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| import streamlit as st | |
| import tensorflow as tf | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import cv2 | |
| from tensorflow.python.keras.models import load_model | |
| from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix | |
| class GradCAM(object): | |
| def __init__(self, model, alpha=0.8, beta=0.3): | |
| self.model = model | |
| self.alpha = alpha | |
| self.beta = beta | |
| def apply_heatmap(self, heatmap, image): | |
| heatmap = cv2.resize(heatmap, image.shape[:-1]) | |
| heatmap = cv2.applyColorMap(np.uint8(255 * heatmap), cv2.COLORMAP_JET) | |
| superimposed_img = cv2.addWeighted(np.array(image).astype(np.float32), self.alpha, | |
| np.array(heatmap).astype(np.float32), self.beta, 0) | |
| return np.array(superimposed_img).astype(np.uint8) | |
| def gradCAM(self, x_test=None, name='block5_conv3', index_class=0): | |
| with tf.GradientTape() as tape: | |
| last_conv_layer = self.model.get_layer(name) | |
| grad_model = tf.keras.Model([self.model.input], [self.model.output, last_conv_layer.output]) | |
| model_out, last_conv_layer = grad_model(np.expand_dims(x_test, axis=0)) | |
| class_out = model_out[:, index_class] | |
| grads = tape.gradient(class_out, last_conv_layer) | |
| pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) | |
| last_conv_layer = last_conv_layer[0] | |
| heatmap = last_conv_layer @ pooled_grads[..., tf.newaxis] | |
| heatmap = tf.squeeze(heatmap) | |
| heatmap = np.maximum(heatmap, 0) | |
| heatmap /= np.max(heatmap) | |
| heatmap = np.array(heatmap) | |
| return self.apply_heatmap(heatmap, x_test) | |
| # Streamlit app | |
| st.title("Grad-CAM Visualization") | |
| uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| try: | |
| # Load the uploaded image | |
| file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8) | |
| img = cv2.imdecode(file_bytes, 1) | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| st.image(img, caption='Uploaded Image.', use_column_width=True) | |
| # Preprocess the image for the model (assuming the model expects 224x224 images) | |
| img_resized = cv2.resize(img, (224, 224)) | |
| img_array = np.expand_dims(img_resized, axis=0) | |
| # Load the model | |
| model_path = 'model_renamed.h5' # Update this path to your model's path | |
| model = tf.keras.models.load_model(model_path) | |
| # Initialize GradCAM | |
| grad_cam = GradCAM(model) | |
| # Compute GradCAM heatmap | |
| heatmap_img = grad_cam.gradCAM(img_array[0]) | |
| # Display the GradCAM heatmap | |
| st.image(heatmap_img, caption='Grad-CAM Heatmap.', use_column_width=True) | |
| except Exception as e: | |
| st.error(f"Error: {e}") |