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cifar_util.py
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import pickle
import os
import numpy as np
import cv2
from PIL import Image
import random
def SaveImage(image_array,im_output_path):
save_im = image_array
save_im = (255.0 / save_im.max() * (save_im - save_im.min())).astype(np.uint8)
im = Image.fromarray(save_im)
im.save(im_output_path)
def LabelToOneHot(label,num_classes):
y = [0]*num_classes
y[label] = 1
return np.array(y)
def unpickle(file):
with open(file, 'rb') as fo:
dict = pickle.load(fo)
return dict
def FlatCifarToImageArray(flat_image_array):
channel_size = flat_image_array.shape[0] / 3
r = flat_image_array[:channel_size].reshape(32,32)
g = flat_image_array[channel_size:2*channel_size].reshape(32,32)
b = flat_image_array[2*channel_size:].reshape(32,32)
return np.dstack([r,g,b])
def LoadCifarData(num_im_to_load):
cifar_dir = "cifar_data"
data_set = "cifar-10-batches-py"
training_dir = "training"
test_dir = "test"
training_path = os.path.join(cifar_dir,data_set,training_dir)
test_path = os.path.join(cifar_dir,data_set,test_dir)
training_batch_files = os.listdir(training_path)
cifar_images = []
cifar_labels = []
for input_i in range(num_im_to_load):
pass
def LoadCifarDataFromImages(num_im_to_load, train = False):
cifar_dir = "cifar_data"
if(train):
images_path = os.path.join(cifar_dir,"cifar_10_images","train")
else:
images_path = os.path.join(cifar_dir,"cifar_10_images","test")
image_names = os.listdir(images_path)
selected_images = random.sample(image_names,num_im_to_load)
cifar_images = []
cifar_labels = []
for selected_image in selected_images:
image_path = os.path.join(images_path,selected_image)
cifar_images.append(cv2.imread(image_path)[...,::-1])
cifar_labels.append( LabelToOneHot(int(selected_image.split("_")[-1].replace(".jpg","")),10) )
return np.array(cifar_images), np.array(cifar_labels)
def LoadGSCifarData(num_im_to_load, train = False):
xs, ys, = LoadCifarDataFromImages(num_im_to_load,train)
gs_xs = []
for image_i in range(num_im_to_load):
image = xs[image_i,:,:,:]
gs_image = np.dot(image[...,:3],[0.299,0.587,0.114])/255
gs_image = gs_image.reshape(32,32,1)
gs_xs.append(gs_image)
return np.array(gs_xs), ys
if __name__ == '__main__':
training = False
test = True
cifar_dir = "cifar_data"
data_set = "cifar-10-batches-py"
training_dir = "training"
test_dir = "test"
training_path = os.path.join(cifar_dir,data_set,training_dir)
test_path = os.path.join(cifar_dir,data_set,test_dir)
training_batch_files = os.listdir(training_path)
test_batch_files = os.listdir(test_path)
output_dir = os.path.join(cifar_dir,"cifar_10_images")
if(not os.path.exists(output_dir)):
os.mkdir(output_dir)
if(training):
output_save_path = os.path.join(output_dir,"training")
if(not os.path.exists(output_save_path)):
os.mkdir(output_save_path)
for batch_i in range(len(training_batch_files))[:]:
training_batch_file = training_batch_files[batch_i]
batch_file_path = os.path.join(training_path,training_batch_file)
training_batch_dict = unpickle(batch_file_path)
batch_size = training_batch_dict["data"].shape[0]
for image_i in range(batch_size)[:]:
gt_label = training_batch_dict["labels"][image_i]
flat_image_array = training_batch_dict["data"][image_i,:]
image_array = FlatCifarToImageArray(flat_image_array)
im_output_path = os.path.join(output_save_path,str(batch_i)+"_"+str(image_i)+"_"+str(gt_label)+".jpg")
SaveImage(image_array,im_output_path)
if(test):
output_save_path = os.path.join(output_dir,"test")
if(not os.path.exists(output_save_path)):
os.mkdir(output_save_path)
for batch_i in range(len(test_batch_files))[:]:
test_batch_file = test_batch_files[batch_i]
batch_file_path = os.path.join(test_path,test_batch_file)
test_batch_dict = unpickle(batch_file_path)
batch_size = test_batch_dict["data"].shape[0]
for image_i in range(batch_size)[:]:
gt_label = test_batch_dict["labels"][image_i]
flat_image_array = test_batch_dict["data"][image_i,:]
image_array = FlatCifarToImageArray(flat_image_array)
im_output_path = os.path.join(output_save_path,str(batch_i)+"_"+str(image_i)+"_"+str(gt_label)+".jpg")
SaveImage(image_array,im_output_path)
# images, labels = LoadCifarDataFromImages(2)
# print(images.shape)
# print(labels.shape)
# print(labels[0])
# #SaveImage(images[0,:,:,:],"test_output.jpg")
# images, labels = LoadGSCifarData(2)
# print(images.shape)
# print(labels.shape)
# print(labels[0])
# SaveImage(images[0,:,:],"gs_test_output.jpg")