IndexError: list index out of range at line 25 - python-3.x

Question

import numpy as np
import cv2, os
import sys
import pandas as pd
import tensorflow as tf
from model import Tensorflow_Model
class dl_model():
EXT_TRAIN_DATA = 'D:\Diabetic_Retinopathy_Detection-master\data\train'
EXT_TEST_DATA = 'D:\Diabetic_Retinopathy_Detection-master\data\test'
EXT_TRAIN_CSV = 'D:\Diabetic_Retinopathy_Detection-master\data\trainLabels.csv'
IMAGE_WIDTH = 512 #1536
IMAGE_HEIGHT = 340 #1024
N_CHANNELS = 3
GENERATOR_BATCH_SIZE = 100
NB_EPOCH_PER_BATCH = 2
NB_EPOCH = 5
def __init__(self, argv):
self.argv = argv
self.BASE_PATH = argv[0]
self.dims_image = {'width': self.IMAGE_WIDTH, 'height': self.IMAGE_HEIGHT, 'channel': self.N_CHANNELS}
self.dims_output = 5
def get_image_name_list(self, path, train_or_not):
if train_or_not:
training_csv = pd.read_csv(path)
headers = training_csv.columns
return np.array([training_csv[headers[0]], training_csv[headers[1]]])
else:
return np.array([os.listdir(path)])
def get_image_names(self):
self.train_image_names_with_labels = self.get_image_name_list(os.path.join(self.BASE_PATH, self.EXT_TRAIN_CSV), 1) # returns a tuple
self.test_image_names = self.get_image_name_list(os.path.join(self.BASE_PATH, self.EXT_TEST_DATA), 0) # returns just names
print('Number of training images: {}\nNumber of testing images: {}'.format(len(self.train_image_names_with_labels[0]), len(self.test_image_names[0])))
def image_transformation(self, image_path):
img = cv2.imread(image_path)
img = cv2.resize(img, (self.IMAGE_WIDTH, self.IMAGE_HEIGHT))
return np.array(img).reshape((self.IMAGE_HEIGHT, self.IMAGE_WIDTH, self.N_CHANNELS))
def image_batch_generator(self, array, batch_size, ext):
path = os.path.join(self.BASE_PATH, ext)
for i in range(0, len(array[0]), batch_size):
batch = array[0][i: i+batch_size]
data_batch = []
for j, image_name in enumerate(batch):
try:
if ext == self.EXT_TRAIN_DATA:
image_path = '{}.jpeg'.format(os.path.join(path, image_name))
data_batch.append((self.image_transformation(image_path), array[1][i+j]))
else:
image_path = '{}'.format(os.path.join(path, image_name))
data_batch.append(self.image_transformation(image_path))
except:
print('Error reading: {}'.format(image_path))
yield(np.array(data_batch))
def execute(self):
with tf.device('/cpu:0'):
self.get_image_names()
training_batch_generator = self.image_batch_generator(self.train_image_names_with_labels, self.GENERATOR_BATCH_SIZE, self.EXT_TRAIN_DATA)
tf_model = Tensorflow_Model(self.dims_image, self.dims_output) # CALCULATE dims_output
# TRAINING PHASE
for i, training_batch in enumerate(training_batch_generator):
if not i > self.NB_EPOCH:
tf_model.train(training_batch)
else:
break
# test_batch_generator = self.image_batch_generator(self.test_image_names, self.BATCH_SIZE, self.EXT_TEST_DATA)
if __name__ == '__main__':
dl_model(sys.argv[1:]).execute()