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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
from skimage import img_as_float
import sys
rospath = '/opt/ros/kinetic/lib/python2.7/dist-packages'
if str(sys.path).find(rospath) != -1:
sys.path.remove(rospath) # in order to import cv2 under python3
print('ROS path temporarily removed.')
import cv2
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 = (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, image_folder, mask_folder,
image_gray = False, mask_gray = True):
img_paths = list()
lbl_paths = list()
# Recursively find all the image files from the path test_path
for img_path in glob.glob(test_path+"/"+image_folder+"/*"):
img_paths.append(img_path)
# Recursively find all the image files from the path label_path
for lbl_path in glob.glob(test_path+"/"+mask_folder+"/*"):
lbl_paths.append(lbl_path)
images = np.zeros((len(img_paths),256,256,3))
labels = np.zeros((len(lbl_paths),256,256,1))
# Read and resize the images
# Get the encoded labels
for i, img_path in enumerate(img_paths):
# Takes as input path to image file and returns
# resized 3 channel RGB image of as numpy array of size (256, 256, 3)
images[i] = np.array(io.imread(img_path, as_gray = image_gray)) / 255
for i, lbl_path in enumerate(lbl_paths):
labels[i] = np.array(io.imread(lbl_path, as_gray = mask_gray)).reshape((256,256,1)) / 255
errmsg1 = 'mismatched dimension: ' + str(len(img_paths))+' images' + str(len(lbl_paths))+' labels'
errmsg2 = 'no files detected'
assert len(img_paths) == len(lbl_paths), errmsg1
assert len(img_paths) > 0, errmsg2
return images
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):
os.mkdir(save_path)
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 mergeIm(test_path, img_folder, mask_folder, results_path, save_path):
list = os.listdir(os.path.join(test_path,img_folder))
number_files = len(list)
# Paths for test, result, label images
path_test = os.path.join(os.path.join(test_path,img_folder), "*.tif")
path_label = os.path.join(os.path.join(test_path, mask_folder), "*.tif")
path_result = os.path.join(results_path, "*.png")
# Information of images
images_test = [cv2.imread(img) for img in glob.glob(path_test)]
images_result = [cv2.imread(img) for img in glob.glob(path_result)]
images_label = [cv2.imread(img) for img in glob.glob(path_label)]
h,w,d = images_test[0].shape
height = h * number_files
width = w * 3
output = np.zeros((height,width,3))
# current row
n = 0
for i in range(number_files):
# test image | result image | ground truth
output[n:n+h,0:w] = images_test[i]
output[n:n+h,w:w*2] = images_result[i]
output[n:n+h,w*2:w*3] = images_label[i]
n += h
cv2.imwrite(os.path.join(save_path), output)