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dataset.py
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import numpy as np
from imageio import imread
from math import cos, sin, pi
import os
from util import flip_image
def load_dataset(name):
if name == 'tentacle':
return TentacleDataset()
elif name in ('cat', 'frog', 'hippo', 'lizard', 'pig', 'scholar',
'turtle'):
return PSDataset(name)
elif name in ('Adirondack', 'Backpack', 'Bicycle1', 'Cable', 'Classroom1',
'Couch', 'Flowers', 'Jadeplant', 'Mask', 'Motorcycle',
'Piano', 'Pipes', 'Playroom', 'Playtable', 'Recycle',
'Shelves', 'Shopvac', 'Sticks', 'Storage', 'Sword1',
'Sword2', 'Umbrella', 'Vintage'):
return MiddleburyDataset(name)
else:
raise Exception('{0} is not a valid dataset name'.format(name))
class TentacleDataset(object):
def __init__(self):
num_views = 9
self.albedo_png = 'output/tentacle_albedo.png'
self.normals_png = 'output/tentacle_normals.png'
self.normals_npy = 'output/tentacle_normals.npy'
self.ncc_png = 'output/tentacle_ncc.png'
self.depth_npy = 'output/tentacle_depth.npy'
self.mesh_ply = 'output/tentacle_mesh_{0}.ply'
self.ncc_temp = 'temp/tentacle_ncc-%03d.png'
self.ncc_gif = 'output/tentacle_ncc.gif'
self.projected_temp = 'temp/tentacle_projected-%03d.png'
self.projected_gif = 'output/tentacle_projected.gif'
self.stereo_downscale_factor = 3
self.mesh_downscale_factor = 0
self.ncc_size = 5
self.depth_weight = 1
self.min_depth = 24
self.max_depth = 45
self.depth_layers = 128
self.chessboard_dims = (5, 5)
self.right = list(map(flip_image, [
np.float32(imread('input/right/%04d.png' % (i + 1)))
for i in range(num_views)]))
self.left = list(map(flip_image, [
np.float32(imread('input/left/%04d.png' % (i + 1)))
for i in range(num_views)]))
self.height = 1920
self.width = 1080
self.left_alpha = self.left[0][:, :, 3]
self.right_alpha = self.right[0][:, :, 3]
for image in self.right:
assert image.shape == (self.height, self.width, 4)
assert (self.right_alpha == image[:, :, 3]).all()
for image in self.left:
assert image.shape == (self.height, self.width, 4)
assert (self.left_alpha == image[:, :, 3]).all()
self.left = list(map(lambda x: x[:, :, :3], self.left))
self.right = list(map(lambda x: x[:, :, :3], self.right))
rotations = (
(0, 0),
(0, 15),
(0, -15),
(-15, 15),
(-15, 15),
(15, 15),
(15, -15),
(15, 0),
(-15, 0),
)
assert len(rotations) == num_views
lights = []
for rotx, roty in rotations:
direction = np.array(((0.0, ), (0.0, ), (1.0, )))
radians_x = rotx / 180.0 * pi
new_x = direction[1] * cos(radians_x) - \
direction[2] * sin(radians_x)
new_y = direction[1] * sin(radians_x) + \
direction[2] * cos(radians_x)
direction[1] = new_x
direction[2] = new_y
radians_y = roty / 180.0 * pi
new_z = direction[2] * cos(radians_y) - \
direction[0] * sin(radians_y)
new_x = direction[2] * sin(radians_y) + \
direction[0] * cos(radians_y)
direction[2] = new_z
direction[0] = new_x
lights.append(direction)
self.lights = np.hstack(lights)
self.K_left = np.array((
(2100, 0, self.width / 2),
(0, 2100, self.height / 2),
(0, 0, 1)
))
self.K_right = np.array((
(2100, 0, self.width / 2),
(0, 2100, self.height / 2),
(0, 0, 1)
))
calib = np.load('input/calibration.npz')
self.Rt_right = calib['Rt_right']
self.Rt_left = calib['Rt_left']
class PSDataset(object):
def __init__(self, name):
num_views = 20
self.albedo_png = 'output/{0}_albedo.png'.format(name)
self.normals_png = 'output/{0}_normals.png'.format(name)
self.normals_npy = 'output/{0}_normals.npy'.format(name)
self.mesh_ply = 'output/' + name + '_mesh_{0}.ply'
self.mesh_downscale_factor = 1
self.right = [
np.dstack(3 * [
np.float32(
imread(
os.path.join('data', 'PSData', name, 'Objects',
'Image_%02d.png' % (i + 1))
))[:, :, np.newaxis]])
for i in range(num_views)
]
self.height = self.right[0].shape[0]
self.width = self.right[0].shape[1]
self.right_alpha = np.ones((self.height, self.width), dtype=np.float32)
for image in self.right:
assert image.shape == (self.height, self.width, 3)
self.lights = np.loadtxt(
os.path.join('data', 'PSData', name, 'light_directions.txt'))
class MiddleburyDataset(object):
def __init__(self, name):
num_views = 1
self.ncc_png = 'output/{0}_ncc.png'.format(name)
self.depth_npy = 'output/{0}_depth.npy'.format(name)
self.ncc_temp = 'temp/{0}_ncc-%03d.png'.format(name)
self.ncc_gif = 'output/{0}_ncc.gif'.format(name)
self.projected_temp = 'temp/{0}_projected-%03d.png'.format(name)
self.projected_gif = 'output/{0}_projected.gif'.format(name)
self.mesh_ply = 'output/' + name + '_mesh_{0}.ply'
self.stereo_downscale_factor = 4
self.mesh_downscale_factor = 1
self.ncc_size = 5
base = os.path.join('data', '{0}-perfect'.format(name))
self.right = [np.float32(imread(os.path.join(base, 'im0.png')))]
self.left = [np.float32(imread(os.path.join(base, 'im1.png')))]
calib = {}
calib_filename = os.path.join(base, 'calib.txt')
with open(calib_filename, 'r') as f:
for line in f:
name, value = line.strip().split('=')
calib[name] = value
baseline = float(calib['baseline'])
doffs = float(calib['doffs'])
vmin = float(calib['vmin'])
vmax = float(calib['vmax'])
self.K_left = self._parse_K(calib['cam1'])
self.K_right = self._parse_K(calib['cam0'])
self.min_depth = baseline * self.K_left[0, 0] / (vmax + doffs)
self.max_depth = baseline * self.K_left[0, 0] / (vmin + doffs)
self.depth_layers = 128
self.height = int(calib['height'])
self.width = int(calib['width'])
self.Rt_left = np.zeros((3, 4), dtype=np.float32)
self.Rt_left[:, :3] = np.identity(3)
self.Rt_right = np.zeros((3, 4), dtype=np.float32)
self.Rt_right[:, :3] = np.identity(3)
self.Rt_right[0, 3] = float(calib['baseline'])
self.left_alpha = np.ones((self.height, self.width), dtype=np.float32)
self.right_alpha = np.ones((self.height, self.width), dtype=np.float32)
for image in self.right:
assert image.shape == (self.height, self.width, 3)
for image in self.left:
assert image.shape == (self.height, self.width, 3)
def _parse_K(self, string):
assert string[0] == '['
assert string[-1] == ']'
string = string[1:-1]
lines = string.split(';')
assert len(lines) == 3
lines = list(map(lambda x: list(map(float, x.strip().split())), lines))
return np.array(lines)