How to append ND numpy arrays to (N+1)D numpy array through loop?
For example I need 30x30 numpy arrays created from images to be fed to a neural net. If I have a directory of images to predict, I should be able to loop through the directory, get image data and create an (n,30,30) shape np array
This is my current method, I intend to reshape each row before feeding to the model
def get_image_vectors(path):
img_list=os.listdir(path)
print(img_list)
X=np.empty((900,))
for img_file in img_list:
img= Image.open(os.path.join(path,img_file))
img_grey= img.convert("L")
resized = img_grey.resize((30,30))
flattened = np.array(resized.getdata())
# print(flattened.shape)
X=np.vstack((X,flattened))
print(img_file,'=>',X.shape)
return X[1:,:]
python arrays numpy
edited Nov 27 '18 at 20:19
TeeKea
3,22851832
asked Nov 27 '18 at 19:50
Amal JossyAmal Jossy
612
add a comment |
For example I need 30x30 numpy arrays created from images to be fed to a neural net. If I have a directory of images to predict, I should be able to loop through the directory, get image data and create an (n,30,30) shape np array
This is my current method, I intend to reshape each row before feeding to the model
def get_image_vectors(path):
img_list=os.listdir(path)
print(img_list)
X=np.empty((900,))
for img_file in img_list:
img= Image.open(os.path.join(path,img_file))
img_grey= img.convert("L")
resized = img_grey.resize((30,30))
flattened = np.array(resized.getdata())
# print(flattened.shape)
X=np.vstack((X,flattened))
print(img_file,'=>',X.shape)
return X[1:,:]
python arrays numpy
edited Nov 27 '18 at 20:19
TeeKea
3,22851832
asked Nov 27 '18 at 19:50
Amal JossyAmal Jossy
612
So what's your question? Do you realize that each call tovstackmakes a new array?vstackworks just as well, if not better, with a list of many arrays. Notice also that you have to play funny games to get things started, defining that "empty' array (which isn't at all like list) and then 'delete' it with[1:].
– hpaulj
Nov 27 '18 at 21:25
add a comment |
For example I need 30x30 numpy arrays created from images to be fed to a neural net. If I have a directory of images to predict, I should be able to loop through the directory, get image data and create an (n,30,30) shape np array
This is my current method, I intend to reshape each row before feeding to the model
def get_image_vectors(path):
img_list=os.listdir(path)
print(img_list)
X=np.empty((900,))
for img_file in img_list:
img= Image.open(os.path.join(path,img_file))
img_grey= img.convert("L")
resized = img_grey.resize((30,30))
flattened = np.array(resized.getdata())
# print(flattened.shape)
X=np.vstack((X,flattened))
print(img_file,'=>',X.shape)
return X[1:,:]
python arrays numpy
edited Nov 27 '18 at 20:19
TeeKea
3,22851832
asked Nov 27 '18 at 19:50
Amal JossyAmal Jossy
612
For example I need 30x30 numpy arrays created from images to be fed to a neural net. If I have a directory of images to predict, I should be able to loop through the directory, get image data and create an (n,30,30) shape np array
This is my current method, I intend to reshape each row before feeding to the model
def get_image_vectors(path):
img_list=os.listdir(path)
print(img_list)
X=np.empty((900,))
for img_file in img_list:
img= Image.open(os.path.join(path,img_file))
img_grey= img.convert("L")
resized = img_grey.resize((30,30))
flattened = np.array(resized.getdata())
# print(flattened.shape)
X=np.vstack((X,flattened))
print(img_file,'=>',X.shape)
return X[1:,:]
python arrays numpy
python arrays numpy
edited Nov 27 '18 at 20:19
TeeKea
3,22851832
asked Nov 27 '18 at 19:50
Amal JossyAmal Jossy
612
edited Nov 27 '18 at 20:19
TeeKea
3,22851832
asked Nov 27 '18 at 19:50
Amal JossyAmal Jossy
612
edited Nov 27 '18 at 20:19
TeeKea
3,22851832
edited Nov 27 '18 at 20:19
TeeKea
3,22851832
edited Nov 27 '18 at 20:19
TeeKea
3,22851832
3,22851832
asked Nov 27 '18 at 19:50
Amal JossyAmal Jossy
612
asked Nov 27 '18 at 19:50
Amal JossyAmal Jossy
612
asked Nov 27 '18 at 19:50
Amal JossyAmal Jossy
612
612
So what's your question? Do you realize that each call tovstackmakes a new array?vstackworks just as well, if not better, with a list of many arrays. Notice also that you have to play funny games to get things started, defining that "empty' array (which isn't at all like list) and then 'delete' it with[1:].
– hpaulj
Nov 27 '18 at 21:25
add a comment |
So what's your question? Do you realize that each call tovstackmakes a new array?vstackworks just as well, if not better, with a list of many arrays. Notice also that you have to play funny games to get things started, defining that "empty' array (which isn't at all like list) and then 'delete' it with[1:].
– hpaulj
Nov 27 '18 at 21:25
So what's your question? Do you realize that each call to
vstack makes a new array? vstack works just as well, if not better, with a list of many arrays. Notice also that you have to play funny games to get things started, defining that "empty' array (which isn't at all like list [1:].– hpaulj
Nov 27 '18 at 21:25
So what's your question? Do you realize that each call to
vstack makes a new array? vstack works just as well, if not better, with a list of many arrays. Notice also that you have to play funny games to get things started, defining that "empty' array (which isn't at all like list [1:].– hpaulj
Nov 27 '18 at 21:25
add a comment |
1 Answer
1
active
oldest
votes
Instead of appending to an existing array, it will probably be better to use a list initially, appending to it, and converting to an array at the end. thus saving many redundant modifications of np arrays.
Here a toy example:
import numpy as np
def get_image_vectors():
X= #Create empty list
for i in range(10):
flattened = np.zeros(900)
X.append(flattened) #Append some np array to it
return np.array(X) #Create array from the list
With result:
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
answered Nov 27 '18 at 20:33
DinariDinari
1,669522
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Instead of appending to an existing array, it will probably be better to use a list initially, appending to it, and converting to an array at the end. thus saving many redundant modifications of np arrays.
Here a toy example:
import numpy as np
def get_image_vectors():
X= #Create empty list
for i in range(10):
flattened = np.zeros(900)
X.append(flattened) #Append some np array to it
return np.array(X) #Create array from the list
With result:
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
answered Nov 27 '18 at 20:33
DinariDinari
1,669522
add a comment |
Instead of appending to an existing array, it will probably be better to use a list initially, appending to it, and converting to an array at the end. thus saving many redundant modifications of np arrays.
Here a toy example:
import numpy as np
def get_image_vectors():
X= #Create empty list
for i in range(10):
flattened = np.zeros(900)
X.append(flattened) #Append some np array to it
return np.array(X) #Create array from the list
With result:
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
answered Nov 27 '18 at 20:33
DinariDinari
1,669522
add a comment |
Instead of appending to an existing array, it will probably be better to use a list initially, appending to it, and converting to an array at the end. thus saving many redundant modifications of np arrays.
Here a toy example:
import numpy as np
def get_image_vectors():
X= #Create empty list
for i in range(10):
flattened = np.zeros(900)
X.append(flattened) #Append some np array to it
return np.array(X) #Create array from the list
With result:
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
answered Nov 27 '18 at 20:33
DinariDinari
1,669522
Instead of appending to an existing array, it will probably be better to use a list initially, appending to it, and converting to an array at the end. thus saving many redundant modifications of np arrays.
Here a toy example:
import numpy as np
def get_image_vectors():
X= #Create empty list
for i in range(10):
flattened = np.zeros(900)
X.append(flattened) #Append some np array to it
return np.array(X) #Create array from the list
With result:
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
answered Nov 27 '18 at 20:33
DinariDinari
1,669522
answered Nov 27 '18 at 20:33
DinariDinari
1,669522
answered Nov 27 '18 at 20:33
DinariDinari
1,669522
answered Nov 27 '18 at 20:33
DinariDinari
1,669522
1,669522
add a comment |
add a comment |
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So what's your question? Do you realize that each call to
vstackmakes a new array?vstackworks just as well, if not better, with a list of many arrays. Notice also that you have to play funny games to get things started, defining that "empty' array (which isn't at all like list) and then 'delete' it with[1:].– hpaulj
Nov 27 '18 at 21:25