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solarPV_identification/CNN/data_loaders.py
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data_loaders.py
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import os
#import tensorflow
import numpy as np
import scipy
import json
#import cv2
from scipy import ndimage
#from matplotlib import pyplot as plt
from PIL import Image
import augmentation_tools
#Cluster paths:
'''
images under:
/work/hyenergy/raw/SwissTopo/RGB_25cm/data_resized/SI_25_2015_1301-13/original/PV/xxx.png
/work/hyenergy/raw/SwissTopo/RGB_25cm/data_resized/SI_25_2015_1301-13/labels/PV/xxxx_label.png
'''
## ========= CHANGE HERE THE PATH ===============================
## ==============================================================
#image_path = "/work/hyenergy/raw/SwissTopo/RGB_25cm/data_resized/"
## ==============================================================
## ==============================================================
#image_folders = ['SI_25_2015_1301-13', 'SI_25_2015_1301-14', 'SI_25_2015_1301-32', 'SI_25_2015_1261-33']
def read_json():
with open('parameters.json') as json_file:
json_data = json.load(json_file)
return json_data
def path_label(filename, label_name, extension_name):
"""
returns the path to the label from a filename of the form :
'SI_25_2015_1242-13_0_750' or 'SI_25_2015_1242-13_0_750.png'
"""
filename = filename.replace(label_name, '').replace(extension_name, '')
return filename + label_name + extension_name
#TODO improve loading of rotations
def load_images(lights=False, rotations=False):
'''
Returns the loaded images, takes 2 arguments, lights and rotations to
give the option to return the different lightnings and/or the rotations
N.B. Images are returned as additional images, not as additional layer (features). Maybe we should add the lighting as a layer?
For sure Sobel filter should be added as layer... # RC
'''
json_data = read_json()
image_path = json_data['image_path']
image_folders = json_data['image_folders']
original_folder = "/original/PV/"
labels_folder = "/labels/PV/"
label_name = "_label"
extension_name = '.png'
original_images = []
labeled_images = []
for folder in image_folders:
for imagefile in os.listdir(image_path + folder + original_folder):
label_dir = os.listdir(image_path + folder + labels_folder)
labelfile = path_label(imagefile, label_name, extension_name)
if imagefile.startswith('SI') and labelfile in label_dir:
original_image = Image.open(image_path + folder + original_folder + imagefile)
labeled_image = Image.open(image_path + folder + labels_folder + labelfile)
if lights:
for image in augmentation_tools.change_contrast_multi(original_image, [-80, 0, 100]):
original_images.append(np.array(image, dtype='uint8'))
labeled_images.append(np.array(labeled_image, dtype='uint8'))
else:
original_images.append(np.array(original_image, dtype='uint8'))
labeled_images.append(np.array(labeled_image, dtype='uint8'))
if rotations:
original_images_rotations = []
labeled_images_rotations = []
for idx in range(len(original_images)):
original_images_rotations = original_images_rotations + augmentation_tools.rotatedImages(original_images[idx])
labeled_images_rotations = labeled_images_rotations + augmentation_tools.rotatedImages(labeled_images[idx])
return original_images_rotations, labeled_images_rotations
return original_images, labeled_images
def add_sobel_layer(images):
'''
Adds sobel layer to all images
:param images: an array of 3 layer numpy images(RGB)
:return: sobel_images: same array with a 4th layer
'''
return 0
def add_light_layer(images):
'''
Adds 3 new layers
:param images: array of 3 layer numpy images(RGB)
:return: light_images: same array with 3 new layers of the altered image
'''
return 0
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