I am rather new to Python and I think I'm trying to attempt something quite complicated? I have repeatedly tried to search for this and think I'm missing some base knowledge as I truly don't understand what I've read.
Effectively I have 3 arrays, which contain x points, y points, and z points. They create an approximately hemispherical shape. I have these points from taking a contour of a 2D axisymmetrical semicircular shape, extracting the x points and y points from it into separate arrays, and creating a "z points" array of zeros of the same length as the previous two arrays.
From this, I then rotate the y points into the z-domain using angles to create a 3D approximation of the 2D contour.
This 3D shape is completely bounded (in that it creates both the bottom and the top), as linked below:
I've butchered the following code from my much longer program, it's the only part that is pertinent to my question however:
c, contours, _ = cv2.findContours(WB, mode = cv2.RETR_EXTERNAL, method = cv2.CHAIN_APPROX_NONE) # Find new contours in recently bisected mask
contours = sorted (contours, key = cv2.contourArea, reverse = True) # # Sort contours (there is only 2 contours left, plinth/background, and droplet)
contour = contours[1] # Second longest contour is the desired contour
xpointsList = [xpoints[0][0] for xpoints in contour] # Create new array of all x co-ordinates
ypointsList = [ypoints[0][1] for ypoints in contour] # Create new array of all y co-ordinates
zpointsList = np.zeros(len(xpointsList)) # # Create an array of 0 values to represent the z domain. True contour sits at 0 on all points in the z-domain
angles = np.arange(0,180,5, dtype = int) # # Creating an array of angles between to form a full drop in degrees of 5
i = 0
b = 0
d = 0
qx = np.zeros(len(xpointsList)*len(angles)) # Setting variable to equal the length of x points times the length of iterative angle calculations
qy = np.zeros(len(ypointsList)*len(angles)) # Setting variable to equal the length of x points times the length of iterative angle calculations
qz = np.zeros(len(zpointsList)*len(angles)) # Setting variable to equal the length of x points times the length of iterative angle calculations
ax = plt.axes(projection='3d')
for b in range(0,len(angles)):
angle = angles[b]
for i in range(0,len(ypointsList)):
qx[i+d] = xpointsList[i]-origin[0] # Setting the x-axis to equal it's current values around the origin
qz[i+d] = origin[0] + math.cos(math.radians(angle)) * (zpointsList[i]) - math.sin(math.radians(angle)) * (ypointsList[i] - origin[1]) # creating a z value based on 10 degree rotations of the y values around the x axis
qy[i+d] = origin[1] + math.sin(math.radians(angle)) * (zpointsList[i]) + math.cos(math.radians(angle)) * (ypointsList[i] - origin[1]) # adapting the y value based on the creation of the z values.
i = i + 1
b = b + 1
d = d + len (xpointsList)
ax.plot3D (qy, qz, qx, color = 'blue', linewidth = 0.1)
Effectively my question is, how do I use this structure to somehow find the volume using the co-ordinate arrays? I think I need to use some kind of scipy.spatial ConvexHull to fill in the areas between my rotations so that it has a surface and work from there?
