def rle_decode(mask_rle, shape=(1280, 1918, 1)):     '''     mask_rle: run-length as string formated (start length)     shape: (height,width) of array to return      returns numpy array, 1 - mask, 0 - background     '''     img = np.zeros(shape[0]*shape[1], dtype=np.uint8)      s = mask_rle.split()     starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]     starts -= 1     ends = starts + lengths         for lo, hi in zip(starts, ends):         img[lo:hi] = 1      img = img.reshape(shape)     return img 

def keras_generator(gen_df, batch_size):     while true:         x_batch = []         y_batch = []          for i in range(batch_size):             img_name, mask_rle = gen_df.sample(1).values[0]              img = cv2.imread('data/train/{}'.format(img_name))             mask = rle_decode(mask_rle)              img = cv2.resize(img, (256, 256))              mask = cv2.resize(mask, (256, 256))              x_batch += [img]              y_batch += [mask]          x_batch = np.array(x_batch) / 255.          y_batch = np.array(y_batch)          yield x_batch, np.expand_dims(y_batch, -1) 

im_id = 5 fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(25, 25)) axes[0].imshow(x[im_id])  axes[1].imshow(pred[im_id, ..., 0] > 0.5)   plt.show()