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convert_cars_2_hdf5_make.py
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convert_cars_2_hdf5_make.py
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from random import shuffle
import glob
import cv2
shuffle_data = True # shuffle the addresses before saving
hdf5_train1_path = '/path_to_file/train1.h5' # address to where you want to save the hdf5 file
hdf5_train2_path = '/path_to_file/train2.h5' # address to where you want to save the hdf5 file
car_train_path = '/path_to_dir/*.jpg'
hdf5_test_path = '/path_to_file/test.h5'
# read addresses and labels from the 'train' folder
addrs = glob.glob(car_train_path)
i= -1
labels = []
for addr in addrs:
i = i + 1
labels.append(i)
if 'Audi' in addr:
labels[i] = 0
elif 'BMW' in addr:
labels[i] = 1
elif 'Buick' in addr:
labels[i] = 2
elif 'Chevrolet' in addr:
labels[i] = 3
elif 'Dodge' in addr:
labels[i] = 4
elif 'Ford' in addr:
labels[i] = 5
elif 'GMC' in addr:
labels[i] = 6
elif 'Honda' in addr:
labels[i] = 7
elif 'Infiniti' in addr:
labels[i] = 8
elif 'Jeep' in addr:
labels[i] = 9
elif 'Kia' in addr:
labels[i] = 10
elif 'Lexus' in addr:
labels[i] = 11
elif 'Lincoln' in addr:
labels[i] = 12
elif 'Mazda' in addr:
labels[i] = 13
elif 'Mercedes-benz' in addr:
labels[i] = 14
elif 'Nissan' in addr:
labels[i] = 15
elif 'Porsche' in addr:
labels[i] = 16
elif 'Toyota' in addr:
labels[i] = 17
elif 'Volkswagen' in addr:
labels[i] = 18
elif 'Volvo' in addr:
labels[i] = 19
# to shuffle data
if shuffle_data:
c = list(zip(addrs, labels))
shuffle(c)
addrs, labels = zip(*c)
train1_addrs = addrs[0:int(0.5*len(addrs))]
train1_labels = labels[0:int(0.5*len(addrs))]
train2_addrs = addrs[int(0.5*len(addrs)):int(0.9*len(addrs))]
train2_labels = labels[int(0.5*len(addrs)):int(0.9*len(addrs))]
test_addrs = addrs[int(0.9*len(addrs)):int(len(addrs))]
test_labels = labels[int(0.9*len(addrs)):int(len(addrs))]
import numpy as np
import h5py
data_order = 'tf' # 'th' for Theano, 'tf' for Tensorflow
SIZE = 224
train_shape = (len(train1_addrs), 3, SIZE, SIZE)
# open a hdf5 file and create earrays
hdf5_file = h5py.File(hdf5_train1_path, mode='w')
hdf5_file.create_dataset("data", train_shape, np.int8)
# hdf5_file.create_dataset("train_mean", train_shape[1:], np.float32)
hdf5_file.create_dataset("label", (len(train1_addrs),), np.int8)
hdf5_file["label"][...] = train1_labels
# a numpy array to save the mean of the images
mean = np.zeros(train_shape[1:], np.float32)
# loop over train addresses
for i in range(len(train1_addrs)):
# print how many images are saved every 1000 images
if i % 1000 == 0 and i > 1:
print 'Train1 data: {}/{}'.format(i, len(train1_addrs))
# read an image and resize to (SIZE, SIZE)
# cv2 load images as BGR, convert it to RGB
addr = train1_addrs[i]
# print addr
img = cv2.imread(addr)
if img is not None:
img = cv2.resize(img, (SIZE, SIZE), interpolation=cv2.INTER_CUBIC)
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# add any image pre-processing here
img = np.rollaxis(img, 2)
# save the image and calculate the mean so far
hdf5_file["data"][i, ...] = img[None]
mean += img / float(len(train1_labels))
# save the mean and close the hdf5 file
# hdf5_file["train_mean"][...] = mean
hdf5_file.close()
train_shape = (len(train2_addrs), 3, SIZE, SIZE)
# open a hdf5 file and create earrays
hdf5_file = h5py.File(hdf5_train2_path, mode='w')
hdf5_file.create_dataset("data", train_shape, np.int8)
# hdf5_file.create_dataset("train_mean", train_shape[1:], np.float32)
hdf5_file.create_dataset("label", (len(train2_addrs),), np.int8)
hdf5_file["label"][...] = train2_labels
# a numpy array to save the mean of the images
mean = np.zeros(train_shape[1:], np.float32)
# loop over train addresses
for i in range(len(train2_addrs)):
# print how many images are saved every 1000 images
if i % 1000 == 0 and i > 1:
print 'Train2 data: {}/{}'.format(i, len(train2_addrs))
# read an image and resize to (SIZE, SIZE)
# cv2 load images as BGR, convert it to RGB
addr = train2_addrs[i]
img = cv2.imread(addr)
if img is not None:
img = cv2.resize(img, (SIZE, SIZE), interpolation=cv2.INTER_CUBIC)
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# add any image pre-processing here
img = np.rollaxis(img, 2)
# save the image and calculate the mean so far
hdf5_file["data"][i, ...] = img[None]
mean += img / float(len(train2_labels))
# save the mean and close the hdf5 file
# hdf5_file["train_mean"][...] = mean
hdf5_file.close()
test_shape = (len(test_addrs), 3, SIZE, SIZE)
# open a hdf5 file and create earrays
hdf5_file = h5py.File(hdf5_test_path, mode='w')
hdf5_file.create_dataset("data", test_shape, np.int8)
# hdf5_file.create_dataset("train_mean", train_shape[1:], np.float32)
hdf5_file.create_dataset("label", (len(test_addrs),), np.int8)
hdf5_file["label"][...] = test_labels
# a numpy array to save the mean of the images
mean = np.zeros(test_shape[1:], np.float32)
# loop over train addresses
for i in range(len(test_addrs)):
# print how many images are saved every 1000 images
if i % 100 == 0 and i > 1:
print 'Test data: {}/{}'.format(i, len(test_addrs))
# read an image and resize to (SIZE, SIZE)
# cv2 load images as BGR, convert it to RGB
addr = test_addrs[i]
img = cv2.imread(addr)
if img is not None:
img = cv2.resize(img, (SIZE, SIZE), interpolation=cv2.INTER_CUBIC)
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# add any image pre-processing here
img = np.rollaxis(img, 2)
# save the image and calculate the mean so far
hdf5_file["data"][i, ...] = img[None]
mean += img / float(len(test_labels))
# save the mean and close the hdf5 file
# hdf5_file["train_mean"][...] = mean
hdf5_file.close()