-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathutils.py
103 lines (87 loc) · 3.38 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
from PIL import Image
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import gdal
# colour map
label_colours = [(0,0,0)
# 0=background
,(128,0,0),(0,128,0),(128,128,0),(0,0,128),(128,0,128)
# 1=aeroplane, 2=bicycle, 3=bird, 4=boat, 5=bottle
,(0,128,128),(128,128,128),(64,0,0),(192,0,0),(64,128,0)
# 6=bus, 7=car, 8=cat, 9=chair, 10=cow
,(192,128,0),(64,0,128),(192,0,128),(64,128,128),(192,128,128)
# 11=diningtable, 12=dog, 13=horse, 14=motorbike, 15=person
,(0,64,0),(128,64,0),(0,192,0),(128,192,0),(0,64,128)]
# 16=potted plant, 17=sheep, 18=sofa, 19=train, 20=tv/monitor
def decode_labels(mask, num_images=1, num_classes=21):
"""Decode batch of segmentation masks.
Args:
mask: result of inference after taking argmax.
num_images: number of images to decode from the batch.
num_classes: number of classes to predict (including background).
Returns:
A batch with num_images RGB images of the same size as the input.
"""
n, h, w, c = mask.shape
assert (n >= num_images), 'Batch size %d should be greater or equal than number of images to save %d.' % (
n, num_images)
outputs = np.zeros((num_images, h, w, 3), dtype=np.uint8)
for i in range(num_images):
img = Image.new('RGB', (len(mask[i, 0]), len(mask[i])))
pixels = img.load()
for j_, j in enumerate(mask[i, :, :, 0]):
for k_, k in enumerate(j):
if k < num_classes:
pixels[k_, j_] = label_colours[k]
outputs[i] = np.array(img)
return outputs
def decode_images(image, num_images=1):
n, h, w, c = image.shape
outputs = np.zeros((num_images, h, w, 3), dtype=np.uint8)
for i in range(3):
scalar = MinMaxScaler()
# scalared = scalar.fit_transform(image[0, :, :, i]) * 255.0
scalared = image[0, :, :, i]
outputs[0, :, :, i] = scalared.astype(np.uint8)
return outputs
def read_img(file_name):
image = gdal.Open(file_name)
im_width = image.RasterXSize
im_height = image.RasterYSize
im_geotrans = image.GetGeoTransform()
im_proj = image.GetProjection()
im_data = image.ReadAsArray(0, 0, im_width, im_height)
if len(im_data.shape) == 3:
im_data = im_data.transpose(1, 2, 0)
del image
return im_proj, im_geotrans, im_data
def write_img(file_name, im_proj, im_geotrans, im_data):
if 'uint8' in im_data.dtype.name:
datatype = gdal.GDT_Byte
# print 'uint8'
elif 'int8' in im_data.dtype.name:
datatype = gdal.GDT_Byte
elif 'int16' in im_data.dtype.name:
datatype = gdal.GDT_UInt16
else:
datatype = gdal.GDT_Float32
if len(im_data.shape) == 3:
im_data = im_data.transpose(2, 0, 1)
im_bands, im_height, im_width = im_data.shape
else:
im_bands, (im_height, im_width) = 1, im_data.shape
driver = gdal.GetDriverByName("GTiff")
dataset = driver.Create(
file_name,
im_width,
im_height,
im_bands,
datatype)
dataset.SetGeoTransform(im_geotrans)
dataset.SetProjection(im_proj)
if im_bands == 1:
dataset.GetRasterBand(1).WriteArray(im_data)
else:
for i in range(im_bands):
dataset.GetRasterBand(i + 1).WriteArray(im_data[i])
del dataset