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trajectory_evaluate.py
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trajectory_evaluate.py
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# ---------------------------------------------------------------------
# Copyright (c) 2018 TU Berlin, Communication Systems Group
# Written by Tobias Senst <[email protected]>
#
# 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 copy
import time
import sys
import numpy as np
import pickle
import util as ut
import file_parser as fp
class Trajectory:
def __init__(self, pos, from_idx, to_idx):
self.points = np.ones(shape=(int(to_idx) - int(from_idx) + 1, 2), dtype=np.float32) * 99999
self.points[0,:] = pos
self.start_index = int(from_idx)
def run(self, flow, index):
idx = index - self.start_index
if idx >= 0 and idx + 1 < self.points.shape[0]:
# position is in (x,y)
pos = self.points[idx,:]
ipos = np.floor(pos).astype(np.int32)
if ipos[0] >= 0 and ipos[1] >= 0 and ipos[0] + 1 < flow.shape[1] and ipos[1] + 1 < flow.shape[0]:
a = pos - ipos
iw00 = (1 - a[0]) * (1 - a[1])
iw01 = a[0] * (1 - a[1])
iw10 = (1 - a[0]) * a[1]
iw11 = 1 - iw00 - iw01 - iw10
self.points[idx + 1, :] = pos + \
flow[ipos[1], ipos[0]] * iw00 + \
flow[ipos[1], ipos[0] + 1] * iw01 + \
flow[ipos[1] + 1, ipos[0]] * iw10 + \
flow[ipos[1] + 1, ipos[0] + 1] * iw11
class TrajectoryEvaluator:
def __init__(self,
thresholds=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25],
dense_person_groundtruth_filename_base="DenseTracks.pb",
person_groundtruth_filename="PersonTracks.pb"):
self.dense_person_groundtruth_filename_base = None
self.person_groundtruth_filename = None
if dense_person_groundtruth_filename_base is not None:
self.dense_person_groundtruth_filename_base = copy.deepcopy(dense_person_groundtruth_filename_base)
if person_groundtruth_filename is not None:
self.person_groundtruth_filename = copy.deepcopy(person_groundtruth_filename)
self.threshold_list = copy.deepcopy(thresholds)
def compute_differenz_trajectories(self, flow_filenames, groundtruth_filename):
print(" Load ", groundtruth_filename)
ret = pickle.load(open(groundtruth_filename, "rb"))
start_points_trajectory = ret["GT_StartPoints"]
gt_trajectory = ret["GT_Trajectories"]
print(" Start TrajectoryEstimator ", flow_filenames[0])
trajectory_list = list()
for r in range(0,start_points_trajectory.shape[0]):
trajectory_list.append(Trajectory(pos = (start_points_trajectory[r,3], start_points_trajectory[r,2]) ,
from_idx = start_points_trajectory[r,0], to_idx= start_points_trajectory[r,1]))
for i, flow_file in enumerate(flow_filenames):
flow = ut.readFlowFiles(flow_file)
for trajectory_item in trajectory_list:
trajectory_item.run(flow, i)
estimated_trajectory = list()
for trajectory_item in trajectory_list:
estimated_trajectory.append(list())
for n in range(trajectory_item.points.shape[0]):
estimated_trajectory[-1].append(trajectory_item.points[n,1])
estimated_trajectory[-1].append(trajectory_item.points[n,0])
return ut.differenz_trajectory_list(gt_trajectory, estimated_trajectory)
def run_sequence(self, sequence_name):
start = time.time()
print("Start ", sequence_name)
flow_filenames = list()
filebase = None
for f in self.file_dict:
if f["dir"] == sequence_name:
filebase = f["basepath"]
flow_filenames.append(f["estflow"])
flow_filenames = sorted(flow_filenames)
result = dict()
gt_filebase = filebase + "/gt_trajectories/" + sequence_name + "/"
if self.person_groundtruth_filename is not None:
person_diff_trajectory = self.compute_differenz_trajectories(flow_filenames,
gt_filebase + self.person_groundtruth_filename)
result["person"] = ut.compute_tracking_error(person_diff_trajectory, self.threshold_list)
print("Done person_groundtruth_filename ")
if self.dense_person_groundtruth_filename_base is not None:
print("Start next")
dense_person_diff_trajectory = self.compute_differenz_trajectories(flow_filenames,
gt_filebase + self.dense_person_groundtruth_filename_base)
result["dense_person"] = ut.compute_tracking_error(dense_person_diff_trajectory, self.threshold_list)
print("Done dense_person_groundtruth_filename_base ")
print(time.time() - start)
return result
def average(self, sequences):
keys = []
if self.dense_person_groundtruth_filename_base is not None:
keys.append("dense_person")
if self.person_groundtruth_filename is not None:
keys.append("person")
result = dict()
no_sequences = 1.0 / len(sequences)
for seq_key in sequences.keys():
for key in keys:
if key in result:
result[key] = result[key] + np.array(sequences[seq_key][key]) * no_sequences
else:
result[key] = np.array(sequences[seq_key][key]) * no_sequences
return result
def run(self, file_dict):
self.file_dict = copy.deepcopy(file_dict)
result = dict()
sequence_name = ["IM01", "IM02", "IM03", "IM04", "IM05"]
static_ret = dict()
for s in sequence_name:
static_ret[s] = self.run_sequence(s)
result["static"] = self.average(static_ret)
result = {**result, **static_ret}
sequence_name = ["IM01_hDyn", "IM02_hDyn", "IM03_hDyn", "IM04_hDyn", "IM05_hDyn"]
dynamic_ret = dict()
for s in sequence_name:
dynamic_ret[s] = self.run_sequence(s)
result["dynamic"] = self.average(dynamic_ret)
result = {**result, **dynamic_ret}
result["all"] = self.average({**dynamic_ret, **static_ret})
return result
def get_statistics(self, basepath):
static_sequence_name = ["IM01", "IM02", "IM03", "IM04", "IM05"]
dynamic_sequence_name = ["IM01_hDyn", "IM02_hDyn", "IM03_hDyn", "IM04_hDyn", "IM05_hDyn"]
sequence_name = static_sequence_name + dynamic_sequence_name
length_list = list()
for seq in sequence_name:
filename_base = basepath + "/gt_trajectories/" + seq + "/"
ret = pickle.load(open(filename_base + self.person_groundtruth_filename, "rb"))
retl = ut.get_trajectory_lengths(ret["GT_Trajectories"])
length_list = length_list + retl
print("Max Length", np.max(length_list))
np.save("Person_Trajectory_Length", np.array(length_list))
print("Done Stored")
def run_parameter(flow_method, basepath):
basepath_dict = {"basepath": basepath,
"images" : basepath + "/images/",
"groundtruth": basepath + "/gt_flow/",
"estimate": basepath + "/estimate/" + flow_method + "/",
"masks": basepath + "/masks/",
}
filenames = fp.create_filename_list(basepath_dict)
evaluator = TrajectoryEvaluator()
ret_dict = evaluator.run(filenames)
ret_dict["name"] = flow_method.replace("/", "")
return ret_dict
def main():
if len(sys.argv) < 2:
print("Please provide the first argument. Root path of the CrowdFlow dataset.")
return
basepath = sys.argv[1]
method_list = list()
for n in range(2, len(sys.argv)):
method_list.append("/" + sys.argv[n] + "/" )
latex_filename = "long_term_results.tex"
result_filename = "long_term_results.pb"
result_dict = list()
for method in method_list:
result_dict.append(run_parameter(method, basepath))
print("Try to save file ", result_filename)
pickle.dump(result_dict, open( result_filename, "wb"))
print("Person Trajectories")
result_str = ut.genTrajectoryLatexTable(filename=result_filename, item_key="dense_person")
print("Dense Person Trajectories")
result_str = result_str + ut.genTrajectoryLatexTable(filename=result_filename, item_key="person")
if len(latex_filename) > 0:
with open(latex_filename, "w") as f:
f.write(result_str)
main()