colmap2nerf.py

#!/usr/bin/env python3

# Copyright (c) 2020-2022, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

import argparse
import os
from pathlib import Path, PurePosixPath

import numpy as np
import json
import sys
import math
import cv2
import os
import shutil

def parse_args():
	parser = argparse.ArgumentParser(description="convert a text colmap export to nerf format transforms.json; optionally convert video to images, and optionally run colmap in the first place")

	parser.add_argument("--video_in", default="", help="run ffmpeg first to convert a provided video file into a set of images. uses the video_fps parameter also")
	parser.add_argument("--video_fps", default=2)
	parser.add_argument("--run_colmap", action="store_true", help="run colmap first on the image folder")
	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="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")
	args = parser.parse_args()
	return args

def do_system(arg):
	print(f"==== running: {arg}")
	err=os.system(arg)
	if err:
		print("FATAL: command failed")
		sys.exit(err)


def run_ffmpeg(args):
	if not os.path.isabs(args.images):
		args.images = os.path.join(os.path.dirname(args.video_in), args.images)
	images=args.images
	video=args.video_in
	fps=float(args.video_fps) or 1.0
	print(f"running ffmpeg with input video file={video}, output image folder={images}, fps={fps}.")
	if (input(f"warning! folder '{images}' will be deleted/replaced. continue? (Y/n)").lower().strip()+"y")[:1] != "y":
		sys.exit(1)
	try:
		shutil.rmtree(images)
	except:
		pass
	do_system(f"mkdir {images}")
	do_system(f"ffmpeg -i {video} -qscale:v 1 -qmin 1 -vf \"fps={fps}\" {images}/%04d.jpg")

def run_colmap(args):
	db=args.colmap_db
	images=args.images
	db_noext=str(Path(db).with_suffix(""))

	if args.text=="text":
		args.text=db_noext+"_text"
	text=args.text
	sparse=db_noext+"_sparse"
	print(f"running colmap with:\n\tdb={db}\n\timages={images}\n\tsparse={sparse}\n\ttext={text}")
	if (input(f"warning! folders '{sparse}' and '{text}' will be deleted/replaced. continue? (Y/n)").lower().strip()+"y")[:1] != "y":
		sys.exit(1)
	if os.path.exists(db):
		os.remove(db)
	do_system(f"colmap feature_extractor --ImageReader.camera_model OPENCV --ImageReader.single_camera 1 --database_path {db} --image_path {images}")
	do_system(f"colmap {args.colmap_matcher}_matcher --database_path {db}")
	try:
		shutil.rmtree(sparse)
	except:
		pass
	do_system(f"mkdir {sparse}")
	do_system(f"colmap mapper --database_path {db} --image_path {images} --output_path {sparse}")
	do_system(f"colmap bundle_adjuster --input_path {sparse}/0 --output_path {sparse}/0 --BundleAdjustment.refine_principal_point 1")
	try:
		shutil.rmtree(text)
	except:
		pass
	do_system(f"mkdir {text}")
	do_system(f"colmap model_converter --input_path {sparse}/0 --output_path {text} --output_type TXT")

def variance_of_laplacian(image):
	return cv2.Laplacian(image, cv2.CV_64F).var()

def sharpness(imagePath):
	image = cv2.imread(imagePath)
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	fm = variance_of_laplacian(gray)
	return fm

def qvec2rotmat(qvec):
	return np.array([
		[
			1 - 2 * qvec[2]**2 - 2 * qvec[3]**2,
			2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
			2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]
		], [
			2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
			1 - 2 * qvec[1]**2 - 2 * qvec[3]**2,
			2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]
		], [
			2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
			2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
			1 - 2 * qvec[1]**2 - 2 * qvec[2]**2
		]
	])

def rotmat(a, b):
	a, b = a / np.linalg.norm(a), b / np.linalg.norm(b)
	v = np.cross(a, b)
	c = np.dot(a, b)
	s = np.linalg.norm(v)
	kmat = np.array([[0, -v[2], v[1]], [v[2], 0, -v[0]], [-v[1], v[0], 0]])
	return np.eye(3) + kmat + kmat.dot(kmat) * ((1 - c) / (s ** 2 + 1e-10))

def closest_point_2_lines(oa, da, ob, db): # returns point closest to both rays of form o+t*d, and a weight factor that goes to 0 if the lines are parallel
	da=da/np.linalg.norm(da)
	db=db/np.linalg.norm(db)
	c=np.cross(da,db)
	denom=(np.linalg.norm(c)**2)
	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:
		ta=0
	if tb>0:
		tb=0
	return (oa+ta*da+ob+tb*db)*0.5,denom

if __name__ == "__main__":
	args = parse_args()
	if args.video_in != "":
		run_ffmpeg(args)
	if args.run_colmap:
		run_colmap(args)
	AABB_SCALE=int(args.aabb_scale)
	SKIP_EARLY=int(args.skip_early)
	IMAGE_FOLDER=args.images
	TEXT_FOLDER=args.text
	OUT_PATH=args.out
	print(f"outputting to {OUT_PATH}...")
	with open(os.path.join(TEXT_FOLDER,"cameras.txt"), "r") as f:
		angle_x=math.pi/2
		for line in f:
			# 1 SIMPLE_RADIAL 2048 1536 1580.46 1024 768 0.0045691
			# 1 OPENCV 3840 2160 3178.27 3182.09 1920 1080 0.159668 -0.231286 -0.00123982 0.00272224
			# 1 RADIAL 1920 1080 1665.1 960 540 0.0672856 -0.0761443
			if line[0]=="#":
				continue
			els=line.split(" ")
			w = float(els[2])
			h = float(els[3])
			fl_x = float(els[4])
			fl_y = float(els[4])
			k1 = 0
			k2 = 0
			p1 = 0
			p2 = 0
			cx = w/2
			cy = h/2
			if (els[1]=="SIMPLE_RADIAL"):
				cx = float(els[5])
				cy = float(els[6])
				k1 = float(els[7])
			elif (els[1]=="RADIAL"):
				cx = float(els[5])
				cy = float(els[6])
				k1 = float(els[7])
				k2 = float(els[8])
			elif (els[1]=="OPENCV"):
				fl_y = float(els[5])
				cx = float(els[6])
				cy = float(els[7])
				k1 = float(els[8])
				k2 = float(els[9])
				p1 = float(els[10])
				p2 = float(els[11])
			else:
				print("unknown camera model ", els[1])
			# fl = 0.5 * w / tan(0.5 * angle_x);
			angle_x= math.atan(w/(fl_x*2))*2
			angle_y= math.atan(h/(fl_y*2))*2
			fovx=angle_x*180/math.pi
			fovy=angle_y*180/math.pi

	print(f"camera:\n\tres={w,h}\n\tcenter={cx,cy}\n\tfocal={fl_x,fl_y}\n\tfov={fovx,fovy}\n\tk={k1,k2} p={p1,p2} ")

	with open(os.path.join(TEXT_FOLDER,"images.txt"), "r") as f:
		i=0
		bottom = np.array([0,0,0,1.]).reshape([1,4])
		out={
			"camera_angle_x":angle_x,
			"camera_angle_y":angle_y,
			"fl_x":fl_x,
			"fl_y":fl_y,
			"k1":k1,
			"k2":k2,
			"p1":p1,
			"p2":p2,
			"cx":cx,
			"cy":cy,
			"w":w,
			"h":h,
			"aabb_scale":AABB_SCALE,"frames":[]
		}

		up=np.zeros(3)
		for line in f:
			line=line.strip()
			if line[0]=="#":
				continue
			i=i+1
			if i < SKIP_EARLY*2:
				continue
			if  i%2==1 :
				elems=line.split(" ") # 1-4 is quat, 5-7 is trans, 9 is filename
				#name = str(PurePosixPath(Path(IMAGE_FOLDER, elems[9])))
				# why is this requireing a relitive path while using ^
				image_rel = os.path.relpath(IMAGE_FOLDER)
				name = str(f"./{image_rel}/{elems[9]}")
				b=sharpness(name)
				print(name, "sharpness=",b)
				image_id = int(elems[0])
				qvec = np.array(tuple(map(float, elems[1:5])))
				tvec = np.array(tuple(map(float, elems[5:8])))
				R = qvec2rotmat(-qvec)
				t = tvec.reshape([3,1])
				m = np.concatenate([np.concatenate([R, t], 1), bottom], 0)
				c2w = np.linalg.inv(m)
				c2w[0:3,2] *= -1 # flip the y and z axis
				c2w[0:3,1] *= -1
				c2w=c2w[[1,0,2,3],:] # swap y and z
				c2w[2,:] *= -1 # flip whole world upside down

				up += c2w[0:3,1]

				frame={"file_path":name,"sharpness":b,"transform_matrix": c2w}
				out["frames"].append(frame)
	nframes = len(out["frames"])
	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


	for f in out["frames"]:
		f["transform_matrix"]=np.matmul(R,f["transform_matrix"]) # rotate up to be the z axis

	# find a central point they are all looking at
	print("computing center of attention...")
	totw=0
	totp=[0,0,0]
	for f in out["frames"]:
		mf=f["transform_matrix"][0:3,:]
		for g in out["frames"]:
			mg=g["transform_matrix"][0:3,:]
			p,w=closest_point_2_lines(mf[:,3],mf[:,2],mg[:,3],mg[:,2])
			if w>0.01:
				totp+=p*w
				totw+=w
	totp/=totw
	print(totp) # the cameras are looking at totp
	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")
	print(f"writing {OUT_PATH}")
	with open(OUT_PATH, "w") as outfile:
		json.dump(out, outfile, indent=2)