I checked out gradient descent using python and numpy but it didn't solve my problem.
I'm trying to get familiar with image-processing and I want to generate a few test arrays to mess around with in Python.
Is there a method (like np.arange) to create a m x n array where the inner entries form some type of gradient?
I did an example of a naive method for generating the desired output.
Excuse my generality of the term gradient, I'm using it in it's simple meaning as smooth transition in color.
#!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
#Set up parameters
m = 15
n = 10
A_placeholder = np.zeros((m,n))
V_m = np.arange(0,m).astype(np.float32)
V_n = np.arange(0,n).astype(np.float32)
#Iterate through combinations
for i in range(m):
m_i = V_m[i]
for j in range(n):
n_j = V_n[j]
A_placeholder[i,j] = m_i * n_j #Some combination
#Relabel
A_gradient = A_placeholder
A_placeholder = None
#Print data
print A_gradient
#[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
[ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18.]
[ 0. 3. 6. 9. 12. 15. 18. 21. 24. 27.]
[ 0. 4. 8. 12. 16. 20. 24. 28. 32. 36.]
[ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45.]
[ 0. 6. 12. 18. 24. 30. 36. 42. 48. 54.]
[ 0. 7. 14. 21. 28. 35. 42. 49. 56. 63.]
[ 0. 8. 16. 24. 32. 40. 48. 56. 64. 72.]
[ 0. 9. 18. 27. 36. 45. 54. 63. 72. 81.]
[ 0. 10. 20. 30. 40. 50. 60. 70. 80. 90.]
[ 0. 11. 22. 33. 44. 55. 66. 77. 88. 99.]
[ 0. 12. 24. 36. 48. 60. 72. 84. 96. 108.]
[ 0. 13. 26. 39. 52. 65. 78. 91. 104. 117.]
[ 0. 14. 28. 42. 56. 70. 84. 98. 112. 126.]]
#Show Image
plt.imshow(A_gradient)
plt.show()
I've tried np.gradient but it didn't give me the desired output.
#print np.gradient(np.array([V_m,V_n]))
#Traceback (most recent call last):
# File "Untitled.py", line 19, in <module>
# print np.gradient(np.array([V_m,V_n]))
# File "/Users/Mu/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.py", line 1458, in gradient
# out[slice1] = (y[slice2] - y[slice3])
#ValueError: operands could not be broadcast together with shapes (10,) (15,)
