#!/usr/bin/env python
# coding: utf-8

# # Heightmap Demo
# stough 202-
# 
# In this quick demo I wanted to show how an image can be seen as a height-map, where the xy-plane is the domain of the image, while the z-axis represents the image intensity. This is easy enough with [matplotlib's 3D plotting](https://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html) capability. Specifically I would refer you to the examples [here](https://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html#surface-plots).

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get_ipython().run_line_magic('matplotlib', 'widget')
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from skimage import color

# For importing from alternative directory sources
import sys  
sys.path.insert(0, '../dip_utils')

from matrix_utils import arr_info
from vis_utils import vis_image


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I = color.rgb2gray(plt.imread('../dip_pics/D_image.jpg'))


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arr_info(I)


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vis_image(I, cmap='gray')


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X, Y = np.meshgrid(range(0,I.shape[0]), range(0,I.shape[1]), indexing='ij')


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fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(111, projection='3d')

ax.plot_surface(X, Y, I, cmap=cm.coolwarm);


# The argument `projection='3d'` means the `ax` returned is a [Axes3D](https://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html) object. We then call that object's `plot_surface` method with a colormap so that the colors change as a function of the image intensity, or z coordinate. 
# 
# Note also that 3D plotting normalizes the units for a square output, even while the original image may not be. Lastly, code that does this surface plotting has been added to [vis_utils](../dip_utils/vis_utils.py) if we need it in the future.

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