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How to create a Numpy array with integer values? (current version doesn't work with getpixel)

I'm using PIL to get the red values from every pixel of a picture. However, I need all the values to be in numpy arrays, because as far as I know, that is the only way to plot a 3D graph with a colourmap.
The problem is when I try to find the required red values using getpixel(), I get the following error:
Traceback (most recent call last):
File "C:\Users\Elitebook\Desktop\PIL\Smoke\get_data_smoke.py", line 14, in <module>
Z=im_rgb.getpixel((X,Y))[0]
File "C:\Users\Elitebook\AppData\Roaming\Python\Python37\site-packages\PIL\Image.py", line 1436, in getpixel
return self.im.getpixel(xy)
TypeError: an integer is required
So far, I have tried using x=x.astype(int) and dtype to get integer values, but none of them worked.
Here is my code:
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
im=Image.open("smoke.jpg")
im_rgb=im.convert("RGB")
w,h=im.size
x=np.arange(1,w+1,1)
y=np.arange(1,h+1,1)
X,Y=np.meshgrid(x,y)
Z=im_rgb.getpixel((X,Y))[0]
fig=plt.figure()
ax=fig.add_subplot(projection='3d')
ax.plot(X,Y,Z)
plt.show()

If you want the image as a Numpy array, just use:
na = np.array(im_rgb)
By the way, the converse operation, turning a Numpy array back into a PIL Image is:
pilImage = Image.fromarray(na)

Your problem is that getpixel needs a sequence of integers. Your inputs X and Y were arrays. Therefore, you need some form of loop to extract the individual indexes:
x = np.arange(1,w+1,1)
y = np.arange(1,h+1,1)
X,Y = np.meshgrid(x,y)
Z = []
for i,j in zip(X,Y):
for ii, jj in zip(i,j):
Z.append(im_rgb.getpixel((int(ii),int(jj))))

KMeans in Python: ValueError: setting an array element with a sequence

I am trying to perform kmeans clustering in Python using numpy and sklearn.
I have a txt file with 45 columns and 645 rows. The first row is Y and remaining 644 rows are X.
My Python code is:
import numpy as np
import matplotlib.pyplot as plt
import csv
from sklearn.cluster import KMeans
#The following code reads the first row and terminates the loop
with open('trainDataXY.txt','r') as f:
read = csv.reader(f)
for first_row in read:
y = list(first_row)
break
#The following code skips the first row and reads rest of the rows
firstLine = True
with open('trainDataXY.txt','r') as f1:
readY = csv.reader(f1)
for rows in readY:
if firstLine:
firstLine=False
continue
x = list(readY)
X = np.array((x,y), dtype=object)
kmean = KMeans(n_clusters=2)
kmean.fit(X)
I get an error at this line: kmean.fit(X)
The error I get is:
Traceback (most recent call last):
File "D:\file_path\kmeans.py", line 25, in <module> kmean.fit(X)
File "C:\Anaconda2\lib\site-packages\sklearn\cluster\k_means_.py",
line 812, in fit X = self._check_fit_data(X)
File "C:\Anaconda2\lib\site-packages\sklearn\cluster\k_means_.py",
line 786, in _check_fit_data X = check_array(X, accept_sparse='csr',
dtype=np.float64)
File "C:\Anaconda2\lib\site-packages\sklearn\utils\validation.py",
line 373, in check_array array = np.array(array, dtype=dtype,
order=order, copy=copy) ValueError: setting an array element with a
sequence.`
trainDataXY.txt
1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,5
47,64,50,39,66,51,46,37,43,37,37,35,36,34,37,38,37,39,104,102,103,103,102,108,109,107,106,115,116,116,120,122,121,121,116,116,131,131,130,132,126,127,131,128,127
47,65,58,30,39,48,47,35,42,37,38,37,37,36,38,38,38,40,104,103,103,103,101,108,110,108,106,116,115,116,121,121,119,121,116,116,133,131,129,132,127,128,132,126,127
49,69,55,28,56,64,50,30,41,37,39,37,38,36,39,39,39,40,105,103,104,104,103,110,110,108,107,116,115,117,120,120,117,121,115,116,134,131,129,134,128,125,134,126,127
51,78,52,46,56,74,50,28,38,38,39,38,38,37,40,39,39,41,96,101,99,104,97,101,111,101,104,115,116,116,119,110,112,119,116,116,135,130,129,135,120,108,133,120,125
55,79,53,65,52,102,55,28,36,39,40,38,39,37,40,39,40,42,79,86,84,105,84,57,110,85,76,117,118,115,110,66,86,117,117,118,123,130,130,129,106,93,130,113,114
48,80,59,81,50,120,63,26,31,39,40,39,40,38,42,37,41,42,53,73,77,90,47,34,76,52,63,106,102,97,80,33,68,105,105,113,115,130,124,111,83,91,128,105,110
45,95,56,86,38,137,60,27,27,39,40,38,40,37,41,52,38,41,24,44,44,79,40,32,48,26,28,63,52,59,42,30,62,79,67,77,116,121,122,114,96,90,126,93,103
45,93,47,86,35,144,60,26,27,39,40,45,39,38,43,87,46,58,33,21,26,62,42,49,49,37,24,33,41,56,29,28,68,79,58,74,115,111,115,119,117,104,132,92,97
48,85,50,83,37,142,62,25,29,57,47,77,43,64,61,115,70,101,41,28,28,48,39,46,42,38,37,47,43,74,32,28,64,86,80,81,127,113,99,130,140,112,139,92,97
48,94,78,77,30,138,57,28,29,91,66,94,61,94,103,129,89,140,38,34,32,38,33,43,38,36,39,50,39,75,31,33,65,89,82,84,127,112,100,133,141,107,136,95,97
45,108,158,77,30,140,67,29,26,104,97,113,92,106,141,137,116,151,33,32,32,43,44,40,37,34,37,54,86,77,55,48,77,112,83,109,120,111,105,124,133,98,129,89,99
48,139,173,64,40,159,61,55,27,115,117,128,106,124,150,139,125,160,27,26,29,54,51,47,36,36,32,80,125,105,97,96,86,130,102,118,117,104,105,118,117,92,130,94,97
131,157,143,66,87,130,57,118,26,124,137,129,133,138,156,133,132,173,29,25,28,81,48,38,48,32,24,134,165,144,149,142,110,145,147,161,114,112,103,118,115,94,126,87,102
160,162,146,78,116,127,52,133,71,116,141,125,125,141,169,115,110,161,69,53,46,97,79,47,76,59,32,148,147,134,165,152,111,155,139,145,116,113,101,118,105,86,123,92,99

Your data matrix should not be of type object. It should be a matrix of numbers of shape n_samples x n_features.
This error usually crops up when people try to convert a list of samples into a data matrix, and each sample is an array or a list, and at least one of the samples does not have the same length as the others. This can be figured out by evaluating np.unique(list(map(len, X))).
In your case it is different. Make sure you obtain a data matrix. The first thing to try is to replace the line X = np.array((x,y), dtype=object) with something that creates a data matrix.
You should also opt for using numpy.recfromcsv to read your data. It will make everything easier to read.

Value Error :Storing data from binary file into numpy 3d arrays

I am trying to read float numbers from a Binary file using Struct module and then storing them in numpy 3D arrays.When I run it as an independent script, it works fine.But when I call it as a class's function from another script(after import) it gives value error
Here is my code.
import struct
from numpy import *
class DCD_read:
def read_cord(self,total_atoms,dcd_data):
cord_data=dcd_data[276:len(dcd_data)] ## binary data string
byte=0
count=0
total_frames=info_dict['nset']
coord=numpy.zeros((total_frames,total_atoms,3)) ## 3d array
for frames in range(0,total_frames):
for atoms in range(0,total_atoms):
x = list(struct.unpack('<f',cord_data[60+byte:64+byte])) ###reading float
byte+=4
y = list(struct.unpack('<f',cord_data[60+byte:64+byte]))
byte+=4
z = list(struct.unpack('<f',cord_data[60+byte:64+byte]))
byte+=4
ls=x
ls.extend(y)
ls.extend(z)
coord[frames][atoms]=ls
return coord
Error:
Traceback (most recent call last):
File "C:\Users\Hira\Documents\PROJECT\md.py", line 24, in <module>
coord=dcd.read_cord(total_atoms,dcd_data)
File "C:\Users\Hira\Documents\PROJECT\DCD_read.py", line 51, in read_cord
coord=numpy.zeros((total_frames,total_atoms,3))
File "C:\Python27\numpy\core\numeric.py", line 148, in ones
a = empty(shape, dtype, order)
ValueError: negative dimensions are not allowed
md.py is the main (calling script) while DCD_read.py is module. Here is code for md.py (main script)
from DCD_read import *
import numpy
dcd_file=open('frame3.dcd',"rb")
dcd_data=dcd_file.read()
dcd=read_dcd()
total_atoms=6141
coord=dcd.read_cord(total_atoms,dcd_data)
Please can any one help???? I hope I explained it completely and clearly.Thanx

Unable to unwrap image in OpenCV

I am trying to convert an image from cartesian to polar so that I can unravel the image, but I am getting a runtime error. If you are curious how this looks visually, see this example.
Code:
import scipy
import scipy.ndimage
import numpy as np
from math import *
import cv2
def logpolar(input):
# This takes a numpy array and returns it in Log-Polar coordinates.
coordinates = np.mgrid[0:max(input.shape[:])*2,0:360] # We create a cartesian array which will be used to compute log-polar coordinates.
log_r = 10**(coordinates[0,:]/(input.shape[0]*2.)*log10(input.shape[1])) # This contains a normalized logarithmic gradient
angle = 2.*pi*(coordinates[1,:]/360.) # This is a linear gradient going from 0 to 2*Pi
# Using scipy's map_coordinates(), we map the input array on the log-polar coordinate. Do not forget to center the coordinates!
lpinput = scipy.ndimage.interpolation.map_coordinates(input,(log_r*np.cos(angle)+input.shape[0]/2.,log_r*np.sin(angle)+input.shape[1]/2.),order=3,mode='constant')
# Returning log-normal...
return lpinput
# Load image
image = cv2.imread("test.jpg")
result = logpolar(image)
Error message in console:
Traceback (most recent call last):
File "test.py", line 23, in <module>
result = logpolar(image)
File "test.py", line 15, in logpolar
lpinput = scipy.ndimage.interpolation.map_coordinates(input,(log_r*np.cos(angle)+input.shape[0]/2.,log_r*np.sin(angle)+input.shape[1]/2.),order=3,mode='constant')
File "/Library/Python/2.7/site-packages/scipy-0.13.0.dev_c31f167_20130415-py2.7-macosx-10.8-intel.egg/scipy/ndimage/interpolation.py", line 295, in map_coordinates
raise RuntimeError('invalid shape for coordinate array')
RuntimeError: invalid shape for coordinate array

My first guess would be that you are passing in a colour image which is 3 dimensional. At first glance I don't think your code could handle that.
My guess was based off of the error you pasted, specifically
"invalid shape for coordinate array"
When using higher dimensional arrays like that usually you have to pass extra parameters around specifying which axis to repeat the operations over and even then sometimes it does not work. I didn't see a repeated extra integer at the end of your argument lists so I figured you weren't trying to handle that case explicitly and might have forgotten to check your array dimensions after reading in the image.
Glad it helped :)

ValueError when trying to save ndarray (Numpy)

I am trying to translate a project I have in MATLAB to Python+Numpy because MATLAB keeps running out of memory. The file I have is rather long, so I have tried to make a minimal example that shows the same error.
Basically I'm making a 2d histogram of a dataset, and want to save it after some processing. The problem is that the numpy.save function throws a "ValueError: setting an array element with a sequence" when I try to save the output of the histogram function. I can't find the problem when I look at the docs of Numpy.
My version of Python is 2.6.6, Numpy version 1.4.1 on a Debian distro.
import numpy as np
import random
n_samples = 5
rows = 5
out_file = file('dens.bin','wb')
x_bins = np.arange(-2.005,2.005,0.01)
y_bins = np.arange(-0.5,n_samples+0.5)
listy = [random.gauss(0,1) for r in range(n_samples*rows)]
dens = np.histogram2d( listy, \
range(n_samples)*rows, \
[y_bins, x_bins])
print 'Write data'
np.savez(out_file, dens)
out_file.close()
Full output:
$ python error.py
Write data
Traceback (most recent call last):
File "error.py", line 19, in <module>
np.savez(out_file, dens)
File "/usr/lib/pymodules/python2.6/numpy/lib/io.py", line 439, in savez
format.write_array(fid, np.asanyarray(val))
File "/usr/lib/pymodules/python2.6/numpy/core/numeric.py", line 312, in asanyarray
return array(a, dtype, copy=False, order=order, subok=True)
ValueError: setting an array element with a sequence.

Note that np.histogram2d actually returns a tuple of three arrays: (hist, x_bins, y_bins). If you want to save all three of these, you have to unpack them as #Francesco said.
dens = np.histogram2d(listy,
range(n_samples)*rows,
[y_bins, x_bins])
np.savez('dens.bin', *dens)
Alternatively, if you only need the histogram itself, you could save just that.
np.savez('dens.bin', dens[0])
If you want to keep track of which of these is which, use the **kwds instead of the *args
denskw = dict(zip(['hist','y_bins','x_bins'], dens))
np.savez('dens.bin', **denskw)
Then, you can load it like
dens = np.load('dens.bin')
hist = dens['hist']# etc