Normalise all elements in deep nested list Python
0
I have image data in the form of a deep nested list of ints:
len(train_data_imgs) = 3889 # number of images in set
len(train_data_imgs[0]) = 100 # height
len(train_data_imgs[0][0]) = 100 # width
len(train_data_imgs[0][0][0]) = 3 # these are ints - RGB pixel values
How can I iterate through these to normalise them between 0 and 1? Simply would require every number to be divided by 255.
python python-3.x image
asked Nov 26 '18 at 13:45
Seb SquireSeb Squire
62
add a comment |
0
I have image data in the form of a deep nested list of ints:
len(train_data_imgs) = 3889 # number of images in set
len(train_data_imgs[0]) = 100 # height
len(train_data_imgs[0][0]) = 100 # width
len(train_data_imgs[0][0][0]) = 3 # these are ints - RGB pixel values
How can I iterate through these to normalise them between 0 and 1? Simply would require every number to be divided by 255.
python python-3.x image
asked Nov 26 '18 at 13:45
Seb SquireSeb Squire
62
What's wrong with dividing every value by 255?
– Yakov Dan
Nov 26 '18 at 13:49
You have 116.670.000 values, just the iteration alone will take about 10 seconds. Are your images in a format that allows bulk operations, such asnumpytypes?
– MisterMiyagi
Nov 26 '18 at 13:52
add a comment |
0
0
0
I have image data in the form of a deep nested list of ints:
len(train_data_imgs) = 3889 # number of images in set
len(train_data_imgs[0]) = 100 # height
len(train_data_imgs[0][0]) = 100 # width
len(train_data_imgs[0][0][0]) = 3 # these are ints - RGB pixel values
How can I iterate through these to normalise them between 0 and 1? Simply would require every number to be divided by 255.
python python-3.x image
asked Nov 26 '18 at 13:45
Seb SquireSeb Squire
62
I have image data in the form of a deep nested list of ints:
len(train_data_imgs) = 3889 # number of images in set
len(train_data_imgs[0]) = 100 # height
len(train_data_imgs[0][0]) = 100 # width
len(train_data_imgs[0][0][0]) = 3 # these are ints - RGB pixel values
How can I iterate through these to normalise them between 0 and 1? Simply would require every number to be divided by 255.
python python-3.x image
python python-3.x image
asked Nov 26 '18 at 13:45
Seb SquireSeb Squire
62
asked Nov 26 '18 at 13:45
Seb SquireSeb Squire
62
asked Nov 26 '18 at 13:45
Seb SquireSeb Squire
62
asked Nov 26 '18 at 13:45
Seb SquireSeb Squire
62
asked Nov 26 '18 at 13:45
Seb SquireSeb Squire
62
62
What's wrong with dividing every value by 255?
– Yakov Dan
Nov 26 '18 at 13:49
You have 116.670.000 values, just the iteration alone will take about 10 seconds. Are your images in a format that allows bulk operations, such asnumpytypes?
– MisterMiyagi
Nov 26 '18 at 13:52
add a comment |
What's wrong with dividing every value by 255?
– Yakov Dan
Nov 26 '18 at 13:49
You have 116.670.000 values, just the iteration alone will take about 10 seconds. Are your images in a format that allows bulk operations, such asnumpytypes?
– MisterMiyagi
Nov 26 '18 at 13:52
What's wrong with dividing every value by 255?
– Yakov Dan
Nov 26 '18 at 13:49
What's wrong with dividing every value by 255?
– Yakov Dan
Nov 26 '18 at 13:49
You have 116.670.000 values, just the iteration alone will take about 10 seconds. Are your images in a format that allows bulk operations, such as
numpy types?– MisterMiyagi
Nov 26 '18 at 13:52
You have 116.670.000 values, just the iteration alone will take about 10 seconds. Are your images in a format that allows bulk operations, such as
numpy types?– MisterMiyagi
Nov 26 '18 at 13:52
add a comment |
1 Answer
1
active
oldest
votes
1
Use NumPy package to do in a line:
# Assuming an image stored in a nested list | here NumPy array
lst = np.arange(27).reshape(3,3,3)
lst
array([[[ 0, 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]]])
lst = lst/255 # That's what you should look for
lst
array([[[0. , 0.00392157, 0.00784314],
[0.01176471, 0.01568627, 0.01960784],
[0.02352941, 0.02745098, 0.03137255]],
[[0.03529412, 0.03921569, 0.04313725],
[0.04705882, 0.05098039, 0.05490196],
[0.05882353, 0.0627451 , 0.06666667]],
[[0.07058824, 0.0745098 , 0.07843137],
[0.08235294, 0.08627451, 0.09019608],
[0.09411765, 0.09803922, 0.10196078]]])
answered Nov 26 '18 at 13:49
dataLeodataLeo
6181419
What's the advantage of using numpy in this case?
– Yakov Dan
Nov 26 '18 at 13:50
1
The same advantage whichNumPycan have over normal pythonlist;) Actually you can go ahead with normal list too. But what's the problem in knowing something better, especially when it's images. :)
– dataLeo
Nov 26 '18 at 13:52
2
The question then is how to get the images into numpy format in the first place, and whether they must be extracted later on again.
– MisterMiyagi
Nov 26 '18 at 13:55
@MisterMiyagi True
– dataLeo
Nov 26 '18 at 13:56
1
There's nothing wrong with using a better tool! However, if you already have an image in memory stored as lists of lists, why would it be better to convert to a numpy array and then use numpy vs just iterating over the lists?
– Yakov Dan
Nov 26 '18 at 13:56
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53482476%2fnormalise-all-elements-in-deep-nested-list-python%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
Use NumPy package to do in a line:
# Assuming an image stored in a nested list | here NumPy array
lst = np.arange(27).reshape(3,3,3)
lst
array([[[ 0, 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]]])
lst = lst/255 # That's what you should look for
lst
array([[[0. , 0.00392157, 0.00784314],
[0.01176471, 0.01568627, 0.01960784],
[0.02352941, 0.02745098, 0.03137255]],
[[0.03529412, 0.03921569, 0.04313725],
[0.04705882, 0.05098039, 0.05490196],
[0.05882353, 0.0627451 , 0.06666667]],
[[0.07058824, 0.0745098 , 0.07843137],
[0.08235294, 0.08627451, 0.09019608],
[0.09411765, 0.09803922, 0.10196078]]])
answered Nov 26 '18 at 13:49
dataLeodataLeo
6181419
What's the advantage of using numpy in this case?
– Yakov Dan
Nov 26 '18 at 13:50
1
The same advantage whichNumPycan have over normal pythonlist;) Actually you can go ahead with normal list too. But what's the problem in knowing something better, especially when it's images. :)
– dataLeo
Nov 26 '18 at 13:52
2
The question then is how to get the images into numpy format in the first place, and whether they must be extracted later on again.
– MisterMiyagi
Nov 26 '18 at 13:55
@MisterMiyagi True
– dataLeo
Nov 26 '18 at 13:56
1
There's nothing wrong with using a better tool! However, if you already have an image in memory stored as lists of lists, why would it be better to convert to a numpy array and then use numpy vs just iterating over the lists?
– Yakov Dan
Nov 26 '18 at 13:56
add a comment |
1
Use NumPy package to do in a line:
# Assuming an image stored in a nested list | here NumPy array
lst = np.arange(27).reshape(3,3,3)
lst
array([[[ 0, 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]]])
lst = lst/255 # That's what you should look for
lst
array([[[0. , 0.00392157, 0.00784314],
[0.01176471, 0.01568627, 0.01960784],
[0.02352941, 0.02745098, 0.03137255]],
[[0.03529412, 0.03921569, 0.04313725],
[0.04705882, 0.05098039, 0.05490196],
[0.05882353, 0.0627451 , 0.06666667]],
[[0.07058824, 0.0745098 , 0.07843137],
[0.08235294, 0.08627451, 0.09019608],
[0.09411765, 0.09803922, 0.10196078]]])
answered Nov 26 '18 at 13:49
dataLeodataLeo
6181419
What's the advantage of using numpy in this case?
– Yakov Dan
Nov 26 '18 at 13:50
1
The same advantage whichNumPycan have over normal pythonlist;) Actually you can go ahead with normal list too. But what's the problem in knowing something better, especially when it's images. :)
– dataLeo
Nov 26 '18 at 13:52
2
The question then is how to get the images into numpy format in the first place, and whether they must be extracted later on again.
– MisterMiyagi
Nov 26 '18 at 13:55
@MisterMiyagi True
– dataLeo
Nov 26 '18 at 13:56
1
There's nothing wrong with using a better tool! However, if you already have an image in memory stored as lists of lists, why would it be better to convert to a numpy array and then use numpy vs just iterating over the lists?
– Yakov Dan
Nov 26 '18 at 13:56
add a comment |
1
1
1
Use NumPy package to do in a line:
# Assuming an image stored in a nested list | here NumPy array
lst = np.arange(27).reshape(3,3,3)
lst
array([[[ 0, 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]]])
lst = lst/255 # That's what you should look for
lst
array([[[0. , 0.00392157, 0.00784314],
[0.01176471, 0.01568627, 0.01960784],
[0.02352941, 0.02745098, 0.03137255]],
[[0.03529412, 0.03921569, 0.04313725],
[0.04705882, 0.05098039, 0.05490196],
[0.05882353, 0.0627451 , 0.06666667]],
[[0.07058824, 0.0745098 , 0.07843137],
[0.08235294, 0.08627451, 0.09019608],
[0.09411765, 0.09803922, 0.10196078]]])
answered Nov 26 '18 at 13:49
dataLeodataLeo
6181419
Use NumPy package to do in a line:
# Assuming an image stored in a nested list | here NumPy array
lst = np.arange(27).reshape(3,3,3)
lst
array([[[ 0, 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]]])
lst = lst/255 # That's what you should look for
lst
array([[[0. , 0.00392157, 0.00784314],
[0.01176471, 0.01568627, 0.01960784],
[0.02352941, 0.02745098, 0.03137255]],
[[0.03529412, 0.03921569, 0.04313725],
[0.04705882, 0.05098039, 0.05490196],
[0.05882353, 0.0627451 , 0.06666667]],
[[0.07058824, 0.0745098 , 0.07843137],
[0.08235294, 0.08627451, 0.09019608],
[0.09411765, 0.09803922, 0.10196078]]])
answered Nov 26 '18 at 13:49
dataLeodataLeo
6181419
answered Nov 26 '18 at 13:49
dataLeodataLeo
6181419
answered Nov 26 '18 at 13:49
dataLeodataLeo
6181419
answered Nov 26 '18 at 13:49
dataLeodataLeo
6181419
6181419
What's the advantage of using numpy in this case?
– Yakov Dan
Nov 26 '18 at 13:50
1
The same advantage whichNumPycan have over normal pythonlist;) Actually you can go ahead with normal list too. But what's the problem in knowing something better, especially when it's images. :)
– dataLeo
Nov 26 '18 at 13:52
2
The question then is how to get the images into numpy format in the first place, and whether they must be extracted later on again.
– MisterMiyagi
Nov 26 '18 at 13:55
@MisterMiyagi True
– dataLeo
Nov 26 '18 at 13:56
1
There's nothing wrong with using a better tool! However, if you already have an image in memory stored as lists of lists, why would it be better to convert to a numpy array and then use numpy vs just iterating over the lists?
– Yakov Dan
Nov 26 '18 at 13:56
add a comment |
What's the advantage of using numpy in this case?
– Yakov Dan
Nov 26 '18 at 13:50
1
The same advantage whichNumPycan have over normal pythonlist;) Actually you can go ahead with normal list too. But what's the problem in knowing something better, especially when it's images. :)
– dataLeo
Nov 26 '18 at 13:52
2
The question then is how to get the images into numpy format in the first place, and whether they must be extracted later on again.
– MisterMiyagi
Nov 26 '18 at 13:55
@MisterMiyagi True
– dataLeo
Nov 26 '18 at 13:56
1
There's nothing wrong with using a better tool! However, if you already have an image in memory stored as lists of lists, why would it be better to convert to a numpy array and then use numpy vs just iterating over the lists?
– Yakov Dan
Nov 26 '18 at 13:56
What's the advantage of using numpy in this case?
– Yakov Dan
Nov 26 '18 at 13:50
What's the advantage of using numpy in this case?
– Yakov Dan
Nov 26 '18 at 13:50
1
1
The same advantage which
NumPy can have over normal python list ;) Actually you can go ahead with normal list too. But what's the problem in knowing something better, especially when it's images. :)– dataLeo
Nov 26 '18 at 13:52
The same advantage which
NumPy can have over normal python list ;) Actually you can go ahead with normal list too. But what's the problem in knowing something better, especially when it's images. :)– dataLeo
Nov 26 '18 at 13:52
2
2
The question then is how to get the images into numpy format in the first place, and whether they must be extracted later on again.
– MisterMiyagi
Nov 26 '18 at 13:55
The question then is how to get the images into numpy format in the first place, and whether they must be extracted later on again.
– MisterMiyagi
Nov 26 '18 at 13:55
@MisterMiyagi True
– dataLeo
Nov 26 '18 at 13:56
@MisterMiyagi True
– dataLeo
Nov 26 '18 at 13:56
1
1
There's nothing wrong with using a better tool! However, if you already have an image in memory stored as lists of lists, why would it be better to convert to a numpy array and then use numpy vs just iterating over the lists?
– Yakov Dan
Nov 26 '18 at 13:56
There's nothing wrong with using a better tool! However, if you already have an image in memory stored as lists of lists, why would it be better to convert to a numpy array and then use numpy vs just iterating over the lists?
– Yakov Dan
Nov 26 '18 at 13:56
add a comment |
draft saved
draft discarded
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53482476%2fnormalise-all-elements-in-deep-nested-list-python%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
What's wrong with dividing every value by 255?
– Yakov Dan
Nov 26 '18 at 13:49
You have 116.670.000 values, just the iteration alone will take about 10 seconds. Are your images in a format that allows bulk operations, such as
numpytypes?– MisterMiyagi
Nov 26 '18 at 13:52