def flow_loader(path):
if '.pfm' in path:
data = readPFM(path)[0]
data[:, :, 2] = 1
return data
else:
return read_flow(path)
def disparity_loader(path):
if '.png' in path:
data = Image.open(path)
data = np.ascontiguousarray(data, dtype=np.float32) / 256
return data
else:
return readPFM(path)[0]
def read_pfm_file(flow_file):
"""
Read from .pfm file
:param flow_file: name of the flow file
:return: optical flow data in matrix
"""
(data, scale) = readPFM(flow_file)
return data
def eval_f(fp):
import warnings
warnings.filterwarnings("ignore")
# gt
if '2015' in args.dataset:
mask_gt = cv2.imread(maskp[fp], 0)
flow_gt = read_flow(maskp[fp].replace('obj_map',
'flow_occ')).astype(np.float32)
validmask = flow_gt[:, :, -1] == 1
bgmask_gt = np.logical_and(validmask, mask_gt == 0).astype(float)
else:
bgmask_gt = cv2.imread(maskp[fp], 0) > 0
shape = bgmask_gt.shape[:2]
validmask = np.ones(shape).astype(bool)
shape = bgmask_gt.shape[:2]
# pred
flowpath = flow_paths[fp]
# KITTI
if '2015' in args.dataset:
idnum = int(flowpath.split('/')[-1].split('_')[-2])
if args.method == 'ours':
maskp_pred = '%s/%s/pm-%06d_10.pfm' % (args.path, args.dataset,
idnum)
mask_pred = readPFM(maskp_pred)[0].astype(int)
bgmask_pred = mask_pred == 0
elif args.method == 'detectron2':
maskp_pred = '/data/gengshay/code/moseg/moseg-detectron2/%06d.png' % idnum
mask_pred = cv2.imread(maskp_pred, 0)
bgmask_pred = mask_pred == 0
elif args.method == 'mrflowss':
bgmask_pred = np.load(
'/home/gengshay/code/SF/mrflow/semantic_rigidity_cvpr2017/output_kitti_smodel/%06d_10.npy'
% idnum)
bgmask_pred = cv2.resize(bgmask_pred.astype(float),
shape[::-1]).astype(bool)
elif args.method == 'mrflowsk':
bgmask_pred = cv2.imread(
'/home/gengshay/code/SF/mrflow/semantic_rigidity_cvpr2017/output_kitti_kmodel/%06d_10.png'
% idnum, 0).astype(bool)
elif args.method == 'u2net':
bgmask_pred = cv2.imread(
'/data/gengshay/code/U-2-Net/test_data/u2net_results/%06d_10.png'
% idnum, 0) < 128
elif args.method == 'rtn':
bgmask_pred = ~cv2.imread(
'/data/gengshay/code/learningrigidity_mod/output_kitti/%06d_10.png'
% idnum, 0).astype(bool)
elif args.method == 'fusionseg':
bgmask_pred = ~np.load(
'/data/gengshay/code/fusionseg/python/kitti/results_liu/joint_davis_val/%06d_10_mask.npy'
% idnum).astype(bool)
elif args.method == 'fusionsegm':
bgmask_pred = ~np.load(
'/data/gengshay/code/fusionseg/python/kitti/results_liu/motion/%06d_10_mask.npy'
% idnum).astype(bool)
elif args.method == 'cosnet':
bgmask_pred = cv2.resize(
cv2.imread(
'/data/gengshay/code/COSNet/result/test/davis_iteration_conf/Results/kitti/%06d_10.png'
% idnum, 0), shape[::-1]) < 128
elif args.method == 'matnet':
bgmask_pred = cv2.imread(
'/data/gengshay/code/MATNet/output/kitti_liu/%06d_10.png' %
idnum, 0) < 128
elif args.method == 'cc':
bgmask_pred = cv2.resize(
np.load('/data/gengshay/code/cc/output/mask-joint/%03d.npy' %
idnum)[0], shape[::-1]) == 1
elif args.method == 'ccm':
bgmask_pred = cv2.resize(
np.load('/data/gengshay/code/cc/output/mask-motion/%03d.npy' %
idnum)[0], shape[::-1]) == 1
elif args.method == 'angle':
maskp_pred = '%s/%s/pm-%06d_10.pfm' % (args.path, args.dataset,
idnum)
mask_pred = readPFM(maskp_pred)[0].astype(int)
bgmask_pred = mask_pred == 0
else:
exit()
# Sintel
else:
passname = flowpath.split('/')[-1].split('_')[-4]
seqname1 = flowpath.split('/')[-1].split('_')[-3]
seqname2 = flowpath.split('/')[-1].split('_')[-2]
framename = int(flowpath.split('/')[-1].split('_')[-1].split('.')[0])
if args.method == 'ours':
maskp_pred = '%s/%s/pm-Sintel_%s_%s_%s_%02d.pfm' % (
args.path, args.dataset, passname, seqname1, seqname2,
framename)
mask_pred = readPFM(maskp_pred)[
0] # label map in {1,...N} given N rigid body predictions
bgmask_pred = mask_pred == 0
elif args.method == 'detectron2':
maskp_pred = '/data/gengshay/code/moseg/smoseg-detectron2/Sintel_%s_%s_%s_%02d.png' % (
passname, seqname1, seqname2, framename)
mask_pred = cv2.imread(maskp_pred, 0)
bgmask_pred = mask_pred == 0
elif args.method == 'mrflowss':
bgmask_pred = np.load(
'/home/gengshay/code/SF/mrflow/semantic_rigidity_cvpr2017/output_sintel_smodel/Sintel_%s_%s_%s_%02d.npy'
% (passname, seqname1, seqname2, framename)).astype(bool)
elif args.method == 'mrflowsk':
bgmask_pred = cv2.imread(
'/home/gengshay/code/SF/mrflow/semantic_rigidity_cvpr2017/output_sintel_kmodel/Sintel_%s_%s_%s_%02d.png'
% (passname, seqname1, seqname2, framename), 0).astype(bool)
elif args.method == 'u2net':
bgmask_pred = cv2.imread(
'/data/gengshay/code/U-2-Net/test_data/u2net_results_sintel/Sintel_%s_%s_%s_%02d.png'
% (passname, seqname1, seqname2, framename), 0) < 128
elif args.method == 'rtn':
bgmask_pred = ~cv2.imread(
'/data/gengshay/code/learningrigidity_mod/output_sintel/Sintel_%s_%s_%s_%02d.png'
% (passname, seqname1, seqname2, framename), 0).astype(bool)
elif args.method == 'fusionseg':
bgmask_pred = ~np.load(
'/data/gengshay/code/fusionseg/python/sintel/results/joint_davis_val/Sintel_%s_%s_%s_%02d_mask.npy'
% (passname, seqname1, seqname2, framename)).astype(bool)
elif args.method == 'fusionsegm':
bgmask_pred = ~np.load(
'/data/gengshay/code/fusionseg/python/sintel/results/motion/Sintel_%s_%s_%s_%02d_mask.npy'
% (passname, seqname1, seqname2, framename)).astype(bool)
elif args.method == 'cosnet':
bgmask_pred = cv2.resize(
cv2.imread(
'/data/gengshay/code/COSNet/result/test/davis_iteration_conf/Results/sintel/Sintel_%s_%s_%s_%02d.png'
% (passname, seqname1, seqname2, framename), 0),
shape[::-1]) < 128
elif args.method == 'matnet':
bgmask_pred = cv2.imread(
'/data/gengshay/code/MATNet/output/sintel/Sintel_%s_%s_%s_%02d.png'
% (passname, seqname1, seqname2, framename), 0) < 128
elif args.method == 'angle':
maskp_pred = '%s/%s/pm-Sintel_%s_%s_%s_%02d.pfm' % (
args.path, args.dataset, passname, seqname1, seqname2,
framename)
mask_pred = readPFM(maskp_pred)[0]
bgmask_pred = mask_pred == 0
else:
exit()
if args.method != 'ours' and args.method != 'detectron2':
_, mask_pred = cv2.connectedComponentsWithAlgorithm(
(1 - bgmask_pred.astype(np.uint8)),
connectivity=8,
ltype=cv2.CV_16U,
ccltype=cv2.CCL_WU)
# bg-iou
try:
bgiou = np.logical_and(
bgmask_gt.astype(bool)[validmask],
bgmask_pred[validmask]).sum() / np.logical_or(
bgmask_gt.astype(bool)[validmask],
bgmask_pred[validmask]).sum()
except:
pdb.set_trace()
# obj-F (penalizes false positive)
# defined in https://arxiv.org/abs/1902.03715 Sec.4
# F=2PR/P+R => 2/(1/P+1/R)
# since P = sum(|c and g|) / sum(|c|) and R = sum(|c and g|) / sum(|g|),
# where c is prediction and g is GT
# 1/P+1/R = [sum(|c|) + sum(|g|)] / sum(|c and g|)
# therefore F = 2*sum(|c and g|) / [sum(|c|) + sum(|g|)]
if '2015' in args.dataset:
gtlist = list(set(mask_gt.flatten()))
M = len(gtlist) - 1
imatrix = np.zeros((M, mask_pred.max()))
fmatrix = np.zeros((M, mask_pred.max()))
for i in range(M): # for all bg instances
objx_mask_gt = mask_gt == gtlist[i + 1]
for j in range(mask_pred.max()):
objx_mask_pred = mask_pred == j + 1
imatrix[i, j] = float(
np.logical_and(objx_mask_gt,
objx_mask_pred)[validmask].sum())
fmatrix[i,
j] = imatrix[i,
j] / (objx_mask_gt[validmask].sum() +
objx_mask_pred[validmask].sum()) * 2
ridx, cidx = scipy.optimize.linear_sum_assignment(1 - fmatrix)
objf = imatrix[ridx, cidx].sum() / ((mask_pred > 0)[validmask].sum() +
(mask_gt > 0)[validmask].sum()) * 2
else:
objf = 0.
return bgiou, objf
import os
import sys
sys.path.insert(0, os.getcwd())
import cv2
import numpy as np
import glob
import argparse
from utils.util_flow import readPFM
parser = argparse.ArgumentParser(description='')
parser.add_argument('--datapath', default='', help='dataset path')
parser.add_argument('--outpath', default='', help='output path')
args = parser.parse_args()
for folder in glob.glob('%s/seq-*' % (args.datapath)):
name = folder.split('seq-')[-1]
dirpath = '%s/%s/' % (args.outpath, name)
print(dirpath)
if not os.path.isdir(dirpath): os.mkdir(dirpath)
for filename in glob.glob('%s/pm*.pfm' % folder):
frameid = filename.split('pm-')[-1].split('.pfm')[0]
print(filename)
mask_pred = readPFM(filename)[0].astype(int) > 0
shape = mask_pred.shape
mask_pred = np.stack(
[np.zeros(shape),
np.zeros(shape),
mask_pred.astype(int) * 128], -1)
cv2.imwrite('%s/%s.png' % (dirpath, frameid), mask_pred)
def pfm_to_flo(pfm_file):
flow_filename = pfm_file[0:pfm_file.find('.pfm')] + '.flo'
(data, scale) = readPFM(pfm_file)
flow = data[:, :, 0:2]
write_flow(flow, flow_filename)
parser.add_argument('--imgpath', default='',
help='dataset path')
args = parser.parse_args()
class Object(object):
def has(self, name: str) -> bool:
return name in self._fields
def __getattr__(self, name: str) -> Any:
if name == "_fields" or name not in self._fields:
raise AttributeError("Cannot find field '{}' in the given Instances!".format(name))
return self._fields[name]
frames=[]
for i,path in enumerate(sorted(glob.glob('%s/pm*'%args.datapath))):
print(path)
pred = readPFM(path)[0]
center_img = cv2.imread(path.replace('pm', 'mvis').replace('.pfm', '.jpg'))
img = cv2.imread('%s/%s.png'%(args.imgpath,path.split('/')[-1].split('pm-')[1].split('.pfm')[0]))
if img is None:
img = cv2.imread('%s/%s.jpg'%(args.imgpath,path.split('/')[-1].split('pm-')[1].split('.pfm')[0]))
shape = pred.shape[:2]
num_instances = int(pred.max())
# if no object detected
if num_instances==0:
_, pred =cv2.connectedComponentsWithAlgorithm((1-(pred==0).astype(np.uint8)),connectivity=8,ltype=cv2.CV_16U,ccltype=cv2.CCL_WU)
num_instances = pred.max()
if num_instances>0:
pred_masks = torch.zeros((num_instances,)+shape).bool()
for k in range(num_instances):