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From a complex 3D shape, I have obtained by tricontourf the equivalent top view of my shape.

I wish now to export this result on a 2D array. I have tried this :

import numpy as np
from shapely.geometry import Polygon
import skimage.draw as skdraw
import matplotlib.pyplot as plt

x = [...]
y = [...]
z = [...]
levels = [....]

cs = plt.tricontourf(x, y, triangles, z, levels=levels)

image = np.zeros((100,100))

for i in range(len(cs.collections)):
    p = cs.collections[i].get_paths()[0]
    v = p.vertices
    x = v[:,0]
    y = v[:,1]
    z = cs.levels[i]

    # to see polygon at level i
    poly = Polygon([(i[0], i[1]) for i in zip(x,y)])
    x1, y1 = poly.exterior.xy
    plt.plot(x1,y1)
    plt.show()


    rr, cc = skdraw.polygon(x, y)
    image[rr, cc] = z

plt.imshow(image)
plt.show()

but unfortunately, from contours vertices only one polygon is created by level (I think), generated at the end an incorrect projection of my contourf in my 2D array.

Do you have an idea to correctly represent contourf in a 2D array ?

Patrick
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  • Do yo want to represent your contours as numbers in a rectangular array? Assigning NaNs to values outside your region? – nicoguaro Jun 03 '16 at 15:54
  • If I remember correctly, `cs.collections[i].get_paths()` returns a *list* of contour line segments corresponding to level `i`. This is in agreement with your referencing only `...get_paths()[0]`. You probably have to loop `for path in ...get_paths()` and plot each path independently. But watch out: there can be open contour lines, so using `Polygon` like that might not be correct (and this might explain your spurious lines across the figure). I suggest using a simple example function with simple contour lines to get a grasp of how these things are done under the hood. – Andras Deak -- Слава Україні Jun 06 '16 at 23:30
  • @nicoguaro : I can't assign value outside my region 'cause I don't know it a priori. – Patrick Jun 08 '16 at 08:47
  • @Andreas : It's getting better (see below) – Patrick Jun 08 '16 at 08:48

1 Answers1

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Considering a inner loop with for path in ...get_paths() as suggested by Andreas, things are better ... but not completely fixed. My code is now :

import numpy as np
import matplotlib.pyplot as plt
import cv2

x = [...]
y = [...]
z = [...]
levels = [....]
...

cs = plt.tricontourf(x, y, triangles, z, levels=levels)

nbpixels = 1024
image = np.zeros((nbpixels,nbpixels))
pixel_size = 0.15 # relation between a pixel and its physical size

for i,collection in enumerate(cs.collections):
    z = cs.levels[i]
    for path in collection.get_paths():
        verts = path.to_polygons()
        for v in verts:
            v = v/pixel_size+0.5*nbpixels # to centered and convert vertices in physical space to image pixels 
            poly = np.array([v], dtype=np.int32) # dtype integer is necessary for the next instruction
            cv2.fillPoly( image, poly, z )

The final image is not so far from the original one (retunred by plt.contourf).

Unfortunately, some empty little spaces still remains in the final image.(see contourf and final image)

Is path.to_polygons() responsible for that ? (considering only array with size > 2 to build polygons, ignoring 'crossed' polygons and passing through isolated single pixels ??).

Patrick
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