def do_post(
self,
lcost,
rcost,
save_path,
display,
interpolate=False,
# added by CCJ;
isLocalExp=True):
imgl = self.imgl.astype(np.float32)
imgr = self.imgr.astype(np.float32)
imgl = (imgl - np.mean(imgl)) / np.std(imgl)
imgr = (imgr - np.mean(imgr)) / np.std(imgr)
#****************
# added by CCJ;
if (isLocalExp):
print 'running local expansion ...'
displ = self.__postprocessing_localExp(lcost.astype(np.float32),
display)
elif (interpolate):
displ = self.__postprocessing_mem_interp(imgl, imgr, lcost, -1,
display)
else:
displ = self.__postprocessing_mem(imgl, imgr, lcost, -1, display)
pfm.save(save_path, displ.astype(np.float32))
print("Saved {}".format(save_path))
def do_post(self, lcost, rcost, save_path, display, interpolate=False):
imgl = self.imgl.astype(np.float32)
imgr = self.imgr.astype(np.float32)
imgl = (imgl - np.mean(imgl)) / np.std(imgl)
imgr = (imgr - np.mean(imgr)) / np.std(imgr)
if (interpolate):
displ = self.__postprocessing_mem_interp(imgl, imgr, lcost, -1,
display)
else:
displ = self.__postprocessing_mem(imgl, imgr, lcost, -1, display)
print "Saving"
pfm.save(save_path, displ.astype(np.float32))
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import sys
import math
import random
import os
sys.path.insert(0, '../pylibs')
sys.path.insert(0, '../src')
import cpputils
import pfmutil as pfm
l_gt_p = "..../Freiburg/driving/disparity/15mm_focallength/scene_forwards/slow/left/"
r_gt_p = "..../Freiburg/driving/disparity/15mm_focallength/scene_forwards/slow/right/"
save_p = ".../Freiburg/driving/disparity/15mm_focallength/scene_forwards/slow/left_nonocc/"
ims = os.listdir(l_gt_p)
for im in ims:
l_gt = pfm.load(l_gt_p + im)[0]
r_gt = pfm.load(r_gt_p + im)[0]
occ = cpputils.make_occ(l_gt, r_gt)
pfm.save(save_p + im, occ)
if __name__ == "__main__":
if 0:
fname = "/mnt/interns/changjiang/PatientPositioning/Datasets/surreal/cmu/train/run0/01_01/01_01_c0001_depth.mat"
dataroot = "/mnt/interns/changjiang/PatientPositioning/Datasets/surreal/small-data/depth"
dep = load_anno_mat(fname)
#print ("type : ", type(dep), "keys : %s" % sorted(dep.keys()))
#for i in range(1,101):
for i in range(0,4):
dep_key_name = 'depth_%d' % (i+1)
dep_name = 'depth_%03d' % i
depth = dep[dep_key_name]
depth[depth == 1e+10] = np.inf
#depth[depth == 1e+10] = 0
cv2.imwrite(dataroot + '/' + dep_name + ".png", depth.astype(np.uint16))
pfm.save(dataroot + '/' + dep_name + ".pfm", 1000.0*depth.astype(np.float32))
#dep_names = ['depth_1', 'depth_10', 'depth_100', 'depth_11', 'depth_12', 'depth_13', 'depth_14', 'depth_15', 'depth_16', 'depth_17', 'depth_18', 'depth_19', 'depth_2', 'depth_20', 'depth_21', 'depth_22', 'depth_23', 'depth_24', 'depth_25', 'depth_26', 'depth_27', 'depth_28', 'depth_29', 'depth_3', 'depth_30', 'depth_31', 'depth_32', 'depth_33', 'depth_34', 'depth_35', 'depth_36', 'depth_37', 'depth_38', 'depth_39', 'depth_4', 'depth_40', 'depth_41', 'depth_42', 'depth_43', 'depth_44', 'depth_45', 'depth_46', 'depth_47', 'depth_48', 'depth_49', 'depth_5', 'depth_50', 'depth_51', 'depth_52', 'depth_53', 'depth_54', 'depth_55', 'depth_56', 'depth_57', 'depth_58', 'depth_59', 'depth_6', 'depth_60', 'depth_61', 'depth_62', 'depth_63', 'depth_64', 'depth_65', 'depth_66', 'depth_67', 'depth_68', 'depth_69', 'depth_7', 'depth_70', 'depth_71', 'depth_72', 'depth_73', 'depth_74', 'depth_75', 'depth_76', 'depth_77', 'depth_78', 'depth_79', 'depth_8', 'depth_80', 'depth_81', 'depth_82', 'depth_83', 'depth_84', 'depth_85', 'depth_86', 'depth_87', 'depth_88', 'depth_89', 'depth_9', 'depth_90', 'depth_91', 'depth_92', 'depth_93', 'depth_94', 'depth_95', 'depth_96', 'depth_97', 'depth_98', 'depth_99']
#print ("depth images : %d" % len(dep_names))
if 0:
video_fname = "/mnt/interns/changjiang/PatientPositioning/Datasets/surreal/cmu/train/run0/01_01/01_01_c0001.mp4"
dataroot = "/mnt/interns/changjiang/PatientPositioning/Datasets/surreal/small-data/images"
vidcap = cv2.VideoCapture(video_fname)
info_fname = "/mnt/interns/changjiang/PatientPositioning/Datasets/surreal/cmu/train/run0/01_01/01_01_c0001_info.mat"
joints2D = load_anno_mat(info_fname)["joints2D"]
jnames = joint_names()
success, image = vidcap.read()
for i in range(0, 4):
key_name = 'frame_%03d' % i
joints = joints2D[:,:,i]
for j in range(0, 14):