Plotting 3D data in Python from multiple images

Question

I'm trying to create a 3D matrix stacking multiple images in order to create a 3D visualization of densities in order to interpolate those data and other operations... right now I'm trying to stack images with this code:

import numpy as np
import matplotlib.pyplot as plt



stacked = np.ndarray(shape=(300,300,20))
for s in range(11):
    s=s+1
    source = cv2.imread('IMAGE_#'+ str(s) +'.png',0)
    m = np.asmatrix(source)
    stacked[:,:,s]=m

x=stacked[:,0]
y=stacked[:,1]
z=stacked[:,2]

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x,y,z)
plt.xlim(0,300)
plt.ylim(0,300)

plt.show()

but the plot results in something like this wrong plot

While I'm expecting a cloud of dots in the middle of the graph. What am I doing wrong?

EDIT 1> To test the code feel free to use 'Image Stacking\StarM '+ str(s) +'.png',0 in the 10th line with this images: https://wetransfer.com/downloads/7a3cdac121427d122787b5e24943d4b320210412123454/edfcc9

EDIT 2> changed the code as follows:

import numpy as np
import matplotlib.pyplot as plt



stacked = np.ndarray(shape=(300,300,20))


for s in range(11):
    s=s+1
    source = cv2.imread('ss\StarM '+ str(s) +'.png',2)
    m = np.asmatrix(source)
    stacked[:,:,s]=m

x=stacked[:,:,0]
y=stacked[:,:,1]
z=stacked[:,:,2]

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x,y,z)
plt.xlim(0,300)
plt.ylim(0,300)

plt.show() 

And now the plot shows: WrongPlot2

thx

Answer 1

ok had to rewrite starting from Pillow I don't know opencv.

Here my code:

from PIL import Image

import numpy as np
import matplotlib.pyplot as plt


size_image = Image.open('StarM 1.png', 'r')

print(size_image.size)

stacked = np.ndarray(shape=(size_image.size[0], size_image.size[1], 11)) #empty array


for s in range(11):
    s = s+1
    source = Image.open('StarM ' + str(s) + '.png', 'r').convert('L') #open image convertiung it to grayscale
    # source.show() #show grayscale image
    # print('source :', source) #print image
    m = np.asarray(source) #convert image to array
    print(m) #print array 
    print(m.shape) #print array shape

    stacked[:, :, s-1] = m #fill empty array with image
    
# print(stacked)
print(stacked.shape) #print array shape


x,y,z =stacked.nonzero() #gets non zero values from stacked
print(x,y,z)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x,y,z)
plt.show()

the result is:

not sure is what you were expecting let me know.

changing line 47 to

x,y,z =(stacked >  100).nonzero() #gets >100 values from stacked

with values ranging from 0 to 255 (remember we are on 8 bit grayscale 0 - 255)

you get nicer pics:

borrowed from Creating a 3D plot from a 3D numpy array

Answer 2

cant test your code without your images, but just moving your code I get more than

one dot

import numpy as np
import matplotlib.pyplot as plt



stacked = np.ndarray(shape=(300,300,20))
for s in range(11):
    s=s+1
    source = cv2.imread('IMAGE_#'+ str(s) +'.png',0)
    print(source)
    m = np.asmatrix(source)
    stacked[:,:,s]=m

    x=stacked[:,0]
    y=stacked[:,1]
    z=stacked[:,2]
    
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    ax.scatter(x,y,z)
    plt.xlim(0,300)
    plt.ylim(0,300)
    
    plt.show()

see: