I'm trying to calculate histogram for an image. I'm using the following formula to calculate the bin
%bin = red*(N^2) + green*(N^1) + blue;
I have to implement the following Matlab functions.
[row, col, noChannels] = size(rgbImage);
hsvImage = rgb2hsv(rgbImage); % Ranges from 0 to 1.
H = zeros(4,4,4);
for col = 1 : columns
for row = 1 : rows
hBin = floor(hsvImage(row, column, 1) * 15);
sBin = floor(hsvImage(row, column, 2) * 4);
vBin = floor(hsvImage(row, column, 3) * 4);
F(hBin, sBin, vBin) = hBin, sBin, vBin + 1;
end
end
When I run the code I get the following error message "Subscript indices must either be real positive integers or logical."
As I am new to Matlab and Image processing, I'm not sure if the problem is with implementing the algorithm or a syntax error.
There are 3 problems with your code. (Four if you count that you changed from H to F your accumulator vector, but I'll assume that's a typo.)
First one, your variable bin can be zero at any moment if the values of a giving pixel are low. And F(0) is not a valid index for a vector or matrix. This is why you are getting that error.
You can solve easily by doing F(bin+1) and keep in mind that your F vector will have your values shifted one position over.
Second error, you are assigning the value bin + 1 to your accumulator vector F, which is not what you want, you want to add 1 every time a pixel in that range is found, what you should do is F(bin+1) = F(bin+1) + 1;. This way the values of F will be increasing all the time.
Third error is simpler, you forgot to implement your bin = red*(N^2) + green*(N^1) + blue; equation
Related
I have a task: given a value N. I should generate a list of length L > 1 such that the sum of the squares of its elements is equal to N.
I wrote a code:
deltas = np.zeros(L)
deltas[0] = (np.random.uniform(-N, N))
i = 1
while i < L and np.sum(np.array(deltas)**2) < N**2:
deltas[i] = (np.random.uniform(-np.sqrt(N**2 - np.sum(np.array(deltas)**2)),\
np.sqrt(N**2 - np.sum(np.array(deltas)**2))))
i += 1
But this approach takes long time, if I generate such list many times. (I think because of loop).
Note, that I don't want my list to consist of just one unique value. The distribution of values does not have to be uniform - I took uniform just for example.
Could you suggest any faster approach? May be there is special function in any lib?
If you didn't mind a few repeating 1s, you could do something like this:
def square_list(integer):
components = []
total = 0
remaining = integer
while total != integer:
component = int(remaining ** 0.5)
remaining -= component ** 2
components.append(component)
total = sum([x ** 2 for x in components])
return components
This code works by finding the taking the largest square, and then decreasing to the next largest square. It continues until the largest square is 1, which could at worse result in 3 1s in a list.
If you are looking for a more random distribution, it might make sense to randomly transform remaining as a separate variable before subtracting from it.
IE:
value = transformation(remaining)
component = int(value ** 0.5)
which should give you more "random" values.
In the code supplied below I am trying to iterate over 2D numpy array [i][k]
Originally it is a code which was written in Fortran 77 which is older than my grandfather. I am trying to adapt it to python.
(for people interested whatabouts: it is a simple hydraulics transients event solver)
Bear in mind that all variables are introduced in my code which I don't paste here.
H = np.zeros((NS,50))
Q = np.zeros((NS,50))
Here I am assigning the first row values:
for i in range(NS):
H[0][i] = HR-i*R*Q0**2
Q[0][i] = Q0
CVP = .5*Q0**2/H[N]
T = 0
k = 0
TAU = 1
#Interior points:
HP = np.zeros((NS,50))
QP = np.zeros((NS,50))
while T<=Tmax:
T += dt
k += 1
for i in range(1,N):
CP = H[k][i-1]+Q[k][i-1]*(B-R*abs(Q[k][i-1]))
CM = H[k][i+1]-Q[k][i+1]*(B-R*abs(Q[k][i+1]))
HP[k][i-1] = 0.5*(CP+CM)
QP[k][i-1] = (HP[k][i-1]-CM)/B
#Boundary Conditions:
HP[k][0] = HR
QP[k][0] = Q[k][1]+(HP[k][0]-H[k][1]-R*Q[k][1]*abs(Q[k][1]))/B
if T == Tc:
TAU = 0
CV = 0
else:
TAU = (1.-T/Tc)**Em
CV = CVP*TAU**2
CP = H[k][N-1]+Q[k][N-1]*(B-R*abs(Q[k][N-1]))
QP[k][N] = -CV*B+np.sqrt(CV**2*(B**2)+2*CV*CP)
HP[k][N] = CP-B*QP[k][N]
for i in range(NS):
H[k][i] = HP[k][i]
Q[k][i] = QP[k][i]
Remember i is for rows and k is for columns
What I am expecting is that for all k number of columns the values should be calculated until T<=Tmax condition is met. I cannot figure out what my mistake is, I am getting the following errors:
RuntimeWarning: divide by zero encountered in true_divide
CVP = .5*Q0**2/H[N]
RuntimeWarning: invalid value encountered in multiply
QP[N][k] = -CV*B+np.sqrt(CV**2*(B**2)+2*CV*CP)
QP[N][k] = -CV*B+np.sqrt(CV**2*(B**2)+2*CV*CP)
ValueError: setting an array element with a sequence.
Looking at your first iteration:
H = np.zeros((NS,50))
Q = np.zeros((NS,50))
for i in range(NS):
H[0][i] = HR-i*R*Q0**2
Q[0][i] = Q0
The shape of H is (NS,50), but when you iterate over a range(NS) you apply that index to the 2nd dimension. Why? Shouldn't it apply to the dimension with size NS?
In numpy arrays have 'C' order by default. Last dimension is inner most. They can have a F (fortran) order, but let's not go there. Thinking of the 2d array as a table, we typically talk of rows and columns, though they don't have a formal definition in numpy.
Lets assume you want to set the first column to these values:
for i in range(NS):
H[i, 0] = HR - i*R*Q0**2
Q[i, 0] = Q0
But we can do the assignment whole rows or columns at a time. I believe new versions of Fortran also have these 'whole-array' functions.
Q[:, 0] = Q0
H[:, 0] = HR - np.arange(NS) * R * Q0**2
One point of caution when translating to Python. Indexing starts with 0; so does ranges and np.arange(...).
H[0][i] is functionally the same as H[0,i]. But when using slices you have to use the H[:,i] format.
I suspect your other iterations have similar problems, but I'll stop here for now.
Regarding the errors:
The first:
RuntimeWarning: divide by zero encountered in true_divide
CVP = .5*Q0**2/H[N]
You initialize H as zeros so it is normal that it complains of division by zero. Maybe you should add a conditional.
The third:
QP[N][k] = -CV*B+np.sqrt(CV**2*(B**2)+2*CV*CP)
ValueError: setting an array element with a sequence.
You define CVP = .5*Q0**2/H[N] and then CV = CVP*TAU**2 which is a sequence. And then you try to assign a derivate form it to QP[N][K] which is an element. You are trying to insert an array to a value.
For the second error I think it might be related to the third. If you could provide more information I would like to try to understand what happens.
Hope this has helped.
I have an image like that:
I have both the mask and the original image. I would like to calculate the colour temperature of ONLY the ducks region.
Right now, I'm iterating through each row and column of the image below and getting pixels where their values are not zero. But I think this isn't the right way to do this. Any suggestions?
What I did was:
xyzImg = cv2.cvtColor(resImage, cv2.COLOR_BGR2XYZ)
x,y,z = cv2.split(xyzImg)
xList=[]
yList=[]
zList=[]
rows=x.shape[0]
cols=x.shape[1]
for i in range(rows):
for j in range(cols):
if (x[i][j]!=0) and (y[i][j]!=0) and (z[i][j]!=0):
xList.append(x[i][j])
yList.append(y[i][j])
zList.append(z[i][j])
xAvg = np.mean(xList)
yAvg = np.mean(yList)
zAvg = np.mean(zList)
xs = xAvg / (xAvg + yAvg + zAvg)
ys = yAvg / (xAvg + yAvg + zAvg)
xyChrome = np.array([xs,ys])
But this is very slow and I don't think its right...
The simplest way would be to use cv2.mean() function.
It takes two arguments src (having 1 to 4 channels) and mask and returns a vector with mean values for individual channels.
Refer to cv2::mask
Here is background information to the problem I am encountering:
1) output is a cell array, each cell contains a matrix of size = 1024 x 1024, type = double
2) labelbout is a cell array which is the identical to output, except that each matrix has been binarized.
3) I am using the function regionprops to extract the mean intensity and centroid values for ROIs (there are multiple ROIs in each image) for each cell of output
4) props is a 5 x 1 struct with 2 fields (centroid and mean intensity)
The problem: I would like to take the mean intensity values for each ROI in every matrix and export to excel. Here is what I have so far:
for i = 1:size(output,2)
props = regionprops(labelboutput{1,i},output{1,i},'MeanIntensity','Centroid');
end
for i = 1:size(output,2)
meanValues = getfield(props(1:length(props),'MeanIntensity'));
end
writetable(struct2table(props), 'advanced_test.xlsx');
There seem to be a few issues:
1) my getfield command is not working and gets the error: "Index exceeds matrix dimensions"
2) when the information is being stored into props, it overwrites the values for each matrix. How do I make props a 5 x n (where n = number of cells in output)?
Please help!!
1) my getfield command is not working and gets the error: "Index exceeds matrix dimensions"
An easier way to get numeric values out of the same field in an array of structs, as an array is: [structArray.fieldName]. In your case this will be:
meanValues = [props.MeanIntensity];
2) when the information is being stored into props, it overwrites the values for each matrix. How do I make props a 5 x n (where n = number of cells in output)?
One option would be to preallocate an empty cell of the necessary dimensions and then fill it in with your regionprops output. Like this:
props = cell(size(output,1),1);
for k = 1:size(output,2)
props{k} = regionprops(labelboutput{1,k},output{1,k},'MeanIntensity','Centroid');
end
for k = 1:size(output,2)
meanValues = [props{k}.MeanIntensity];
end
...
Another option would be to combine your loops so that you can use your matrix data before it is overwritten. Like this:
for i = 1:size(output,2)
props = regionprops(labelboutput{1,i},output{1,i},'MeanIntensity','Centroid');
meanValues = [props.MeanIntensity];
% update this call to place props in non-overlapping parts of your file (e.g. append)
% writetable(struct2table(props), 'advanced_test.xlsx');
end
The bad thing about this second one is it has a file I/O step right inside your loop which can really slow things down; not to mention you will need to curtail your writetable call so it places the resulting table in non-overlapping regions of 'advanced_test.xlsx'.
I am attempting to solve this set of equations:
696x + 405y + 61z = 1385699
618x + 463y + 81z = 1401476
573x + 476y + 113z = 1407438
508x + 537y + 117z = 1418256
473x + 566y + 123z = 1427224
x,y and z are removed in the worksheet.
Using the following formula: E2:E5 {=MMULT(MINVERSE("A1:C5"),"D1:D5")}
But I keep encountering value errors.
Can MINVERSE only be used with a 3x3 matrix?
Since your system of equations is overdetermined you may instead be looking to find the pseudoinverse or least squares estimate. To calculate this, select a 1 x 3 range such as F1:H1 then enter the array formula:
=LINEST(D1:D5,A1:C5,0)
entered by holding down CTRL+SHIFT+ENTER. For the given example, this returns {z,y,x} = {1191.8,1345.3,1103.7}. Note that the results coincide with MINVERSE for square matrices.