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generator.py
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generator.py
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import os
import cv2
import random
import numpy as np
from visualize import visualize
from preprocess.datagen import label_generator
from preprocess.augmentation import rotation, translate, crop, noise, salt
def train_generator(sample_per_batch, batch_number):
""" Generating training data """
train_image_file = []
directory = '../EgoGesture Dataset/'
folder_name = ['SingleOne', 'SingleTwo', 'SingleThree', 'SingleFour', 'SingleFive',
'SingleSix', 'SingleSeven', 'SingleEight']
for folder in folder_name:
train_image_file = train_image_file + os.listdir(directory + folder + '/')
for i in range(0, 10):
random.shuffle(train_image_file)
print('Training Dataset Size: {0}'.format(len(train_image_file)))
while True:
for i in range(0, batch_number - 1):
start = i * sample_per_batch
end = (i + 1) * sample_per_batch
x_batch = []
y_batch_prob = []
y_batch_pos = []
for n in range(start, end):
image_name = train_image_file[n]
try:
image, probability, position = label_generator(directory=directory,
image_name=image_name,
type='')
except cv2.error:
print(image_name)
continue
# 1.0 Original image
x_batch.append(image)
y_batch_prob.append(probability)
pos = np.array([position, ] * 10)
y_batch_pos.append(pos)
# visualize(image, probability, pos[0])
""" Augmentation """
# 2.0 Original + translate
im, pos = translate(image, probability, position)
x_batch.append(im)
y_batch_prob.append(probability)
pos = np.array([pos, ] * 10)
y_batch_pos.append(pos)
# visualize(im, probability, pos[0])
# 3.0 Original + rotation
im, pos = rotation(image, probability, position)
x_batch.append(im)
y_batch_prob.append(probability)
pos = np.array([pos, ] * 10)
y_batch_pos.append(pos)
# visualize(im, probability, pos[0])
# 4.0 Original + salt
im, pos = salt(image, probability, position)
x_batch.append(im)
y_batch_prob.append(probability)
pos = np.array([pos, ] * 10)
y_batch_pos.append(pos)
# visualize(im, probability, pos[0])
# 5.0 Original + crop
im, pos = crop(image, probability, position)
x_batch.append(im)
y_batch_prob.append(probability)
pos = np.array([pos, ] * 10)
y_batch_pos.append(pos)
# visualize(im, probability, pos)
# 6.0 Original + noise
im, pos = noise(image, probability, position)
x_batch.append(im)
y_batch_prob.append(probability)
pos = np.array([pos, ] * 10)
y_batch_pos.append(pos)
# visualize(im, probability, pos[0])
# 7.0 Original + rotate + translate
im, pos = rotation(image, probability, position)
im, pos = translate(im, probability, pos)
x_batch.append(im)
y_batch_prob.append(probability)
pos = np.array([pos, ] * 10)
y_batch_pos.append(pos)
# visualize(im, probability, pos)
# 8.0 Original + rotate + crop
im, pos = rotation(image, probability, position)
im, pos = crop(im, probability, pos)
x_batch.append(im)
y_batch_prob.append(probability)
pos = np.array([pos, ] * 10)
y_batch_pos.append(pos)
# visualize(im, probability, pos[0])
x_batch = np.asarray(x_batch)
x_batch = x_batch.astype('float32')
x_batch = x_batch / 255.
y_batch_prob = np.asarray(y_batch_prob)
y_batch_pos = np.asarray(y_batch_pos)
y_batch_pos = y_batch_pos.astype('float32')
y_batch_pos = y_batch_pos / 128.
y_batch = [y_batch_prob, y_batch_pos]
yield (x_batch, y_batch)
def valid_generator(sample_per_batch, batch_number):
""" Generating validation data """
valid_image_file = []
directory = '../EgoGesture Dataset/'
folder_name = ['SingleOneValid', 'SingleTwoValid', 'SingleThreeValid', 'SingleFourValid', 'SingleFiveValid',
'SingleSixValid', 'SingleSevenValid', 'SingleEightValid']
for folder in folder_name:
valid_image_file = valid_image_file + os.listdir(directory + folder + '/')
# print(len(valid_image_file))
while True:
for i in range(0, batch_number - 1):
start = i * sample_per_batch
end = (i + 1) * sample_per_batch
x_batch = []
y_batch_prob = []
y_batch_pos = []
for n in range(start, end):
image_name = valid_image_file[n]
try:
image, probability, position = label_generator(directory=directory,
image_name=image_name,
type='Valid')
except cv2.error:
print(image_name)
continue
# 1.0 Original image
x_batch.append(image)
y_batch_prob.append(probability)
pos = np.array([position, ] * 10)
y_batch_pos.append(pos)
x_batch = np.asarray(x_batch)
x_batch = x_batch.astype('float32')
x_batch = x_batch / 255.
y_batch_prob = np.asarray(y_batch_prob)
y_batch_pos = np.asarray(y_batch_pos)
y_batch_pos = y_batch_pos.astype('float32')
y_batch_pos = y_batch_pos / 128.
y_batch = [y_batch_prob, y_batch_pos]
yield (x_batch, y_batch)
if __name__ == '__main__':
gen = train_generator(sample_per_batch=100, batch_number=220)
batch_x, batch_y = next(gen)
print(batch_x)
print(batch_y)