I have the following output for a
[ 1. 3. 5. 7. 9. 11. 13. 15. 17. 19. 21. 23. 25. 27.
29. 31. 33. 35. 37. 39. 41. 43. 45. 47. 97. 99. 101. 103.
105. 107. 109. 111. 113. 115. 117. 119. 121. 123. 125. 127. 129. 131.
133. 135. 137. 139. 141. 143.]
I want to reshape it to the below
[[1. 3. 5. 7. 9. 11. 13. 15.]
[17. 19. 21. 23. 25. 27. 29. 31.]
[33. 35. 37. 39. 41. 43. 45. 47.]
[97. 99. 101. 103. 105. 107. 109. 111.]
[113. 115. 117. 119. 121. 123. 125. 127.]
[129. 131. 133. 135. 137. 139. 141. 143.]]
I tried to use a.resize(6, 8), but it gives me this error: "resize only works on single-segment arrays"
Also, when I am trying to use a.reshape(6, 8), it gives me the same array.
I don't understand what is the reason for that as I have tested another array and worked well.
try a.reshape((8, 6))
notice the double parentheses
a = np.array([1., 3., 5., 7., 9., 11., 13., 15., 17., 19., 21., 23., 25., 27.,
29., 31., 33., 35., 37., 39., 41., 43., 45., 47., 97., 99., 101., 103.,
105., 107., 109., 111., 113., 115., 117., 119., 121., 123., 125., 127., 129., 131.,
133., 135., 137., 139., 141., 143.])
print(a.reshape((8, 6)))
out:
[[ 1. 3. 5. 7. 9. 11.]
[ 13. 15. 17. 19. 21. 23.]
[ 25. 27. 29. 31. 33. 35.]
[ 37. 39. 41. 43. 45. 47.]
[ 97. 99. 101. 103. 105. 107.]
[109. 111. 113. 115. 117. 119.]
[121. 123. 125. 127. 129. 131.]
[133. 135. 137. 139. 141. 143.]]
Process finished with exit code 0
do notice that for the output you requested, the dimensions should be
a.reshape((6,8))
out:
[[ 1. 3. 5. 7. 9. 11. 13. 15.]
[ 17. 19. 21. 23. 25. 27. 29. 31.]
[ 33. 35. 37. 39. 41. 43. 45. 47.]
[ 97. 99. 101. 103. 105. 107. 109. 111.]
[113. 115. 117. 119. 121. 123. 125. 127.]
[129. 131. 133. 135. 137. 139. 141. 143.]]
Process finished with exit code 0
you can read about NumPy's reshape here: reshape documentation
Try
b = a.reshape((8,6))
and keep in mind 2 things, for future use of similar methods:
the reshape method takes a tuple as input, in that case (8,6) , calling b = a.reshape(8,6) gives 2 int arguments to the method instead of the tuple it expects. always pay attention to the expected values. you can investigate that by just hovering over a function in pycharm and most editors.
in numpy, many methods do not manipulate the given object but rather return a new value for you to use.
it is healthy to always check for that in documentation, in order to avoid catastrophic heartbreaks, trust me.
Related
I have this file which has an array of data written to it:
[[[ 32. 28. 28. ... 24. 24. 24.]
[ 30. 29. 29. ... 24. 24. 24.]
[ 29. 29. 28. ... 24. 24. 24.]
...
[137. 138. 129. ... 34. 34. 34.]
[140. 139. 128. ... 31. 34. 34.]
[136. 135. 122. ... 30. 30. 33.]]
[[ 40. 40. 40. ... 33. 33. 33.]
[ 38. 38. 37. ... 33. 33. 33.]
[ 37. 37. 37. ... 33. 33. 33.]
...
[140. 137. 132. ... 41. 43. 42.]
[139. 136. 129. ... 42. 43. 43.]
[140. 139. 133. ... 40. 42. 43.]]
[[ 10. 8. 7. ... 4. 4. 4.]
[ 8. 7. 7. ... 4. 4. 4.]
[ 7. 6. 6. ... 4. 4. 4.]
...
[101. 103. 94. ... 12. 13. 13.]
[105. 104. 92. ... 12. 13. 13.]
[ 99. 99. 99. ... 9. 10. 11.]]]
I do not know how to read from this file and use it within my code. Any help would be great! I have this within my code so far:
# Read and pre-process input images
n, c, h, w = net.inputs[input_blob].shape
images = np.ndarray(shape=(n, c, h, w))
for i in range(n):
image = cv2.imread(args.input[i])
if image.shape[:-1] != (h, w):
log.warning("Image {} is resized from {} to {}".format(args.input[i], image.shape[:-1], (h, w)))
image = cv2.resize(image, (w, h))
# Swapping Red and Blue channels
#image[:, :, [0, 2]] = image[:, :, [2, 0]]
# Change data layout from HWC to CHW
image = image.transpose((2, 0, 1))
images[i] = image
eoim = image
eoim16 = eoim.astype(np.float16)
val = []
preprocessed_image_path = 'C:/Users/Owner/Desktop/Ubotica/IOD/cloud_detect/'
formated_image_file = "output_patch_fp"
f = open(preprocessed_image_path + "/" + formated_image_file + ".txt", 'r')
val = f
print(f)
print(val)
# divide by 255 to get value in range 0->1 if necessary (depends on input pixel format)
if(eoim16.max()>1.0):
eoim16 = np.divide(eoim16,255)
print(eoim16)
#f.close()
#print(val)
#val = np.reshape(val, (3,512,512))
eoim16 = np.ndarray(shape=(c, h, w))
#res = val
# calling the instance method using the object cloudDetector
res = cloudDetector.infer(eoim16)
res = res[out_blob]
But when I try to print out val and f (just to see if the data matches and is actually being read within my code nothing appears. Is there any way to solve this so that my array reads into val and I can use the data within my code? Much appreciated!
Try using the eval function. It takes strings and interprets them as Python code.
a = eval(fileData)
print(a)
Suppose I have some nearest neighbor classifier. For a new observation it computes the distance between the new observation and all observations in the "known" data set. It returns the class label of the observation, that has the smallest distance to the new observation.
import numpy as np
known_obs = np.random.randint(0, 10, 40).reshape(8, 5)
new_obs = np.random.randint(0, 10, 80).reshape(16, 5)
labels = np.random.randint(0, 2, 8).reshape(8, )
def my_dist(x1, known_obs, axis=0):
return (np.square(np.linalg.norm(x1 - known_obs, axis=axis)))
def nn_classifier(n, known_obs, labels, axis=1, distance=my_dist):
return labels[np.argmin(distance(n, known_obs, axis=axis))]
def classify_batch(new_obs, known_obs, labels, classifier=nn_classifier, distance=my_dist):
return [classifier(n, known_obs, labels, distance=distance) for n in new_obs]
print(classify_batch(new_obs, known_obs, labels, nn_classifier, my_dist))
For performance reasons I would like to avoid the for loop in the classify_batch function. Is there a way to use numpy operations to apply the nn_classifier function to each row of new_obs?
I already tried apply_along_axis but as often mentioned it is convenient but not fast.
The key to avoiding the loop is to express the action on the (16,8) array of 'distances'. The labels[] and argmin steps just cloud the issue.
If I set labels = np.arange(8), then this
arr = np.array([my_dist(n, known_obs, axis=1) for n in new_obs])
print(arr)
print(np.argmin(arr, axis=1))
produces the same thing. It still has a list comprehension, but we are closer to 'source'.
[[ 32. 115. 22. 116. 162. 86. 161. 117.]
[ 106. 31. 142. 164. 92. 106. 45. 103.]
[ 44. 135. 94. 18. 94. 50. 87. 135.]
[ 11. 92. 57. 67. 79. 43. 118. 106.]
[ 40. 67. 126. 98. 50. 74. 75. 175.]
[ 78. 61. 120. 148. 102. 128. 67. 191.]
[ 51. 48. 57. 133. 125. 35. 110. 14.]
[ 47. 28. 93. 91. 63. 49. 32. 88.]
[ 61. 86. 23. 141. 159. 85. 146. 22.]
[ 131. 70. 155. 149. 129. 127. 44. 138.]
[ 97. 138. 87. 117. 223. 77. 130. 122.]
[ 151. 78. 211. 161. 131. 115. 46. 164.]
[ 13. 50. 31. 69. 59. 43. 80. 40.]
[ 131. 108. 157. 161. 207. 85. 102. 146.]
[ 39. 106. 67. 23. 61. 67. 70. 88.]
[ 54. 51. 74. 68. 42. 86. 35. 65.]]
[2 1 3 0 0 1 7 1 7 6 5 6 0 5 3 6]
With
print((new_obs[:,None,:] - known_obs[None,:,:]).shape)
I get a (16,8,5) array. So can I apply the linalg.norm on the last axis?
This seems to do the trick
np.square(np.linalg.norm(diff, axis=-1))
So together:
diff = (new_obs[:,None,:] - known_obs[None,:,:])
dist = np.square(np.linalg.norm(diff, axis=-1))
idx = np.argmin(dist, axis=1)
print(idx)
I have a 601x350x200x146 numpy float64 array which, according to my calculations takes about 22.3 Gb of memory. My output of free -m tells me I have about 100Gb of free memory so it fits fine. However, when integrating with
result = np.trapz(large_arr, axis=3)
I get a memory error. I understand that this is because of the intermediate arrays that numpy.trapz has to create to perform the integration. But I'm looking to see if there's a way around it, or at least a way to minimize the extra use of memory.
I have read about memory errors and I know of things to avoid this: one is placing a gc.collect() call before the integration. I tried this and it didn't work.
The other one is using the *= operators such as writing arr*=a instead of arr=arr*a, which I can't really do here. So I don't know what else to try.
Does anyone know of a way to do this operation without raising a memory error?
You can reproduce the error with:
arr = np.ones((601,350,200,146), dtype=np.float64)
arr=np.trapz(arr, axis=3)
although you'll have to scale down the size to match your memory size.
numpy.trapz provides some convenience, but the actual calculation is very simple. To avoid large temporary arrays, just implement it yourself:
In [37]: x.shape
Out[37]: (2, 4, 4, 10)
Here's the result of numpy.trapz(x, axis=3):
In [38]: np.trapz(x, axis=3)
Out[38]:
array([[[ 43. , 48.5, 46.5, 67. ],
[ 35.5, 39.5, 52.5, 35. ],
[ 44.5, 47.5, 34.5, 39.5],
[ 54. , 40. , 46.5, 50.5]],
[[ 42. , 60. , 55.5, 51. ],
[ 51.5, 40. , 52. , 42.5],
[ 48.5, 43. , 32. , 36.5],
[ 42.5, 38. , 38. , 45. ]]])
Here's the calculation written to use no large intermediate arrays. (The slice x[:,:,:,1:-1] does not copy the data associated with the array.)
In [48]: 0.5*(x[:,:,:,0] + 2*x[:,:,:,1:-1].sum(axis=3) + x[:,:,:,-1])
Out[48]:
array([[[ 43. , 48.5, 46.5, 67. ],
[ 35.5, 39.5, 52.5, 35. ],
[ 44.5, 47.5, 34.5, 39.5],
[ 54. , 40. , 46.5, 50.5]],
[[ 42. , 60. , 55.5, 51. ],
[ 51.5, 40. , 52. , 42.5],
[ 48.5, 43. , 32. , 36.5],
[ 42.5, 38. , 38. , 45. ]]])
If x has shape (m, n, p, q), the few temporary arrays that are generated in that expression all have shape (m, n, p).
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,)
A_placeholder[i,j] = m_i * n_j
Any operation like that can be expressed in numpy using broadcasting
A = np.arange(m)[:, None] * np.arange(n)[None, :]
Scikit-Image has quite a few methods available for blob detection:
Laplacian of Gaussian (LoG)
Difference of Gaussian (DoG)
Determinant of Hessian (DoH)
All three return an array that contains a single point within the bounds of the found components:
>>> from skimage import data, feature
>>> img = data.coins()
>>> feature.blob_doh(img)
array([[ 121. , 271. , 30. ],
[ 123. , 44. , 23.55555556],
[ 123. , 205. , 20.33333333],
[ 124. , 336. , 20.33333333],
[ 126. , 101. , 20.33333333],
[ 126. , 153. , 20.33333333],
[ 156. , 302. , 30. ],
[ 185. , 348. , 30. ],
[ 192. , 212. , 23.55555556],
[ 193. , 275. , 23.55555556],
[ 195. , 100. , 23.55555556],
[ 197. , 44. , 20.33333333],
[ 197. , 153. , 20.33333333],
[ 260. , 173. , 30. ],
[ 262. , 243. , 23.55555556],
[ 265. , 113. , 23.55555556],
[ 270. , 363. , 30. ]])
I'd like to use that information to produce lists that contains the coordinates of all the points in a given component.
I could just iterate through the whole image myself starting with the seeds and just collect all the points in a dict with the key being the point provide by blob detection, but I imagine it would rather slow unless I'm using cython(more than willing to be wrong about this, as I'm fairly new to python). More truthfully, I simply think there is probably a better way then just doing it myself.