import numpy as np
from math import acos
from math import sqrt
from math import pi
import colorsys
import cv2
opt = lambda: None
opt.spp = 100
opt.width = 1280
opt.height = 720
opt.noise = False
opt.path_obj = 'content/dragon/dragon.obj'
# # # # # # # # # # # # # # # # # # # # # # # # #
visii.initialize(headless=True, verbose=True)
if not opt.noise is True:
visii.enable_denoiser()
camera = visii.entity.create(name="camera",
transform=visii.transform.create("camera"),
camera=visii.camera.create(
name="camera",
aspect=float(opt.width) / float(opt.height)))
camera.get_transform().look_at(
at=(0, 0.1, 0.1),
up=(0, 0, 1),
eye=(0, 3.0, 0.2),
)
import visii
import noise
import random
import numpy as np
opt = lambda: None
opt.spp = 400
opt.width = 1920
opt.height = 1080
opt.noise = False
opt.out = '15_camera_motion_car_blur.png'
opt.control = True
# # # # # # # # # # # # # # # # # # # # # # # # #
visii.initialize()
visii.set_dome_light_intensity(.8)
visii.resize_window(int(opt.width), int(opt.height))
# # # # # # # # # # # # # # # # # # # # # # # # #
# load the textures
dome = visii.texture.create_from_file("dome", "content/teatro_massimo_2k.hdr")
# we can add HDR images to act as dome
visii.set_dome_light_texture(dome)
visii.set_dome_light_rotation(
visii.angleAxis(visii.pi() * .5, visii.vec3(0, 0, 1)))
car_speed = 0
car_speed_x = car_speed
car_speed_y = -2 * car_speed
opt = lambda: None
opt.spp = 512
opt.width = 500
opt.height = 500
opt.outf = "09_metadata"
# # # # # # # # # # # # # # # # # # # # # # # # #
if os.path.isdir(opt.outf):
print(f'folder {opt.outf}/ exists')
else:
os.mkdir(opt.outf)
print(f'created folder {opt.outf}/')
# # # # # # # # # # # # # # # # # # # # # # # # #
visii.initialize(headless=False, verbose=True, lazy_updates=True)
visii.enable_denoiser()
camera = visii.entity.create(name="camera",
transform=visii.transform.create("camera"),
camera=visii.camera.create(
name="camera",
aspect=float(opt.width) / float(opt.height)))
camera.get_transform().look_at(at=(0, 0, 0), up=(0, 0, 1), eye=(0, 1, 1))
visii.set_camera_entity(camera)
# # # # # # # # # # # # # # # # # # # # # # # # #
# Create a scene to use for exporting segmentations
opt.nb_objs = 10000
opt.spp = 16
opt.width = 1920
opt.height = 1080
opt.out = '02_random_scene.png'
# visii uses sets of components to represent a scene.
# We can increase the max component limit here if necessary.
# In this case, we'll need 16 meshes, a material for each object,
# and finally a transform for each object as well as one more for the camera.
visii.initialize(
headless=True,
verbose=True,
lazy_updates=True,
max_entities=opt.nb_objs + 1,
max_transforms=opt.nb_objs + 1,
max_materials=opt.nb_objs,
max_meshes=16
# these are also available
# max_lights, max_textures, & max_cameras
)
# Turn on the denoiser
visii.enable_denoiser()
# Create a camera
camera = visii.entity.create(name="camera",
transform=visii.transform.create("camera"),
camera=visii.camera.create(
name="camera",
aspect=float(opt.width) / float(opt.height)))
def f(frame_ids):
# headless - no window
# verbose - output number of frames rendered, etc..
visii.initialize(headless=True, verbose=False)
# Use a neural network to denoise ray traced
visii.enable_denoiser()
# set up dome background
negatives = list(glob.glob("negatives/*.jpg"))
visii.set_dome_light_intensity(1)
# create an entity that will serve as our camera.
camera = visii.entity.create(name="camera")
# To place the camera into our scene, we'll add a "transform" component.
# (All visii objects have a "name" that can be used for easy lookup later.)
camera.set_transform(visii.transform.create(name="camera_transform"))
# To make our camera entity act like a "camera", we'll add a camera component
camera.set_camera(
visii.camera.create_from_fov(
name="camera_camera",
field_of_view=1.4, # note, this is in radians
aspect=opt.width / float(opt.height)))
# Finally, we'll select this entity to be the current camera entity.
# (visii can only use one camera at the time)
visii.set_camera_entity(camera)
# lets store the camera look at information so we can export it
camera_struct_look_at = {
'at': [0, 0, 0],
'up': [0, 0, 1],
'eye': [-1, 0, 0]
}
# Lets set the camera to look at an object.
# We'll do this by editing the transform component.
camera.get_transform().look_at(at=camera_struct_look_at['at'],
up=camera_struct_look_at['up'],
eye=camera_struct_look_at['eye'])
# This function loads a mesh ignoring .mtl
mesh = visii.mesh.create_from_file(opt.entity, opt.model)
# creates visii entity using loaded mesh
obj_entity = visii.entity.create(
name=opt.entity + "_entity",
mesh=mesh,
transform=visii.transform.create(opt.entity + "_entity"),
material=visii.material.create(opt.entity + "_entity"),
)
# obj_entity.get_light().set_intensity(0.05)
# you can also set the light color manually
# obj_entity.get_light().set_color((1,0,0))
# Add texture to the material
material = visii.material.get(opt.entity + "_entity")
texture = visii.texture.create_from_file(opt.entity, "./models/Cutie.PNG")
material.set_base_color_texture(texture)
# Lets add the cuboid to the object we want to export
add_cuboid(opt.entity + "_entity", opt.debug)
# lets keep track of the entities we want to export
entities_to_export = [opt.entity + "_entity"]
# Loop where we change and render each frame
for i in tqdm(frame_ids):
# load a random negtive onto the dome
negative = cv2.imread(random.choice(negatives))
# Skip dark backgrounds (20/255)
if np.mean(negative) < 20:
continue
# Fix lighting of background and make it small within the FOV
background = make_background(negative)
cv2.imwrite("test" + str(i) + ".png", background)
dome = visii.texture.create_from_file("dome", "test" + str(i) + ".png")
visii.set_dome_light_texture(dome)
visii.set_dome_light_rotation(
visii.angleAxis(visii.pi() * .5, visii.vec3(0, 0, 1)))
stretch_factor = 2
scale = [
random.uniform(1, stretch_factor), # width
random.uniform(1, stretch_factor), # length
random.uniform(1, stretch_factor) # height
]
obj_entity.get_transform().set_scale(scale)
# create random rotation while making usre the entity is facing forward in each frame
rot = [
random.uniform(-10, 10), # Roll
random.uniform(-15, 15), # Pitch
random.uniform(-45, 45) # Yaw
]
q = Quaternion.from_euler(rot[0], rot[1], rot[2], degrees=True)
position = [
random.uniform(0, 4), # X Depth
random.uniform(-1, 1), # Y
random.uniform(-1, 1) # Z
]
# Scale the position based on depth into image to make sure it remains in frame
position[1] *= position[0]
position[2] *= position[0]
obj_entity.get_transform().set_position(tuple(position))
obj_entity.get_transform().set_rotation((q.x, q.y, q.z, q.w))
# use random to make 95 % probability the frame data goes into training and
# 5% chance it goes in test folder
folder = ''
if random.randint(0, 100) < opt.test_percent:
folder = opt.entity + '_test/'
else:
folder = opt.entity + '_training/'
# Render the scene
visii.render_to_file(width=opt.width,
height=opt.height,
samples_per_pixel=opt.spp,
file_path=opt.out + folder + opt.entity + str(i) +
'.png')
# set up JSON
export_to_ndds_file(filename=opt.out + folder + opt.entity + str(i) +
'.json',
obj_names=entities_to_export,
width=opt.width,
height=opt.height,
camera_struct=camera_struct_look_at)
# remove current negative from the dome
visii.clear_dome_light_texture()
visii.texture.remove("dome")
os.remove("test" + str(i) + ".png")
visii.deinitialize()