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Macro.py
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Macro.py
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# ==================== Pre Process training image ===============
from scipy import ndimage
from imageio import imread
# from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
filenames = os.listdir(path + "Cvt")
train_files = []
categories = []
image_width = 128
image_height = 128
channels = 3
nb_classes = 1
# append stringlist to ndarray type
for filename in filenames:
category = filename.split('.')[0]
if category == 'dog':
train_files.append(filename)
categories.append(1)
else:
train_files.append(filename)
categories.append(0)
trainfiles = np.array(train_files)
category = np.array(categories)
# ===================== Split Data ===========================
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(trainfiles, category, test_size = .5, random_state = 55)
Save split data to file
np.save ('x_train.npy', x_train)
np.save ('y_train.npy', y_train)
np.save ('x_test.npy', x_test)
np.save ('y_test.npy', y_test)
# ================== put data into dataframe ===================
import cv2
import time
print('Converting images into array')
i = 0
rawdata = []
for file in x_train:
img = cv2.imread(path + "Cvt/" + file, 0)
rawdata.append(img.flatten())
i += 1
if i % 2500 == 0:
print("%d images to array" % i)
print('Saving progress')
start = time.time()
np.save('dataframe.npy', rawdata)
end = time.time()
print(end - start)
# ====================== Process test data ========================
print('Converting images into array')
i = 0
rawdat = []
for file in x_test:
img = cv2.imread(path + "Cvt/" + file, 0)
rawdat.append(img.flatten())
i += 1
if i % 2500 == 0:
print("%d images to array" % i)
import time
print('Saving progress')
start = time.time()
# df.to_csv('rawtest.csv', index = False)
np.save('rawtest.npy', rawdat)
end = time.time()
print(end - start)