/
visualize.py
126 lines (88 loc) · 3.17 KB
/
visualize.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
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
import numpy as np
import time
import logging
import os.path
import sys
from PIL import Image
def load(pathRaw, pathNpy):
#log("Loading data...")
datasetFull = None
# Load the data from file and, if necessary, save RAW file as NPY file to speed future use
if( os.path.exists(pathNpy) ):
log("Loading NPY dataset: {0}".format(pathNpy) )
datasetFull = np.load(pathNpy)
else:
log("Loading RAW file")
log("Loading RAW dataset: {0}".format(pathRaw) )
datasetFull = np.loadtxt(open(pathRaw,"rb"),delimiter=",",skiprows=1)
log("Saving RAW data into NPY dataset: {0}".format(pathNpy) )
np.save(pathNpy, datasetFull)
return datasetFull
def log(message):
logging.info(message)
def execute(dataset, items):
log("Executing...")
log("items: {0}".format(items))
m, n = dataset.shape
targets = dataset[:,0].astype(int)
images = dataset[:,1:n].astype(int)
for i in items:
image = images[i]
target = targets[i]
log("Image: {0}".format(i))
visualize(target, image)
view(image)
def visualize(target, flatImage):
log("Visualizing...")
log("Target: {0}".format(target))
image = flatImage.astype(int).reshape(28,28).astype(int)
m,n = image.shape
str = "\n\n "
for i in range(n):
str = str + "{0:3d} ".format(i)
for i in range(m):
row = image[i]
rowstr = ""
for j in range(n):
value = image[i][j]
rowstr = rowstr + "{0:03d}.".format(value)
str = str + "\n{0:3d}: {1}".format(i, rowstr)
str = str + "\n"
log(str)
def view(flatImage):
m,n = 28,28
img = Image.new( 'RGB', (m,n), "black") # create a new black image
pixels = img.load() # create the pixel map
#for i in range(img.size[0]): # for every pixel:
# for j in range(img.size[1]):
# pixels[i,j] = (i, j, 100) # set the colour accordingly
#img.show()
image = flatImage.astype(int).reshape(m,n).astype(int)
m,n = image.shape
for i in range(m):
for j in range(n):
value = image[i][j]
# Equal RGB values (255 == white and 0 == black)
pixels[i,j] = (value, value, value)
img.show()
def main():
#print( 'Number of arguments: {0}'.format(len(sys.argv)) )
#print( 'Argument List: {0}'.format(str(sys.argv)) )
start = 1
if len(sys.argv) > 1:
start = int(sys.argv[1])
end = start + 1
if len(sys.argv) > 2:
end = int(sys.argv[2])
logging.getLogger('').handlers = []
logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
log("Started mainline")
trainingFileRaw = "data/train.csv"
trainingFileNpy = "data/train.npy"
dataset = load(trainingFileRaw, trainingFileNpy)
m, n = dataset.shape
log("Full data set: rows: {0}, features: {1}".format(m,n))
predictions = execute(dataset, range(start, end))
log("Completed mainline")
if __name__ == "__main__":
main()