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| import streamlit as st | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import keras | |
| from sklearn.preprocessing import MinMaxScaler | |
| from keras.models import Model | |
| from keras.layers import Conv2D | |
| import cv2 | |
| import tensorflow as tf | |
| model = keras.models.load_model("model.keras") | |
| uploaded_img = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) | |
| options = ['1st Convolution', '2nd Convolution', '3rd Convolution'] | |
| selected_option = st.selectbox('Choose an option:', options) | |
| conv_layers = [layer for layer in model.layers if isinstance(layer, Conv2D)] | |
| fig = plt.figure(figsize=(12, 4)) | |
| layer_ind = options.index(selected_option) | |
| # selected_layer = conv_layers[layer_ind] | |
| scaler = MinMaxScaler() | |
| # for i in range(3): | |
| for j in range(6): | |
| layer=conv_layers[layer_ind] | |
| weights=layer.get_weights()[0][:,:,0,j] | |
| norm_weights = scaler.fit_transform(weights) | |
| plt.subplot(2,3,j+1) | |
| plt.imshow(norm_weights,cmap='gray') | |
| plt.title(f"Filters {j+1}") | |
| plt.axis('off') | |
| plt.tight_layout() | |
| st.pyplot(fig) | |
| if uploaded_img is not None: | |
| #st.image(uploaded_img, caption="Uploaded Image", use_column_width=True) | |
| file_bytes = np.frombuffer(uploaded_img.read(), np.uint8) | |
| img = cv2.imdecode(file_bytes, cv2.IMREAD_GRAYSCALE) | |
| img_resized = cv2.resize(img,(28,28),interpolation=cv2.INTER_AREA) | |
| #img_norm = img_resized.astype('float32') / 255.0 | |
| input_img = img_resized.reshape(1,28,28,1) | |
| st.image(img_norm, caption="Uploaded Image (Resized to 28x28)", use_container_width =True, channels="GRAY") | |
| #layer_ind = options.index(selected_option) | |
| selected_layer = conv_layers[layer_ind] | |
| #func_model = Model(inputs = model.layers[0].input, outputs = model.selected_layer.output) | |
| func_model = Model(inputs = model.layers[0].input, outputs = selected_layer.output) | |
| fm = func_model.predict(input_img) | |
| fm = fm[0] | |
| if layer_ind == 0: | |
| fig1 = plt.figure(figsize=(12, 4)) | |
| for i in range(6): | |
| plt.subplot(2, 3, i + 1) | |
| plt.imshow(fm[:, :, i], cmap='gray') | |
| plt.title(f"Feature Map {i+1}") | |
| plt.axis('off') | |
| elif layer_ind == 1: | |
| fig1 = plt.figure(figsize=(25, 15)) | |
| for i in range(16): | |
| plt.subplot(2, 8, i + 1) | |
| plt.imshow(fm[:, :, i], cmap='gray') | |
| plt.title(f"Feature Map {i+1}") | |
| plt.axis('off') | |
| elif layer_ind == 2: | |
| fig1 = plt.figure(figsize=(100, 50)) | |
| for i in range(120): | |
| plt.subplot(30, 4, i + 1) | |
| plt.imshow(fm[:, :, i],cmap="gray") | |
| plt.title(f"Feature Map {i+1}") | |
| plt.axis('off') | |
| plt.tight_layout() | |
| st.pyplot(fig1) | |