move functions

data/dataset.py

@@ -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

@@ -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

@@ -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__)) + '/..')
@@ -58,4 +59,52 @@ def get_normals_from_depth(depth_image, depth_mask, camera_intrinsic):
 
 
 def center_crop_resize(img, new_center, size):
-    pass
+    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