eval_disp.py

import warnings
warnings.filterwarnings("ignore")
import numpy as np
import glob
from matplotlib import pyplot as plt
from texttable import Texttable
from utils.readpfm import readPFM
import cv2
import pdb
import os
import matplotlib.patches as patches

import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--method', type=str ,default='HSM',
                    help='resolution')
parser.add_argument('--indir', type=str ,default='/ssd/carla-highres/carlaF/',
                    help='resolution')
parser.add_argument('--gtdir', type=str ,default='/ssd/carla-highres/testF/',
                    help='resolution')
args = parser.parse_args()

method = args.method
numr = (0.1450+0.1537)*3668.61736086358178
levels = [[0,25],[25,60],[60,115],[0,115]]


blist = np.load('%s/blist.npy'%args.gtdir, encoding='latin1')[()]
t = Texttable()
t.set_deco(Texttable.HEADER)
t.set_cols_dtype(['t','f','f','f','f','f','f'])
t.set_cols_width([32,10,10,10,10,10,10])
t.add_row(['Image', 'avgerr','rms','bad-1','bad-2','bad-4','time'])

for level in levels:
    imgnames=[i for i in glob.glob('%s/*'%args.indir) if os.path.isdir(i)]
    avgerrs = np.zeros(len(imgnames)+1)
    rmss = np.zeros(len(imgnames)+1)
    bad5s = np.zeros(len(imgnames)+1)
    bad10s = np.zeros(len(imgnames)+1)
    bad20s = np.zeros(len(imgnames)+1)
    times = np.zeros(len(imgnames)+1)

    for i,imgname in enumerate(imgnames):
        if not os.path.isdir(imgname):
            continue
        gt = readPFM('%s/%s/disp0GT.pfm'%(args.gtdir,imgname.split('/')[-1]))[0]
        mask = gt!=np.inf
        gt_depth = gt.copy()
        gt_depth[mask] = numr/gt_depth[mask]
        mask = mask*(gt_depth>level[0])*(gt_depth<level[1])
        if imgname.split('/')[-1] in blist.keys():
            for b in blist[imgname.split('/')[-1]]:
                mask[int(b[0][1]):int(b[1][1]),\
                     int(b[0][0]):int(b[1][0])] = 0

        disp = readPFM('%s/disp0%s.pfm'%(imgname,method))[0]
        if disp.shape != gt.shape:
            ratio = float(gt.shape[1])/disp.shape[1]
            disp=cv2.resize(disp,(gt.shape[1],gt.shape[0]))*ratio

        errmap = np.abs(gt-disp)*mask
        avgerr = errmap[mask].mean()
        rms = np.sqrt((errmap[mask]**2).mean())
        bad5map = (errmap>1) * mask
        bad5 = bad5map[mask].sum()/float(mask.sum())*100

        bad10map = (errmap>2) * mask
        bad10 = bad10map[mask].sum()/float(mask.sum())*100

        bad20map = (errmap>4) * mask
        bad20 = bad20map[mask].sum()/float(mask.sum())*100

        avgerrs[i] = avgerr
        rmss[i] = rms
        bad5s[i] = bad5
        bad10s[i] = bad10
        bad20s[i] = bad20
        with open('%s/time%s.txt'%(imgname,method)) as f:
            times[i]=float(f.readlines()[0])

    imgnames += ['-'.join([str(i) for i in level])+'m']
    avgerrs[-1]= avgerrs.mean()
    rmss[-1] =    rmss.mean()
    bad5s[-1] =    bad5s.mean()
    bad10s[-1] =    bad10s.mean()
    bad20s[-1] =    bad20s.mean()
    times[-1] =    times.mean()
    t.add_rows(list(zip([i.split('/')[-1] for i in imgnames],avgerrs,rmss,bad5s,bad10s,bad20s,times))[-1:],header=False)
    #t.add_rows(zip([i.split('/')[-1] for i in imgnames],avgerrs,rmss,bad5s,bad10s,bad20s,times),header=False)

    
t.set_cols_align(['r','r','r','r','r','r','r'])
print (t.draw())