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depthfusion.py
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depthfusion.py
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#!/usr/bin/env python
"""
Copyright 2019, Yao Yao, HKUST.
Convert MVSNet output to Gipuma format for post-processing.
"""
from __future__ import print_function
import argparse
import os
import time
import glob
import random
import math
import re
import sys
import shutil
from struct import *
sys.path.insert(0, '..')
from utils.utils import read_pfm, write_pfm
import cv2
import numpy as np
def read_gipuma_dmb(path):
'''read Gipuma .dmb format image'''
with open(path, "rb") as fid:
image_type = unpack('<i', fid.read(4))[0]
height = unpack('<i', fid.read(4))[0]
width = unpack('<i', fid.read(4))[0]
channel = unpack('<i', fid.read(4))[0]
array = np.fromfile(fid, np.float32)
array = array.reshape((width, height, channel), order="F")
return np.transpose(array, (1, 0, 2)).squeeze()
def write_gipuma_dmb(path, image):
'''write Gipuma .dmb format image'''
image_shape = np.shape(image)
width = image_shape[1]
height = image_shape[0]
if len(image_shape) == 3:
channels = image_shape[2]
else:
channels = 1
if len(image_shape) == 3:
image = np.transpose(image, (2, 0, 1)).squeeze()
with open(path, "wb") as fid:
# fid.write(pack(1))
fid.write(pack('<i', 1))
fid.write(pack('<i', height))
fid.write(pack('<i', width))
fid.write(pack('<i', channels))
image.tofile(fid)
return
def ucsnet_to_gipuma_dmb(in_path, out_path):
'''convert ucsnet .pfm output to Gipuma .dmb format'''
image, _ = read_pfm(in_path)
write_gipuma_dmb(out_path, image)
return
def ucsnet_to_gipuma_cam(in_path, out_path):
'''convert ucsnet camera to gipuma camera format'''
cam_txt = open(in_path).readlines()
f = lambda xs: list(map(lambda x: list(map(float, x.strip().split())), xs))
extr_mat = f(cam_txt[1:5])
intr_mat = f(cam_txt[7:10])
extr_mat = np.array(extr_mat, np.float32)
intr_mat = np.array(intr_mat, np.float32)
intrinsic = np.zeros((4, 4), np.float32)
intrinsic[:3, :3] = intr_mat
projection_matrix = np.matmul(intrinsic, extr_mat)
projection_matrix = projection_matrix[0:3][:]
f = open(out_path, "w")
for i in range(0, 3):
for j in range(0, 4):
f.write(str(projection_matrix[i][j]) + ' ')
f.write('\n')
f.write('\n')
f.close()
return
def fake_gipuma_normal(in_depth_path, out_normal_path):
depth_image = read_gipuma_dmb(in_depth_path)
image_shape = np.shape(depth_image)
normal_image = np.ones_like(depth_image)
normal_image = np.reshape(normal_image, (image_shape[0], image_shape[1], 1))
normal_image = np.tile(normal_image, [1, 1, 3])
normal_image = normal_image / 1.732050808
mask_image = np.squeeze(np.where(depth_image > 0, 1, 0))
mask_image = np.reshape(mask_image, (image_shape[0], image_shape[1], 1))
mask_image = np.tile(mask_image, [1, 1, 3])
mask_image = np.float32(mask_image)
normal_image = np.multiply(normal_image, mask_image)
normal_image = np.float32(normal_image)
write_gipuma_dmb(out_normal_path, normal_image)
return
def ucsnet_to_gipuma(dense_folder, gipuma_point_folder):
image_folder = os.path.join(dense_folder, 'rgb')
cam_folder = os.path.join(dense_folder, 'cam')
depth_folder = os.path.join(dense_folder, 'depth')
gipuma_cam_folder = os.path.join(gipuma_point_folder, 'cams')
gipuma_image_folder = os.path.join(gipuma_point_folder, 'images')
if not os.path.isdir(gipuma_point_folder):
os.mkdir(gipuma_point_folder)
if not os.path.isdir(gipuma_cam_folder):
os.mkdir(gipuma_cam_folder)
if not os.path.isdir(gipuma_image_folder):
os.mkdir(gipuma_image_folder)
# convert cameras
image_names = os.listdir(image_folder)
for image_name in image_names:
image_prefix = os.path.splitext(image_name)[0]
in_cam_file = os.path.join(cam_folder, 'cam_'+image_prefix+'.txt')
out_cam_file = os.path.join(gipuma_cam_folder, image_name+'.P')
ucsnet_to_gipuma_cam(in_cam_file, out_cam_file)
# copy images to gipuma image folder
image_names = os.listdir(image_folder)
for image_name in image_names:
in_image_file = os.path.join(image_folder, image_name)
out_image_file = os.path.join(gipuma_image_folder, image_name)
shutil.copyfile(in_image_file, out_image_file)
# convert depth maps and fake normal maps
gipuma_prefix = '2333__'
for image_name in image_names:
image_prefix = os.path.splitext(image_name)[0]
sub_depth_folder = os.path.join(gipuma_point_folder, gipuma_prefix+image_prefix)
if not os.path.isdir(sub_depth_folder):
os.mkdir(sub_depth_folder)
in_depth_pfm = os.path.join(depth_folder, image_prefix+'_prob_filtered.pfm')
#
out_depth_dmb = os.path.join(sub_depth_folder, 'disp.dmb')
fake_normal_dmb = os.path.join(sub_depth_folder, 'normals.dmb')
ucsnet_to_gipuma_dmb(in_depth_pfm, out_depth_dmb)
fake_gipuma_normal(out_depth_dmb, fake_normal_dmb)
def probability_filter(dense_folder, prob_threshold, s=3):
'''
filter with stage 3.
:param dense_folder:
:param prob_threshold:
:return:
'''
image_folder = os.path.join(dense_folder, 'rgb')
depth_folder = os.path.join(dense_folder, 'depth')
conf_folder = os.path.join(dense_folder, 'confidence')
# convert cameras
image_names = os.listdir(image_folder)
for image_name in image_names:
image_prefix = os.path.splitext(image_name)[0]
init_depth_map_path = os.path.join(depth_folder, 'dep_'+image_prefix+'_3.pfm')
prob_map_path = os.path.join(conf_folder, 'conf_'+image_prefix+'_1.pfm')
out_depth_map_path = os.path.join(depth_folder, image_prefix+'_prob_filtered.pfm')
depth_map, _ = read_pfm(init_depth_map_path)
prob_map, _ = read_pfm(prob_map_path)
h, w = depth_map.shape
prob_map = cv2.resize(prob_map, (w, h), interpolation=cv2.INTER_LINEAR)
depth_map[prob_map < prob_threshold] = 0
write_pfm(out_depth_map_path, depth_map)
def depth_map_fusion(point_folder, fusibile_exe_path, disp_thresh, num_consistent):
cam_folder = os.path.join(point_folder, 'cams')
image_folder = os.path.join(point_folder, 'images')
depth_min = 0.001
depth_max = 100000
normal_thresh = 360
cmd = fusibile_exe_path
cmd = cmd + ' -input_folder ' + point_folder + '/'
cmd = cmd + ' -p_folder ' + cam_folder + '/'
cmd = cmd + ' -images_folder ' + image_folder + '/'
cmd = cmd + ' --depth_min=' + str(depth_min)
cmd = cmd + ' --depth_max=' + str(depth_max)
cmd = cmd + ' --normal_thresh=' + str(normal_thresh)
cmd = cmd + ' --disp_thresh=' + str(disp_thresh)
cmd = cmd + ' --num_consistent=' + str(num_consistent)
print (cmd)
os.system(cmd)
return
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dense_folder', type=str, default = '')
parser.add_argument('--fusibile_exe_path', type=str, default = '')
parser.add_argument('--prob_threshold', type=float, default = '0.8')
parser.add_argument('--disp_threshold', type=float, default = '0.25')
parser.add_argument('--num_consistent', type=float, default = '3')
args = parser.parse_args()
dense_folder = args.dense_folder
fusibile_exe_path = args.fusibile_exe_path
prob_threshold = args.prob_threshold
disp_threshold = args.disp_threshold
num_consistent = args.num_consistent
point_folder = os.path.join(dense_folder, 'points_ucsnet')
if not os.path.isdir(point_folder):
os.mkdir(point_folder)
# probability filter
print ('filter depth map with probability map')
probability_filter(dense_folder, prob_threshold)
# convert to gipuma format
print ('Convert ucsnet output to gipuma input')
ucsnet_to_gipuma(dense_folder, point_folder)
# depth map fusion with gipuma
print ('Run depth map fusion & filter')
depth_map_fusion(point_folder, fusibile_exe_path, disp_threshold, num_consistent)