I have an array in three dimensions (x, y, z) and an indexing vector. This vector has a size equal to the dimension x of the array. Its objective is to index a specific y bringing their respective z, i.e., the expected result has dimension (x, z).
I wrote a code that works as expected, but does anyone know if a Numpy function can replace the for loop and solve the problem more optimally?
arr = np.random.rand(100,5,2)
result = np.random.rand(100,2)
id = [np.random.randint(0, 5) for _ in range(100)]
for i in range(100):
result[i] = arr[i,id[i]]
You can achieve this with this piece of code:
import numpy as np
arr = np.random.randn(100, 5, 2)
ids = np.random.randint(0, 5, size=100)
res = arr[range(100), ids]
res.shape # (100, 2)
I am trying to manually convert a BGR image to HSV. I need to find the maximum pixel value each of 3 image channels (numPy arrays) and create a new array which contains the maximum of the 3 channels.
def convertBGRtoHSV(image):
# normalize image
scaledImage = image // 256
# split image into 3 channels
B, G, R = cv2.split(scaledImage)
# find the shape of each array
heightB, widthB = B.shape
V = []
for h_i in range(0, height):
for w_i in range(0, width):
V[h_i][w_i] = max(B[h_i][w_i], G[h_i][w_i], R[h_i][w_i])
I am getting this error: IndexError: list index out of range
I know this loop is incorrect. I know to access the value of a pixel in an array you must say the location as such as x[:,:] but I am not sure how to loop over all the pixels of each image and make a new array with the max value of each array element.
If possible I would like to know how to use a numPy "Vectorized Operation" to accomplish this as well as the for loop.
There is a builtin function for element-wise maximum:
V = np.maximum(np.maximum(R, G), B)
... and you are done
Following up on my comment:
import cv2
import numpy as np
image = cv2.imread(image)
height, width, _ = image.shape
# initialize your output array 'v'
v = np.zeros((height, width))
# loop over each index in ranges dictated by the image shape
for row in range(height):
for col in range(width):
# assign the maximum value across the 3rd dimension (color channel)
# from the original image to your output array
v[row, col] = max(image[row, col, :])
I a have tuple a with the shape of (3,1) and I would like to construct a 2D matrix X with the dimension(3,2). After X is constructed, there is a need to multiply X'*X which is supposed to be in the shape of (2,2)
enter image description here
import numpy as np
thistuple = (1, 2, 3)
arr=np.ones(shape=(len(thistuple),2))
tuple_index=0
for i in range(0,len(arr)):
for j in range(0,len(arr[0])):
if(tuple_index>=len(thistuple)):
break
arr[i][j]=thistuple[tuple_index]
tuple_index+=1
rez = arr.T
result = np.dot(rez,arr)
print(result)
The above code will work for an tuple of shape n*1 in python.
I am trying to do some numpy matrix math because I need to replicate the repmat function from MATLAB. I know there are a thousand examples online, but I cannot seem to get any of them working.
The following is the code I am trying to run:
def getDMap(image, mapSize):
newSize = (float(mapSize[0]) / float(image.shape[1]), float(mapSize[1]) / float(image.shape[0]))
sm = cv.resize(image, (0,0), fx=newSize[0], fy=newSize[1])
for j in range(0, sm.shape[1]):
for i in range(0, sm.shape[0]):
dmap = sm[:,:,:]-np.array([np.tile(sm[j,i,:], (len(sm[0]), len(sm[1]))) for k in xrange(len(sm[2]))])
return dmap
The function getDMap(image, mapSize) expects an OpenCV2 HSV image as its image argument, which is a numpy array with 3 dimensions: [:,:,:]. It also expects a tuple with 2 elements as its imSize argument, of course making sure the function passing the arguments takes into account that in numpy arrays the rows and colums are swapped (not: x, y, but: y, x).
newSize then contains a tuple containing fracions that are used to resize the input image to a specific scale, and sm becomes a resized version of the input image. This all works fine.
This is my goal:
The following line:
np.array([np.tile(sm[i,j,:], (len(sm[0]), len(sm[1]))) for k in xrange(len(sm[2]))]),
should function equivalent to the MATLAB expression:
repmat(sm(j,i,:),[size(sm,1) size(sm,2)]),
This is my problem:
Testing this, an OpenCV2 image with dimensions 800x479x3 is passed as the image argument, and (64, 48) (a tuple) is passed as the imSize argument.
However when testing this, I get the following ValueError:
dmap = sm[:,:,:]-np.array([np.tile(sm[i,j,:], (len(sm[0]),
len(sm[1]))) for k in xrange(len(sm[2]))])
ValueError: operands could not be broadcast together with
shapes (48,64,3) (64,64,192)
So it seems that the array dimensions do not match and numpy has a problem with that. But my question is what? And how do I get this working?
These 2 calculations match:
octave:26> sm=reshape(1:12,2,2,3)
octave:27> x=repmat(sm(1,2,:),[size(sm,1) size(sm,2)])
octave:28> x(:,:,2)
7 7
7 7
In [45]: sm=np.arange(1,13).reshape(2,2,3,order='F')
In [46]: x=np.tile(sm[0,1,:],[sm.shape[0],sm.shape[1],1])
In [47]: x[:,:,1]
Out[47]:
array([[7, 7],
[7, 7]])
This runs:
sm[:,:,:]-np.array([np.tile(sm[0,1,:], (2,2,1)) for k in xrange(3)])
But it produces a (3,2,2,3) array, with replication on the 1st dimension. I don't think you want that k loop.
What's the intent with?
for i in ...:
for j in ...:
data = ...
You'll only get results from the last iteration. Did you want data += ...? If so, this might work (for a (N,M,K) shaped sm)
np.sum(np.array([sm-np.tile(sm[i,j,:], (N,M,1)) for i in xrange(N) for j in xrange(M)]),axis=0)
z = np.array([np.tile(sm[i,j,:], (N,M,1)) for i in xrange(N) for j in xrange(M)]),axis=0)
np.sum(sm - z, axis=0) # let numpy broadcast sm
Actually I don't even need the tile. Let broadcasting do the work:
np.sum(np.array([sm-sm[i,j,:] for i in xrange(N) for j in xrange(M)]),axis=0)
I can get rid of the loops with repeat.
sm1 = sm.reshape(N*M,L) # combine 1st 2 dim to simplify repeat
z1 = np.repeat(sm1, N*M, axis=0).reshape(N*M,N*M,L)
x1 = np.sum(sm1 - z1, axis=0).reshape(N,M,L)
I can also apply broadcasting to the last case
x4 = np.sum(sm1-sm1[:,None,:], 0).reshape(N,M,L)
# = np.sum(sm1[None,:,:]-sm1[:,None,:], 0).reshape(N,M,L)
With sm I have to expand (and sum) 2 dimensions:
x5 = np.sum(np.sum(sm[None,:,None,:,:]-sm[:,None,:,None,:],0),1)
len(sm[0]) and len(sm[1]) are not the sizes of the first and second dimensions of sm. They are the lengths of the first and second row of sm, and should both return the same value. You probably want to replace them with sm.shape[0] and sm.shape[1], which are equivalent to your Matlab code, although I am not sure that it will work as you expect it to.
I have a cell array of numbers but the majority of the cell array is empty for example:
x =
[] [6] [] [4] [] [] [] [1]
I have a matching array y
y = [1, 3,1,5,7,3,1,5]
I want to get the index of the numbers from the cell array x and use them to get the corresponding values from y. So x(2) matches with y(2). I convert x to a array using
x = cell2mat(x);
But the problem is that it returns
x = [6,4,1]
This does not allow me to get the correct index so I can then sort X and then sort Y accordingly so the same indices match up. I tried to use sort that does not work for cell arrays.
Just use y(x); that will return indices 6, 4, and 1 from the y vector.
Note that the order of the returned matrix will depend on the order of the indices in x; if you want to sort x, do it before running y(x).