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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}") |