move functions

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