def main():
IMG_SIZE = (80, 80)
channels = 1
char_path = r'../input/the-simpsons-characters-dataset/simpsons_dataset'
char_dict = {}
for char in os.listdir(char_path):
char_dict[char] = len(os.listdir(os.path.join(char_path, char)))
# Sort in descending order
char_dict = caer.sort_dict(char_dict, descending=True)
# Get top 10 characters
characters = []
count = 0
for i in char_dict:
characters.append(i[0])
count += 1
if count >= 10:
break
# Create the training data
train = caer.preprocess_from_dir(char_path,
characters,
channels=channels,
IMG_SIZE=IMG_SIZE,
isShuffle=True)
# Separate feature set and labels
featureSet, labels = caer.sep_train(train, IMG_SIZE=IMG_SIZE)
# Normalize the featureSet in (0, 1)
featureSet = caer.normalize(featureSet)
labels = to_categorical(labels, len(characters))
x_train, x_test, y_train, y_test = caer.train_val_split(featureSet,
labels,
val_ratio=0.2)
del train
del featureSet
del labels
gc.collect()
BATCH_SIZE = 32
EPOCHS = 10
# Create new data generator
data_gen = canaro.generators.imageDataGenerator()
train_gen = data_gen.flow(np.array(x_train),
np.array(y_train),
batch_size=BATCH_SIZE)
# Create a model
model = canaro.models.createSimpsonsModel(IMG_SIZE=IMG_SIZE,
channels=channels,
output_dim=len(characters),
loss='binary_crossentropy',
decay=1e-6,
learning_rate=0.001,
momentum=0.9,
nesterov=True)
callbacks_list = [LearningRateScheduler(canaro.lr_schedule)]
training = model.fit(train_gen,
steps_per_epoch=len(x_train) // BATCH_SIZE,
epochs=EPOCHS,
validation_data=(np.array(x_test), np.array(y_test)),
validation_steps=len(y_test) // BATCH_SIZE,
callbacks=callbacks_list)
test_path = r'../input/the-simpsons-characters-dataset/kaggle_simpson_testset/kaggle_simpson_testset/charles_montgomery_burns_0.jpg'
img = cv2.imread(test_path)
predictions = model.predict(prepare(img))
print(characters[np.argmax(predictions[0])])
import matplotlib.pyplot as plt
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import LearningRateScheduler
IMG_SIZE = (80,80)
channels = 1
char_path = r'../input/the-simpsons-characters-dataset/simpsons_dataset'
# Creating a character dictionary, sorting it in descending order
char_dict = {}
for char in os.listdir(char_path):
char_dict[char] = len(os.listdir(os.path.join(char_path,char)))
# Sort in descending order
char_dict = caer.sort_dict(char_dict, descending=True)
char_dict
# Getting the first 10 categories with the most number of images
characters = []
count = 0
for i in char_dict:
characters.append(i[0])
count += 1
if count >= 10:
break
characters
# Create the training data
train = caer.preprocess_from_dir(char_path, characters, channels=channels, IMG_SIZE=IMG_SIZE, isShuffle=True)
image_size = (80, 80)
# gray scale image, hence only 1 channel
channels = 1
base_path = 'simposons/simpsons_dataset'
trial_dict = {}
for file in os.listdir(base_path):
subfile_path = os.path.join(base_path, file)
subfiles = glob('{}/*'.format(subfile_path))
# print(file, len(subfiles))
trial_dict[file] = len(subfiles)
print(trial_dict)
# sort in descending order
trial_dict = caer.sort_dict(unsorted_dict=trial_dict, descending=True)
print(trial_dict)
characters = []
count = 0
for keys in trial_dict:
characters.append(keys[0])
count += 1
if count > 10:
break
print(characters)
# create a training data
train = caer.preprocess_from_dir(DIR=base_path,
classes=characters,