| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import cv2 | |
| import keras | |
| import gradio as gr | |
| SHAPE = (224, 224, 3) | |
| predictor_disease_risk = keras.models.load_model('predictor_Disease_Risk.h5') | |
| predictor_dr = keras.models.load_model('predictor_DR.h5') | |
| predictor_mh = keras.models.load_model('predictor_MH.h5') | |
| predictor_odc = keras.models.load_model('predictor_ODC.h5') | |
| predictor_tsln = keras.models.load_model('predictor_TSLN.h5') | |
| predictor_dn = keras.models.load_model('predictor_DN.h5') | |
| predictor_armd = keras.models.load_model('predictor_ARMD.h5') | |
| predictor_mya = keras.models.load_model('predictor_MYA.h5') | |
| predictor_brvo = keras.models.load_model('predictor_BRVO.h5') | |
| def cut_and_resize(image): | |
| LOW_TOL = 20 | |
| img_bw = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| img_bw[img_bw<=LOW_TOL] = 0 | |
| y_nonzero, x_nonzero = np.nonzero(img_bw) | |
| image = image[np.min(y_nonzero):np.max(y_nonzero), np.min(x_nonzero): np.max(x_nonzero), ] | |
| return cv2.resize(image, SHAPE[:2], interpolation = cv2.INTER_LINEAR) | |
| def simple_normalizer(X): | |
| return X / 255.0 | |
| def predict (image_path): | |
| image = simple_normalizer(cut_and_resize(cv2.imread(image_path))) | |
| result = predictor_disease_risk.predict(np.array([image]))[0][0] | |
| dr = predictor_dr.predict(np.array([image]))[0][0] | |
| mh = predictor_mh.predict(np.array([image]))[0][0] | |
| odc = predictor_odc.predict(np.array([image]))[0][0] | |
| tsln = predictor_tsln.predict(np.array([image]))[0][0] | |
| dn = predictor_dn.predict(np.array([image]))[0][0] | |
| armd = predictor_armd.predict(np.array([image]))[0][0] | |
| mya = predictor_mya.predict(np.array([image]))[0][0] | |
| brvo = predictor_brvo.predict(np.array([image]))[0][0] | |
| diseases = { | |
| 'DR' : float(dr), | |
| 'MH' : float(mh), | |
| 'ODC' : float(odc), | |
| 'DN' : float(dn), | |
| 'TSLN': float(tsln), | |
| 'ARMD': float(armd), | |
| 'MYA' : float(mya), | |
| 'BRVO': float(brvo) | |
| } | |
| to_delete = [] | |
| for k,v in diseases.items(): | |
| if v < 0.05: | |
| to_delete.append(k) | |
| for k in to_delete: | |
| del diseases[k] | |
| if len(diseases) == 0: | |
| diseases = {'No specific disease': 0.0} | |
| return ( | |
| {'Enferma': float(result), 'Sana': 1 - float(result)}, diseases | |
| ) | |
| title = 'Retinal Disease Predictor' | |
| description = 'Modelo de deep learning que permite clasificar im谩genes de la retina en patol贸gicas y no patol贸gicas. Si detecta una retina enferma, realiza un diagn贸stico de la enfermedad concreta entre las siguientes: Diabetic Retinopathy (DR), Media Haze (MH), Optic Disk Cupping (ODC), Drusen (DN), Tessellation (TSLN), Age Related Macular Disease (ARMD), Myopia (MYA), Branch Retinal Vein Occlusion (BRVO) . Las im谩genes deben tener fondo negro.' | |
| article = 'Proyecto HORUS (Helping Oftalmoscopy of Retina Using Supervised Learning' | |
| interface = gr.Interface( | |
| predict, | |
| inputs = [gr.inputs.Image(source="upload",type="filepath", label="Imagen")], | |
| outputs= [gr.outputs.Label(num_top_classes=2, label='Retina'), gr.outputs.Label(num_top_classes=4, label='Enfermedad')], | |
| title = title, description = description, article = article, | |
| theme = 'peach', | |
| examples = ['10.png', '82.png', '15.png', '25.png', '48.png', '61.png', '37.png', '631.png', '23.png', '8.png'] | |
| ) | |
| interface.launch() |