render transparent background pngs?

[felixgeen]

Hello,

I was wondering if there is any way to render out transparent images from the NGP? I am using the render.py script which outputs JPG with black background by default. It would be amazing to render without a solid colour background.

Best,
Felix

[felixgeen]

I have managed to modify the render script to output PNG but I cannot get it to work with transparent backgrounds, it just renders black background PNGs. Any help would be much appreciated, here is the script so far (I used ChatGPT to get some help haha):

import os, sys, shutil
import argparse
from tqdm import tqdm
from PIL import Image

import common
import pyngp as ngp # noqa
import numpy as np

def write_image(path, image, quality=100):
	image = np.maximum(np.minimum(image, 1.0), 0.0)
	image = (image * 255).astype(np.uint8)
	image = Image.fromarray(image)
	save_options = {'quality': quality, 'transparency': 0}
	image.save(path, format='PNG', **save_options)

def render_video(resolution, numframes, scene, name, bb, spp, fps, exposure=0):
	testbed = ngp.Testbed(ngp.TestbedMode.Nerf)
	# testbed.load_snapshot("data/toy/base.msgpack")
	# testbed.load_camera_path("data/toy/base_cam.json")
	testbed.load_snapshot(os.path.join(scene, "base.msgpack"))
	testbed.load_camera_path(os.path.join(scene, "base_cam.json"))
    
	if bb:
		bbs = bb.split(",")
		testbed.render_aabb = ngp.BoundingBox([bbs[0], bbs[1], bbs[2]], [bbs[3], bbs[4], bbs[5]])
    
	if 'temp' in os.listdir():
		shutil.rmtree('temp')
	os.makedirs('temp')	
    
	for i in tqdm(list(range(min(numframes,numframes+1))), unit="frames", desc=f"Rendering"):
		testbed.camera_smoothing = i > 0
		frame = testbed.render(resolution[0], resolution[1], spp, True, float(i)/numframes, float(i + 1)/numframes, fps, shutter_fraction=0.5)
		common.write_image(f"temp/{i:04d}.png", np.clip(frame * 2**exposure, 0.0, 1.0), quality=100)

	os.system(f"ffmpeg -r {fps} -i temp/%04d.png -c:v libx264 -pix_fmt yuv420p {name}.mp4")
#	shutil.rmtree('temp')


def parse_args():
	parser = argparse.ArgumentParser(description="render neural graphics primitives testbed, see documentation for how to")
	parser.add_argument("--scene", "--training_data", default="", help="The scene to load. Can be the scene's name or a full path to the training data.")

	parser.add_argument("--width", "--screenshot_w", type=int, default=1920, help="Resolution width of the render video")
	parser.add_argument("--height", "--screenshot_h", type=int, default=1080, help="Resolution height of the render video")
	parser.add_argument("--n_seconds", type=int, default=1, help="Number of steps to train for before quitting.")
	parser.add_argument("--fps", type=int, default=60, help="number of fps")
	parser.add_argument("--bounding_box", type=str, default="", help="Bounding box info, x,y,z min, x,y,z max. Ensure that min greater than max")
	parser.add_argument("--render_name", type=str, default="", help="name of the result video")
    
	args = parser.parse_args()
	return args

if __name__ == "__main__":
	args = parse_args()	

	render_video([args.width, args.height], args.n_seconds*args.fps, args.scene, args.render_name, args.bounding_box, spp=8, fps=args.fps)

[Tom94]

testbed.background_color = [0.0, 0.0, 0.0, 0.0] should do the trick.

[felixgeen]

That works! thank you so much!

Script for transparent PNGs:

#!/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 os, sys, shutil
import argparse
from tqdm import tqdm
from PIL import Image

import common
import pyngp as ngp # noqa
import numpy as np

def write_image(path, image, quality=100):
	image = np.maximum(np.minimum(image, 1.0), 0.0)
	image = (image * 255).astype(np.uint8)
	image = Image.fromarray(image)
	save_options = {'quality': quality, 'transparency': 0}
	image.save(path, format='PNG', **save_options)

def render_video(resolution, numframes, scene, name, bb, spp, fps, exposure=0):
	testbed = ngp.Testbed(ngp.TestbedMode.Nerf)
	# testbed.load_snapshot("data/toy/base.msgpack")
	# testbed.load_camera_path("data/toy/base_cam.json")
	testbed.load_snapshot(os.path.join(scene, "base.msgpack"))
	testbed.load_camera_path(os.path.join(scene, "base_cam.json"))
	testbed.background_color = [0.0, 0.0, 0.0, 0.0]
    
	if bb:
		bbs = bb.split(",")
		testbed.render_aabb = ngp.BoundingBox([bbs[0], bbs[1], bbs[2]], [bbs[3], bbs[4], bbs[5]])
    
	if 'temp' in os.listdir():
		shutil.rmtree('temp')
	os.makedirs('temp')	
    
	for i in tqdm(list(range(min(numframes,numframes+1))), unit="frames", desc=f"Rendering"):
		testbed.camera_smoothing = i > 0
		frame = testbed.render(resolution[0], resolution[1], spp, True, float(i)/numframes, float(i + 1)/numframes, fps, shutter_fraction=0.5)
		common.write_image(f"temp/{i:04d}.png", np.clip(frame * 2**exposure, 0.0, 1.0), quality=100)

	os.system(f"ffmpeg -r {fps} -i temp/%04d.png -c:v libx264 -pix_fmt yuv420p {name}.mp4")
#	shutil.rmtree('temp')


def parse_args():
	parser = argparse.ArgumentParser(description="render neural graphics primitives testbed, see documentation for how to")
	parser.add_argument("--scene", "--training_data", default="", help="The scene to load. Can be the scene's name or a full path to the training data.")

	parser.add_argument("--width", "--screenshot_w", type=int, default=1920, help="Resolution width of the render video")
	parser.add_argument("--height", "--screenshot_h", type=int, default=1080, help="Resolution height of the render video")
	parser.add_argument("--n_seconds", type=int, default=1, help="Number of steps to train for before quitting.")
	parser.add_argument("--fps", type=int, default=60, help="number of fps")
	parser.add_argument("--bounding_box", type=str, default="", help="Bounding box info, x,y,z min, x,y,z max. Ensure that min greater than max")
	parser.add_argument("--render_name", type=str, default="", help="name of the result video")
    
	args = parser.parse_args()
	return args

if __name__ == "__main__":
	args = parse_args()	

	render_video([args.width, args.height], args.n_seconds*args.fps, args.scene, args.render_name, args.bounding_box, spp=8, fps=args.fps)