Basic data set generation
import numpy as np
import os
from scipy.misc import imread, imresize
import matplotlib.pyplot as plt
%matplotlib inline
print ("Package loaded")
cwd = os.getcwd()
print ("Current folder is %s" % (cwd) )
Package loaded
Current folder is /home/enginius/github/tensorflow-101/notebooks
SPECIFY THE FOLDER PATHS
+ RESHAPE SIZE + GRAYSCALE
paths = {"../../img_dataset/celebs/Arnold_Schwarzenegger"
, "../../img_dataset/celebs/Junichiro_Koizumi"
, "../../img_dataset/celebs/Vladimir_Putin"
, "../../img_dataset/celebs/George_W_Bush"}
imgsize = [64, 64]
use_gray = 1
data_name = "custom_data"
print ("Your images should be at")
for i, path in enumerate(paths):
print (" [%d/%d] %s/%s" % (i, len(paths), cwd, path))
print ("Data will be saved to %s"
% (cwd + '/data/' + data_name + '.npz'))
Your images should be at
[0/4] /home/enginius/github/tensorflow-101/notebooks/../../img_dataset/celebs/George_W_Bush
[1/4] /home/enginius/github/tensorflow-101/notebooks/../../img_dataset/celebs/Arnold_Schwarzenegger
[2/4] /home/enginius/github/tensorflow-101/notebooks/../../img_dataset/celebs/Junichiro_Koizumi
[3/4] /home/enginius/github/tensorflow-101/notebooks/../../img_dataset/celebs/Vladimir_Putin
Data will be saved to /home/enginius/github/tensorflow-101/notebooks/data/custom_data.npz
RGB 2 GRAY FUNCTION
def rgb2gray(rgb):
if len(rgb.shape) is 3:
return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])
else:
return rgb
LOAD IMAGES
nclass = len(paths)
valid_exts = [".jpg",".gif",".png",".tga", ".jpeg"]
imgcnt = 0
for i, relpath in zip(range(nclass), paths):
path = cwd + "/" + relpath
flist = os.listdir(path)
for f in flist:
if os.path.splitext(f)[1].lower() not in valid_exts:
continue
fullpath = os.path.join(path, f)
currimg = imread(fullpath)
if use_gray:
grayimg = rgb2gray(currimg)
else:
grayimg = currimg
graysmall = imresize(grayimg, [imgsize[0], imgsize[1]])/255.
grayvec = np.reshape(graysmall, (1, -1))
curr_label = np.eye(nclass, nclass)[i:i+1, :]
if imgcnt is 0:
totalimg = grayvec
totallabel = curr_label
else:
totalimg = np.concatenate((totalimg, grayvec), axis=0)
totallabel = np.concatenate((totallabel, curr_label), axis=0)
imgcnt = imgcnt + 1
print ("Total %d images loaded." % (imgcnt))
Total 681 images loaded.
DIVIDE TOTAL DATA INTO TRAINING AND TEST SET
def print_shape(string, x):
print ("Shape of '%s' is %s" % (string, x.shape,))
randidx = np.random.randint(imgcnt, size=imgcnt)
trainidx = randidx[0:int(3*imgcnt/5)]
testidx = randidx[int(3*imgcnt/5):imgcnt]
trainimg = totalimg[trainidx, :]
trainlabel = totallabel[trainidx, :]
testimg = totalimg[testidx, :]
testlabel = totallabel[testidx, :]
print_shape("trainimg", trainimg)
print_shape("trainlabel", trainlabel)
print_shape("testimg", testimg)
print_shape("testlabel", testlabel)
Shape of 'trainimg' is (408, 4096)
Shape of 'trainlabel' is (408, 4)
Shape of 'testimg' is (273, 4096)
Shape of 'testlabel' is (273, 4)
SAVE TO NPZ
savepath = cwd + "/data/" + data_name + ".npz"
np.savez(savepath, trainimg=trainimg, trainlabel=trainlabel
, testimg=testimg, testlabel=testlabel, imgsize=imgsize, use_gray=use_gray)
print ("Saved to %s" % (savepath))
Saved to /home/enginius/github/tensorflow-101/notebooks/data/custom_data.npz
LOAD TO CHECK!
cwd = os.getcwd()
loadpath = cwd + "/data/" + data_name + ".npz"
l = np.load(loadpath)
l.files
trainimg_loaded = l['trainimg']
trainlabel_loaded = l['trainlabel']
testimg_loaded = l['testimg']
testlabel_loaded = l['testlabel']
print ("%d train images loaded" % (trainimg_loaded.shape[0]))
print ("%d test images loaded" % (testimg_loaded.shape[0]))
print ("Loaded from to %s" % (savepath))
408 train images loaded
273 test images loaded
Loaded from to /home/enginius/github/tensorflow-101/notebooks/data/custom_data.npz
PLOT RANDOMLY SELECTED TRAIN IMAGES
ntrain_loaded = trainimg_loaded.shape[0]
batch_size = 10;
randidx = np.random.randint(ntrain_loaded, size=batch_size)
for i in randidx:
currimg = np.reshape(trainimg_loaded[i, :], (imgsize[0], -1))
currlabel_onehot = trainlabel_loaded[i, :]
currlabel = np.argmax(currlabel_onehot)
if use_gray:
currimg = np.reshape(trainimg[i, :], (imgsize[0], -1))
plt.matshow(currimg, cmap=plt.get_cmap('gray'))
plt.colorbar()
else:
currimg = np.reshape(trainimg[i, :], (imgsize[0], imgsize[1], 3))
plt.imshow(currimg)
title_string = "[%d] %d-class" % (i, currlabel)
plt.title(title_string)
plt.show()










PLOT RANDOMLY SELECTED TEST IMAGES
ntest_loaded = testimg_loaded.shape[0]
batch_size = 3;
randidx = np.random.randint(ntest_loaded, size=batch_size)
for i in randidx:
currimg = np.reshape(testimg_loaded[i, :], (imgsize[0], -1))
currlabel_onehot = testlabel_loaded[i, :]
currlabel = np.argmax(currlabel_onehot)
if use_gray:
currimg = np.reshape(testimg[i, :], (imgsize[0], -1))
plt.matshow(currimg, cmap=plt.get_cmap('gray'))
plt.colorbar()
else:
currimg = np.reshape(testimg[i, :], (imgsize[0], imgsize[1], 3))
plt.imshow(currimg)
title_string = "[%d] %d-class" % (i, currlabel)
plt.title(title_string)
plt.show()


