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Assembly Graph Clustering Tools
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# Assembly Parts Clustering
## Description
The goal of this project is to split large CAD assembly models automatically into subassemblies using community detection algorithms.
This project contains scripts for:
- Creating different variations of contact matrices
- Different clustering algorithms (Louvain and Girvan-Newman) on these contact matrices
## Installation
Install utilizing Anaconda3 and [environment.yml](environment.yml).
## How to use the project
1. Load CAD Model in STEP format in [data/step_files](data/step_files/). A sample file "centrifugal_pump.stp" from [GrabCAD](https://grabcad.com/library/centrifugal-pump-41) is given.
2. Run the script [main.py](src/main.py)
3. Follow the instructions in the Python console.
## Acknowledgements
This work is part of the research project “Internet of Construction” that is funded by the Federal Ministry of Education and Research of Germany within the indirective on a joint funding initiative in the field of innovation for production, services and labor of tomorrow (funding number: 02P17D081) and supported by the project management agency “Projekttr¨ager Karlsruhe (PTKA)”. The authors are responsible for the content.
Main authors of the code: Sören Münker, Daniel Swoboda, Karim El Zaatari, Nehel Malhotra, Lucas Manasses Pinheiro de Souza.
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# Graph Clustering
Graph clustering (or community detection) describes a number of techniques for identifying connected components in a graph.
They usually use a measure of centrality for nodes (for which there are many suggested definitions in literature) to identify
important "central" nodes, or use edge cutting to identify weak connections within the graph. Graphs of n nodes are usually represented
by adjacency matrices A (n x n), where each element Aij represents the connection between nodes i and j. If Aij = 0, then there is no
edge from i to j. In an unweighted digraph, Aij = Aji and Aij = 1 iff there is an edge between i and j. In a weighted graph Aij != 0 if
there is an edge between the nodes. In an undirected graph Aij = Aji does not (always) hold.
## Idea
Preparing the STEP file naturally yields several potential candidate matrices that can be seen as adjacency matrices of a graph corresponding to
the input model:
- **Contact Matrix:** The contact matrix for an input model with n shapes is a n x n matrix where Aij = 1 if shapes i and j are in contact. It is interesting
to consider since graph clusters are assumed to be groups of parts with strong connectivity (e.g. many parts in a cluster are in contact with each other). Thus
it is reasonable to assume that they should be consider as one subassembly.
- **Norms Matrix:** The norms matrix for an input model with n shapes is a n x n matrix where Aij is equal to a norm of the distance of shape i to shape j in
euclidian space. It is a symmetrical matrix along the diagonal with Aii = 0 for all i and thus represents a fully connected graph excluding self loops. It
is intersting to consider this graph/matrix sincce objects that are closer together have smaller edge weights between them and thus are likely to be grouped
together by a clustering algorithm.
- **Weighted Contact Matrix:** This matrix combines the two above by taking the contact matrix and replacing every Aij with Aij = 1 from the contact matrix with
the corresponding entry from the norms matrix. Thus the resulting graph has edge weights corresponding to the distances in euclidian space and edges corresponding
to actual connectivity.
## Approach
Two graph clustering algorithms were implemented and can be used by the user: Garvin-Newman and Louvain. Garvin-Newman relies on cutting edges to identify connected
components (edge-betweenness centrality) wheras Louvain relies on comparing the number of links within in a community compared to the number of expected
links between nodes in the overall graph.
## Results (Preliminary)
Preliminary results show that the input for the clustering algorithm should be chosen according to the selected STEP model.
Different models yield better separation into connected components with different input matrices.
This suggests, that the best approach would be to provide visual representations of the segmentation results and then let the
user choose the appropriate suggestion.
environment.yml 0 → 100644
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name: ioc_part_clustering
channels:
- conda-forge
- defaults
dependencies:
- bzip2=1.0.8=h8ffe710_4
- ca-certificates=2022.6.15=h5b45459_0
- certifi=2022.6.15=py37h03978a9_0
- colorama=0.4.5=pyhd8ed1ab_0
- curl=7.83.1=h789b8ee_0
- double-conversion=3.1.7=h0e60522_0
- eigen=3.4.0=h2d74725_0
- expat=2.4.8=h39d44d4_0
- ffmpeg=4.3.1=ha925a31_0
- font-ttf-dejavu-sans-mono=2.37=hab24e00_0
- font-ttf-inconsolata=3.000=h77eed37_0
- font-ttf-source-code-pro=2.038=h77eed37_0
- font-ttf-ubuntu=0.83=hab24e00_0
- fontconfig=2.14.0=hce3cb01_0
- fonts-conda-ecosystem=1=0
- fonts-conda-forge=1=0
- freeimage=3.18.0=h6676e37_9
- freetype=2.10.4=h546665d_1
- gl2ps=1.4.2=h0597ee9_0
- glew=2.1.0=h39d44d4_2
- hdf4=4.2.15=h0e5069d_3
- hdf5=1.10.6=nompi_h5268f04_1114
- icu=69.1=h0e60522_0
- imath=3.1.5=h12d4b20_0
- jpeg=9e=h8ffe710_1
- jsoncpp=1.9.4=h2d74725_3
- jxrlib=1.1=h8ffe710_2
- krb5=1.19.3=h1176d77_0
- lcms2=2.12=h2a16943_0
- lerc=3.0=h0e60522_0
- libclang=13.0.1=default_h81446c8_0
- libcurl=7.83.1=h789b8ee_0
- libdeflate=1.12=h8ffe710_0
- libiconv=1.16=he774522_0
- libnetcdf=4.8.1=nompi_hf689e7d_100
- libogg=1.3.4=h8ffe710_1
- libpng=1.6.37=h1d00b33_2
- libraw=0.20.2=hee1bdec_1
- libssh2=1.10.0=h680486a_2
- libtheora=1.1.1=h8d14728_1005
- libtiff=4.4.0=h2ed3b44_1
- libwebp-base=1.2.2=h8ffe710_1
- libxml2=2.9.14=hf5bbc77_0
- libzip=1.8.0=hfed4ece_1
- libzlib=1.2.12=h8ffe710_1
- loguru=0.6.0=py37h03978a9_1
- lz4-c=1.9.3=h8ffe710_1
- occt=7.5.1=h60997fb_2
- openexr=3.1.5=hab3b255_0
- openjpeg=2.4.0=hb211442_1
- openssl=1.1.1p=h8ffe710_0
- pip=21.2.4=py37haa95532_0
- proj=7.2.0=h1cfcee9_2
- pugixml=1.11.4=h0e60522_0
- python=3.7.13=h6244533_0
- python_abi=3.7=2_cp37m
- pythonocc-core=7.5.1=py37h8c6f293_1
- qt=5.12.9=h556501e_6
- rapidjson=1.1.0=ha925a31_1002
- setuptools=61.2.0=py37haa95532_0
- six=1.16.0=pyh6c4a22f_0
- sqlite=3.38.3=h2bbff1b_0
- tbb=2020.2=h2d74725_4
- tbb-devel=2020.2=h2d74725_4
- utfcpp=3.2.1=h57928b3_0
- vc=14.2=h21ff451_1
- vs2015_runtime=14.27.29016=h5e58377_2
- vtk=9.0.1=qt_py37hc2356ab_210
- wheel=0.37.1=pyhd3eb1b0_0
- win32_setctime=1.1.0=pyhd8ed1ab_0
- wincertstore=0.2=py37haa95532_2
- xz=5.2.5=h62dcd97_1
- zlib=1.2.12=h8ffe710_1
- zstd=1.5.2=h6255e5f_1
- pip:
- appdirs==1.4.4
- better-exchook==1.20220510.124015
- cycler==0.11.0
- fonttools==4.33.3
- imageio==2.19.3
- joblib==1.1.0
- kiwisolver==1.4.3
- matplotlib==3.5.2
- networkx==2.6.3
- numpy==1.21.6
- packaging==21.3
- pandas==1.3.5
- pillow==9.1.1
- pyparsing==3.0.9
- pyqt5==5.15.7
- pyqt5-qt5==5.15.2
- pyqt5-sip==12.11.0
- python-dateutil==2.8.2
- python-louvain==0.16
- pytz==2022.1
- pyvista==0.34.1
- scikit-learn==1.0.2
- scipy==1.7.3
- scooby==0.5.12
- seaborn==0.11.2
- sklearn==0.0
- threadpoolctl==3.1.0
- typing-extensions==4.2.0
prefix: C:\Users\adam-uqef35nm77fzn5b\Anaconda3\envs\ioc_part_clustering
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" Assembly Planning using Machine Learning
" This file provides functionality to perform different visual data analysis operations
" (C) 2021 - Nehel Malhotra, Karim El Zaatari - InVEST 2021/22
" MIT License
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
# two-dimensional PCA of a set of n-dimensional vectors
def pca_2(data):
pca = PCA(2)
projected = pca.fit_transform(data)
plt.scatter(projected[:, 0], projected[:, 1], alpha=0.3)
#plt.xlabel('component 1')
#plt.ylabel('component 2')
plt.colorbar()
plt.show()
\ No newline at end of file
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" Assembly Planning using Machine Learning
" This file provides clustering functionality for distances based histgorams
" (C) 2021 - Daniel Swoboda <swoboda@kbsg.rwth-aachen.de> - InVEST 2021/22
" MIT License
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
import numpy as np
import random
import analysis
import matplotlib.pyplot as plt
from sklearn.cluster import OPTICS # <- Machine Learning comes here
from util import *
# computes a histogram out of the given array with bins of size step_size and cutoff at max_dist
def cl_compute_histogram(norms:np.ndarray, step_size, max_dist):
hist, edges = np.histogram(norms, bins=np.arange(0, step=step_size, stop=max_dist-(step_size/2)), range=(0, max_dist))
return hist #/ (np.arange(step_size, step=step_size, stop=max_dist-(step_size/2)) ** distance_scaledown)
def cl_compute_max_norm(points):
max_dist = max(
np.linalg.norm(points[from_idx] - points[to_idx]) for from_idx in range(0, len(points)) for to_idx in
range(0, from_idx)) / 2
return max_dist
# compute the max distance between to shapes to discretize the space of distances correctly
def cl_compute_step_max(cogs,steps):
max_dist = cl_compute_max_norm(cogs)
step_size = max_dist / steps
# compute the histograms
return max_dist, step_size
# clusters the shapes based on histograms that represent the neighborhood of each shape
# in a discretized way. Returns a cluster dictionary, having an array of shape indices per cluster,
# the raw clustering result and the histograms
def cl_compute_clusters(histograms, min_samples=2,eps=0.5):
#clustering = DBSCAN(eps=eps, min_samples=min_samples).fit(histograms) # UPDATE THE EPS VALUE BASED ON SIZE OF OBJECT
clustering = OPTICS(min_samples=min_samples).fit(histograms) # UPDATE THE EPS VALUE BASED ON SIZE OF OBJECT
cluster_dict = {}
for idx in range(len(clustering.labels_)):
if clustering.labels_[idx] in cluster_dict:
cluster_dict[clustering.labels_[idx]].append(idx)
else:
cluster_dict[clustering.labels_[idx]] = [idx]
return cluster_dict, clustering
def compute_histogram_mesh(vertice, cog):
steps = 50
max_dist, step_size = cl_compute_step_max(np.append(vertice,np.array([cog]),axis=0),steps=steps)#50 works well
distances = vertice - cog
norms = np.linalg.norm(distances, axis=-1)
histogram = np.zeros(steps-1)
if step_size != 0.0:
histogram = cl_compute_histogram(norms, step_size, max_dist)
return histogram
def compute_histogram_neighbors(cogs, cog):
steps = 10
distances = cogs - cog
norm = np.linalg.norm(distances, axis=-1)
max_dist, step_size = cl_compute_step_max(cogs, steps=steps)
return cl_compute_histogram(norm, step_size, max_dist)
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" Assembly Planning using Machine Learning
" Provides functions to do community detection on different graph inputs akin to adjacency matrices
" (C) 2021 - Karim El Zaatari <arimzaatary@gmail.com - InVEST 2021/22
" MIT License
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
import networkx as nx
import community as community_louvain
def edge_to_remove(graph):
G_dict = nx.edge_betweenness_centrality(graph)
edge = ()
# extract the edge with the highest betweenness centrality
for key, value in sorted (G_dict.items(), key = lambda item: item[1], reverse= True):
edge = key
break
return edge
#returns result of girvan_newman community detection as list of lists with
#each list being a community of shapes
def girvan_newman(graph):
# find number of connected components
sg = nx.connected_components(graph)
sg_counts = nx.number_connected_components(graph)
while (sg_counts == 1):
graph.remove_edge(edge_to_remove(graph)[0],edge_to_remove(graph)[1])
sg = nx.connected_components(graph)
sg_counts = nx.number_connected_components(graph)
#girvan newman
node_groups = []
for i in sg:
node_groups.append(list(i))
return node_groups
#returns result of louvain community detection as list of lists with
#each list being a community of shapes
def louvain(graph):
community = community_louvain.best_partition(graph)
community_dict = {}
for shape_idx in community:
if community[shape_idx] not in community_dict.keys():
community_dict[community[shape_idx]] = [shape_idx]
else:
community_dict[community[shape_idx]].append(shape_idx)
return list(community_dict.values())
def generate_nx_graph(m):
cm_graph = nx.Graph()
for id in range (len(m)):
cm_graph.add_node(id)
for i in range (len(m)):
for j in range (len(m)):
if m[i][j] != 0:
cm_graph.add_edge(i,j,weight = m[i][j])
return cm_graph
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import random
from sklearn.decomposition import PCA
from numpy import sign
import argparse
import collections
import analysis
import copy
import pprint
import better_exchook
import networkx as nx
import seaborn as sns; sns.set()
from gui import *
from communities import *
from disassembly import *
from process import *
from OCC.Display.WebGl import threejs_renderer
from OCC.Core.BRepPrimAPI import BRepPrimAPI_MakeTorus
from OCC.Core.gp import gp_Vec
from OCC.Extend.ShapeFactory import translate_shp
import pickle as pkl # added by Lucas
# configure
visual_confirmation = True
export = False
experimental = True
#community_algorithm = "Louvain" #"GM" for Girvan-Newman, or "Louvain" for Louvain.
community_algorithm = "Louvain" #"GM" for Girvan-Newman, or "Louvain" for Louvain.
histogram_algorithm = "mesh" #"mesh" for a mesh-based approach, "neihbors" for a neihborhood-based approach.
visualisation = "production" #"demo" to show every step, "production" to only show states in which the HITL interacts.
autoconfirm = True
autoconfirm_threshold = 5
# always change when needed
main_path="C:/Users/adam-uqef35nm77fzn5b/Documents/20_Dissertation/20_Tools/IoC_Scheduling/graph-clustering-tools"
# Find a way to optimize the import of files
import os
files = []
path = "../data/step_files/"
files.append("cp_reduced")
#files.append("20230324_IoC_Demo")
#files.append("delta_robot")
#files.append("centrifugal_pump")
#files.append("cylinder_radial_engine")
#files.append("IoC_canopy")
#file = "vertical_drop_lift"
#file = "tower_crane"
file = files[0]
# creating the DF
result_data_0 = [
["VERIFICATION"],
["id"],
["product_name"],
["n_parts"],
["n_columns"],
["n_rows"],
["isolated_nodes"],
["n_isolates"],
["n_sub_graphs"],
["n_nodes_min_subgraph"],
["n_nodes_avg_subgraph"],
["n_nodes_max_subgraph"]
]
df_1 = pd.DataFrame(result_data_0)
list_matrices_names=['cm','norms','cm_with_norms']
#list_matrices_names=['cm_with_norms']
filename_cm = main_path+"/matrices/"+list_matrices_names[0]+file+".csv"
filename_norms = main_path+"/matrices/"+list_matrices_names[1]+file+".csv"
filename_cm_with_norms = main_path+"/matrices/"+list_matrices_names[2]+file+".csv"
#print("ex_rq2_"+str(cont))
"""
Load the initial files
"""
# set up command line args
parser = argparse.ArgumentParser(description='Assembly Planning using ML')
parser.add_argument('-cm', type=bool, help='should the contact matrix be written to a file?', required=False)
args = parser.parse_args()
# import the file
print("Loading shapes")
shapes_names_colors, shapes = read_or_gen_p_shapes(path, file)
# get props
props = GProp_GProps()
# get the contact matrix
print("Loading contact matrix")
cm, names = read_or_gen_p_cm(path, file, shapes_names_colors = shapes_names_colors)
print(cm)
# Calculating fetures for the excel file
number_of_parts = len(shapes)
print("number_of_parts: ",number_of_parts)
number_of_columns = len(shapes)
print("number_of_columns: ",number_of_columns)
number_of_rows = len(shapes)
print("number_of_rows: ",number_of_rows)
# Other Matrices
# get centers of gravity
print("Loading COGs")
cogs = read_or_gen_p_cog(path, file, shapes = shapes, props = props)
# get distance norm matrix
print("Loading norms matrix")
norms = read_or_gen_p_norms(path, file, cogs = cogs)
#print(len(norms))
# get the vertices matrix
print("Loading vertices matrix")
vertices = read_or_gen_p_vertices(path, file, shapes = shapes)
# compute distance-infused contact matrix
print("Computing distance-infused contact matrix")
cm_with_norms = cm.copy()
for i in range (len(cm_with_norms)):
for j in range(len(cm_with_norms[i])):
if cm_with_norms [i][j] ==1:
cm_with_norms [i][j] = norms [i][j]
matrices_list = []
matrices_list.append(cm)
matrices_list.append(norms)
matrices_list.append(cm_with_norms)
#%% creating dictionary for the names, indexes and matrices and extra info
thisdict = {
"indexes" : np.arange(0,len(names)),
"names" : names,
"contact_matrix" : cm,
"norms_matrix" : norms,
"distance_infused_cm" : cm_with_norms,
"number_of_parts" : number_of_parts,
"number_of_columns" : number_of_columns,
"number_of_rows" : number_of_rows,
}
with open(main_path+"/liasons_graphs/"+file+"/Extracted_Info_CAD_file_"+file+'.pkl', 'wb') as f:
pkl.dump(thisdict, f)
#with open(main_path+"/liasons_graphs/"+file+"/Extracted_Info_CAD_file_"+file+'.pkl', 'rb') as f:
# loaded_dict = pkl.load(f)
"""
# from the networkx (before Community detection)
cont=0
for matrix in matrices_list:
#G = nx.from_pandas_adjacency(df)
G = nx.from_numpy_matrix(matrix)
isolated_nodes = list(nx.isolates(G))
print("isolated_nodes: ",isolated_nodes)
n_isolates = len(isolated_nodes)
print("n_isolates: ",n_isolates)
sub_graphs=list(G.subgraph(c) for c in nx.connected_components(G))
n_sub_graphs = len(sub_graphs)
print("n_sub_graphs: ",n_sub_graphs)
sub_graphs_n_nodes = []
for i, sg in enumerate(sub_graphs):
print ("subgraph {} has {} nodes".format(i, sg.number_of_nodes()))
sub_graphs_n_nodes.append(sg.number_of_nodes())
#print ("\tNodes:", sg.nodes(data=True))
#print ("\tEdges:", sg.edges())
min_n_nodes = min(sub_graphs_n_nodes)
print("min_n_nodes: ",min_n_nodes)
max_n_nodes = max(sub_graphs_n_nodes)
print("max_n_nodes: ",max_n_nodes)
def Average(lst):
return sum(lst) / len(lst)
avg_n_nodes = Average(sub_graphs_n_nodes)
print("avg_n_nodes: ",avg_n_nodes)
#nx.draw(G, with_labels=False)
#
#plt.savefig(os.path.join(main_path+"/liasons_graphs/"+file+"/"+str(cont)+"_"+list_matrices_names[cont]+"_"+file))
#
plt.close()
#plt.show()
cont=cont+1
"""
#
#%% Eliminate isolates before community detection - reducing matrices - reduction based on CM
G = nx.from_numpy_matrix(cm)
isolated_nodes = list(nx.isolates(G))
#print(isolated_nodes)
cm_reduced = np.delete(cm, isolated_nodes, axis=0)
cm_reduced = np.delete(cm_reduced, isolated_nodes, axis=1)
norms_reduced = np.delete(norms, isolated_nodes, axis=0)
norms_reduced = np.delete(norms_reduced, isolated_nodes, axis=1)
cm_with_norms_reduced = np.delete(cm_with_norms, isolated_nodes, axis=0)
cm_with_norms_reduced = np.delete(cm_with_norms_reduced, isolated_nodes, axis=1)
matrices_list_reduced = []
matrices_list_reduced.append(cm_reduced)
matrices_list_reduced.append(norms_reduced)
matrices_list_reduced.append(cm_with_norms_reduced)
#%%
# functions for cimmunity detection
displayed_communities = 0
def gui_community_monitor_switcher_callback(selected, *kwargs):
global display,partitions_colored,displayed_communities
gui_import_as_multiple_shapes(display, partitions_colored[displayed_communities])
displayed_communities+=1
if displayed_communities == len(partitions_colored):
displayed_communities = 0
# apply graph clustering on the input model to identify compounds of components
def graph_clustering(matrices_list, shapes, shapes_names_colors):
global displayed_communities, partitions_colored
print("Compute communities for",len(matrices_list),"input matrices")
partitions_colored = []
partitions_list = []
graph_list = []
cont=0
# control variables # added by Lucas
number_communities_list = [] # Number of communities
average_size_communities = [] # Avg size of communities (number of nodes / per communities)
std_size_communities = [] # Standard deviation of size of communities
computing_time = []
for m in matrices_list:
program_starts = time.time() # added by Lucas
partitions_colored.append(dict())
print("Round", len(partitions_colored), "of", len(matrices_list))
#create copy of shapes_names_colors to set the colors according to the cluster
for shape in shapes_names_colors:
partitions_colored[len(partitions_colored) -1][shape] = shapes_names_colors[shape].copy()
local_shapes_names_colors = partitions_colored[len(partitions_colored) -1]
#create the graph object from the given adjacency matrix-style input matrix
cm_graph = generate_nx_graph(m)
graph_list.append(cm_graph)
#compute the cluster
communities = []
if community_algorithm == "GM":
communities = girvan_newman(cm_graph.copy())
elif community_algorithm == "Louvain":
communities = louvain(cm_graph.copy())
partitions_list.append(communities)
for community in communities:
#col = Quantity_Color(random.random(),random.random(),random.random(),Quantity_TOC_RGB)
r = random.random()
g = random.random()
b = random.random()
col = (r, g, b)
for item in community:
local_shapes_names_colors[shapes[item]][1] = col
print("Computed",len(communities), "clusters, with sizes",[len(x) for x in communities])
# Number of communities
number_communities_list.append(len(communities)) # added by Lucas
# Avg size of communities (number of nodes / per communities)
from statistics import mean # install and add to .ylm file
average_size_communities.append(mean([len(x) for x in communities])) # added by Lucas
#print('average_size_communities: ',average_size_communities)
# Standard deviation of size of communities
std_size_communities.append(np.std([len(x) for x in communities]))
#visual feedback for the selection
#gui_import_as_multiple_shapes(display, partitions_colored[displayed_communities])
my_ren = threejs_renderer.ThreejsRenderer()
gui_import_as_multiple_shapes_webGL(my_ren, partitions_colored[displayed_communities], event=None)
my_ren.render()
print("Showing displayed communities", displayed_communities)
displayed_communities = displayed_communities + 1
now = time.time() # added by Lucas
print("It has been {0} seconds since the loop started".format(now - program_starts)) # added by Lucas
computing_time.append(now - program_starts) # added by Lucas
input("Press enter to select the currently displayed partitions, click on the screen to change the partition")
import pyautogui # install and add to .ylm file
im = pyautogui.screenshot(region=(325,125, 1025, 800))
im.save(main_path+"/liasons_graphs/"+file+"/"+str(cont)+"_"+list_matrices_names[cont]+"_"+community_algorithm+"_"+file+"_screenshot.png")
cont=cont+1
return partitions_list, graph_list, number_communities_list, average_size_communities, std_size_communities, computing_time
#%%
#init the gui
#display, start_display, add_menu, add_function_to_menu = init_display()
from random import randint
color_list = []
for i in range(100):
r = random.random()
g = random.random()
b = random.random()
color_list.append((r,g,b))
#color_list.append('#%06X' % randint(0, 0xFFFFFF))
partitions_list_reduced, graph_list, number_communities_list, average_size_communities, std_size_communities, computing_time = graph_clustering([cm_reduced, norms_reduced, cm_with_norms_reduced], shapes, shapes_names_colors.copy())
#partitions_list_reduced, graph_list, number_communities_list, average_size_communities, std_size_communities, computing_time = graph_clustering([cm_with_norms_reduced], shapes, shapes_names_colors.copy())
print("(#) Original number_of_parts: ",number_of_parts)
print("(#) Reduced number_of_parts: ", len(cm_reduced))
print("(#) number_communities_list: ",number_communities_list)
print("(#) average_size_communities: ",average_size_communities)
print("(#) std_size_communities: ",std_size_communities)
print("(#) computing_time_list: ",computing_time)
# Saving DATA
list_matrices_names_with_algorithm=['cm_'+community_algorithm,'norms_'+community_algorithm,'cm_with_norms_'+community_algorithm]
list_file_names = [file,file,file]
original_number_of_parts_list = [number_of_parts,number_of_parts,number_of_parts]
reduced_number_of_parts_list = [len(cm_reduced),len(cm_reduced),len(cm_reduced)]
df = pd.DataFrame(data={"list_file_names" : list_file_names,"original_number_of_parts_list": original_number_of_parts_list, "reduced_number_of_parts_list" : reduced_number_of_parts_list,"Matrix": list_matrices_names_with_algorithm, "number_communities_list": number_communities_list, "average_size_communities": average_size_communities, "std_size_communities": std_size_communities, "computing_time_list": computing_time})
#df = pd.DataFrame(data={list_matrices_names_with_algorithm, number_communities_list, average_size_communities, std_size_communities, computing_time})
#df.set_index(list_matrices_names_with_algorithm)
df.to_csv(main_path+"/liasons_graphs/"+file+"/"+"Data_"+community_algorithm+"_community_detection"+"_"+file+'.csv', sep=',',index=False,header=True)
#print("partitions_list: ",partitions_list)
#print("len partitions_list[0]: ",partitions_list[0])
#print("len partitions_list[0][0]: ",partitions_list[0][0])
#print("len partitions_list[0][1]: ",partitions_list[0][1])
#print("len partitions_list[0][2]: ",partitions_list[0][2])
#print("graph_list: ",graph_list[0])
#quit()
cont=0;
cont2=0;
for matrix in matrices_list_reduced:
fig = plt.figure()
node_color = []
matrix_array_index = np.arange(0,len(matrix))
partitions_list_per_matrix = partitions_list_reduced[cont2]
print(partitions_list_per_matrix)
for node in matrix_array_index:
cont=0;
for partition in partitions_list_per_matrix:
if node in matrix_array_index[partition]:
node_color.append(color_list[cont])
cont=cont+1
#print(cont)
print("Building resulting graph...")
G = nx.from_numpy_matrix(matrix)
nx.draw(G, node_color=node_color, with_labels=True, font_size=8, font_color="black", font_weight="bold")
print("Dumping graph in pickle...")
file_test = open(main_path+"/liasons_graphs/"+file+"/"+str(cont2)+"_"+list_matrices_names[cont2]+"_"+community_algorithm+"_community_detection"+"_"+file+'.mpl', 'wb')
pkl.dump(fig, file_test)
file_test = open(main_path+"/liasons_graphs/"+file+"/"+str(cont2)+"_"+list_matrices_names[cont2]+"_"+community_algorithm+"_community_detection"+"_"+file+'.mpl', 'rb')
figure = pkl.load(file_test)
#figure.show()
#print(list_matrices_names[cont2])
plt.savefig(os.path.join(main_path+"/liasons_graphs/"+file+"/"+str(cont2)+"_"+list_matrices_names[cont2]+"_"+community_algorithm+"_community_detection"+"_"+file))
#
plt.close()
#plt.show()
cont2=cont2+1
#%% Exclude isolates from matrices (cm and cm_with_norms), since norms has no isoloted nodes
'''
cont=0;
cont2=0;
for matrix in matrices_list:
node_color = []
matrix_array_index = np.arange(0,len(matrix))
partitions_list_per_matrix = partitions_list[cont2]
#print("partitions_list_per_matrix: ",partitions_list_per_matrix)
G = nx.from_numpy_matrix(matrix)
print("List of isolates: ",list(nx.isolates(G)))
#list(nx.isolates(G))
for node in matrix_array_index:
cont=0;
for partition in partitions_list_per_matrix:
if (node in matrix_array_index[partition]) and (node not in list(nx.isolates(G))) : #and (len(partition) > 1):
node_color.append(color_list[cont])
cont=cont+1
#print(cont)
#print("Tamanho do node_color: ",len(node_color))
G.remove_nodes_from(list(nx.isolates(G)))
#print("List of isolates",list(nx.isolates(G)))
#print("isolates",nx.isolates(G))
nx.draw(G, node_color=node_color, with_labels=True, font_size=8, font_color="black", font_weight="bold")
#print(list_matrices_names[cont2])
plt.savefig(os.path.join(main_path+"/liasons_graphs/"+file+"/3_"+file+"_"+ list_matrices_names[cont2]+"_community_detection"+"_"+community_algorithm+"_without_isolates"))
#
plt.show()
cont2=cont2+1
'''
#%%
# result_data = [
# [" "],
# ["ex_rq2_"+str(cont)],
# [file],
# [number_of_parts],
# [number_of_columns],
# [number_of_rows],
# [isolated_nodes],
# [n_isolates],
# [n_sub_graphs],
# [min_n_nodes],
# [avg_n_nodes],
# [max_n_nodes]
# ]
#
# result_data_df = pd.DataFrame(result_data)
#
# df_2 = result_data_df
#
# df_f = pd.concat([df_1,df_2], axis=1)
#
# df_1 = df_f
#
#print(df_f)
#
#product_name = "RQ2_experiment_log"
#df_f.to_excel(os.path.join(main_path+"/",product_name + "_graph_analysis_results.xlsx"),index=False,header=None)
\ No newline at end of file
src/contact.py 0 → 100644
+ 78
− 0
View file @ 2d99b8b1
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" Assembly Planning using Machine Learning
" This file provides functionality to compute a contact matrix from a STEP file
" (C) 2021 - Nehel Malhotra, Karim El Zaatari - InVEST 2021/22
" MIT License
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
import numpy as np
import pandas as pd
import itertools
from OCC.Core.BRepMesh import BRepMesh_IncrementalMesh
from OCC.Core.BRepExtrema import BRepExtrema_ShapeProximity,BRepExtrema_ShapeList,BRepExtrema_TriangleSet
from OCC.Extend.ShapeFactory import translate_shp
from OCC.Core.gp import gp_Pnt, gp_Vec
#write a contact matrix as a csv file
def write_contact_matrix(matrix, names, filename):
contacts = cm_transpose_add(matrix)
# df_contact = pd.DataFrame(contacts,index=names,columns=names)
df_contact = pd.DataFrame(contacts)
df_contact.to_csv(filename)
# Added by Lucas
def write_norms_matrix(matrix, names, filename):
contacts = cm_transpose_add(matrix)
# df_contact = pd.DataFrame(contacts,index=names,columns=names)
df_contact = pd.DataFrame(contacts)
df_contact.to_csv(filename)
# Added by Lucas
def write_cm_with_norms_matrix(matrix, names, filename):
contacts = cm_transpose_add(matrix)
# df_contact = pd.DataFrame(contacts,index=names,columns=names)
df_contact = pd.DataFrame(contacts)
df_contact.to_csv(filename)
def cm_transpose_add(df_array):
df_array = df_array + df_array.T - np.diag(np.diag(df_array))
return df_array
def cm_proximity(shapes_object,make_csv=False):
names = []
for shape in shapes_object:
label, _ = shapes_object[shape]
names.append(label)
print("Meshing ", len(shapes_object), " shapes")
i = 0
for shape in shapes_object:
BRepMesh_IncrementalMesh(shape, 1.0)
print("progress: ", (i/len(shapes_object)) * 100, "%")
i+=1
print("Computing contact matrix")
contacts = np.zeros(shape=[len(names),len(names)])
for i, j in itertools.combinations(np.arange(len(list(shapes_object.keys()))), 2):
shape_a = list(shapes_object.keys())[i]
shape_b = list(shapes_object.keys())[j]
proximity = BRepExtrema_ShapeProximity(shape_a, shape_b)
proximity.Perform()
shapes_in_contact_a = []
shapes_in_contact_b = []
print("progress: ", ((i*len(shapes_object)+j) / (len(shapes_object)**2))*100 , "%")
if proximity.IsDone():
subs1 = proximity.OverlapSubShapes1().Keys()
subs2 = proximity.OverlapSubShapes2().Keys()
for sa in subs1:
temp = translate_shp(proximity.GetSubShape1(sa), gp_Vec(0, 0, 5))
shapes_in_contact_a.append(temp)
for sa in subs2:
temp = translate_shp(proximity.GetSubShape2(sa), gp_Vec(0, 0, 5))
shapes_in_contact_b.append(temp)
if len(shapes_in_contact_a) and len(shapes_in_contact_b):
contacts[i,j] = 1
return contacts, names
\ No newline at end of file
src/gui.py 0 → 100644
+ 34
− 0
View file @ 2d99b8b1
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" Assembly Planning using Machine Learning
" This file provides PyOCC GUI utility functions for the project
" (C) 2021 - Daniel Swoboda <swoboda@kbsg.rwth-aachen.de> - InVEST 2021/22
" MIT License
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
from OCC.Core.Quantity import Quantity_Color, Quantity_TOC_RGB
import random
#import shapes for displaying
def gui_import_as_multiple_shapes(display, shapes_names_colors, event=None):
display.EraseAll()
for shpt_lbl_color in shapes_names_colors:
label, c = shapes_names_colors[shpt_lbl_color][:2]
display.DisplayColoredShape(shpt_lbl_color, color=c)
display.FitAll()
def gui_import_as_multiple_shapes_webGL(my_ren, shapes_names_colors, event=None):
for shpt_lbl_color in shapes_names_colors:
label, c = shapes_names_colors[shpt_lbl_color][:2]
print("label", label, "c", c)
#r = random.random()
#g = random.random()
#b = random.random()
#c = (r, g, b)
#todo:change to proper color setting before this random thingy
my_ren.DisplayShape(shpt_lbl_color, color=c, mesh_quality=0.5)
my_ren.render()
\ No newline at end of file
src/main.py 0 → 100644
+ 0
− 0
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src/marginal_items.py 0 → 100644
+ 94
− 0
View file @ 2d99b8b1
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" Assembly Planning using Machine Learning
" This file provides functionality to identify peripharal shapes in a model
" (C) 2021 - Nehel Malhotra, Karim El Zaatari - InVEST 2021/22
" MIT License
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
from OCC.Core.GProp import GProp_GProps
from OCC.Core.Quantity import Quantity_Color, Quantity_TOC_RGB
from OCC.Display.SimpleGui import init_display
from OCC.Core.BRepAlgoAPI import BRepAlgoAPI_Fuse
from OCC.Core.TopTools import TopTools_ListOfShape
import argparse
from util import *
from contact import *
from clustering import *
from gui import *
import analysis
"""
Python OCC GUI Code
"""
displayed_communities = 0
#this defines a simple on-click callback that prints shape information
def gui_simple_callback(selected, *kwargs):
global display,shapes_list,displayed_communities
gui_import_as_multiple_shapes(display, shapes_list[displayed_communities])
print("I am showing the displayed community",displayed_communities)
displayed_communities+=1
if displayed_communities == len(shapes_list):
displayed_communities = 0
"""
Support Functions
"""
# function that filters items in contact with a specific shape and checks distance to COG of fuse. The item is removed if it the farthest
# from fuse cog (compared to its neighbors)
def item_eliminate(index):
contact_list =[]
for ind in len(matrix):
if matrix[index][ind] == 1:
contact_list.append(norms_fuse_center[ind])
if norms_fuse_center[index]> max(contact_list):
shapes.remove(shapes[index])
return shapes
# combine all components into a single item
def object_fuse(objects):
fuse = objects[0]
for shape in objects[1:]:
fuse = BRepAlgoAPI_Fuse(fuse, shape).Shape()
return fuse
"""
Main Logic
"""
path = "../data/step_files/"
file = input("Which file do you want to edit? ")
# import the file
print("Loading shapes")
shapes_names_colors, shapes = read_or_gen_p_shapes(path, file)
print(shapes)
# get props
props = GProp_GProps()
# get the contact matrix
print("Loading contact matrix")
matrix, names = read_or_gen_p_cm(path, file, shapes_names_colors = shapes_names_colors)
print("Computing COGs")
cogs = np.array([get_center_of_gravity(props, shape) for shape in shapes])
print("Computing COG Distances")
norms = np.array([cl_compute_norms_modified(idx, cogs) for idx in range(len(cogs))])
print("Compute center of gravity of fusion")
fuse = object_fuse(shapes)
center_gravity = np.array([get_center_of_gravity(props, fuse)])
print("compute distance between fuse and each item")
distances_fuse_center = cogs - center_gravity
norms_fuse_center = np.array(np.linalg.norm(distances_fuse_center, axis = -1))
index_farthest_element = np.argmax(norms_fuse_center)
print(norms_fuse_center)
print(index_farthest_element)
src/minimal_renderer.py 0 → 100644
+ 20
− 0
View file @ 2d99b8b1
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" Assembly Planning using Machine Learning
" Renders a serialized set of shapes from a generated pickle
" (C) 2021 - Daniel Swoboda <swoboda@kbsg.rwth-aachen.de> - InVEST 2021/22
" MIT License
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
from util import *
from gui import *
from OCC.Display.SimpleGui import init_display
path = "../data/step_files/"
file = "vertical_drop_lift"
shapes_names_colors, list_of_shapes = read_or_gen_p_shapes(path, file)
display, start_display, add_menu, add_function_to_menu = init_display()
gui_import_as_multiple_shapes(display, shapes_names_colors)
start_display()
\ No newline at end of file
src/preprocessor.py 0 → 100644
+ 22
− 0
View file @ 2d99b8b1
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" Assembly Planning using Machine Learning
" Goes through the step_files directory and performs the preprocessing on all objects
" (C) 2021 - Daniel Swoboda <swoboda@kbsg.rwth-aachen.de> - InVEST 2021/22
" MIT License
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
import util
import process
from os import listdir
from os.path import isfile, join
path = "../data/step_files"
files = [f[:-4] for f in listdir(path) if isfile(join(path, f)) and ".stp" in f]
for file in files:
print("Working on ", file)
util.read_or_gen_p_shapes(path="../data/step_files/", name=file, override=True)
util.read_or_gen_p_cm(path="../data/step_files/", name=file, override=True)
util.read_or_gen_p_cog(path="../data/step_files/", name=file, override=True)
\ No newline at end of file
src/process.py 0 → 100644
+ 171
− 0
View file @ 2d99b8b1
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" Assembly Planning using Machine Learning
" This file provides utility functions for the project
" (C) 2021 - Daniel Swoboda <swoboda@kbsg.rwth-aachen.de> - InVEST 2021/22
" MIT License
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import random
import itertools
from sklearn.cluster import DBSCAN # <- Machine Learning comes here
from sklearn.cluster import OPTICS # <- Machine Learning comes here
from sklearn.decomposition import PCA
import argparse
from OCC.Core.GProp import GProp_GProps
from OCC.Core.BRepGProp import brepgprop_VolumeProperties
from OCC.Core.Quantity import Quantity_Color, Quantity_TOC_RGB
from OCC.Display.SimpleGui import init_display
from OCC.Extend.DataExchange import read_step_file_with_names_colors
from OCC.Core.Bnd import Bnd_Box
from OCC.Core.BRepMesh import BRepMesh_IncrementalMesh
from OCC.Core.BRepBndLib import brepbndlib_Add
from OCC.Core.BRepExtrema import BRepExtrema_ShapeProximity,BRepExtrema_ShapeList,BRepExtrema_TriangleSet
from OCC.Extend.ShapeFactory import translate_shp
from OCC.Core.gp import gp_Pnt, gp_Vec
import pickle as p
import contact
from util import *
# get the shapes and shapes with names and colors from a step file
def get_shapes(filepath):
snc = read_step_file_with_names_colors(filepath)
list_of_shapes = list(snc.keys())
shapes_names_colors = {}
#convert to rgb tuple to allow storage
for shape in list_of_shapes:
shapes_names_colors[shape] = (snc[shape][0],(snc[shape][1].Red(), snc[shape][1].Green(), snc[shape][1].Blue()))
return shapes_names_colors, list_of_shapes
# gets the vertices that make up the mesh of the given shape
def get_vertices(shape):
te = TopologyExplorer(shape)
vertices = []
for vertex in te.vertices():
x = BRep_Tool.Pnt(vertex).X()
y = BRep_Tool.Pnt(vertex).Y()
z = BRep_Tool.Pnt(vertex).Z()
vertices.append([x,y,z])
return vertices
def process_gen_cm(shapes_names_colors):
return contact.cm_proximity(shapes_names_colors)
def process_write(object, filepath):
f = open(filepath, 'wb')
p.dump(object, f)
f.close()
def process_load(filepath):
f = open(filepath, "rb")
object = p.load(f)
f.close()
return object
def path_name_to_path(path, name):
if path[-1:] != "/":
path_p_s = path + "/" + name + "_S.pickle"
path_p_cm = path + "/" + name + "_CM.pickle"
path_p_cog = path + "/" + name + "_COG.pickle"
path_p_norms = path + "/" + name + "_NRM.pickle"
path_p_vertices = path + "/" + name + "_VRT.pickle"
path_stp = path + "/" + name + ".stp"
else:
path_p_s = path + name + "_S.pickle"
path_p_cm = path + name + "_CM.pickle"
path_p_cog = path + name + "_COG.pickle"
path_p_norms = path + name + "_NRM.pickle"
path_p_vertices = path + name + "_VRT.pickle"
path_stp = path + name + ".stp"
return path_p_s, path_p_cm, path_p_cog, path_p_norms, path_p_vertices, path_stp
def read_or_gen_p_shapes(path, name, override = False):
path_p_s, path_p_cm, path_p_cog, path_p_norms, path_p_vertices, path_stp = path_name_to_path(path, name)
try:
if override:
raise Exception()
shape_tuple = process_load(path_p_cm)
except:
print("Shapes not found, computing and serializing to pickle for future use!")
shapes_names_colors, list_of_shapes = get_shapes(path_stp)
process_write((shapes_names_colors, list_of_shapes), path_p_cm)
shape_tuple = process_load(path_p_cm)
shapes_names_colors = {}
for shape in shape_tuple[0].keys():
shapes_names_colors[shape] = [shape_tuple[0][shape][0],rgb_color(shape_tuple[0][shape][1])]
return shapes_names_colors, shape_tuple[1]
# try to read a pickle file for the given name in the given path, if none exists, create the contact matrix and serialize it
def read_or_gen_p_cm(path, name, shapes_names_colors = None, override = False):
path_p_s, path_p_cm, path_p_cog, path_p_norms, path_p_vertices, path_stp= path_name_to_path(path, name)
try:
if override:
raise Exception()
return process_load(filepath=path_p_s)
except:
print("Contact matrix not found, computing and serializing to pickle for future use!")
if shapes_names_colors is None:
shapes_names_colors, list_of_shapes = get_shapes(path_stp)
process_write(contact.cm_proximity(shapes_names_colors), path_p_s)
return process_load(filepath=path_p_s)
# try to read a pickle file for the given name in the given path, if none exists, create the COG array
def read_or_gen_p_cog(path, name, shapes=None, props=None, override=False):
path_p_s, path_p_cm, path_p_cog, path_p_norms, path_p_vertices, path_stp = path_name_to_path(path, name)
try:
if override:
raise Exception()
return process_load(filepath=path_p_cog)
except:
print("COGs not found, computing and serializing to pickle for future use!")
if shapes is None:
shapes_names_colors, shapes = read_or_gen_p_shapes(path, name)
props = GProp_GProps()
cogs = np.array([get_center_of_gravity(props, shape) for shape in shapes])
process_write(cogs, path_p_cog)
return process_load(filepath=path_p_cog)
# try to read a pickle file for the given name in the given path, if none exists, create the COG array
def read_or_gen_p_norms(path, name, cogs=None, override=False):
path_p_s, path_p_cm, path_p_cog, path_p_norms, path_p_vertices, path_stp = path_name_to_path(path, name)
try:
if override:
raise Exception()
return process_load(filepath=path_p_norms)
except:
print("COGs not found, computing and serializing to pickle for future use!")
if cogs is None:
cogs = read_or_gen_p_cog(path, name)
norms = np.array([compute_norms(idx, cogs) for idx in range(len(cogs))])
process_write(norms, path_p_norms)
return process_load(filepath=path_p_norms)
# try to read a pickle file for the given name in the given path, if none exists, create the vertices array
def read_or_gen_p_vertices(path, name, shapes=None, override=False):
path_p_s, path_p_cm, path_p_cog, path_p_norms, path_p_vertices, path_stp = path_name_to_path(path, name)
try:
if override:
raise Exception()
return process_load(filepath=path_p_vertices)
except:
print("Vertices not found, computing and serializing to pickle for future use!")
if shapes is None:
shapes_names_colors, shapes = read_or_gen_p_shapes(path, name, override = False)
vertices = np.array([get_vertices(shape) for shape in shapes])
process_write(vertices, path_p_vertices)
return process_load(filepath=path_p_vertices)
\ No newline at end of file
src/screw_handling.py 0 → 100644
+ 49
− 0
View file @ 2d99b8b1
from OCC.Extend.DataExchange import write_step_file
from util import *
from gui import *
from OCC.Display.SimpleGui import init_display
#this defines a simple on-click callback that prints shape information
def gui_simple_callback(selected, *kwargs):
for shape in selected:
if shape in list_of_shapes:
# remove item whose key correponds to shape from shape_name_colors
del shapes_names_colors[shape]
# remove "shape" from list_of_shapes
list_of_shapes.remove(shape)
# update rendering
gui_import_as_multiple_shapes(display, shapes_names_colors)
def filter_items(shapes_names_colors, items_to_remove):
new_shapes_names_colors = {}
for key in shapes_names_colors:
if shapes_names_colors[key][0] not in items_to_remove:
new_shapes_names_colors[key] = shapes_names_colors[key]
return new_shapes_names_colors, list(new_shapes_names_colors.keys())
path = "../data/step_files/"
file = input("Please enter the file you want to edit: ")
output_file = input("Please enter the file name you want to save to: ")
items_to_remove = input("Please enter a list of names (comma separated) for items you want to remove: ").split(",")
#read the step file
shapes_names_colors, list_of_shapes = read_or_gen_p_shapes(path, file)
#filter by name
shapes_names_colors, list_of_shapes = filter_items(shapes_names_colors, items_to_remove)
display, start_display, add_menu, add_function_to_menu = init_display()
gui_import_as_multiple_shapes(display, shapes_names_colors)
display.register_select_callback(gui_simple_callback)
input("Press any key to save the result: ")
for item in shapes_names_colors:
write_step_file(item, path+output_file+".stp", application_protocol="AP203")
src/util.py 0 → 100644
+ 194
− 0
View file @ 2d99b8b1
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" Assembly Planning using Machine Learning
" This file provides utilities to process STEP files for usage with PythonOCC
" (C) 2021 - Daniel Swoboda <swoboda@kbsg.rwth-aachen.de> - InVEST 2021/22
" MIT License
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
from OCC.Core.GProp import GProp_GProps
from OCC.Core.BRepGProp import brepgprop_VolumeProperties
from OCC.Core.Quantity import Quantity_Color, Quantity_TOC_RGB
from OCC.Extend.DataExchange import read_step_file_with_names_colors
from OCC.Core.Bnd import Bnd_Box
from OCC.Core.BRepMesh import BRepMesh_IncrementalMesh
from OCC.Core.BRepBndLib import brepbndlib_Add
from OCC.Core.BRep import BRep_Tool, BRep_Builder
from OCC.Core.BRepAlgoAPI import BRepAlgoAPI_Fuse
from OCC.Extend.DataExchange import write_stl_file
from OCC.Core.StlAPI import stlapi_Read, StlAPI_Writer
from OCC.Core.TopoDS import (
topods,
TopoDS_Wire,
TopoDS_Vertex,
TopoDS_Edge,
TopoDS_Face,
TopoDS_Shell,
TopoDS_Solid,
TopoDS_Shape,
TopoDS_Compound,
TopoDS_CompSolid,
topods_Edge,
topods_Vertex,
TopoDS_Iterator,
)
from OCC.Extend.TopologyUtils import WireExplorer, TopologyExplorer
import numpy as np
import shutil
import random
import os
import contact
# converts values r,g,b in [0-1] to Quantity_Color object for rendering
def rgb_color(r, g, b):
return Quantity_Color(r, g, b, Quantity_TOC_RGB)
# converts values r,g,b in [0-1] to Quantity_Color object for rendering
def rgb_color(ct):
return Quantity_Color(ct[0], ct[1], ct[2], Quantity_TOC_RGB)
def rgb_color_random():
return Quantity_Color(random.random(), random.random(), random.random(), Quantity_TOC_RGB)
# returns the mass of a shape assuming uniform density
def get_mass(props:GProp_GProps, shape):
brepgprop_VolumeProperties(shape, props)
return props.Mass()
# returns the center of gravity of a shape assuming uniform density distribution
def get_center_of_gravity(props:GProp_GProps, shape):
brepgprop_VolumeProperties(shape, props)
return props.CentreOfMass().Coord()
def get_boundingbox(shape, tol=1e-6, use_mesh=True):
bbox = Bnd_Box()
bbox.SetGap(tol)
if use_mesh:
mesh = BRepMesh_IncrementalMesh()
mesh.SetParallelDefault(True)
mesh.SetShape(shape)
mesh.Perform()
if not mesh.IsDone():
raise AssertionError("Mesh not done.")
brepbndlib_Add(shape, bbox, use_mesh)
xmin, ymin, zmin, xmax, ymax, zmax = bbox.Get()
return xmin, ymin, zmin, xmax, ymax, zmax, xmax-xmin, ymax-ymin, zmax-zmin
#given a list of tuples, combine two pairs if they have a mutual element
#iteratively until no more tuples can be combined.
# e.g. (2,1) (2,3) -> (1,2,3) and (2,4) (4,7) -> (2,4,7)
# ----> (1,2,3) (2,4,7) -> (1,2,3,4,7)
def combine_pairs(groups):
out = []
while len(groups) > 0:
first, *rest = groups
first = set(first)
lf = -1
while len(first) > lf:
lf = len(first)
rest2 = []
for r in rest:
if len(first.intersection(set(r))) > 0:
first |= set(r)
else:
rest2.append(r)
rest = rest2
out.append(first)
groups = rest
return out
# computes the norms for the centers of gravity to determine the distances
def compute_norms(shape_idx: int, coords:np.array):
distances = coords - coords[shape_idx]
return np.linalg.norm(distances, axis=-1)
#combines given list of shapes into a TopoDS_Compound
def combine_shapes(shapes):
list_contains_null_shape = True
compound = TopoDS_Compound()
builder = BRep_Builder()
builder.MakeCompound(compound)
for shape in shapes:
if shape.IsNull():
list_contains_null_shape = False
continue
builder.Add(compound, shape)
return compound, list_contains_null_shape
#combines given list of shapes into a TopoDS_Compound
def combine_shapes_from_indexlist(shapes, indexlist):
list_contains_null_shape = True
compound = TopoDS_Compound()
builder = BRep_Builder()
builder.MakeCompound(compound)
for index in indexlist:
if shapes[index].IsNull():
list_contains_null_shape = False
continue
builder.Add(compound, shapes[index])
return compound
#combine given list of shapes into a single TopoDS_Solid using the BRep Fuse algorithm
def combine_shapes_iteratively(shapes):
shape = shapes[0]
list_contains_null_shape = True
if len(shapes) > 1:
for add_shape in shapes[1:]:
if add_shape.IsNull():
list_contains_null_shape = False
continue
shape = BRepAlgoAPI_Fuse(shape, add_shape).Shape()
return shape, list_contains_null_shape
def export_shapes_to_stl(dirpath, shapes):
if os.path.isdir(dirpath):
shutil.rmtree(dirpath)
os.mkdir(dirpath, 0o777)
os.chdir(dirpath)
for i in range(0,len(shapes)):
print("Writing", str(i)+".stl", i+1, "of", len(shapes))
write_stl_file(shapes[i], str(i)+".stl")
os.chdir("..")
def remove_stl_export_dir(dirpath):
if os.path.isdir(dirpath):
shutil.rmtree(dirpath)
#this export function assumes that the dictionary keeps its order (as of assertion)
#the assumption holds for every python version since 2019.
def export_tree_to_stl_recursive(level, dirpath, tree, exporter):
os.mkdir(dirpath, 0o777)
for i in range(0, len(tree["data"])):
shape_data = tree["data"][list(tree["data"].keys())[i]]
shape_name = shape_data[0]
shape_color = shape_data[1]
shape_duplicates = shape_data[2]
shape = list(tree["data"].keys())[i]
#export
os.chdir(dirpath)
exporter.Write(shape, str(i)+".stl")
if shape_duplicates is not None and len(shape_duplicates) > 0:
os.mkdir(dirpath+"/"+str(i)+"-duplicates", 0o777)
os.chdir(dirpath+"/"+str(i)+"-duplicates")
for duplicate in shape_duplicates:
exporter.Write(duplicate, str(shape_duplicates.index(duplicate))+".stl")
os.chdir("..")
if tree["children"] is not None:
shape_node = tree["children"][i]
export_tree_to_stl_recursive(level+1, dirpath+"/"+str(i), shape_node, exporter)
def export_tree_to_stl(dirpath, tree):
exporter = StlAPI_Writer()
exporter.SetASCIIMode(False)
export_tree_to_stl_recursive(0, dirpath+"-root", tree, exporter)
\ No newline at end of file
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