random.shuffle(imgNames)
rgb1Batch[b, :, :, :], labelWVBatch[b, :, :, 0] = prepareImage(k)
k += 1
yield rgb1Batch, labelWVBatch
batch_size = 4
traingen = generator(imgList[:trainCount], batch_size=batch_size)
testgen = generator(imgList[trainCount:], batch_size=batch_size)
testCount = len(imgList[trainCount:])
model = FaceModel()
model.summary()
model.load_weights('./weights/faceModel.hdf5')
if not testMode:
model.fit_generator(generator=traingen,
validation_data=testgen,
steps_per_epoch=int(trainCount / batch_size),
validation_steps=int(testCount / batch_size),
epochs=30000,
verbose=1,
callbacks=[
ModelCheckpoint('./weights/faceModel.hdf5',
verbose=1,
monitor='val_loss',
save_best_only=False)
])
else:
k = 0
from configurations import DATASET_IMAGES
from helpers import load_tf_image
def grouper(iterable, n, fillvalue=None):
"Collect data into fixed-length chunks or blocks"
# grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
args = [iter(iterable)] * n
return zip_longest(*args, fillvalue=fillvalue)
if __name__ == '__main__':
# Create path:descriptor lookup
model = FaceModel()
model.load_weights('./checkpoints/my_checkpoint')
image_paths = []
descs = []
for person_folder in DATASET_IMAGES.iterdir():
if not person_folder.is_dir():
continue
for img_path in person_folder.glob('*.jpg'):
image_paths.append(img_path)
# Process 100 images at once
batches = list(grouper(image_paths, 200))
for batch in tqdm(batches):
images = tf.stack([
load_tf_image(img_path, augment=False) for img_path in batch
