model/cnn_brain_model_9_11.h5") | |
| resnet = tf.keras.models.load_model("saved_model/resnet_brain_model_9_11.h5") | |
| vgg = tf.keras.models.load_model("saved_model/vgg_brain_model_9_11.h5") | |
| ### load file | |
| uploaded_file = st.file_uploader("Choose a image file", type="jpg") | |
| map_dict = {0: 'demented', | |
| 1: 'glioma', | |
| 2: 'healthy', | |
| 3: 'meningioma', | |
| 4: 'pituitary'} | |
| if uploaded_file is not None: | |
| file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8) | |
| opencv_image = cv2.imdecode(file_bytes, 1) | |
| image = Image.fromarray(opencv_image, 'RGB') | |
| image = image.resize((200, 200)) | |
| image=np.array(image) | |
| input_img = np.expand_dims(image, axis=0) | |
| st.image(image, channels="RGB") | |
| img=[] | |
| img.append(input_img) | |
| img = np.array(img) | |
| print(img.shape) | |
| img = img.astype(np.float32) / 255.0 | |
| Genrate_pred = st.button("Generate Prediction") | |
| if Genrate_pred: | |
| prediction = cnn.predict(img[0]).argmax() | |
| st.title("Predicted Label for the image by cnn model is {}".format(map_dict [prediction])) | |
| prediction = resnet.predict(img[0]).argmax() | |
| st.title("Predicted Label for the image by resnet model is {}".format(map_dict [prediction])) | |
| prediction = vgg.predict(img[0]).argmax() | |
| st.title("Predicted Label for the image by vgg model is {}".format(map_dict [prediction])) | |