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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 loadedCurrent folder is /home/enginius/github/tensorflow-101/notebooks# Training set folder paths = { "../../img_dataset/celebs/Arnold_Schwarzenegger" , "../../img_dataset/celebs/Junichiro_Koizumi" , "../../img_dataset/celebs/Vladimir_Putin" , "../../img_dataset/celebs/George_W_Bush" } # The reshape size imgsize = [, ] # Grayscale use_gray = # Save name 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_PutinData will be saved to /home/enginius/github/tensorflow-101/notebooks/data/custom_data.npzdef rgb2gray (rgb) : if len(rgb.shape) is : return np.dot(rgb[...,:], [ 0.299 , 0.587 , 0.114 ]) else : # print ("Current Image if GRAY!") return rgbnclass = len(paths)valid_exts = [ ".jpg" , ".gif" , ".png" , ".tga" , ".jpeg" ]imgcnt = for i, relpath in zip(range(nclass), paths): path = cwd + "/" + relpath flist = os.listdir(path) for f in flist: if os.path.splitext(f)[].lower() not in valid_exts: continue fullpath = os.path.join(path, f) currimg = imread(fullpath) # Convert to grayscale if use_gray: grayimg = rgb2gray(currimg) else : grayimg = currimg # Reshape graysmall = imresize(grayimg, [imgsize[], imgsize[]])/ grayvec = np.reshape(graysmall, (, -1 )) # Save curr_label = np.eye(nclass, nclass)[i:i+, :] if imgcnt is : totalimg = grayvec totallabel = curr_label else : totalimg = np.concatenate((totalimg, grayvec), axis=) totallabel = np.concatenate((totallabel, curr_label), axis=) imgcnt = imgcnt + print ( "Total %d images loaded." % (imgcnt))Total 681 images loaded.def print_shape (string, x) : print ( "Shape of '%s' is %s" % (string, x.shape,))randidx = np.random.randint(imgcnt, size=imgcnt)trainidx = randidx[:int(*imgcnt/)]testidx = randidx[int(*imgcnt/):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)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 them! cwd = os.getcwd()loadpath = cwd + "/data/" + data_name + ".npz" l = np.load(loadpath) # See what's in here l.files # Parse data trainimg_loaded = l[ 'trainimg' ]trainlabel_loaded = l[ 'trainlabel' ]testimg_loaded = l[ 'testimg' ]testlabel_loaded = l[ 'testlabel' ] print ( "%d train images loaded" % (trainimg_loaded.shape[])) print ( "%d test images loaded" % (testimg_loaded.shape[])) print ( "Loaded from to %s" % (savepath))408 train images loaded273 test images loadedLoaded from to /home/enginius/github/tensorflow-101/notebooks/data/custom_data.npzntrain_loaded = trainimg_loaded.shape[]batch_size = ;randidx = np.random.randint(ntrain_loaded, size=batch_size) for i in randidx: currimg = np.reshape(trainimg_loaded[i, :], (imgsize[], -1 )) currlabel_onehot = trainlabel_loaded[i, :] currlabel = np.argmax(currlabel_onehot) if use_gray: currimg = np.reshape(trainimg[i, :], (imgsize[], -1 )) plt.matshow(currimg, cmap=plt.get_cmap( 'gray' )) plt.colorbar() else : currimg = np.reshape(trainimg[i, :], (imgsize[], imgsize[], )) plt.imshow(currimg) title_string = "[%d] %d-class" % (i, currlabel) plt.title(title_string) plt.show()
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