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move functions

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luni
2023-01-16 16:38:08 +00:00
parent 4dee8e4106
commit b17baf157b
3 changed files with 56 additions and 45 deletions
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data/dataset.py
+1 -1
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@@ -19,7 +19,7 @@ from torch.utils.data import Dataset
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
from data.render import Render, DepthRenderer
from superSMPL.IO import readPC2Frame, writePC2
from utils.data_utils import Frame, read_frames_file, mask_faces, get_normals_from_depth
from utils.data_utils import Frame, read_frames_file, mask_faces, get_normals_from_depth, get_camera
from cloth3d.read import DataReader
from cloth3d.util import quads2tris
data/render.py
+5 -43
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@@ -10,7 +10,7 @@ from deodr.pytorch.differentiable_renderer_pytorch import Scene3DPytorch, Camera
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
from cloth3d.util import proj
from utils.data_utils import intrinsic, extrinsic, get_camera
class RenderCore:
def __init__(self, max_depth=0, depth_scale=1):
@@ -70,45 +70,6 @@ class RenderCore:
return depth
""" CAMERA """
def intrinsic():
RES_X = 640
RES_Y = 480
f_mm = 50 # blender default
sensor_w_mm = 36 # blender default
sensor_h_mm = sensor_w_mm * RES_Y / RES_X
fx_px = f_mm * RES_X / sensor_w_mm
fy_px = f_mm * RES_Y / sensor_h_mm
u = RES_X / 2
v = RES_Y / 2
return np.array([[fx_px, 0, u],
[0, fy_px, v],
[0, 0, 1]], np.float32)
def extrinsic(camLoc):
R_w2bc = np.array([[0, 1, 0],
[0, 0, 1],
[1, 0, 0]], np.float32)
T_w2bc = -1 * R_w2bc.dot(camLoc)
R_bc2cv = np.array([[1, 0, 0],
[0, -1, 0],
[0, 0, -1]], np.float32)
R_w2cv = R_bc2cv.dot(R_w2bc)
T_w2cv = R_bc2cv.dot(T_w2bc)
return np.concatenate((R_w2cv, T_w2cv[:,None]), axis=1)
def proj(camLoc):
return intrinsic().dot(extrinsic(camLoc))
class Render:
def __init__(self, max_depth=0, depth_scale=1, size=(640, 480)):
self.render_core = RenderCore(max_depth=max_depth)
@@ -161,16 +122,17 @@ class DepthRenderer(nn.Module):
# R, T = look_at_view_transform(2.7, 0, 0)
# cameras = FoVPerspectiveCameras(device='cuda', R=R, T=T)
ext = torch.tensor(extrinsic(cam).astype(np.float32), device='cuda')
# ext = torch.tensor(extrinsic(cam).astype(np.float32), device='cuda')
# ext = proj(cam)
# fp = np.array([240 * (480 / 36), 320 * (640 / 36)], dtype=np.float32)
img_size = np.array([480, 640], dtype=np.float32)
# K = np.eye(4, dtype=np.float32)
# K[:3,:3] = intrinsic()
K = torch.tensor(intrinsic()[None, :, :], device='cuda')
# K = torch.tensor(intrinsic()[None, :, :], device='cuda')
# cameras = PerspectiveCameras(device='cuda', R=ext[None, :, :3], T=ext[None, :, 3], focal_length=1,)# principal_point=fp[None, :], image_size=img_size[None, :])
cameras = cameras_from_opencv_projection(R=ext[None, :, :3], tvec=ext[None, :, 3], camera_matrix=K, image_size=torch.tensor(img_size[None, :], device='cuda'))
# cameras = cameras_from_opencv_projection(R=ext[None, :, :3], tvec=ext[None, :, 3], camera_matrix=K, image_size=torch.tensor(img_size[None, :], device='cuda'))
cameras, K = get_camera(cam, [480, 640], verts.get_device())
raster_settings = RasterizationSettings(
image_size=(480, 640),
utils/data_utils.py
+49
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@@ -5,6 +5,7 @@ import torch
import numpy as np
import scipy.io as sio
from math import cos, sin
from pytorch3d.utils import cameras_from_opencv_projection
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
@@ -59,3 +60,51 @@ def get_normals_from_depth(depth_image, depth_mask, camera_intrinsic):
def center_crop_resize(img, new_center, size):
pass
""" CAMERA """
def intrinsic():
RES_X = 640
RES_Y = 480
f_mm = 50 # blender default
sensor_w_mm = 36 # blender default
sensor_h_mm = sensor_w_mm * RES_Y / RES_X
fx_px = f_mm * RES_X / sensor_w_mm
fy_px = f_mm * RES_Y / sensor_h_mm
u = RES_X / 2
v = RES_Y / 2
return np.array([[fx_px, 0, u],
[0, fy_px, v],
[0, 0, 1]], np.float32)
def extrinsic(camLoc):
R_w2bc = np.array([[0, 1, 0],
[0, 0, 1],
[1, 0, 0]], np.float32)
T_w2bc = -1 * R_w2bc.dot(camLoc)
R_bc2cv = np.array([[1, 0, 0],
[0, -1, 0],
[0, 0, -1]], np.float32)
R_w2cv = R_bc2cv.dot(R_w2bc)
T_w2cv = R_bc2cv.dot(T_w2bc)
return np.concatenate((R_w2cv, T_w2cv[:,None]), axis=1)
def proj(camLoc):
return intrinsic().dot(extrinsic(camLoc))
def get_camera(cam, img_size, device):
size = np.array(img_size, dtype=np.float32)
ext = torch.tensor(extrinsic(cam).astype(np.float32), device=device)
K = torch.tensor(intrinsic()[None, :, :], device=device)
cameras = cameras_from_opencv_projection(R=ext[None, :, :3], tvec=ext[None, :, 3], camera_matrix=K, image_size=torch.tensor(size[None, :], device=device))
return cameras, K