/
my_utils.py
executable file
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my_utils.py
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import random
import numpy as np
import trimesh
import os
import torch
from termcolor import colored
import pymesh
def grey_print(x):
print(colored(x, "grey"))
def red_print(x):
print(colored(x, "red"))
def green_print(x):
print(colored(x, "green"))
def yellow_print(x):
print(colored(x, "yellow"))
def blue_print(x):
print(colored(x, "blue"))
def magenta_print(x):
print(colored(x, "magenta"))
def cyan_print(x):
print(colored(x, "cyan"))
def white_print(x):
print(colored(x, "white"))
def print_arg(opt):
cyan_print("PARAMETER: ")
for a in opt.__dict__:
print(
" "
+ colored(a, "yellow")
+ " : "
+ colored(str(opt.__dict__[a]), "cyan")
)
def int_2_boolean(x):
if x == 1:
return True
else:
return False
def test_orientation(input_mesh):
"""
This fonction tests wether widest axis of the input mesh is the Z axis
input mesh
output : boolean or warning
"""
point_set = input_mesh.vertices
bbox = np.array([[np.max(point_set[:,0]), np.max(point_set[:,1]), np.max(point_set[:,2])], [np.min(point_set[:,0]), np.min(point_set[:,1]), np.min(point_set[:,2])]])
extent = bbox[0] - bbox[1]
if not np.argmax(np.abs(extent)) == 1:
print("The widest axis is not the Y axis, you should make sure the mesh is aligned on the Y axis for the autoencoder to work (check out the example in /data)")
return
def clean(input_mesh, prop=None):
"""
This function remove faces, and vertex that doesn't belong to any face. Intended to be used before a feed forward pass in pointNet
Input : mesh
output : cleaned mesh
"""
print("cleaning ...")
print("number of point before : " , np.shape(input_mesh.vertices)[0])
pts = input_mesh.vertices
faces = input_mesh.faces
faces = faces.reshape(-1)
unique_points_index = np.unique(faces)
unique_points = pts[unique_points_index]
print("number of point after : " , np.shape(unique_points)[0])
mesh = trimesh.Trimesh(vertices=unique_points, faces=np.array([[0,0,0]]), process=False)
if prop is not None:
new_prop = prop[unique_points_index]
return mesh, new_prop
else:
return mesh
def center(input_mesh):
"""
This function center the input mesh using it's bounding box
Input : mesh
output : centered mesh and translation vector
"""
bbox = np.array([[np.max(input_mesh.vertices[:,0]), np.max(input_mesh.vertices[:,1]), np.max(input_mesh.vertices[:,2])], [np.min(input_mesh.vertices[:,0]), np.min(input_mesh.vertices[:,1]), np.min(input_mesh.vertices[:,2])]])
tranlation = (bbox[0] + bbox[1]) / 2
points = input_mesh.vertices - tranlation
mesh = trimesh.Trimesh(vertices=points, faces=input_mesh.faces, process= False)
return mesh, tranlation
def scale(input_mesh, mesh_ref):
"""
This function scales the input mesh to have the same volume as a reference mesh Intended to be used before a feed forward pass in pointNet
Input : file path
mesh_ref : reference mesh path
output : scaled mesh
"""
area = np.power(mesh_ref.volume / input_mesh.volume, 1.0/3)
mesh= trimesh.Trimesh( vertices = input_mesh.vertices * area, faces= input_mesh.faces, process = False)
return mesh, area
def uniformize(input):
input = pymesh.form_mesh(input.vertices, input.faces)
input, _ = pymesh.split_long_edges(input, 0.005)
return input
def rot(input_mesh, theta = np.pi/2):
# rotation around X axis of angle theta
point = input_mesh.vertices
rot_matrix = np.array([[1,0,0],[0,np.cos(theta),-np.sin(theta)],[0,np.sin(theta),np.cos(theta)]])
point_set = point.dot(np.transpose(rot_matrix, (1, 0)))
#center the rotated mesh
bbox = np.array([[np.max(point_set[:,0]), np.max(point_set[:,1]), np.max(point_set[:,2])], [np.min(point_set[:,0]), np.min(point_set[:,1]), np.min(point_set[:,2])]])
tranlation = (bbox[0] + bbox[1]) / 2
point_set = point_set - tranlation
mesh = trimesh.Trimesh(vertices=point_set, faces=input_mesh.faces, process = False)
return mesh
#initialize the weighs of the network for Convolutional layers and batchnorm layers
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
def adjust_learning_rate(optimizer, epoch, phase):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
if (epoch%phase==(phase-1)):
for param_group in optimizer.param_groups:
param_group['lr'] = param_group['lr']/10.
def plant_seeds(randomized_seed=False):
if randomized_seed:
print("Randomized seed")
manualSeed = random.randint(1, 10000)
else:
print("Used fix seed")
manualSeed = 1
print("Random Seed: ", manualSeed)
random.seed(manualSeed)
torch.manual_seed(manualSeed)
np.random.seed(manualSeed)
class AverageValueMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def get_random_color(pastel_factor = 0.5):
return [(x+pastel_factor)/(1.0+pastel_factor) for x in [random.uniform(0,1.0) for i in [1,2,3]]]
def color_distance(c1,c2):
return sum([abs(x[0]-x[1]) for x in zip(c1,c2)])
def generate_new_color(existing_colors,pastel_factor = 0.5):
max_distance = None
best_color = None
for i in range(0,100):
color = get_random_color(pastel_factor = pastel_factor)
if not existing_colors:
return color
best_distance = min([color_distance(color,c) for c in existing_colors])
if not max_distance or best_distance > max_distance:
max_distance = best_distance
best_color = color
return best_color
#Example:
def get_colors(num_colors=10):
colors = []
for i in range(0,num_colors):
colors.append(generate_new_color(colors,pastel_factor = 0.9))
for i in range(0,num_colors):
for j in range(0,3):
colors[i][j] = int(colors[i][j]*256)
colors[i].append(255)
return colors
CHUNK_SIZE = 150
lenght_line = 60
def my_get_n_random_lines(path, n=5):
MY_CHUNK_SIZE = lenght_line * (n+2)
lenght = os.stat(path).st_size
with open(path, 'r') as file:
file.seek(random.randint(400, lenght - MY_CHUNK_SIZE))
chunk = file.read(MY_CHUNK_SIZE)
lines = chunk.split(os.linesep)
return lines[1:n+1]
def sampleSphere(N):
rand = np.random.rand(N)
theta = 2*np.pi*np.random.rand(N)
phi = np.arccos(1 - 2*np.random.rand(N))
x = np.sin(phi) * np.cos(theta)
y = np.sin(phi) * np.sin(theta)
z = np.cos(phi)
sphere = np.array([x,y,z]).transpose(1,0)
return sphere
if __name__ == '__main__':
#To make your color choice reproducible, uncomment the following line:
#random.seed(10)
sampleSphere(1000)