def _get_image_files(self, path):
'''
Each path should contain a data and labels folder containing images.
Creates a list of tuples containing path name for data and label.
'''
tiles = util.get_image_files(os.path.join(path, 'data'))
labels = util.get_image_files(os.path.join(path, 'labels'))
self._is_valid_dataset(tiles, labels)
return list(zip(tiles, labels))
def calculate_std_from_dataset(path, dataset):
#dataset = util.get_dataset(path)
variance = []
#for set in dataset:
folder_path = os.path.join(path, dataset, 'data')
tiles = util.get_image_files(folder_path)
samples = []
i = 0
for tile in tiles:
i += 1
print(tile)
im = Image.open(os.path.join(folder_path, tile), 'r')
s, v = get_image_estimate(im)
samples.append(s)
variance.append(v)
if (i % 10 == 0):
print("Progress", i)
#Average standard deviation by averaging the variances and taking square root of average.
#Source: http://bit.ly/1VS5pmT
combined = np.concatenate(samples)
print("Real std", np.std(combined))
dataset_std = np.sqrt(np.sum(variance) / len(variance))
return dataset_std
def calculate_std_from_dataset(path, dataset):
#dataset = util.get_dataset(path)
variance = []
#for set in dataset:
folder_path = os.path.join(path, dataset, 'data')
tiles = util.get_image_files(folder_path)
samples = []
i = 0
for tile in tiles:
i += 1
print(tile)
im = Image.open(os.path.join(folder_path, tile), 'r')
s, v = get_image_estimate(im)
samples.append(s)
variance.append(v)
if(i%10 == 0):
print("Progress", i)
#Average standard deviation by averaging the variances and taking square root of average.
#Source: http://bit.ly/1VS5pmT
combined = np.concatenate(samples)
print("Real std" , np.std(combined))
dataset_std = np.sqrt(np.sum(variance) / len(variance))
return dataset_std
os.makedirs(sub_dir + "/data")
dataset_base = "/home/olav/Pictures/Norwegian_roads_dataset"
dataset_dest = "/home/olav/Pictures/Norwegian_roads_dataset_alpha"
datasets = loader.get_dataset(dataset_base)
color_to_alpha = False
content = ["data"]
create_dataset_structure(dataset_dest, datasets)
for set in datasets:
for t in content:
rel_path = "/" + set + "/" + t
images = loader.get_image_files(dataset_base + rel_path)
for img_path in images:
src_path = dataset_base + rel_path + "/" + img_path
dest_path = dataset_dest + rel_path + "/" + img_path
src_im = Image.open(src_path)
im = src_im.convert('RGBA')
if color_to_alpha:
pixel_data = im.load()
height = im.size[1]
width = im.size[0]
for y in xrange(height): # For each row ...
for x in xrange(width): # Iterate through each column ...
