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convert_colors_HDF5.py
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convert_colors_HDF5.py
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from random import shuffle
import glob
import cv2
shuffle_data = True # shuffle the addresses before saving
images_path = '/path_to_color_dataset_images/*.jpg'
train_hdf5_path = '/path_to_files/train.h5' # address to where you want to save the hdf5 file
test_hdf5_path = '/path_to_files/test.h5'
# read addresses and labels from the 'train' folder
addrs = glob.glob(images_path)
i= -1
labels = []
for addr in addrs:
i = i + 1
labels.append(i)
if 'black' in addr:
labels[i] = 0 # 0 = Coupe, 1=Convertible, 2 = Sedan, 3 = Van, 4 = SUV, 5 = Truck, 6 = Wagon, 7 = Hatchback.
elif 'white' in addr:
labels[i] = 1
elif 'gray' in addr:
labels[i] = 2
elif 'green' in addr:
labels[i] = 3
elif 'cyan' in addr:
labels[i] = 4
elif 'blue' in addr:
labels[i] = 5
elif 'red' in addr:
labels[i] = 6
elif 'yellow' in addr:
labels[i] = 7
else:
labels[i] = 10
print 'Error at ' + i + 'Failed to label'
print addr
exit()
# to shuffle data
if shuffle_data:
c = list(zip(addrs, labels))
shuffle(c)
addrs, labels = zip(*c)
train_addrs = addrs[0:int(0.85*len(addrs))]
train_labels = labels[0:int(0.85*len(addrs))]
test_addrs = addrs[int(0.85*len(addrs)):int(len(addrs))]
test_labels = labels[int(0.85*len(addrs)):int(len(addrs))]
import numpy as np
import h5py
data_order = 'tf' # 'th' for Theano, 'tf' for Tensorflow
SIZE = 227
train_shape = (len(train_addrs), 3, SIZE, SIZE)
# open a hdf5 file and create earrays
hdf5_file = h5py.File(train_hdf5_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(train_addrs),), np.int8)
hdf5_file["label"][...] = train_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(train_addrs)):
# print how many images are saved every 1000 images
if i % 1000 == 0 and i > 1:
print 'Train data: {}/{}'.format(i, len(train_addrs))
# read an image and resize to (SIZE, SIZE)
# cv2 load images as BGR, convert it to RGB
addr = train_addrs[i]
img = cv2.imread(addr)
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(train_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(test_hdf5_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)
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()