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| import streamlit as st | |
| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.applications.vgg16 import preprocess_input | |
| from tensorflow.keras.preprocessing import image | |
| # Loading Models | |
| # braintumor_model = load_model('models/brain_tumor_binary.h5') | |
| braintumor_model = load_model('models/braintumor.h5') | |
| # Configuring Streamlit | |
| st.set_page_config(page_title="Brain Tumor Prediction App", page_icon=":brain:") | |
| def preprocess_image(img): | |
| # If it's a NumPy array, use it directly | |
| if isinstance(img, np.ndarray): | |
| img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) | |
| else: | |
| # Convert Gradio image data to bytes | |
| img_bytes = img.read() | |
| # Convert to NumPy array | |
| nparr = np.frombuffer(img_bytes, np.uint8) | |
| # Decode image | |
| img_gray = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE) | |
| # Crop and preprocess the grayscale image | |
| img_processed = preprocess_imgs([img_gray], (224, 224)) | |
| return img_processed | |
| # Handle binary decision | |
| def binary_decision(confidence): | |
| return 1 if confidence >= 0.5 else 0 | |
| def predict_braintumor(img): | |
| # Preprocess the image | |
| img_processed = preprocess_image(img) | |
| # Make prediction | |
| pred = braintumor_model.predict(img_processed) | |
| # Handle binary decision | |
| confidence = pred[0][0] | |
| return "Brain Tumor Not Found!" if binary_decision(confidence) == 1 else "Brain Tumor Found!" | |
| def preprocess_imgs(set_name, img_size): | |
| set_new = [] | |
| for img in set_name: | |
| img = cv2.resize(img, dsize=img_size, interpolation=cv2.INTER_CUBIC) | |
| set_new.append(preprocess_input(img)) | |
| return np.array(set_new) | |
| def crop_imgs(set_name, add_pixels_value=0): | |
| set_new = [] | |
| for img in set_name: | |
| gray = cv2.GaussianBlur(img, (5, 5), 0) | |
| thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1] | |
| thresh = cv2.erode(thresh, None, iterations=2) | |
| thresh = cv2.dilate(thresh, None, iterations=2) | |
| cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) | |
| cnts = cnts[0] if len(cnts) == 2 else cnts[1] | |
| c = max(cnts, key=cv2.contourArea) | |
| extLeft = tuple(c[c[:, :, 0].argmin()][0]) | |
| extRight = tuple(c[c[:, :, 0].argmax()][0]) | |
| extTop = tuple(c[c[:, :, 1].argmin()][0]) | |
| extBot = tuple(c[c[:, :, 1].argmax()][0]) | |
| ADD_PIXELS = add_pixels_value | |
| new_img = img[extTop[1] - ADD_PIXELS:extBot[1] + ADD_PIXELS, | |
| extLeft[0] - ADD_PIXELS:extRight[0] + ADD_PIXELS].copy() | |
| set_new.append(new_img) | |
| return np.array(set_new) | |
| # Gradio interface | |
| iface = gr.Interface( | |
| fn=predict_braintumor, | |
| inputs="image", | |
| outputs="text", | |
| examples=[["examples/1_no.jpeg"], ["examples/2_no.jpeg"], ["examples/3_no.jpg"], ["examples/Y1.jpg"], ["examples/Y2.jpg"], ["examples/Y3.jpg"]] | |
| ) | |
| iface.launch() | |