<jupyter_start><jupyter_text>Generating Results on Datasets<jupyter_code>%load_ext autoreload
%autoreload 2
%matplotlib inline
import os
import sys
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [14, 8]

sys.path.append('../..')
from pytracking.analysis.plot_results import plot_results, print_results, print_per_sequence_results
from pytracking.evaluation import Tracker, get_dataset, trackerlist<jupyter_output>The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload<jupyter_text>Plots for OTB, NFS and UAV<jupyter_code>trackers = []
trackers.extend(trackerlist('atom', 'default', None, 'ATOM'))


dataset = get_dataset('eotb')
plot_results(trackers, dataset, 'EOTB', merge_results=True, plot_types=('success', 'prec','norm_prec'), 
             skip_missing_seq=True, force_evaluation=True, plot_bin_gap=0.05, exclude_invalid_frames=False)<jupyter_output>100%|██████████| 81/81 [00:04<00:00, 18.50it/s]<jupyter_text>Plots for LaSOT<jupyter_code>trackers = []
trackers.extend(trackerlist('atom', 'default', range(0,5), 'ATOM'))
trackers.extend(trackerlist('dimp', 'dimp18', range(0,5), 'DiMP18'))
trackers.extend(trackerlist('dimp', 'dimp50', range(0,5), 'DiMP50'))
trackers.extend(trackerlist('dimp', 'prdimp18', range(0,5), 'PrDiMP18'))
trackers.extend(trackerlist('dimp', 'prdimp50', range(0,5), 'PrDiMP50'))

dataset = get_dataset('lasot')
plot_results(trackers, dataset, 'LaSOT', merge_results=True, plot_types=('success'), 
             skip_missing_seq=False, force_evaluation=True, plot_bin_gap=0.05)<jupyter_output><empty_output><jupyter_text>Tables for OTB, NFS, UAV and LaSOT<jupyter_code>trackers = []
trackers.extend(trackerlist('atom', 'default', range(0,5), 'ATOM'))
trackers.extend(trackerlist('dimp', 'dimp18', range(0,5), 'DiMP18'))
trackers.extend(trackerlist('dimp', 'dimp50', range(0,5), 'DiMP50'))
trackers.extend(trackerlist('dimp', 'prdimp18', range(0,5), 'PrDiMP18'))
trackers.extend(trackerlist('dimp', 'prdimp50', range(0,5), 'PrDiMP50'))

dataset = get_dataset('otb')
print_results(trackers, dataset, 'OTB', merge_results=True, plot_types=('success', 'prec', 'norm_prec'))

dataset = get_dataset('nfs')
print_results(trackers, dataset, 'NFS', merge_results=True, plot_types=('success', 'prec', 'norm_prec'))

dataset = get_dataset('uav')
print_results(trackers, dataset, 'UAV', merge_results=True, plot_types=('success', 'prec', 'norm_prec'))

dataset = get_dataset('otb', 'nfs', 'uav')
print_results(trackers, dataset, 'OTB+NFS+UAV', merge_results=True, plot_types=('success', 'prec', 'norm_prec'))

dataset = get_dataset('lasot')
print_results(trackers, dataset, 'LaSOT', merge_results=True, plot_types=('success', 'prec', 'norm_prec'))<jupyter_output><empty_output><jupyter_text>Filtered per-sequence results<jupyter_code># Print per sequence results for sequences where all trackers fail, i.e. all trackers have average overlap in percentage of less than 10.0
filter_criteria = {'mode': 'ao_max', 'threshold': 100.0}
dataset = get_dataset('eotb')
print_per_sequence_results(trackers, dataset, 'EOTB', merge_results=True, filter_criteria=filter_criteria, force_evaluation=False)
# Print per sequence results for sequences where at least one tracker fails, i.e. a tracker has average overlap in percentage of less than 10.0
filter_criteria = {'mode': 'ao_min', 'threshold': 99.0}
dataset = get_dataset('eotb')
print_per_sequence_results(trackers, dataset, 'OTB+NFS+UAV', merge_results=True, filter_criteria=filter_criteria, force_evaluation=False)
# Print per sequence results for sequences where the trackers have differing behavior.
# i.e. average overlap in percentage for different trackers on a sequence differ by at least 40.0
filter_criteria = {'mode': 'delta_ao', 'threshold': 40.0}
dataset = get_dataset('otb', 'nfs', 'uav')
print_per_sequence_results(trackers, dataset, 'OTB+NFS+UAV', merge_results=True, filter_criteria=filter_criteria, force_evaluation=False)
# Print per sequence results for all sequences
filter_criteria = None
dataset = get_dataset('eotb')
print_per_sequence_results(trackers, dataset, 'OTB+NFS+UAV', merge_results=False, filter_criteria=filter_criteria, force_evaluation=False)<jupyter_output>| ATOM       |
bottle_sim (ID=0)            | 68.86      |
cow_high (ID=1)              | 85.10      |
ship_low (ID=2)              | 6.29       |
truck_hdr (ID=3)             | 9.54       |
cup_low (ID=4)               | 7.14       |
giraffe_hdr (ID=5)           | 3.39       |
whale_low (ID=6)             | 39.70      |
toy_car_low (ID=7)           | 65.55      |
giraffe2 (ID=8)              | 61.51      |
dog3 (ID=9)                  | 71.55      |
ball_motion (ID=10)          | 11.45      |
elephant_motion (ID=11)      | 63.30      |
fighter3 (ID=12)             | 36.14      |
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