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data.py
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data.py
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from __future__ import print_function
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
import glob
import skimage.io as io
import skimage.transform as trans
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:
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="rgb",
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=(512, 512), 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"
"""
if aug_dict is None:
image_datagen = ImageDataGenerator()
mask_datagen = ImageDataGenerator()
else:
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 valGenerator(batch_size, train_path, image_folder, mask_folder, aug_dict, image_color_mode="rgb",
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=(512, 512), 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"
"""
if aug_dict is None:
image_datagen = ImageDataGenerator()
mask_datagen = ImageDataGenerator()
else:
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, target_size=(512, 512, 3), flag_multi_class=False, as_gray=True):
all_test_image_names = os.listdir(test_path)
for image_name in all_test_image_names:
img = io.imread(os.path.join(test_path, image_name), as_gray=as_gray)
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]
output = (img > 0.5).astype(np.uint8) * 255
io.imsave(os.path.join(save_path, "%d_predict.tif" % i), output)