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unet_proc.py
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# -*- coding: utf-8 -*-
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
import glob
import skimage.io as io
import skimage.transform as trans
from skimage import img_as_ubyte
Sky = [128, 128, 128]
Building = [128, 0, 0]
Pole = [192, 192, 128]
Road = [128, 64, 128]
Pavement = [60, 40, 222]
Tree = [128, 128, 0]
SignSymbol = [192, 128, 128]
Fence = [64, 64, 128]
Car = [64, 0, 128]
Pedestrian = [64, 64, 0]
Bicyclist = [0, 128, 192]
Unlabelled = [0, 0, 0]
COLOR_DICT = np.array([Sky, Building, Pole, Road, Pavement,
Tree, SignSymbol, Fence, Car, Pedestrian, Bicyclist, Unlabelled])
def adjustData(img, mask, flag_multi_class, num_class):
if (flag_multi_class):
img = img / 255
mask = mask[:, :, :, 0] if (len(mask.shape) == 4) else mask[:, :, 0]
new_mask = np.zeros(mask.shape + (num_class,))
for i in range(num_class):
# for one pixel in the image, find the class in mask and convert it into one-hot vector
# index = np.where(mask == i)
# index_mask = (index[0],index[1],index[2],np.zeros(len(index[0]),dtype = np.int64) + i) if (len(mask.shape) == 4) else (index[0],index[1],np.zeros(len(index[0]),dtype = np.int64) + i)
# new_mask[index_mask] = 1
new_mask[mask == i, i] = 1
new_mask = np.reshape(new_mask, (new_mask.shape[0], new_mask.shape[1] * new_mask.shape[2],
new_mask.shape[3])) if flag_multi_class else np.reshape(new_mask, (
new_mask.shape[0] * new_mask.shape[1], new_mask.shape[2]))
mask = new_mask
elif (np.max(img) > 1):
# 2020-2-5: see if we can accept the original image
img = img / 255
mask = mask / 255
mask[mask > 0.5] = 1
mask[mask <= 0.5] = 0
return (img, mask)
def trainGenerator(batch_size, train_path, image_folder, mask_folder, aug_dict, image_color_mode="grayscale",
mask_color_mode="grayscale", image_save_prefix="image", mask_save_prefix="mask",
flag_multi_class=False, num_class=2, save_to_dir=None, target_size=(256, 256), seed=1):
'''
can generate image and mask at the same time
use the same seed for image_datagen and mask_datagen to ensure the transformation for image and mask is the same
if you want to visualize the results of generator, set save_to_dir = "your path"
'''
image_datagen = ImageDataGenerator(**aug_dict)
mask_datagen = ImageDataGenerator(**aug_dict)
image_generator = image_datagen.flow_from_directory(
train_path,
classes=[image_folder],
class_mode=None,
color_mode=image_color_mode,
target_size=target_size,
batch_size=batch_size,
save_to_dir=save_to_dir,
save_prefix=image_save_prefix,
seed=seed)
mask_generator = mask_datagen.flow_from_directory(
train_path,
classes=[mask_folder],
class_mode=None,
color_mode=mask_color_mode,
target_size=target_size,
batch_size=batch_size,
save_to_dir=save_to_dir,
save_prefix=mask_save_prefix,
seed=seed)
train_generator = zip(image_generator, mask_generator)
for (img, mask) in train_generator:
img, mask = adjustData(img, mask, flag_multi_class, num_class)
yield (img, mask)
def testGenerator(test_path, num_image=30, target_size=(256, 256), flag_multi_class=False, as_gray=True):
photo_files = os.listdir(test_path)
for photo_file in photo_files:
file_to_open = os.path.join(test_path, photo_file)
if os.path.isfile(file_to_open):
# img = io.imread(os.path.join(test_path, "%d.png" % i), as_gray=as_gray)
img = io.imread(file_to_open, as_gray=as_gray)
# 2020-2-5: See if we don't need to /255
img = img / 255
img = trans.resize(img, target_size)
img = np.reshape(img, img.shape + (1,)) if (not flag_multi_class) else img
img = np.reshape(img, (1,) + img.shape)
yield img
def geneTrainNpy(image_path, mask_path, flag_multi_class=False, num_class=2, image_prefix="image", mask_prefix="mask",
image_as_gray=True, mask_as_gray=True):
image_name_arr = glob.glob(os.path.join(image_path, "%s*.png" % image_prefix))
image_arr = []
mask_arr = []
for index, item in enumerate(image_name_arr):
img = io.imread(item, as_gray=image_as_gray)
img = np.reshape(img, img.shape + (1,)) if image_as_gray else img
mask = io.imread(item.replace(image_path, mask_path).replace(image_prefix, mask_prefix), as_gray=mask_as_gray)
mask = np.reshape(mask, mask.shape + (1,)) if mask_as_gray else mask
img, mask = adjustData(img, mask, flag_multi_class, num_class)
image_arr.append(img)
mask_arr.append(mask)
image_arr = np.array(image_arr)
mask_arr = np.array(mask_arr)
return image_arr, mask_arr
def labelVisualize(num_class, color_dict, img):
img = img[:, :, 0] if len(img.shape) == 3 else img
img_out = np.zeros(img.shape + (3,))
for i in range(num_class):
img_out[img == i, :] = color_dict[i]
return img_out / 255
def saveResult(save_path, npyfile, flag_multi_class=False, num_class=2):
for i, item in enumerate(npyfile):
img = labelVisualize(num_class, COLOR_DICT, item) if flag_multi_class else item[:, :, 0]
io.imsave(os.path.join(save_path, "%d_predict.png" % i), img)
def saveResult_V2(save_path, npyfile, file_list, flag_multi_class=False, num_class=2):
# print(file_list)
# print(npyfile)
for i, item in enumerate(npyfile):
img = labelVisualize(num_class, COLOR_DICT, item) if flag_multi_class else item[:, :, 0]
file_name = file_list[i]
filename_no_ext, file_ext = os.path.splitext(file_name)
# file_name = filename_no_ext + '_mask' + file_ext
# Seems PNG and JPG is preferred
# If using others like TIF, when converting it to
# 8-bit image there could be problem
# file_name = filename_no_ext + '_mask' + file_ext
file_name = filename_no_ext + '_mask.png'
# io.imsave(os.path.join(save_path, file_name), img)
# This is to suppress the warning message when converting the image
# to low contrast one
io.imsave(os.path.join(save_path, file_name), img_as_ubyte(img), check_contrast=False)