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scanning.py
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scanning.py
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import vedo
import numpy as np
import os
import trimesh
from PIL import Image
import open3d as o3d
import math
import imageio
from tqdm import tqdm
from data import models
# vedo.settings.useDepthPeeling = True
vedo.settings.use_depth_peeling = True
# if there is an error with libGL, apply this fix to the conda scan environment: https://stackoverflow.com/a/72427700
class Scanner:
def __init__(self, model="bunny", steps=30):
"""
:param model: model to scan
:param steps: scanner steps around z-axis
"""
self.model = models[model]
self.steps=steps
self.factor = 360/steps
def scan(self, n_rays=10):
"""
:param n_rays: number of rays to shoot for one scanner step
"""
# good video to see how triangulation based range scanner works: https://www.micro-epsilon.de/2D_3D/laser-scanner/
print("\nScanning")
origin = np.tile(self.model["scanner_pos"],(n_rays,1))
# dest = np.expand_dims(np.linspace(-0.5,1.5,num=n),axis=1)
dest = np.expand_dims(np.linspace(-0.8,1.2,num=n_rays),axis=1)
z = np.zeros(shape=(n_rays,2))
z+=[0.0,0.0]
dest = np.concatenate([z,dest],axis=1)
self.mesh = trimesh.load(self.model["mesh"],use_embree=False,force="mesh")
# self.mesh = list(mesh.geometry.items())[0][1]
points = np.empty(shape=(0,3))
spos = np.empty(shape=(0,3))
self.pointsArray = []
self.sensorsArray = []
for i in tqdm(range(self.steps)):
R = trimesh.transformations.rotation_matrix(self.factor*math.pi/180, [0, 0, 1])
# Ry = trimesh.transformations.rotation_matrix(i*3.1415/180, [1, 0, 0])
# R=trimesh.transformations.concatenate_matrices(Ry, Rz)
origin = np.matmul(origin,R[:3,:3])
dest+=[0,0.3/self.steps,0]
dest = np.matmul(dest,R[:3,:3])
# Get the intersections
# locations, index_ray, index_tri = mesh.ray.intersects_location(ray_origins=origin, ray_directions=dest-origin)
index_ray, index_tri, locations = self.mesh.ray.intersects_id(ray_origins=origin, ray_directions=dest-origin,
return_locations=True,multiple_hits=False)
points = np.concatenate([points,locations])
spos = np.concatenate([spos,origin[:locations.shape[0]]])
self.pointsArray.append(locations)
self.sensorsArray.append(origin[:locations.shape[0]])
# save the point cloud
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(points)
pcd.normals = o3d.utility.Vector3dVector(spos-points)
o3d.io.write_point_cloud(os.path.join(self.model["path"],"pointcloud.ply"),pcd)
np.savez(os.path.join(self.model["path"],"pointcloud.npz"),points=points,sensor_position=spos)
def scanMVS(self,n_points=5000):
n_cam = self.model["mvs_pos"].shape[0]
print("\nScanning MVS")
self.mesh = trimesh.load(self.model["mesh"],use_embree=False,force="mesh")
points = np.empty(shape=(0,3))
spos = np.empty(shape=(0,3))
# cameras = np.array([[0,2,1.2],[-2,0,1],[2.5,1,1.2],[-3,-3,1.8]])
# cameras = np.array([[-2,1,1],[2.5,1,1.2],[-1,-3,1.8]])
for c in tqdm(self.model["mvs_pos"]):
dest = np.random.uniform(low=self.mesh.bounds[0,:],high=self.mesh.bounds[1,:],size=(int(n_points/n_cam),3))
origin = np.tile(c,(int(n_points/n_cam),1))
# Get the intersections
# locations, index_ray, index_tri = mesh.ray.intersects_location(ray_origins=origin, ray_directions=dest-origin)
index_ray, index_tri, locations = self.mesh.ray.intersects_id(ray_origins=origin, ray_directions=dest-origin,
return_locations=True,multiple_hits=False)
points = np.concatenate([points,locations])
spos = np.concatenate([spos,origin[:locations.shape[0]]])
self.points = points
self.spos = spos
# save the point cloud
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(points)
pcd.normals = o3d.utility.Vector3dVector(spos-points)
o3d.io.write_point_cloud(os.path.join(self.model["path"],"pointcloud_mvs.ply"),pcd)
np.savez(os.path.join(self.model["path"],"pointcloud_mvs.npz"),points=points,sensor_position=spos)
def setCamera(self,plt):
plt.camera.SetPosition(self.model["cam"]["position"])
plt.camera.SetFocalPoint(self.model["cam"]["focal_point"])
plt.camera.SetViewUp(self.model["cam"]["viewup"])
plt.camera.SetDistance(self.model["cam"]["distance"])
plt.camera.SetClippingRange(self.model["cam"]["clipping_range"])
def scanVisualizeMVS(self,interactive=False,size=(500,500),texture=True,duration=80):
print("\nvisualize MVS Scanning")
mesh = vedo.Mesh(self.model["mesh"], c=[90, 90, 90])
if texture:
mesh.texture(self.model["texture"])
brightness = 1.5
else:
mesh=mesh.compute_normals().phong()
brightness = 2.5
mesh.lighting("default")
points = vedo.Points(self.points,c=[255,255,0],r=10)
sensors = vedo.Points(self.spos, c=[255, 165, 0], r=20)
origin = vedo.Point(self.model["scanner_pos"])
# [default, metallic, plastic, shiny, glossy, ambient, off]
os.makedirs(os.path.join(self.model["path"], "mvs"), exist_ok=True)
images=[]
for i in tqdm(range(self.steps)):
R = trimesh.transformations.rotation_matrix(self.factor*math.pi/180, [0, 0, 1])
mesh.apply_transform(R)
points.apply_transform(R)
sensors.apply_transform(R)
rays = vedo.shapes.Lines(points,sensors,c='y',lw=1,res=24,alpha=0.4)
plt = vedo.Plotter(axes=1, offscreen=np.invert(interactive))
plt += mesh
plt += rays.lighting("ambient")
plt += points.lighting("ambient")
plt += sensors.lighting("ambient")
# lp=vedo.Points(sensors.points()[0],c='g',r=15.0)
# lp=vedo.Point((-0.3,-0.3,0.8),c='g',r=15.0)
origin.apply_transform(R)
lp=origin.points()[0]+[0,0,0.3]
lp=vedo.Point(lp,c='g',r=15.0)
plt+= vedo.Light(lp, c='w', intensity=brightness)
self.setCamera(plt)
plt.show(interactive=interactive, size=size,axes=0,roll=0,
azimuth=-i*self.factor,resetcam=False)
img="img{:03d}.png".format(i)
vedo.io.screenshot(os.path.join(self.model["path"], "mvs", img))
plt.close()
images.append(Image.open(os.path.join(self.model["path"], "mvs", img)))
images[0].save(os.path.join(self.model["path"], "scan_mvs.gif"),
save_all=True, append_images=images[1:], optimize=True, duration=duration, loop=0)
def scanVisualize(self,interactive=False,size=(500,500),texture=True,duration=80,trail=[0,0]):
print("\nvisualize Scanning")
mesh = vedo.Mesh(self.model["mesh"], c=[90, 90, 90])
if texture:
mesh.texture(self.model["texture"])
brightness = 1.5
else:
mesh=mesh.compute_normals().phong()
brightness = 2.5
mesh.lighting("default")
# [default, metallic, plastic, shiny, glossy, ambient, off]
os.makedirs(os.path.join(self.model["path"], "scn"), exist_ok=True)
scanlines=[]
images=[]
for i in tqdm(range(self.steps)):
R = trimesh.transformations.rotation_matrix(self.factor*math.pi/180, [0, 0, 1])
mesh.apply_transform(R)
scanline = vedo.Points(self.pointsArray[i],c=[255,255,0],r=10)
scanline.apply_transform(R)
scanlines.append(scanline)
sensors = vedo.Points(self.sensorsArray[i],c=[255,165,0],r=20)
sensors.apply_transform(R)
rays = vedo.shapes.Lines(scanline,sensors,c='y',lw=4,res=24,alpha=0.6)
plt = vedo.Plotter(axes=1, offscreen=np.invert(interactive))
plt += mesh
plt += rays.lighting("ambient")
plt += scanline.lighting("ambient")
if trail[0] > 0:
last_n = trail[0]
every = trail[1]
rr = np.arange(every,last_n+every,every)
### uncomment to visualize last n=7 scanlines
if(len(scanlines)>last_n):
for step in rr:
plt += scanlines[-step].lighting("ambient")
plt += sensors.lighting("ambient")
light=sensors.points()[0]
light+=[0,0,0.3]
lp=vedo.Point(light,c='g',r=15.0)
# lp=vedo.Point((-0.3,-0.3,0.8),c='g',r=15.0)
plt+= vedo.Light(lp, c='w', intensity=brightness)
self.setCamera(plt)
plt.show(interactive=interactive, size=size,axes=0,roll=0,
azimuth=-i*self.factor,resetcam=False)
img="img{:03d}.png".format(i)
vedo.io.screenshot(os.path.join(self.model["path"], "scn", img))
plt.close()
images.append(Image.open(os.path.join(self.model["path"], "scn", img)))
images[0].save(os.path.join(self.model["path"], "scan.gif"),
save_all=True, append_images=images[1:], optimize=True, duration=duration, loop=0)
def pointsVisualize(self,interactive=False,size=(500,500),with_mesh=False,duration=80):
print("\nvisualize Points")
points = np.empty(shape=(0,3))
normals = np.empty(shape=(0,3))
mesh = vedo.Mesh(self.model["mesh"])
mesh.texture(self.model["texture"])
mesh.lighting("default")
# [default, metallic, plastic, shiny, glossy, ambient, off]
name = "pts_mesh" if with_mesh else "pts"
images = []
os.makedirs(os.path.join(self.model["path"], name), exist_ok=True)
for i in tqdm(range(self.steps)):
R = trimesh.transformations.rotation_matrix(self.factor*math.pi/180, [0, 0, 1])
mesh.apply_transform(R)
sensors = vedo.Points(self.sensorsArray[i],c=[255,165,0],r=20)
sensors.apply_transform(R)
# pointcloud
# iteratively extent the point cloud for 2nd visualisation
points = np.concatenate([points, self.pointsArray[i]])
normals = np.concatenate([normals, self.sensorsArray[i]-self.pointsArray[i]])
# pc = vedo.Points(points,c=[113, 189, 247],r=6).lighting("default")
pc = vedo.Points(points,c=[93, 169, 227],r=6).lighting("default")
# [default, metallic, plastic, shiny, glossy, ambient, off]
pc.compute_normals_with_pca()
pc.pointdata["Normals"]=normals
pc.apply_transform(R)
plt = vedo.Plotter(axes=1, offscreen=np.invert(interactive))
plt+= vedo.Light(sensors.points()[0], c='w', intensity=1.5)
plt+=pc
if(with_mesh):
plt+=mesh
self.setCamera(plt)
plt.show(interactive=interactive, size=size,axes=0,roll=0,
azimuth=-i*self.factor,resetcam=False)
img = "img{:03d}.png".format(i)
vedo.io.screenshot(os.path.join(self.model["path"], name, img))
images.append(Image.open(os.path.join(self.model["path"], name, img)))
plt.close()
images[0].save(os.path.join(self.model["path"], name+".gif"),
save_all=True, append_images=images[1:], optimize=True, duration=duration, loop=0)
def GIF(self,duration=0.1):
print("\nMake GIFs")
images = []
for img in sorted(os.listdir(os.path.join(self.model["path"], "img"))):
images.append(imageio.imread(os.path.join(self.model["path"], "img", img)))
imageio.mimsave(os.path.join(self.model["path"], "scanning.gif"), images, duration=duration)
images = []
for img in sorted(os.listdir(os.path.join(self.model["path"],"pts"))):
images.append(imageio.imread(os.path.join(self.model["path"],"pts", img)))
imageio.mimsave(os.path.join(self.model["path"],"points.gif"), images, duration=duration)
def GIF_PIL(self,duration=80):
from PIL import Image, ImageOps
print("\nMake GIFs")
images = []
for img in sorted(os.listdir(os.path.join(self.model["path"], "img"))):
im=Image.open(os.path.join(self.model["path"], "img", img))
# im.show()
# im=ImageOps.crop(im,(80,70,0,100))
# im.show()
images.append(im)
images[0].save(os.path.join(self.model["path"], "scanning.gif"),
save_all=True, append_images=images[1:], optimize=True, duration=duration, loop=0)
images = []
for img in sorted(os.listdir(os.path.join(self.model["path"], "pts"))):
images.append(Image.open(os.path.join(self.model["path"], "pts", img)))
images[0].save(os.path.join(self.model["path"], "points.gif"),
save_all=True, append_images=images[1:], optimize=True, duration=duration, loop=0)
def gtVisualize(self,interactive=False,size=(500,500),lw=0.0,texture=False,duration=80):
print("\nvisualize Mesh")
images = []
name = "gt"
sensor = self.model["scanner_pos"]
mesh = vedo.Mesh(self.model["mesh"], c=[90, 90, 90])
if lw > 0.0:
mesh.lineWidth(lw=lw)
name+=str(lw)
if texture:
# mesh.texture(os.path.join(self.path, "0", "DefaultMaterial_baseColor.jpg"))
mesh.texture(self.model["texture"])
name+="_tex"
brightness = 1.0
else:
brightness = 2.5
# mesh=mesh.compute_normals().phong()
os.makedirs(os.path.join(self.model["path"], "gt"), exist_ok=True)
for i in tqdm(range(self.steps)):
R = trimesh.transformations.rotation_matrix(self.factor*math.pi/180, [0, 0, 1])
mesh.apply_transform(R)
sensor=sensor@R[:3,:3]
plt = vedo.Plotter(axes=0, offscreen=np.invert(interactive))
self.setCamera(plt)
plt += mesh
plt += vedo.Light(sensor, c='w', intensity=brightness)
plt.show(interactive=interactive, size=size, axes=0, roll=0,
azimuth=-i * self.factor, resetcam=False)
vedo.io.screenshot(os.path.join(self.model["path"], "gt", "img{:03d}.jpg".format(i)))
plt.close()
images.append(Image.open(os.path.join(self.model["path"], "gt", "img{:03d}.jpg".format(i))))
# images.append(imageio.imread(os.path.join(self.model["path"],"mesh", "img{:03d}.png".format(i))))
images[0].save(os.path.join(self.model["path"], name+".gif"),
save_all=True, append_images=images[1:], optimize=True, duration=duration, loop=0)
# imageio.mimsave(os.path.join(self.model["path"],"points.gif"), images, duration=duration)
def meshVisualize(self,interactive=False,size=(500,500),lw=0.0,texture=False,duration=80):
print("\nvisualize Mesh")
images = []
name = "mesh"
sensor = self.model["scanner_pos"]
mesh = vedo.Mesh(os.path.join(self.model["path"], "pointcloud_rt_5.0.ply"), c=[90, 90, 90])
mesh.compute_normals().phong()
if lw > 0.0:
mesh.lineWidth(lw=lw)
name+=str(lw)
if texture:
mesh.texture(self.model["texture"])
# mesh.texture(os.path.join(self.path, "0", "bunny1ww.jpg"))
name+="_tex"
os.makedirs(self.model["mesh"], exist_ok=True)
for i in tqdm(range(self.steps)):
vedo.settings.useDepthPeeling = True
R = trimesh.transformations.rotation_matrix(self.factor*math.pi/180, [0, 0, 1])
mesh.apply_transform(R)
sensor=sensor@R[:3,:3]
plt = vedo.Plotter(axes=0, offscreen=np.invert(interactive))
self.setCamera(plt)
plt += mesh
plt += vedo.Light(sensor, c='w', intensity=2.5)
plt.show(interactive=interactive, size=size, axes=0, roll=0,
azimuth=-i * self.factor, resetcam=False)
vedo.io.screenshot(os.path.join(self.model["path"], "mesh", "img{:03d}.png".format(i)))
plt.close()
images.append(Image.open(os.path.join(self.model["path"], "mesh", "img{:03d}.png".format(i))))
# images.append(imageio.imread(os.path.join(self.path,"mesh", "img{:03d}.png".format(i))))
images[0].save(os.path.join(self.model["path"], name+".gif"),
save_all=True, append_images=images[1:], optimize=True, duration=duration, loop=0)
imageio.mimsave(os.path.join(self.model["path"],"points.gif"), images, duration=duration)
def pcVisualize(self,interactive=False,size=(500,500),with_mesh=False,type="range",duration=80):
print("\nvisualize Pointcloud")
images = []
# sensor = np.array([-0.85,-0.85,0.80])
sensor = self.model["scanner_pos"]
if with_mesh:
mesht = vedo.Mesh(self.model["mesh"])
# mesh.texture(os.path.join(self.path, "0", "CH_NPC_MOB_Elephant_A01_MI_GRN_baseColor.png"))
if self.model["texture"]:
mesht.texture(self.model["texture"])
mesht.lighting("default")
if type == "mvs":
pc = vedo.load(os.path.join(self.model["path"], "pointcloud_mvs.ply"))
else:
pc = vedo.load(os.path.join(self.model["path"], "pointcloud.ply"))
pc = vedo.Points(pc, c=[93, 169, 227], r=6).lighting("default")
pc.compute_normals_with_pca()
name = "pc_mesh" if with_mesh else "pc"
os.makedirs(os.path.join(self.model["path"], name), exist_ok=True)
for i in tqdm(range(self.steps)):
R = trimesh.transformations.rotation_matrix(self.factor*math.pi/180, [0, 0, 1])
if with_mesh:
mesht.apply_transform(R)
pc.apply_transform(R)
sensor=sensor@R[:3,:3]
# pointcloud
plt = vedo.Plotter(axes=0, offscreen=np.invert(interactive))
self.setCamera(plt)
plt += pc
if with_mesh:
plt +=mesht
plt += vedo.Light(sensor, c='w', intensity=1.5)
plt.show(interactive=interactive, size=size, axes=0, roll=0,
azimuth=-i * self.factor, resetcam=False)
vedo.io.screenshot(os.path.join(self.model["path"], name, "img{:03d}.png".format(i)))
plt.close()
images.append(Image.open(os.path.join(self.model["path"], name, "img{:03d}.png".format(i))))
images[0].save(os.path.join(self.model["path"], name+".gif"),
save_all=True, append_images=images[1:], optimize=True, duration=duration, loop=0)
if __name__ == "__main__":
size=(600,600)
interactive=False
# sc=Scanner(alpha=15)
# sc=Scanner(alpha=60,n=100)
sc=Scanner(model="bunny",steps=360)
sc.scan(n_rays=100)
sc.scanVisualize(interactive=interactive,size=size,texture=True,trail=(20,5))
# sc.scanMVS(n_points=3000)
# sc.scanVisualizeMVS(interactive=interactive,size=size,texture=False)
sc.pointsVisualize(interactive=False,size=size,with_mesh=True)
# sc.pointsVisualize(interactive=False,size=size,with_mesh=False)
# sc.pcVisualize(interactive=False,size=size,with_mesh=True, type="mvs")
# sc.pcVisualize(with_mesh=False,type="mvs")
# sc.gtVisualize(interactive=interactive,size=size,lw=0.0,texture=False)
# sc.gtVisualize(interactive=interactive,size=size,lw=0.0,texture=True)
# sc.meshVisualize(interactive=interactive,size=size,lw=0.0,texture=False)
# sc.meshVisualize(interactive=interactive,size=size,lw=0.15)