dynamic_predictor/davis/motion_mask_davis.py

#!/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_davis.py --label_path /home/remote/main/data/davis/DAVIS/Annotations/480p --results_path /home/remote/project/DyGS/InstantSplat/data/davis

python motion_mask_davis.py --label_path /home/remote/main/data/davis/DAVIS/Annotations/480p --results_path /home/remote/project/DyGS/InstantSplat/baselines/3dgs/davis

'''




seq_list = ['soapbox', 'camel', 'motocross-jump', 'dog', 'car-shadow', 'blackswan', 'horsejump-high', 'parkour']

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))