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import tensorflow as tf
from tensorflow.keras.models import Model
from keras.models import load_model
import gradio as gr
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.applications.densenet import preprocess_input, decode_predictions
import numpy as np
from scipy import ndimage
from skimage import exposure
from skimage.transform import resize
from PIL import Image
import matplotlib.pyplot as plt
import cv2
model = load_model('Densenet.h5')
model.load_weights("pretrained_model.h5")
layer_name = 'conv5_block16_concat'
class_names = ['Cardiomegaly', 'Emphysema', 'Effusion', 'Hernia', 'Infiltration', 'Mass', 'Nodule', 'Atelectasis', 'Pneumothorax', 'Pleural_Thickening', 'Pneumonia', 'Fibrosis', 'Edema', 'Consolidation', 'No Finding']
def get_gradcam(model, img, layer_name):
img_array = img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = preprocess_input(img_array)
grad_model = Model(inputs=model.inputs, outputs=[model.get_layer(layer_name).output, model.output])
with tf.GradientTape() as tape:
conv_outputs, predictions = grad_model(img_array)
class_idx = tf.argmax(predictions[0])
output = conv_outputs[0]
grads = tape.gradient(predictions, conv_outputs)[0]
guided_grads = tf.cast(output > 0, 'float32') * tf.cast(grads > 0, 'float32') * grads
weights = tf.reduce_mean(guided_grads, axis=(0, 1))
cam = tf.reduce_sum(tf.multiply(weights, output), axis=-1)
heatmap = np.maximum(cam, 0)
heatmap /= tf.reduce_max(heatmap)
heatmap_img = plt.cm.jet(heatmap)[..., :3]
# Load the original image
original_img = Image.fromarray(img)
# Resize the heatmap to match the original image size
heatmap_img = Image.fromarray((heatmap_img * 255).astype(np.uint8))
heatmap_img = heatmap_img.resize(original_img.size)
# Overlay the heatmap on the original image
overlay_img = Image.blend(original_img, heatmap_img, 0.5)
# Return the overlayed image
return overlay_img
def custom_decode_predictions(predictions, class_labels):
decoded_predictions = []
for pred in predictions:
# Get indices of top predicted classes
top_indices = pred.argsort()[-4:][::-1] # Change 5 to the number of top classes you want to retrieve
# Decode each top predicted class
decoded_pred = [(class_labels[i], pred[i]) for i in top_indices]
decoded_predictions.append(decoded_pred)
return decoded_predictions
def classify_image(img):
img = cv2.resize(img, (540, 540), interpolation=cv2.INTER_AREA)
img_array = img_to_array(img)
#img_array = exposure.equalize_hist(img_array)
img_array = np.expand_dims(img_array, axis=0)
img_array = preprocess_input(img_array)
predictions1 = model.predict(img_array)
decoded_predictions = custom_decode_predictions(predictions1, class_names)
overlay_img = get_gradcam(model, img, layer_name)
# Return the decoded predictions and the overlayed image
return decoded_predictions, overlay_img
# Gradio interface
iface = gr.Interface(
fn=classify_image,
inputs="image",
outputs=["text", "image"], # Add an "image" output for the overlayed image
title="Xray Classification - KIMS",
description="Classify cxr into 'Cardiomegaly', 'Emphysema', 'Effusion', 'Hernia', 'Infiltration', 'Mass', 'Nodule', 'Atelectasis', 'Pneumothorax', 'Pleural_Thickening', 'Pneumonia', 'Fibrosis', 'Edema', 'Consolidation', 'No Finding'. Built by Dr Sai Koundinya")
# Launch the interface,
iface.launch( share=True)
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