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import argparse
import glob
import math
import subprocess
import numpy as np
import os
import tqdm
import torch
import torch.nn as nn
import cv2
from darkfeat import DarkFeat
from utils import matching
def darkfeat_pre(img, cuda):
H, W = img.shape[0], img.shape[1]
inp = img.copy()
inp = inp.transpose(2, 0, 1)
inp = torch.from_numpy(inp)
inp = torch.autograd.Variable(inp).view(1, 3, H, W)
if cuda:
inp = inp.cuda()
return inp
if __name__ == '__main__':
# Parse command line arguments.
parser = argparse.ArgumentParser()
parser.add_argument('--H', type=int, default=int(640))
parser.add_argument('--W', type=int, default=int(960))
parser.add_argument('--histeq', action='store_true')
parser.add_argument('--model_path', type=str)
parser.add_argument('--dataset_dir', type=str, default='/data/hyz/MID/')
opt = parser.parse_args()
sizer = (opt.W, opt.H)
focallength_x = 4.504986436499113e+03/(6744/sizer[0])
focallength_y = 4.513311442889859e+03/(4502/sizer[1])
K = np.eye(3)
K[0,0] = focallength_x
K[1,1] = focallength_y
K[0,2] = 3.363322177533149e+03/(6744/sizer[0])# * 0.5
K[1,2] = 2.291824660547715e+03/(4502/sizer[1])# * 0.5
Kinv = np.linalg.inv(K)
Kinvt = np.transpose(Kinv)
cuda = True
if cuda:
darkfeat = DarkFeat(opt.model_path).cuda().eval()
for scene in ['Indoor', 'Outdoor']:
base_save = './result/' + scene + '/'
dir_base = opt.dataset_dir + '/' + scene + '/'
pair_list = sorted(os.listdir(dir_base))
for pair in tqdm.tqdm(pair_list):
opention = 1
if scene == 'Outdoor':
pass
else:
if int(pair[4::]) <= 17:
opention = 0
else:
pass
name=[]
files = sorted(os.listdir(dir_base+pair))
for file_ in files:
if file_.endswith('.cr2'):
name.append(file_[0:9])
ISO = ['00100', '00200', '00400', '00800', '01600', '03200', '06400', '12800']
if opention == 1:
Shutter_speed = ['0.005','0.01','0.025','0.05','0.17','0.5']
else:
Shutter_speed = ['0.01','0.02','0.05','0.1','0.3','1']
E_GT = np.load(dir_base+pair+'/GT_Correspondence/'+'E_estimated.npy')
F_GT = np.dot(np.dot(Kinvt,E_GT),Kinv)
R_GT = np.load(dir_base+pair+'/GT_Correspondence/'+'R_GT.npy')
t_GT = np.load(dir_base+pair+'/GT_Correspondence/'+'T_GT.npy')
id0, id1 = sorted([ int(i.split('/')[-1]) for i in glob.glob(f'{dir_base+pair}/?????') ])
cnt = 0
for iso in ISO:
for ex in Shutter_speed:
dark_name1 = name[0] + iso+'_'+ex+'_'+scene+'.npy'
dark_name2 = name[1] + iso+'_'+ex+'_'+scene+'.npy'
if not opt.histeq:
dst_T1_None = f'{dir_base}{pair}/{id0:05d}-npy-nohisteq/{dark_name1}'
dst_T2_None = f'{dir_base}{pair}/{id1:05d}-npy-nohisteq/{dark_name2}'
img1_orig_None = np.load(dst_T1_None)
img2_orig_None = np.load(dst_T2_None)
dir_save = base_save + pair + '/None/'
img_input1 = darkfeat_pre(img1_orig_None.astype('float32')/255.0, cuda)
img_input2 = darkfeat_pre(img2_orig_None.astype('float32')/255.0, cuda)
else:
dst_T1_histeq = f'{dir_base}{pair}/{id0:05d}-npy/{dark_name1}'
dst_T2_histeq = f'{dir_base}{pair}/{id1:05d}-npy/{dark_name2}'
img1_orig_histeq = np.load(dst_T1_histeq)
img2_orig_histeq = np.load(dst_T2_histeq)
dir_save = base_save + pair + '/HistEQ/'
img_input1 = darkfeat_pre(img1_orig_histeq.astype('float32')/255.0, cuda)
img_input2 = darkfeat_pre(img2_orig_histeq.astype('float32')/255.0, cuda)
result1 = darkfeat({'image': img_input1})
result2 = darkfeat({'image': img_input2})
mkpts0, mkpts1, _ = matching.match_descriptors(
cv2.KeyPoint_convert(result1['keypoints'].detach().cpu().float().numpy()), result1['descriptors'].detach().cpu().numpy(),
cv2.KeyPoint_convert(result2['keypoints'].detach().cpu().float().numpy()), result2['descriptors'].detach().cpu().numpy(),
ORB=False
)
POINT_1_dir = dir_save+f'DarkFeat/POINT_1/'
POINT_2_dir = dir_save+f'DarkFeat/POINT_2/'
subprocess.check_output(['mkdir', '-p', POINT_1_dir])
subprocess.check_output(['mkdir', '-p', POINT_2_dir])
np.save(POINT_1_dir+dark_name1[0:-3]+'npy',mkpts0)
np.save(POINT_2_dir+dark_name2[0:-3]+'npy',mkpts1)
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