1. # -*- coding: utf-8 -*-
2. """
3. Created on Thu Mar  7 11:14:23 2019
4.  
5. @author: Student
6. """
7.  
8. #class VectorExample():
9. #    def __init__(self, input_list):
10. #        self.vector = input_list
11. #        
12. #    def foreach(self, other, input_function):
13. #        return VectorExample([input_function(self.vector[i], other.vector[i]) for i in range(len(self.vector))])
14. #        
15. #    def __add__(self,other):
16. #        return self.foreach(other, lambda a,b: a+b)
17. #    
18. #    def __sub__(self, other):
19. #        return self.foreach(other, lambda a,b: a-b)
20. #    
21. #    def __mul__(self, other):
22. #        return self.foreach(other, lambda a,b: a*b)
23. #    
24. #    def __truediv__(self,other):
25. #        return self.foreach(other, lambda a,b: a/b)
26. #    
27. #    def __str__(self):
28. #        return str(self.vector)
29. #
30. #
31. #def run():
32. #    vector_1 = VectorExample([1, 13, 4])
33. #    vector_2 = VectorExample([3, -2, 2])
34. #
35. #    print(vector_1 + vector_2)
36. #    print(vector_1 - vector_2)
37. #    print(vector_1 * vector_2)
38. #    print(vector_1 / vector_2)
39. #    
40. #if __name__== "__main__":
41. #   run()
42.  
43. import os
44. import numpy as np
45. from matplotlib  import pyplot as plt
46. from skimage import io
47. import random
48.  
49. #def run():
50. #    example_array = np.array([
51. #            [8,1,1,1],
52. #            [9,7,5,3],
53. #            [4,6,8,10],
54. #            [2,3,5,7]
55. #    
56. #    ])
57. #    
58. #    noise_1 = np.random.randn(128,256)
59. #    noise_2 = np.random.randn(128,256)
60. #    
61. #    vmin=-0.5;vmax=0.5
62. #    plt.figure()
63. #    plt.subplot(2,1,1)
64. #    plt.imshow(noise_1,cmap='gray', vmin=vmin, vmax=vmax)
65. #    plt.axis('off')
66. #    plt.subplot(2,1,2)
67. #    plt.imshow(noise_2, cmap='gray', vmin=vmin, vmax=vmax)
68. #    plt.axis('off')
69. #    plt.show()
70. #    
71. #    
72. #
73. #run()
74.    
75.  
76. #def run():
77. #    
78. #    x_size = 512
79. #    y_size = 512
80. #    number_of_points = 500
81. #    vector1 = np.random.uniform(0,x_size,number_of_points)
82. #    vector2 = np.random.uniform(0,y_size,number_of_points)
83. #    
84. #    x_origin=256
85. #    y_origin=256
86. #    radius=100
87. #    is_inside=np.square(vector1-x_origin) + np.square(vector2-y_origin) < radius*radius
88. #    
89. #    image = np.zeros((y_size,x_size))
90. #    grid_x, grid_y = np.meshgrid(np.arange(x_size), np.arange(y_size))
91. #    
92. #    image[np.square(grid_x - x_origin)+np.square(grid_y - y_origin)<radius*radius] = 1
93. #    
94. ##    plt.figure()
95. ##    plt.subplot(1,2,1)
96. ##    plt.imshow(grid_x, cmap='gray')
97. ##    plt.subplot(1,2,2)
98. ##    plt.imshow(grid_y, cmap='gray')
99. ##    plt.show()
100. #    
101. #    vector1_inside = vector1[is_inside]
102. #    vector2_inside = vector2[is_inside]
103. #    
104. #    vector1_outside = vector1[np.logical_not(is_inside)]
105. #    vector2_outside = vector2[np.logical_not(is_inside)]    
106. #    
107. #    plt.figure()
108. #    plt.imshow(image, cmap='gray')
109. #    plt.plot(vector1_inside, vector2_inside,"r.")
110. #    plt.plot(vector1_outside, vector2_outside, "b.")
111. #    plt.xlabel("X Coordinates")
112. #    plt.ylabel("Y Coordinates")
113. #    plt.xlin([0, x_size])
114. #    plt.ylin([0, y_size])
115. #    plt.legend(["Outside","Inside"])
116. #    plt.show()
117. #    
118. #run()
119.    
120.    
121. def run():
122.  
123.     current_path = os.path.abspath(os.path.dirname(__file__))
124.     image_folder=os.path.join(current_path, "sample_cifar")
125.     file_names = os.listdir(image_folder)
126.    
127.     random.shuffle(file_names)
128.     file_names = file_names[0:25]
129.    
130.     print(file_names)
131.    
132.     plt.figure()
133.     i=1
134.     for file_name in file_names:
135.         current_class = file_name.split('_')[1].split('.')[0].capitalize()
136.         plt.subplot(5,5,i)
137.         image_path = os.path.join(image_folder, file_name)
138.         image = io.imread(image_path)
139.         i+=1
140.         plt.axis('off')
141.         plt.title(current_class)
142.     plt.show()
143.    
144. run()
145.    
146.    
147.    
148.