Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| from PIL import Image | |
| import tensorflow as tf | |
| import numpy as np | |
| import cv2 | |
| from skimage import img_as_float | |
| from skimage.segmentation import chan_vese | |
| from tensorflow.keras.applications.efficientnet import preprocess_input | |
| model = tf.keras.models.load_model('efficientnetb2_model_v2.h5') | |
| # Define class labels | |
| CLASSES = ['Basal cell carcinoma', 'Benign keratosis', 'Melanoma'] | |
| def inpainted_image(image): | |
| gray_image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2GRAY) | |
| median_filtered_image = cv2.medianBlur(gray_image, 5) | |
| kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 15)) | |
| bottom_hat = cv2.morphologyEx(median_filtered_image, cv2.MORPH_BLACKHAT, kernel) | |
| _, hair_mask = cv2.threshold(bottom_hat, 10, 255, cv2.THRESH_BINARY) | |
| inpainted_image = cv2.inpaint(median_filtered_image, hair_mask, 3, cv2.INPAINT_TELEA) | |
| return inpainted_image | |
| def gac_segmentation(image): | |
| image = img_as_float(image) | |
| segmented_image = chan_vese(image, mu=0.25, lambda1=1, lambda2=1, tol=1e-3, max_num_iter=40, | |
| dt=0.5, init_level_set="checkerboard", extended_output=True) | |
| return segmented_image[0] | |
| def preprocess_image(image): | |
| image = image.resize((260, 260)) | |
| image = preprocess_input(np.array(image)) | |
| return image | |
| # Function to make predictions | |
| def predict(image): | |
| image = preprocess_image(image) | |
| image = np.expand_dims(image, axis=0) | |
| prediction = model.predict(image) | |
| predicted_label_index = np.argmax(prediction) | |
| predicted_label = CLASSES[predicted_label_index] | |
| return predicted_label, prediction | |
| def main(): | |
| st.title('Skin Lesion Classifier') | |
| st.write('Upload an image of a skin lesion for classification.') | |
| uploaded_image = st.file_uploader('Choose an image...', type=['jpg', 'png', 'jpeg']) | |
| if uploaded_image is not None: | |
| image = Image.open(uploaded_image) | |
| st.image(image, caption='Uploaded Image', use_column_width=True) | |
| if st.button('Classify'): | |
| with st.spinner('Processing and Classifying...'): | |
| # Preprocess the image | |
| inpainted_img = inpainted_image(image) | |
| # Apply GAC segmentation | |
| segmented_img = gac_segmentation(inpainted_img) | |
| # Convert segmented image to a format suitable for prediction | |
| segmented_img = (segmented_img * 255).astype(np.uint8) | |
| segmented_img_pil = Image.fromarray(segmented_img).convert("RGB").resize((260, 260)) | |
| # Predict | |
| predicted_label, prediction = predict(segmented_img_pil) | |
| # Display results | |
| st.image(segmented_img, caption='Preprocessed and Segmented Image', use_column_width=True) | |
| st.write('Predicted Label:', predicted_label) | |
| st.write('Prediction Scores:', prediction) | |
| if __name__ == '__main__': | |
| main() | |