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| import numpy as np | |
| import pandas as pd | |
| import cv2 as cv | |
| import streamlit as st | |
| import matplotlib.pyplot as plt | |
| from skimage.feature import graycomatrix, graycoprops | |
| import joblib | |
| indextable = ['dissimilarity', 'contrast', | |
| 'homogeneity', 'energy', 'correlation', 'Label'] | |
| obj = { | |
| 0.0: "Normal", | |
| 1.0: "Cataract", | |
| 2.0: "Glaucoma", | |
| 3.0: 'Retina Disease' | |
| } | |
| width, height = 400, 400 | |
| distance = 10 | |
| teta = 90 | |
| # Code to extract features from Image using Gray Level Co occurrence Image | |
| def get_feature(matrix, name): | |
| feature = graycoprops(matrix, name) | |
| result = np.average(feature) | |
| return result | |
| def preprocessingImage(image): | |
| test_img = cv.cvtColor(image, cv.COLOR_BGR2RGB) | |
| test_img_gray = cv.cvtColor(test_img, cv.COLOR_RGB2GRAY) | |
| test_img_thresh = cv.adaptiveThreshold( | |
| test_img_gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, 11, 3) | |
| cnts = cv.findContours( | |
| test_img_thresh, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) | |
| cnts = cnts[0] if len(cnts) == 2 else cnts[1] | |
| cnts = sorted(cnts, key=cv.contourArea, reverse=True) | |
| for c in cnts: | |
| x, y, w, h = cv.boundingRect(c) | |
| test_img_ROI = test_img[y:y+h, x:x+w] | |
| break | |
| test_img_ROI_resize = cv.resize(test_img_ROI, (width, height)) | |
| test_img_ROI_resize_gray = cv.cvtColor( | |
| test_img_ROI_resize, cv.COLOR_RGB2GRAY) | |
| return test_img_ROI_resize_gray | |
| def extract(path): | |
| data_eye = np.zeros((5, 1)) | |
| # path = cv.imread(path) | |
| img = preprocessingImage(path) | |
| glcm = graycomatrix(img, [distance], [teta], | |
| levels=256, symmetric=True, normed=True) | |
| for i in range(len(indextable[:-1])): | |
| features = [] | |
| feature = get_feature(glcm, indextable[i]) | |
| features.append(feature) | |
| data_eye[i, 0] = features[0] | |
| return pd.DataFrame(np.transpose(data_eye), columns=indextable[:-1]) | |
| """ | |
| Return predicted class with its probability | |
| """ | |
| model = joblib.load("model.pkl") | |
| def predict(path): | |
| X = extract(path) | |
| y = model.predict(X)[0] | |
| prob = model.predict_proba(X)[0, int(y)] | |
| return (obj[y], prob) | |