Plot = mp. Using this context manager to supress it.ĭef _exit_(self, exc_type, exc_val, exc_tb):ĭef show(radius, thickness, noise_scale, noise_strength, seed, bump_angle, bump_width, bump_height): Unlike, concatenate (), it joins arrays along a new axis. Important points: stack () is used for joining multiple NumPy arrays. One of the important functions of this library is stack (). # Meshplot left an annoying print statement in their code. NumPy is a famous Python library used for working with arrays. The code using meshplot is like following: # I have used jupyter notebook to run the code. If it would have been worked I could have seen the effects of the change of various parameters. I can visualize using meshplot library but interactive change is not working. I could have implemented different libraries to get the desired shapes using union but I need the desired shape only by distorting some vertices from the torus. The shapes should have the same number of vertices and faces. Important Note: I need torus shapes with bumps at different angles (one bump per torus). It seems like the x_bump is not adding any effect. I tried using different width and height of the bump but it is not showing up. Pcd = o3d.io.read_triangle_mesh('torus_bump_500/torus_bump_1.ply') Igl.write_triangle_mesh(f"torus_bump_500/torus_bump_.ply", verts, faces)įor visualizing I use the following code: pcd.compute_vertex_normals() ![]() X_warp = rearrange(x_warp, 'v h w d -> h w d v') X_dist = np.linalg.norm((x - gaussian_center), axis=0) X_warp = gradient_noise(x, noise_scale, noise_strength, seed) Verts, faces, normals, values = measure.marching_cubes(sdf, level=0) X = np.stack(np.meshgrid(coords, coords, coords)) Vector_noise = np.stack(np.gradient(scalar_noise))įor idx, bump_angle in tqdm(enumerate(np.linspace(-1, 1, 2))): Scalar_noise = center_crop(scalar_noise, shape=x.shape) Scalar_noise = zoom(scalar_noise, zoom=scale) Slices = tuple()ĭef gradient_noise(x, scale, strength, seed=None): import numpy as np import matplotlib.pyplot as plt Define the step function def stepfunction (x, a): def rect (x): return np.where ( (x > 0) & (x < 1), 1, 0) f np.sum ( a k-1 rect (x - k) for k in range (1, len (a) + 1), axis0) return f Set the random seed for reproducibility np.ed (42) Generate random values fo. # Crop an n-dimensional image with a centered cropping region Return np.linalg.norm(q, axis=0) - thickness Expected outcome and what I got is like the following figure.įrom sklearn.preprocessing import MinMaxScaler I can see the torus but not the bump when visualize using open3d. How does the numpy reshape() method reshape arrays? Have you struggled understanding how it works or have you ever been confused? This tutorial will walk you through reshaping in numpy.The code is supposed to create bump at different angle on torus 3d shape. It takes me many hours to research, learn, and put together tutorials. Consider being a patron and supporting my work?ĭonate and become a patron: If you find value in what I do and have learned something from my site, please consider becoming a patron. This tutorial is also available on Medium, Towards Data Science. Get source code for this RMarkdown script here. Create a 3D array by stacking the arrays along different axes/dimensions.Concatenate/stack arrays with np.stack() and np.hstack().Flatten/ravel to 1D arrays with ravel(). ![]() This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |