Cam-Le/landslide

1import tensorflow as tf2import numpy as np3import cv24from PIL import Image5 6 7def postProcessingPixelLevelThresholding(masks_pred, threshold=0.5):8  """9    # apply thresholding10    # input:11        - masks_pred  (Bx128x128x2):12        - threshold  (default=0.5): smaller threshold -> more focus on landslide pixel, larger threshold -> strictly focus on landslide pixel,13    # output:14        - mask_list  (Bx128x128x2):15  """16  masks_pred = masks_pred.numpy()17  c1 = masks_pred[..., 0]18  c2 = np.where(masks_pred[..., 1] > threshold, 1, 0)19  c1 = np.expand_dims(c1,axis=-1)20  c2 = np.expand_dims(c2,axis=-1)21  new_masks = np.concatenate((c1,c2), axis=-1)22  new_masks = tf.convert_to_tensor(new_masks, dtype=tf.float32)23 24  return new_masks25 26 27def postProcessingMorphology(masks_pred, op=1):28    """29        # apply morphology30        # input:31            - masks_pred  (Bx128x128x2):32        # output:33            - mask_list  (Bx128x128x2):34      """35    masks_pred = masks_pred.numpy()36    masks_pred = np.argmax(masks_pred, axis=-1) # Bx128x12837    masks_pred = masks_pred.astype(np.uint8)38 39    kernel = np.ones((4, 4), np.uint8)40    if op == 0: # opening - removing salt noise 41        masks_erosion  = cv2.erode(masks_pred, kernel, iterations=1)42        new_masks      = cv2.dilate(masks_erosion, kernel, iterations=1)43    else:       # closing - removing pepper noise44        masks_dilation = cv2.dilate(masks_pred, kernel, iterations=1)45        new_masks      = cv2.erode(masks_dilation, kernel, iterations=1)46  47    new_masks = tf.convert_to_tensor(new_masks, dtype=tf.uint8)48    new_masks = tf.one_hot(new_masks, depth=2, axis=-1)49 50    return tf.cast(new_masks, dtype=tf.float32)51 52 53def funcProcessingVotingMask(masks):54    masks = tf.image.resize(masks,[128,128],method=tf.image.ResizeMethod.BILINEAR)55    return np.asarray(masks)56 57def postProcessingVotingMask(masks_pred):58    """59        # apply voting60        # input:61            - masks_preds 3 x (Bx128x128x2):62        # output:63            - mask_list  (Bx128x128x2):64      """65 66    y_64  = masks_pred[2] 67    y_128 = masks_pred[1]68    y_256 = masks_pred[0]69 70    y_64   = funcProcessingVotingMask(y_64)71    y_128  = np.asarray(y_128)72    y_256  = funcProcessingVotingMask(y_256)73 74    new_masks = (y_64 + y_128 + y_256)/3.75    new_masks = tf.convert_to_tensor(new_masks)76 77    return tf.cast(new_masks, dtype=tf.float32)78 79 80def postProcessingMultiAngle(y,y90,y180,y270):81    """82        # apply multi angle83        # input:84            - masks_preds 4 x 3 x (Bx128x128x2):85        # output:86            - mask_list  (Bx128x128x2):87    """88    y90  = tf.image.rot90(y90,  k=3)89    y180 = tf.image.rot90(y180, k=2)90    y270 = tf.image.rot90(y270, k=1)91 92    new_masks = (y + y90 + y180 + y270) / 4.93    new_masks = tf.convert_to_tensor(new_masks)94 95    return tf.cast(new_masks, dtype=tf.float32)96