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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 DAVISEvaluation
default_davis_path = 'data/ref-davis/DAVIS'
time_start = time()
parser = argparse.ArgumentParser()
parser.add_argument('--davis_path', type=str, help='Path to the DAVIS folder containing the JPEGImages, Annotations, '
'ImageSets, Annotations_unsupervised folders',
required=False, default=default_davis_path)
parser.add_argument('--set', type=str, help='Subset to evaluate the results', default='val') # val subset
parser.add_argument('--task', type=str, help='Task to evaluate the results', default='unsupervised',
choices=['semi-supervised', 'unsupervised'])
parser.add_argument('--results_path', type=str, help='Path to the folder containing the sequences folders',
required=True)
args, _ = parser.parse_known_args()
csv_name_global = f'global_results-{args.set}.csv'
csv_name_per_sequence = f'per-sequence_results-{args.set}.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)
if os.path.exists(csv_name_global_path) and os.path.exists(csv_name_per_sequence_path):
print('Using precomputed results...')
table_g = pd.read_csv(csv_name_global_path)
table_seq = pd.read_csv(csv_name_per_sequence_path)
else:
print(f'Evaluating sequences for the {args.task} task...')
# Create dataset and evaluate
dataset_eval = DAVISEvaluation(davis_root=args.davis_path, task=args.task, gt_set=args.set)
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="%.5f")
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="%.5f")
print(f'Per-sequence results saved in {csv_name_per_sequence_path}')
# Print the results
sys.stdout.write(f"--------------------------- Global results for {args.set} ---------------------------\n")
print(table_g.to_string(index=False))
sys.stdout.write(f"\n---------- Per sequence results for {args.set} ----------\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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