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| import streamlit as st | |
| from PIL import Image | |
| import os | |
| import numpy as np | |
| import keras | |
| from keras.preprocessing.image import ImageDataGenerator | |
| from keras.applications import inception_v3 | |
| st.title("Vehicle Orientation Assessment") | |
| st.markdown("### 🚗🚘🚔🚖🚍 Classification Application") | |
| st.markdown("This application predicts the orientation of the Vehicle out of 9 Orientations relative to Driver side") | |
| menu = ["Select image from the below list", "Upload From Computer"] | |
| choice = st.sidebar.radio(label="Menu", options=["Select image from the below list", "choose your own image"]) | |
| # | |
| if choice == "Select image from the below list": | |
| file = st.sidebar.selectbox("choose your image", os.listdir("Images")) | |
| uploaded_file = os.path.join(os.getcwd(), "Images", file) | |
| else: | |
| uploaded_file = st.sidebar.file_uploader("Please upload an image:", type=['jpeg', 'jpg', 'png']) | |
| # Loading model | |
| model = keras.models.load_model('model/model_optimized') | |
| def predict(img_file): | |
| """[Predicting whether an image is blur or not] | |
| Args: | |
| img_file ([path]): [image file path] | |
| Returns: | |
| [string, float]: [Label for the image and also its score]] | |
| """ | |
| img = Image.open(img_file) | |
| new_img = np.asarray(img) | |
| image = keras.preprocessing.image.image_utils.load_img(img_file, | |
| grayscale=False, | |
| color_mode="rgb", | |
| target_size=(224, 224), | |
| interpolation="nearest", ) | |
| image_array = keras.preprocessing.image.image_utils.img_to_array(image) | |
| image_array = keras.applications.inception_v3.preprocess_input(image_array, data_format=None) | |
| input_arr = np.array([image_array]) # Convert single image to a batch. | |
| classes = {'Driver side front': 0, | |
| 'Driver side rear': 1, | |
| 'Passenger side front': 2, | |
| 'Passenger side rear': 3, | |
| 'Driver side': 4, | |
| 'Front': 5, | |
| 'Passenger side': 6, | |
| 'Rear': 7, | |
| 'Unknown': 8} | |
| # Label and score prediction based on pre-trained random forest model | |
| pred = model.predict(input_arr) | |
| class_label = {i for i in classes if classes[i] == pred.argmax(axis=1)} | |
| proba = np.max(pred) | |
| # Label and Score formatting | |
| pred_class_label = ''.join(class_label) | |
| pred_score = round(proba, 4) | |
| # Display image | |
| st.image(img, caption="Uploaded image", use_column_width=True) | |
| return pred_class_label, pred_score | |
| # pred_class_label, pred_score = predict('Images/12397.jpg') | |
| if uploaded_file is not None: | |
| pred_class_label, pred_score = predict(uploaded_file) | |
| st.write("**Orientation:**", pred_class_label) | |
| st.write("**Probability:**", f'{round(pred_score*100)}%') | |
| expander = st.expander("For more details !!") | |
| expander.write({"Orientation": pred_class_label, | |
| "Probability": pred_score}) | |