what is difference between np.copy and np.copy() ? I think np.copy() creates copy of numpy array object and np.copy is attribute of the object. But when i try
print(np.copy) or print(someObject.copy()) it shows message
'<built-in method copy of numpy.ndarray object at 0x00000214E46468D0>'
or
<function copy at 0x00000214E2B3BD30>
I'm expecting some value or object. Can you explain to me what is happening here?
edit: What is difference between
SomeObject.copy()
and
SomeObject.copy
?
Your print just displays the function or method identity. It doesn't run/evaluate either.
If x is an numpy array, np.copy(x) and x.copy() do the same thing.
More generally np.copy(x) will return an array regardless of what x is, while x.copy() uses the copy method defined for the x class. Those may be different.
In [175]: x = np.array([1, 2, 3])
In [176]: np.copy(x)
Out[176]: array([1, 2, 3])
In [177]: x.copy()
Out[177]: array([1, 2, 3])
In [178]: y = [1, 2, 3]
In [179]: np.copy(y)
Out[179]: array([1, 2, 3])
In [180]: y.copy()
Out[180]: [1, 2, 3]
They are two different things. print(np.copy) copies numpy. print(someObject.copy) copies the object.
After the thread strange implicit conversion of data type in numpy, I found another strange conversion with numpy
import numpy as np
a = np.array([1,2,3], dtype=int)
c = np.append(a, [])
print the c gives:
array([1., 2., 3.])
However, if:
c = np.append(a, [4])
gives:
array([1, 2, 3, 4])
why is there such strange automatic conversion? It does not make any sense at all
The empty list has to be first turned into an array:
In [149]: np.array([])
Out[149]: array([], dtype=float64)
np.append actually does:
In [151]: np.ravel([])
Out[151]: array([], dtype=float64)
The append code:
def append(arr, values, axis=None):
arr = asanyarray(arr)
if axis is None:
if arr.ndim != 1:
arr = arr.ravel()
values = ravel(values)
axis = arr.ndim-1
return concatenate((arr, values), axis=axis)
I'm starting off with a numpy array of an image.
In[1]:img = cv2.imread('test.jpg')
The shape is what you might expect for a 640x480 RGB image.
In[2]:img.shape
Out[2]: (480, 640, 3)
However, this image that I have is a frame of a video, which is 100 frames long. Ideally, I would like to have a single array that contains all the data from this video such that img.shape returns (480, 640, 3, 100).
What is the best way to add the next frame -- that is, the next set of image data, another 480 x 640 x 3 array -- to my initial array?
A dimension can be added to a numpy array as follows:
image = image[..., np.newaxis]
Alternatively to
image = image[..., np.newaxis]
in #dbliss' answer, you can also use numpy.expand_dims like
image = np.expand_dims(image, <your desired dimension>)
For example (taken from the link above):
x = np.array([1, 2])
print(x.shape) # prints (2,)
Then
y = np.expand_dims(x, axis=0)
yields
array([[1, 2]])
and
y.shape
gives
(1, 2)
You could just create an array of the correct size up-front and fill it:
frames = np.empty((480, 640, 3, 100))
for k in xrange(nframes):
frames[:,:,:,k] = cv2.imread('frame_{}.jpg'.format(k))
if the frames were individual jpg file that were named in some particular way (in the example, frame_0.jpg, frame_1.jpg, etc).
Just a note, you might consider using a (nframes, 480,640,3) shaped array, instead.
Pythonic
X = X[:, :, None]
which is equivalent to
X = X[:, :, numpy.newaxis] and
X = numpy.expand_dims(X, axis=-1)
But as you are explicitly asking about stacking images,
I would recommend going for stacking the list of images np.stack([X1, X2, X3]) that you may have collected in a loop.
If you do not like the order of the dimensions you can rearrange with np.transpose()
You can use np.concatenate() use the axis parameter to specify the dimension that should be concatenated. If the arrays being concatenated do not have this dimension, you can use np.newaxis to indicate where the new dimension should be added:
import numpy as np
movie = np.concatenate((img1[:,np.newaxis], img2[:,np.newaxis]), axis=3)
If you are reading from many files:
import glob
movie = np.concatenate([cv2.imread(p)[:,np.newaxis] for p in glob.glob('*.jpg')], axis=3)
Consider Approach 1 with reshape method and Approach 2 with np.newaxis method that produce the same outcome:
#Lets suppose, we have:
x = [1,2,3,4,5,6,7,8,9]
print('I. x',x)
xNpArr = np.array(x)
print('II. xNpArr',xNpArr)
print('III. xNpArr', xNpArr.shape)
xNpArr_3x3 = xNpArr.reshape((3,3))
print('IV. xNpArr_3x3.shape', xNpArr_3x3.shape)
print('V. xNpArr_3x3', xNpArr_3x3)
#Approach 1 with reshape method
xNpArrRs_1x3x3x1 = xNpArr_3x3.reshape((1,3,3,1))
print('VI. xNpArrRs_1x3x3x1.shape', xNpArrRs_1x3x3x1.shape)
print('VII. xNpArrRs_1x3x3x1', xNpArrRs_1x3x3x1)
#Approach 2 with np.newaxis method
xNpArrNa_1x3x3x1 = xNpArr_3x3[np.newaxis, ..., np.newaxis]
print('VIII. xNpArrNa_1x3x3x1.shape', xNpArrNa_1x3x3x1.shape)
print('IX. xNpArrNa_1x3x3x1', xNpArrNa_1x3x3x1)
We have as outcome:
I. x [1, 2, 3, 4, 5, 6, 7, 8, 9]
II. xNpArr [1 2 3 4 5 6 7 8 9]
III. xNpArr (9,)
IV. xNpArr_3x3.shape (3, 3)
V. xNpArr_3x3 [[1 2 3]
[4 5 6]
[7 8 9]]
VI. xNpArrRs_1x3x3x1.shape (1, 3, 3, 1)
VII. xNpArrRs_1x3x3x1 [[[[1]
[2]
[3]]
[[4]
[5]
[6]]
[[7]
[8]
[9]]]]
VIII. xNpArrNa_1x3x3x1.shape (1, 3, 3, 1)
IX. xNpArrNa_1x3x3x1 [[[[1]
[2]
[3]]
[[4]
[5]
[6]]
[[7]
[8]
[9]]]]
a = np.expand_dims(a, axis=-1)
or
a = a[:, np.newaxis]
or
a = a.reshape(a.shape + (1,))
There is no structure in numpy that allows you to append more data later.
Instead, numpy puts all of your data into a contiguous chunk of numbers (basically; a C array), and any resize requires allocating a new chunk of memory to hold it. Numpy's speed comes from being able to keep all the data in a numpy array in the same chunk of memory; e.g. mathematical operations can be parallelized for speed and you get less cache misses.
So you will have two kinds of solutions:
Pre-allocate the memory for the numpy array and fill in the values, like in JoshAdel's answer, or
Keep your data in a normal python list until it's actually needed to put them all together (see below)
images = []
for i in range(100):
new_image = # pull image from somewhere
images.append(new_image)
images = np.stack(images, axis=3)
Note that there is no need to expand the dimensions of the individual image arrays first, nor do you need to know how many images you expect ahead of time.
You can use stack with the axis parameter:
img.shape # h,w,3
imgs = np.stack([img1,img2,img3,img4], axis=-1) # -1 = new axis is last
imgs.shape # h,w,3,nimages
For example: to convert grayscale to color:
>>> d = np.zeros((5,4), dtype=int) # 5x4
>>> d[2,3] = 1
>>> d3.shape
Out[30]: (5, 4, 3)
>>> d3 = np.stack([d,d,d], axis=-2) # 5x4x3 -1=as last axis
>>> d3[2,3]
Out[32]: array([1, 1, 1])
I followed this approach:
import numpy as np
import cv2
ls = []
for image in image_paths:
ls.append(cv2.imread('test.jpg'))
img_np = np.array(ls) # shape (100, 480, 640, 3)
img_np = np.rollaxis(img_np, 0, 4) # shape (480, 640, 3, 100).
This worked for me:
image = image[..., None]
This will help you add axis anywhere you want
import numpy as np
signal = np.array([[0.3394572666491664, 0.3089068053925853, 0.3516359279582483], [0.33932706934615525, 0.3094755563319447, 0.3511973743219001], [0.3394407172182317, 0.30889042266755573, 0.35166886011421256], [0.3394407172182317, 0.30889042266755573, 0.35166886011421256]])
print(signal.shape)
#(4,3)
print(signal[...,np.newaxis].shape) or signal[...:none]
#(4, 3, 1)
print(signal[:, np.newaxis, :].shape) or signal[:,none, :]
#(4, 1, 3)
there is three-way for adding new dimensions to ndarray .
first: using "np.newaxis" (something like #dbliss answer)
np.newaxis is just given an alias to None for making it easier to
understand. If you replace np.newaxis with None, it works the same
way. but it's better to use np.newaxis for being more explicit.
import numpy as np
my_arr = np.array([2, 3])
new_arr = my_arr[..., np.newaxis]
print("old shape", my_arr.shape)
print("new shape", new_arr.shape)
>>> old shape (2,)
>>> new shape (2, 1)
second: using "np.expand_dims()"
Specify the original ndarray in the first argument and the position
to add the dimension in the second argument axis.
my_arr = np.array([2, 3])
new_arr = np.expand_dims(my_arr, -1)
print("old shape", my_arr.shape)
print("new shape", new_arr.shape)
>>> old shape (2,)
>>> new shape (2, 1)
third: using "reshape()"
my_arr = np.array([2, 3])
new_arr = my_arr.reshape(*my_arr.shape, 1)
print("old shape", my_arr.shape)
print("new shape", new_arr.shape)
>>> old shape (2,)
>>> new shape (2, 1)
Lets say I have
q=2
y=[5,10,5,15,20,25,30,35,5,10,15,20]
n=len(y)
and I want to make a matrix with n x q dimensions where the first row would be [5,10], the second row would be [10,5], and the third would be [5,15] ...etc.
Is there a way to do this or would I have to use a for loop and concatenate function?
Our good friend index_tricks to the rescue:
import numpy as np
#illustrate functionality on a 2d array
y=np.array([5,10,5,15,20,25,30,35,5,10,15,20]).reshape(2,-1)
def running_view(arr, window, axis=-1):
"""
return a running view of length 'window' over 'axis'
the returned array has an extra last dimension, which spans the window
"""
shape = list(arr.shape)
shape[axis] -= (window-1)
assert(shape[axis]>0)
return np.lib.index_tricks.as_strided(
arr,
shape + [window],
arr.strides + (arr.strides[axis],))
print running_view(y, 2)
It returns a view into the original array, so O(1) performance.
Edit: generalized to include an optional axis parameter for nd-arrays.
Since NumPy arrays are row-major ordered by default, you can directly reshape() to "wrap" an array to the rows of a matrix (assuming the number of columns divides the length of the array).
import numpy as np
def as_matrix(x, ncols):
nrows = len(x) // ncols
return np.array(x).reshape(nrows, ncols)
as_matrix(y, 2)
#> array([[ 5, 10],
#> [ 5, 15],
#> [20, 25],
#> [30, 35],
#> [ 5, 10],
#> [15, 20]])
I'm trying to plot a histogram with matplotlib.
I need to convert my one-line 2D Array
[[1,2,3,4]] # shape is (1,4)
into a 1D Array
[1,2,3,4] # shape is (4,)
How can I do this?
Adding ravel as another alternative for future searchers. From the docs,
It is equivalent to reshape(-1, order=order).
Since the array is 1xN, all of the following are equivalent:
arr1d = np.ravel(arr2d)
arr1d = arr2d.ravel()
arr1d = arr2d.flatten()
arr1d = np.reshape(arr2d, -1)
arr1d = arr2d.reshape(-1)
arr1d = arr2d[0, :]
You can directly index the column:
>>> import numpy as np
>>> x2 = np.array([[1,2,3,4]])
>>> x2.shape
(1, 4)
>>> x1 = x2[0,:]
>>> x1
array([1, 2, 3, 4])
>>> x1.shape
(4,)
Or you can use squeeze:
>>> xs = np.squeeze(x2)
>>> xs
array([1, 2, 3, 4])
>>> xs.shape
(4,)
reshape will do the trick.
There's also a more specific function, flatten, that appears to do exactly what you want.
the answer provided by mtrw does the trick for an array that actually only has one line like this one, however if you have a 2d array, with values in two dimension you can convert it as follows
a = np.array([[1,2,3],[4,5,6]])
From here you can find the shape of the array with np.shape and find the product of that with np.product this now results in the number of elements. If you now use np.reshape() to reshape the array to one length of the total number of element you will have a solution that always works.
np.reshape(a, np.product(a.shape))
>>> array([1, 2, 3, 4, 5, 6])
Use numpy.flat
import numpy as np
import matplotlib.pyplot as plt
a = np.array([[1,0,0,1],
[2,0,1,0]])
plt.hist(a.flat, [0,1,2,3])
The flat property returns a 1D iterator over your 2D array. This method generalizes to any number of rows (or dimensions). For large arrays it can be much more efficient than making a flattened copy.