def batch_gen(folder, batch_shape):
'''Resize images to 512, randomly crop a 256 square, and normalize'''
files = np.asarray(get_files(folder))
while True:
X_batch = np.zeros(batch_shape, dtype=np.float32)
idx = 0
while idx < batch_shape[0]: # Build batch sample by sample
try:
f = np.random.choice(files)
X_batch[idx] = get_img_random_crop(f, resize=512, crop=256).astype(np.float32)
X_batch[idx] /= 255. # Normalize between [0,1]
assert(not np.isnan(X_batch[idx].min()))
except Exception as e:
# Do not increment idx if we failed
print(e)
continue
idx += 1
yield X_batch
def batch_gen(folder, batch_shape):
'''Resize images to 512, randomly crop a 256 square, and normalize'''
files = np.asarray(get_files(folder))
while True:
X_batch = np.zeros(batch_shape, dtype=np.float32)
idx = 0
while idx < batch_shape[0]: # Build batch sample by sample
try:
f = np.random.choice(files)
X_batch[idx] = get_img_random_crop(f, resize=512, crop=256).astype(np.float32)
X_batch[idx] /= 255. # Normalize between [0,1]
assert(not np.isnan(X_batch[idx].min()))
except Exception as e:
# Do not increment idx if we failed
print(e)
continue
idx += 1
yield X_batch