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imagePath='The IQ-OTHNCCD lung cancer dataset/Bengin cases/Bengin case (1).jpg' img = cv.imread(imagePath, cv.IMREAD_GRAYSCALE) img = cv.resize(img, (528, 528)) numOfImg = 1 test = img.copy() path_save='result' segment_result = get_segmented_lungs(imagePath, numOfImg, show_on_window=True) list_filename_img = os.listdir(path_save) list_filename_img=sorted(list_filename_img, reverse=False) list_img = [{"filename": os.path.join('..',path_save, filename),"title":filename[:-4].replace("_"," ")} for filename in list_filename_img] # path_image=os.path.join(path_save, list_filename_img[-1]) path_image="dataset/after_preprocessing/train_test/test/Bengin/Bengin(3).jpg" filename_model_hybrid = os.path.join('.',"best_model", 'cnn3_svm_bestParam.pkl') model_hybrid= pickle.load(open(filename_model_hybrid,'rb')) img = cv.imread(path_image, cv.IMREAD_GRAYSCALE) img = cv.resize(img, (128, 128)) predict_img = np.expand_dims(img, 0) predict_img = predict_img.reshape(-1, 128, 128, 1) extraction_predict_img = cnn3_hybrid.predict(predict_img) result_predict_img = model_hybrid.predict(extraction_predict_img) print("🚀 ~ file: myapp.py:150 ~ result_predict_img:", result_predict_img)
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