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#!/usr/bin/env python
import os
import sys
from time import time
import argparse
import numpy as np
import pandas as pd
from davis2017.evaluation import MaskEvaluation
'''
python motion_mask.py --label_path /home/remote/data/sintel/training/dynamic_label_perfect --results_path /home/remote/project/DyGS/InstantSplat/baselines/3dgs/sintel
python motion_mask.py --label_path /home/remote/data/sintel/training/dynamic_label_perfect --results_path /home/remote/project/DyGS/InstantSplat/data/sintel_pose_dec8_baseline
'''
'''
python /home/kai/monster/monst3r/main/evaluation/sintel/motion_mask.py --label_path /home/kai/monster/monst3r/main/data/sintel/training/dynamic_label_perfect --results_path /home/kai/monster/github/monst3r/results/sintel_pose
J&F-Mean J-Mean J-Recall J-Decay F-Mean F-Recall F-Decay
0.414534 0.371022 0.337043 0.0 0.458046 0.437202 0.0
python /home/kai/monster/monst3r/main/evaluation/sintel/motion_mask.py --label_path /home/kai/monster/monst3r/main/data/sintel/training/dynamic_label_perfect --results_path /home/kai/monster/monst3r/monst3r_assets/results/EqMSeg_fix_monst3r/pred_mask_avg_pred1
J&F-Mean J-Mean J-Recall J-Decay F-Mean F-Recall F-Decay
0.558321 0.593482 0.689984 0.0 0.52316 0.529412 0.0
python /home/kai/monster/monst3r/main/evaluation/sintel/motion_mask.py --label_path /home/kai/monster/monst3r/main/data/sintel/training/dynamic_label_perfect --results_path /home/kai/monster/monst3r/monst3r_assets/results/EqMSeg_fix_encoder/50
J&F-Mean J-Mean J-Recall J-Decay F-Mean F-Recall F-Decay
0.570488 0.594178 0.629571 0.0 0.546798 0.54213 0.0
python evaluation/sintel/motion_mask.py --label_path /home/kai/monster/monst3r/data/sintel/training/dynamic_label_perfect --results_path results/sintel_pose_123456789_from_monst3r_lrx0.2/dynamic_mask_nn
J&F-Mean J-Mean J-Recall J-Decay F-Mean F-Recall F-Decay
0.352903 0.378218 0.443561 0.0 0.327589 0.340223 0.0
python evaluation/sintel/motion_mask.py --label_path /home/kai/monster/monst3r/data/sintel/training/dynamic_label_perfect --results_path results/sintel_pose_123456789_from_monst3r_lrx0.2/dynamic_mask_raft
--------------------------- Global results ---------------------------
J&F-Mean J-Mean J-Recall J-Decay F-Mean F-Recall F-Decay
0.362136 0.309305 0.252782 0.0 0.414966 0.424483 0.0
'''
seq_list = ["alley_2", "ambush_4", "ambush_5", "ambush_6", "cave_2", "cave_4", "market_2",
"market_5", "market_6", "shaman_3", "sleeping_1", "sleeping_2", "temple_2", "temple_3"]
time_start = time()
parser = argparse.ArgumentParser()
parser.add_argument('--label_path', type=str, help='Subset to evaluate the results', default='all')
parser.add_argument('--results_path', type=str, help='Subset to evaluate the results', default='all')
args, _ = parser.parse_known_args()
csv_name_global = f'global_results.csv'
csv_name_per_sequence = f'per-sequence_results.csv'
# Check if the method has been evaluated before, if so read the results, otherwise compute the results
csv_name_global_path = os.path.join(args.results_path, csv_name_global)
csv_name_per_sequence_path = os.path.join(args.results_path, csv_name_per_sequence)
print(f'Evaluating sequences...')
# Create dataset and evaluate
dataset_eval = MaskEvaluation(root=args.label_path, sequences=seq_list)
metrics_res = dataset_eval.evaluate(args.results_path)
J, F = metrics_res['J'], metrics_res['F']
# Generate dataframe for the general results
g_measures = ['J&F-Mean', 'J-Mean', 'J-Recall', 'J-Decay', 'F-Mean', 'F-Recall', 'F-Decay']
final_mean = (np.mean(J["M"]) + np.mean(F["M"])) / 2.
g_res = np.array([final_mean, np.mean(J["M"]), np.mean(J["R"]), np.mean(J["D"]), np.mean(F["M"]), np.mean(F["R"]),
np.mean(F["D"])])
g_res = np.reshape(g_res, [1, len(g_res)])
table_g = pd.DataFrame(data=g_res, columns=g_measures)
with open(csv_name_global_path, 'w') as f:
table_g.to_csv(f, index=False, float_format="%.3f")
print(f'Global results saved in {csv_name_global_path}')
# Generate a dataframe for the per sequence results
seq_names = list(J['M_per_object'].keys())
seq_measures = ['Sequence', 'J-Mean', 'F-Mean']
J_per_object = [J['M_per_object'][x] for x in seq_names]
F_per_object = [F['M_per_object'][x] for x in seq_names]
table_seq = pd.DataFrame(data=list(zip(seq_names, J_per_object, F_per_object)), columns=seq_measures)
with open(csv_name_per_sequence_path, 'w') as f:
table_seq.to_csv(f, index=False, float_format="%.3f")
print(f'Per-sequence results saved in {csv_name_per_sequence_path}')
# Print the results
sys.stdout.write(f"--------------------------- Global results ---------------------------\n")
print(table_g.to_string(index=False))
# sys.stdout.write(f"\n---------- Per sequence results ----------\n")
# print(table_seq.to_string(index=False))
total_time = time() - time_start
sys.stdout.write('\nTotal time:' + str(total_time))
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