def getBatch(batchSize, path): allPaths = image.list_pictures(path) allIms = np.zeros((batchSize, 3, sampSize, sampSize)) for i in xrange(batchSize): f1 = int(np.sign(np.random.rand() - .5)) f2 = int(np.sign(np.random.rand() - .5)) im = image.load_img(allPaths[int(np.random.rand() * len(allPaths))]).resize((newSize, newSize)) r = int(np.random.rand() * (newSize - sampSize)) c = int(np.random.rand() * (newSize - sampSize)) im = im.crop((r, c, r + sampSize, c + sampSize)) im = image.random_rotation(im, 5) allIms[i, :, :, :] = image.img_to_array(im) allIms[i, :, :, :] = allIms[i, :, ::-f1, ::-f2] return allIms/255.
default=False,
action='store_true')
parser.add_argument('-v', '--verbose', default=False, action='store_true')
parser.add_argument('--force', default=False, action='store_true')
args = parser.parse_args()
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
if args.input.endswith('.tsv') or args.input.endswith('.txt'):
allPaths = np.loadtxt(args.input, dtype=str).tolist()
else:
allPaths = image.list_pictures(args.input)
# Generate encodings
if not args.encoding:
# build CAE model
def get_encoding(i):
paths = split_paths[i]
device = devices[i]
tf.keras.backend.clear_session()
os.environ["CUDA_VISIBLE_DEVICES"] = device
cae = modelCAE(args.filterSize, args.poolSize, args.sampSize, 1,
args.weights)
# build encode model
filterSize,
border_mode='same',
weights=cl.layers[12].get_weights()))
encode.add(MaxPooling2D(pool_size=(poolSize, poolSize)))
encode.add(Activation('relu'))
encode.add(Flatten())
encode.add(Dense(1024, weights=cl.layers[16].get_weights()))
encode.add(Activation('relu'))
encode.add(Dense(100, weights=cl.layers[18].get_weights()))
encode.add(Dropout(0.5))
encode.add(Activation('relu'))
encode.compile(loss='mse', optimizer='adam')
###############################
allPaths = image.list_pictures(imPath)
repAll = np.zeros((len(allPaths), 100))
for i in xrange(len(repAll)):
for co in xrange(10):
repAll[i, :] += sum(encode.predict(getOne(100, allPaths[i])))
print(i)
repAll /= 1000.
K = 100
pca = PCA(n_components=K, whiten=True)
reduced = pca.fit_transform(repAll)
vari = pca.explained_variance_ratio_
nDims = np.where(np.cumsum(vari) > .98)[0][0]
# repAll = reduced[:,:nDims]
nDims = reduced.shape[1]
valFrac = float(sys.argv[2]) # fraction for validation
trnBalance = '1' == sys.argv[3] # balance training set classes?
print('-' * 50)
print('Training Fraction: ' + str(trnFrac))
print('Validation Fraction: ' + str(valFrac))
print('Testing Fraction: ' + str(1 - trnFrac - valFrac))
print('-' * 50)
if trnBalance:
print('Balancing Training Set')
else:
print('Not Balancing Training Set')
print('-' * 50)
allPaths = np.asarray(image.list_pictures(imDir))
np.random.shuffle(allPaths)
numIms = len(allPaths)
trainIms = allPaths[:numIms * trnFrac]
testIms = allPaths[numIms * (trnFrac + valFrac):]
valIms = allPaths[numIms * trnFrac:numIms * (trnFrac + valFrac)]
# pull sample classes
labels = {}
classNames = {}
classIms = {}
classCount = 0
fi = open(classFile)
for lin in fi:
im, cl = lin.strip('\n').split('\t')
if cl in classNames:
