opticalflow_evaluate.py

# ---------------------------------------------------------------------
# Copyright (c) 2018 TU Berlin, Communication Systems Group
# Written by Tobias Senst <senst@nue.tu-berlin.de>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# ---------------------------------------------------------------------
import file_parser as fp
import cv2
import sys
import pickle
import copy
import util as ut
import progressbar

def run_parameter(config_item):
    result = dict()
    result["ee"] = 0
    result["R1"] = 0
    result["R2"] = 0
    result["R3"] = 0
    result["no_points"] = 0
    # load ground truth optical flow
    flow_gt         = ut.readFlowFiles(config_item["files"]["gtflow"])
    # load ground truth mask indicating foreground and background flow vectors
    mask_rgb        = cv2.imread(config_item["files"]["mask"])
    #  load estimated optical flow
    est_flow = ut.readFlowFiles(config_item["files"]["estflow"])
    # compute short term errors
    result = ut.compute_error(est_flow, flow_gt, mask_rgb)

    return (config_item, result)


def main():
    if len(sys.argv) < 2:
        print("Please provide the first argument that is the root path of the CrowdFlow dataset.")
        return
    basepath = sys.argv[1]

    parameter_list = list()

    for n in range(2, len(sys.argv)):
        parameter_list.append({"flow_method": "/" + sys.argv[n] + "/" })

    latex_filename = "short_term_results.tex"
    result_filename = "short_term_results.pb"


    result_list = list()
    bar = progressbar.ProgressBar()

    for n, parameter in enumerate(parameter_list):
        basepath_dict = {"basepath": basepath,
                         "images" : basepath + "/images/",
                         "groundtruth": basepath + "/gt_flow/",
                         "estimate": basepath + "/estimate/" + parameter["flow_method"] + "/",
                         "masks": basepath + "/masks/",
                         }

        filenames = fp.create_filename_list(basepath_dict)
        config_list = ut.create_config(parameter, filenames)

        bar.start(max_value=len(config_list) * len(parameter_list))
        for c, config_item in enumerate(config_list):
            result = run_parameter(config_item)
            result_list.append(copy.deepcopy(result))
            bar.update(c + n * len(config_list))

    bar.finish()
    print("\n")
    print("\n")
    print("Save short term evaluation file ",  result_filename)
    out_dict = {"result": result_list}
    pickle.dump(out_dict, open( result_filename, "wb"))

    result_str = ut.getLatexTable(result_filename)
    print(result_str)
    if len(latex_filename) > 0:
        with open(latex_filename, "w") as f:
            f.write(result_str)
main()