reduced scale of examples

README.md

@@ -52,17 +52,20 @@ Automatic tests can be performed by running `stl_tools/test/test_stl.py`.
 Run the file `examples.py` to produce a few sample STL files from images included in `examples/example_data`.
 
 The first example converts the commonly-used [Lena test image](http://en.wikipedia.org/wiki/Lenna) to an STL file
-
+The "solid" keyword argument sets whether to create a solid geometry (with sides and a bottom) or not.
+The algorithm used to generate the sides and bottom have not yet been optimized, so may double the file size
+at the moment. We'll generate this example without a bottom.
 ```python
 from stl_tools import numpy2stl
 
 from scipy.misc import lena, imresize
 from scipy.ndimage import gaussian_filter
 
-A = imresize(lena(), (256,256)) # load Lena image, shrink in half
-A = gaussian_filter(A, 1) # smoothing
 
-numpy2stl(A, "examples/Lena.stl", scale=0.1)
+A = imresize(lena(), (256, 256))  # load Lena image, shrink in half
+A = gaussian_filter(A, 1)  # smoothing
+
+numpy2stl(A, "examples/Lena.stl", scale=0.1, solid=False)
 ```
 
 Source image vs. output geometry:
@@ -72,17 +75,18 @@ Source image vs. output geometry:
 
 ---
 
-The next three examples convert logos to STL, using color information to achieve appropriate 3D layering
+The next two examples convert logos to STL, using color information to achieve appropriate 3D layering.
+For this example, we'll generate a solid geometry (solid=True), for comparison to the first example.
 
 Python code:
 
 ```python
 from pylab import imread
 
-A = 256 * imread("examples/example_data/NASA.png") # 0 - 256 (8 bit) scale
-A = A[:,:, 2] + 1.0*A[:,:, 0] # Compose RGBA channels to give depth
+A = 256 * imread("examples/example_data/NASA.png")
+A = A[:, :, 2] + 1.0*A[:,:, 0] # Compose RGBA channels to give depth
 A = gaussian_filter(A, 1)  # smoothing
-numpy2stl(A, "examples/NASA.stl", scale=0.05, mask_val=5.)
+numpy2stl(A, "examples/NASA.stl", scale=0.05, mask_val=5., solid=True)
 ```
 Equivalent command-line syntax:
 ```bash
@@ -97,11 +101,10 @@ Equivalent command-line syntax:
 Python code:
 
 ```python
-A = 256.*imread("examples/example_data/openmdao.png") # 0 - 256 (8 bit) scale
-A =  A[:,:,0] + 1.*A[:,:,3] # Compose elements from RGBA  channels to give depth
-A = gaussian_filter(A, 2) # smoothing
-
-numpy2stl(A, "examples/OpenMDAO-logo.stl", scale=0.05, mask_val = 1.)
+A = 256 * imread("examples/example_data/openmdao.png")
+A =  A[:, :, 0] + 1.*A[:,:, 3] # Compose some elements from RGBA to give depth
+A = gaussian_filter(A, 2)  # smoothing
+numpy2stl(A, "examples/OpenMDAO-logo.stl", scale=0.05, mask_val=1., solid=False)
 ```
 
 Equivalent command-line syntax:

examples.py

@@ -10,22 +10,18 @@
 
 A = imresize(lena(), (256, 256))  # load Lena image, shrink in half
 A = gaussian_filter(A, 1)  # smoothing
-numpy2stl(A, "examples/Lena.stl", scale=0.1)
-
-from scipy.misc import lena, imresize
-A = imresize(lena(), (256, 256))
-A = gaussian_filter(A, 1)  # smoothing
-numpy2stl(A, "examples/Lena.stl", scale=0.1)
+numpy2stl(A, "examples/Lena.stl", scale=0.1, solid=False)
 
 A = 256 * imread("examples/example_data/NASA.png")
-A = A[:,:, 2] + 1.0*A[:,:, 0] # Compose RGBA channels to give depth
+A = A[:, :, 2] + 1.0*A[:,:, 0] # Compose RGBA channels to give depth
 A = gaussian_filter(A, 1)  # smoothing
-numpy2stl(A, "examples/NASA.stl", scale=0.05, mask_val=5.)
+numpy2stl(A, "examples/NASA.stl", scale=0.05, mask_val=5., solid=True)
 
 A = 256 * imread("examples/example_data/openmdao.png")
-A =  A[:,:, 0] + 1.*A[:,:, 3] # Compose some elements from RGBA to give depth
+A =  A[:, :, 0] + 1.*A[:,:, 3] # Compose some elements from RGBA to give depth
 A = gaussian_filter(A, 2)  # smoothing
-numpy2stl(A, "examples/OpenMDAO-logo.stl", scale=0.05, mask_val=1.)
+numpy2stl(A, "examples/OpenMDAO-logo.stl",
+          scale=0.05, mask_val=1., solid=False)
 
 text = ("$\oint_{\Gamma} (A\, dx + B\, dy) = \iint_{U} \left(\\frac{\partial "
         "B}{\partial x} - \\frac{\partial A}{\partial y}\\right)\ dxdy$ \n\n "

setup.py

@@ -1,7 +1,7 @@
 from setuptools import setup, find_packages
 
 setup(name='stl_tools',
-      version='0.2',
+      version='0.2.2',
       install_requires=['numpy', 'scipy', 'matplotlib'],
       description="Generate STL files from numpy arrays and text",
       author='Tristan Hearn',
@@ -10,7 +10,7 @@
       license='Apache 2.0',
       packages=['stl_tools'],
       entry_points={
-      'console_scripts':
-      ['image2stl=stl_tools.image2stl:image2stl']
+          'console_scripts':
+          ['image2stl=stl_tools.image2stl:image2stl']
       }
       )

stl_tools/numpy2stl.py

@@ -62,7 +62,7 @@ def numpy2stl(A, fn, scale=0.1, mask_val=-np.inf, ascii=False,
               max_width=235.,
               max_depth=140.,
               max_height=150.,
-              solid=True,
+              solid=False,
               min_thickness_percent=0.1):
     """
     Reads a numpy array, and outputs an STL file
@@ -105,8 +105,8 @@ def numpy2stl(A, fn, scale=0.1, mask_val=-np.inf, ascii=False,
 
     facets = []
     mask = np.zeros((m, n))
+    print("Creating top mesh...")
     for i, k in product(range(m - 1), range(n - 1)):
-        print("Creating top mesh...")
 
         this_pt = np.array([i - m / 2., k - n / 2., A[i, k]])
         top_right = np.array([i - m / 2., k + 1 - n / 2., A[i, k + 1]])