fix regression in colmap2nerf.py that caused it to fail to find the center of...

fix regression in colmap2nerf.py that caused it to fail to find the center of the cameras point of interest, due to a sign-flip in the closest-point-of-approach function. this should make it more robust at correctly centering new datasets

scripts/colmap2nerf.py

@@ -29,7 +29,7 @@ def parse_args():
 	parser.add_argument("--colmap_matcher", default="sequential", choices=["exhaustive","sequential","spatial","transitive","vocab_tree"], help="select which matcher colmap should use. sequential for videos, exhaustive for adhoc images")
 	parser.add_argument("--colmap_db", default="colmap.db", help="colmap database filename")
 	parser.add_argument("--images", default="images", help="input path to the images")
-	parser.add_argument("--text", default="text", help="input path to the colmap text files (set automatically if run_colmap is used)")
+	parser.add_argument("--text", default="colmap_text", help="input path to the colmap text files (set automatically if run_colmap is used)")
 	parser.add_argument("--aabb_scale", default=16, choices=["1","2","4","8","16"], help="large scene scale factor. 1=scene fits in unit cube; power of 2 up to 16")
 	parser.add_argument("--skip_early", default=0, help="skip this many images from the start")
 	parser.add_argument("--out", default="transforms.json", help="output path")
@@ -132,9 +132,9 @@ def closest_point_2_lines(oa, da, ob, db): # returns point closest to both rays
 	t=ob-oa
 	ta=np.linalg.det([t,db,c])/(denom+1e-10)
 	tb=np.linalg.det([t,da,c])/(denom+1e-10)
-	if ta<0:
+	if ta>0:
 		ta=0
-	if tb<0:
+	if tb>0:
 		tb=0
 	return (oa+ta*da+ob+tb*db)*0.5,denom
 
@@ -214,7 +214,7 @@ if __name__ == "__main__":
 			"h":h,
 			"aabb_scale":AABB_SCALE,"frames":[]
 		}
-		centroid=np.zeros(3)
+
 		up=np.zeros(3)
 		for line in f:
 			line=line.strip()
@@ -243,31 +243,20 @@ if __name__ == "__main__":
 				c2w=c2w[[1,0,2,3],:] # swap y and z
 				c2w[2,:] *= -1 # flip whole world upside down
 
-				centroid += c2w[0:3,3]
 				up += c2w[0:3,1]
 
 				frame={"file_path":name,"sharpness":b,"transform_matrix": c2w}
 				out["frames"].append(frame)
 	nframes = len(out["frames"])
-	centroid *= 1/nframes
 	up = up / np.linalg.norm(up)
 	print("up vector was ", up)
 	R=rotmat(up,[0,0,1]) # rotate up vector to [0,0,1]
 	R=np.pad(R,[0,1])
 	R[-1,-1]=1
-	avglen=0.
-	for f in out["frames"]:
-		f["transform_matrix"][0:3,3]-=centroid
-		avglen+=np.linalg.norm(f["transform_matrix"][0:3,3])
-	avglen/=nframes
-	print("avg camera distance from origin ", avglen)
+
 
 	for f in out["frames"]:
-		#print(f["transform_matrix"])
-		f["transform_matrix"][0:3,3]*=3./avglen     # scale to "nerf sized"
 		f["transform_matrix"]=np.matmul(R,f["transform_matrix"]) # rotate up to be the z axis
-		# f["transform_matrix"][2,3]+=0.5 # shift up a bit as cameras under ground are rare
-		#print(f["transform_matrix"])
 
 	# find a central point they are all looking at
 	print("computing center of attention...")
@@ -286,6 +275,14 @@ if __name__ == "__main__":
 	for f in out["frames"]:
 		f["transform_matrix"][0:3,3]-=totp
 
+	avglen=0.
+	for f in out["frames"]:
+		avglen+=np.linalg.norm(f["transform_matrix"][0:3,3])
+	avglen/=nframes
+	print("avg camera distance from origin ", avglen)
+	for f in out["frames"]:
+		f["transform_matrix"][0:3,3]*=4./avglen     # scale to "nerf sized"
+
 	for f in out["frames"]:
 		f["transform_matrix"]=f["transform_matrix"].tolist()
 	print(nframes,"frames")