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processing.py
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processing.py
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import glob
import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
from tensorflow.keras import layers
import time
import numpy as np
from sklearn import metrics
from scipy import interpolate
import tensorflow as tf
import tensorflow_addons as tfa
def load(image_file):
image = tf.io.read_file(image_file)
image = tf.image.decode_jpeg(image)
w = tf.shape(image)[1]
w = w // 4
rgb_pos = image[:, :w, :]
nir_pos = image[:, w * 1:w * 2, :]
rgb_neg = image[:, w * 2:w * 3, :]
nir_neg = image[:, w * 3:w * 4, :]
rgb_pos = tf.cast(rgb_pos, tf.float32)
nir_pos = tf.cast(nir_pos, tf.float32)
rgb_neg = tf.cast(rgb_neg, tf.float32)
nir_neg = tf.cast(nir_neg, tf.float32)
return rgb_pos, nir_pos, rgb_neg, nir_neg
# cell 3: data augmentation
def resize(input_l, input_r, target_l, target_r, height, width):
input_l = tf.image.resize(input_l, [height, width],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
input_r = tf.image.resize(input_r, [height, width],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
target_l = tf.image.resize(target_l, [height, width],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
target_r = tf.image.resize(target_r, [height, width],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return input_l, input_r, target_l, target_r
def random_crop(input_l, input_r, target_l, target_r):
stacked_image = tf.stack([input_l, input_r, target_l, target_r], axis=0)
cropped_image = tf.image.random_crop(stacked_image, size=[4, IMG_HEIGHT, IMG_WIDTH, 3])
return cropped_image[0], cropped_image[1], cropped_image[2], cropped_image[3]
# normalizing the images to [-1, 1]
def normalize(input_l, input_r, target_l, target_r):
input_l = (input_l / 127.5) - 1
input_r = (input_r / 127.5) - 1
target_l = (target_l / 127.5) - 1
target_r = (target_r / 127.5) - 1
return input_l, input_r, target_l, target_r
def random_jitter(input_l, input_r, target_l, target_r):
# resize to 68x68
#input_l, input_r, target_l, target_r = resize(input_l, input_r, target_l, target_r, 68, 68)
# crop
#input_l, input_r, target_l, target_r = random_crop(input_l, input_r, target_l, target_r)
# flip_left_right
if tf.random.uniform(()) > 0.5:
input_l = tf.image.flip_left_right(input_l)
input_r = tf.image.flip_left_right(input_r)
target_l = tf.image.flip_left_right(target_l)
target_r = tf.image.flip_left_right(target_r)
# flip_up_down
if tf.random.uniform(()) > 0.5:
input_l = tf.image.flip_up_down(input_l)
input_r = tf.image.flip_up_down(input_r)
target_l = tf.image.flip_up_down(target_l)
target_r = tf.image.flip_up_down(target_r)
# brighness change
if tf.random.uniform(()) > 0.5:
rand_value = tf.random.uniform((), minval=-5.0, maxval=5.0)
input_l = input_l + rand_value
rand_value = tf.random.uniform((), minval=-5.0, maxval=5.0)
input_r = input_r + rand_value
rand_value = tf.random.uniform((), minval=-5.0, maxval=5.0)
target_l = target_l + rand_value
rand_value = tf.random.uniform((), minval=-5.0, maxval=5.0)
target_r = target_r + rand_value
# contrast change
if tf.random.uniform(()) > 0.5:
rand_value = tf.random.uniform((), minval=0.8, maxval=1.2)
mean_value = tf.reduce_mean(input_l)
input_l = (input_l - mean_value) * rand_value + mean_value
rand_value = tf.random.uniform((), minval=0.8, maxval=1.2)
mean_value = tf.reduce_mean(input_r)
input_r = (input_r - mean_value) * rand_value + mean_value
rand_value = tf.random.uniform((), minval=0.8, maxval=1.2)
mean_value = tf.reduce_mean(target_l)
target_l = (target_l - mean_value) * rand_value + mean_value
rand_value = tf.random.uniform((), minval=0.8, maxval=1.2)
mean_value = tf.reduce_mean(target_r)
target_r = (target_r - mean_value) * rand_value + mean_value
# clip value
input_l = tf.clip_by_value(input_l, clip_value_min=0.0, clip_value_max=255.0)
input_r = tf.clip_by_value(input_r, clip_value_min=0.0, clip_value_max=255.0)
target_l = tf.clip_by_value(target_l, clip_value_min=0.0, clip_value_max=255.0)
target_r = tf.clip_by_value(target_r, clip_value_min=0.0, clip_value_max=255.0)
# rotate positive samples for making hard positive cases
if tf.random.uniform(()) > 0.5:
if tf.random.uniform(()) < 0.5:
input_l = tf.image.rot90(input_l,k=1) # 90
input_r = tf.image.rot90(input_r,k=1) # 90
else:
input_l = tf.image.rot90(input_l,k=3) # 270
input_r = tf.image.rot90(input_r,k=3) # 270
return input_l, input_r, target_l, target_r
def load_image_train(image_file):
input_l, input_r, target_l, target_r = load(image_file)
input_l, input_r, target_l, target_r = random_jitter(input_l, input_r, target_l, target_r)
input_l, input_r, target_l, target_r = normalize(input_l, input_r, target_l, target_r)
return input_l, input_r, target_l, target_r
def load_image_test(image_file):
input_l, input_r, target_l, target_r = load(image_file)
input_l, input_r, target_l, target_r = resize(input_l, input_r, target_l, target_r, IMG_HEIGHT, IMG_WIDTH)
input_l, input_r, target_l, target_r = normalize(input_l, input_r, target_l, target_r)
return input_l, input_r, target_l, target_r