797 lines
36 KiB
Python
797 lines
36 KiB
Python
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import tikzplotlib
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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import os.path as osp
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import torch
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import pickle
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import json
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from lib.test.evaluation.environment import env_settings
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from lib.test.analysis.extract_results import extract_results
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def get_plot_draw_styles():
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plot_draw_style = [
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# {'color': (1.0, 0.0, 0.0), 'line_style': '-'},
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# {'color': (0.0, 1.0, 0.0), 'line_style': '-'},
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{'color': (0.0, 1.0, 0.0), 'line_style': '-'},
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{'color': (0.0, 0.0, 0.0), 'line_style': '-'},
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{'color': (1.0, 0.0, 1.0), 'line_style': '-'},
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{'color': (0.0, 1.0, 1.0), 'line_style': '-'},
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{'color': (0.5, 0.5, 0.5), 'line_style': '-'},
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{'color': (136.0 / 255.0, 0.0, 21.0 / 255.0), 'line_style': '-'},
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{'color': (1.0, 127.0 / 255.0, 39.0 / 255.0), 'line_style': '-'},
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{'color': (0.0, 162.0 / 255.0, 232.0 / 255.0), 'line_style': '-'},
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{'color': (0.0, 0.5, 0.0), 'line_style': '-'},
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{'color': (1.0, 0.5, 0.2), 'line_style': '-'},
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{'color': (0.1, 0.4, 0.0), 'line_style': '-'},
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{'color': (0.6, 0.3, 0.9), 'line_style': '-'},
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{'color': (0.4, 0.7, 0.1), 'line_style': '-'},
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{'color': (0.2, 0.1, 0.7), 'line_style': '-'},
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{'color': (0.7, 0.6, 0.2), 'line_style': '-'}]
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return plot_draw_style
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def check_eval_data_is_valid(eval_data, trackers, dataset):
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""" Checks if the pre-computed results are valid"""
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seq_names = [s.name for s in dataset]
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seq_names_saved = eval_data['sequences']
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tracker_names_f = [(t.name, t.parameter_name, t.run_id) for t in trackers]
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tracker_names_f_saved = [(t['name'], t['param'], t['run_id']) for t in eval_data['trackers']]
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return seq_names == seq_names_saved and tracker_names_f == tracker_names_f_saved
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def merge_multiple_runs(eval_data):
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new_tracker_names = []
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ave_success_rate_plot_overlap_merged = []
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ave_success_rate_plot_center_merged = []
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ave_success_rate_plot_center_norm_merged = []
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avg_overlap_all_merged = []
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ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap'])
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ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center'])
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ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm'])
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avg_overlap_all = torch.tensor(eval_data['avg_overlap_all'])
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trackers = eval_data['trackers']
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merged = torch.zeros(len(trackers), dtype=torch.uint8)
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for i in range(len(trackers)):
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if merged[i]:
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continue
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base_tracker = trackers[i]
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new_tracker_names.append(base_tracker)
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match = [t['name'] == base_tracker['name'] and t['param'] == base_tracker['param'] for t in trackers]
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match = torch.tensor(match)
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ave_success_rate_plot_overlap_merged.append(ave_success_rate_plot_overlap[:, match, :].mean(1))
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ave_success_rate_plot_center_merged.append(ave_success_rate_plot_center[:, match, :].mean(1))
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ave_success_rate_plot_center_norm_merged.append(ave_success_rate_plot_center_norm[:, match, :].mean(1))
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avg_overlap_all_merged.append(avg_overlap_all[:, match].mean(1))
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merged[match] = 1
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ave_success_rate_plot_overlap_merged = torch.stack(ave_success_rate_plot_overlap_merged, dim=1)
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ave_success_rate_plot_center_merged = torch.stack(ave_success_rate_plot_center_merged, dim=1)
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ave_success_rate_plot_center_norm_merged = torch.stack(ave_success_rate_plot_center_norm_merged, dim=1)
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avg_overlap_all_merged = torch.stack(avg_overlap_all_merged, dim=1)
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eval_data['trackers'] = new_tracker_names
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eval_data['ave_success_rate_plot_overlap'] = ave_success_rate_plot_overlap_merged.tolist()
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eval_data['ave_success_rate_plot_center'] = ave_success_rate_plot_center_merged.tolist()
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eval_data['ave_success_rate_plot_center_norm'] = ave_success_rate_plot_center_norm_merged.tolist()
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eval_data['avg_overlap_all'] = avg_overlap_all_merged.tolist()
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return eval_data
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def get_tracker_display_name(tracker):
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if tracker['disp_name'] is None:
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if tracker['run_id'] is None:
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disp_name = '{}_{}'.format(tracker['name'], tracker['param'])
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else:
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disp_name = '{}_{}_{:03d}'.format(tracker['name'], tracker['param'],
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tracker['run_id'])
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else:
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disp_name = tracker['disp_name']
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return disp_name
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def plot_draw_save(y, x, scores, trackers, plot_draw_styles, result_plot_path, plot_opts):
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plt.rcParams['text.usetex']=True
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plt.rcParams["font.family"] = "Times New Roman"
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# Plot settings
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font_size = plot_opts.get('font_size', 25)
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font_size_axis = plot_opts.get('font_size_axis', 20)
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line_width = plot_opts.get('line_width', 2)
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font_size_legend = plot_opts.get('font_size_legend', 15)
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plot_type = plot_opts['plot_type']
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legend_loc = plot_opts['legend_loc']
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if 'attr' in plot_opts:
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attr = plot_opts['attr']
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else:
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attr = None
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xlabel = plot_opts['xlabel']
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ylabel = plot_opts['ylabel']
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ylabel = "%s"%(ylabel.replace('%','\%'))
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xlim = plot_opts['xlim']
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ylim = plot_opts['ylim']
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title = r"\textbf{%s}" %(plot_opts['title'])
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print
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matplotlib.rcParams.update({'font.size': font_size})
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matplotlib.rcParams.update({'axes.titlesize': font_size_axis})
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matplotlib.rcParams.update({'axes.titleweight': 'black'})
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matplotlib.rcParams.update({'axes.labelsize': font_size_axis})
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fig, ax = plt.subplots()
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index_sort = scores.argsort(descending=False)
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plotted_lines = []
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legend_text = []
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for id, id_sort in enumerate(index_sort):
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if trackers[id_sort]['disp_name'].startswith('SAMURAI'):
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alpha = 1.0
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line_style = '-'
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if trackers[id_sort]['disp_name'] == 'SAMURAI-L':
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color = (1.0, 0.0, 0.0)
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elif trackers[id_sort]['disp_name'] == 'SAMURAI-B':
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color = (0.0, 0.0, 1.0)
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elif trackers[id_sort]['disp_name'].startswith('SAM2.1'):
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alpha = 0.8
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line_style = '--'
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if trackers[id_sort]['disp_name'] == 'SAM2.1-L':
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color = (1.0, 0.0, 0.0)
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elif trackers[id_sort]['disp_name'] == 'SAM2.1-B':
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color = (0.0, 0.0, 1.0)
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else:
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alpha = 0.5
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color = plot_draw_styles[index_sort.numel() - id - 1]['color']
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line_style = ":"
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line = ax.plot(x.tolist(), y[id_sort, :].tolist(),
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linewidth=line_width,
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color=color,
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linestyle=line_style,
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alpha=alpha)
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plotted_lines.append(line[0])
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tracker = trackers[id_sort]
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disp_name = get_tracker_display_name(tracker)
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legend_text.append('{} [{:.1f}]'.format(disp_name, scores[id_sort]))
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try:
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# add bold to top method
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# for i in range(1,2):
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# legend_text[-i] = r'\textbf{%s}'%(legend_text[-i])
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for id, id_sort in enumerate(index_sort):
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if trackers[id_sort]['disp_name'].startswith('SAMTrack'):
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legend_text[id] = r'\textbf{%s}'%(legend_text[id])
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ax.legend(plotted_lines[::-1], legend_text[::-1], loc=legend_loc, fancybox=False, edgecolor='black',
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fontsize=font_size_legend, framealpha=1.0)
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except:
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pass
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ax.set(xlabel=xlabel,
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ylabel=ylabel,
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xlim=xlim, ylim=ylim,
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title=title)
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ax.grid(True, linestyle='-.')
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fig.tight_layout()
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def tikzplotlib_fix_ncols(obj):
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"""
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workaround for matplotlib 3.6 renamed legend's _ncol to _ncols, which breaks tikzplotlib
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"""
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if hasattr(obj, "_ncols"):
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obj._ncol = obj._ncols
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for child in obj.get_children():
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tikzplotlib_fix_ncols(child)
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tikzplotlib_fix_ncols(fig)
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# tikzplotlib.save('{}/{}_plot.tex'.format(result_plot_path, plot_type))
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if attr is not None:
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fig.savefig('{}/{}_{}_plot.pdf'.format(result_plot_path, plot_type, attr), dpi=300, format='pdf', transparent=True)
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else:
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fig.savefig('{}/{}_plot.pdf'.format(result_plot_path, plot_type), dpi=300, format='pdf', transparent=True)
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plt.draw()
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def check_and_load_precomputed_results(trackers, dataset, report_name, force_evaluation=False, **kwargs):
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# Load data
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settings = env_settings()
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# Load pre-computed results
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result_plot_path = os.path.join(settings.result_plot_path, report_name)
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eval_data_path = os.path.join(result_plot_path, 'eval_data.pkl')
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if os.path.isfile(eval_data_path) and not force_evaluation:
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with open(eval_data_path, 'rb') as fh:
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eval_data = pickle.load(fh)
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else:
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# print('Pre-computed evaluation data not found. Computing results!')
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eval_data = extract_results(trackers, dataset, report_name, **kwargs)
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if not check_eval_data_is_valid(eval_data, trackers, dataset):
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# print('Pre-computed evaluation data invalid. Re-computing results!')
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eval_data = extract_results(trackers, dataset, report_name, **kwargs)
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# pass
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else:
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# Update display names
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tracker_names = [{'name': t.name, 'param': t.parameter_name, 'run_id': t.run_id, 'disp_name': t.display_name}
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for t in trackers]
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eval_data['trackers'] = tracker_names
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with open(eval_data_path, 'wb') as fh:
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pickle.dump(eval_data, fh)
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return eval_data
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def get_auc_curve(ave_success_rate_plot_overlap, valid_sequence):
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ave_success_rate_plot_overlap = ave_success_rate_plot_overlap[valid_sequence, :, :]
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auc_curve = ave_success_rate_plot_overlap.mean(0) * 100.0
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auc = auc_curve.mean(-1)
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return auc_curve, auc
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def get_prec_curve(ave_success_rate_plot_center, valid_sequence):
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ave_success_rate_plot_center = ave_success_rate_plot_center[valid_sequence, :, :]
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prec_curve = ave_success_rate_plot_center.mean(0) * 100.0
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prec_score = prec_curve[:, 20]
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return prec_curve, prec_score
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def plot_per_attribute_results(trackers, dataset, report_name, merge_results=False,
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plot_types=('success'), **kwargs):
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# Load data
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settings = env_settings()
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plot_draw_styles = get_plot_draw_styles()
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# Load pre-computed results
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result_plot_path = os.path.join(settings.result_plot_path, report_name)
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eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, **kwargs)
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tracker_names = eval_data['trackers']
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valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool)
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attr_folder = 'data/LaSOT/att'
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attr_list = ['Illumination Variation', 'Partial Occlusion', 'Deformation', 'Motion Blur', 'Camera Motion', 'Rotation', 'Background Clutter', 'Viewpoint Change', 'Scale Variation', 'Full Occlusion', 'Fast Motion', 'Out-of-View', 'Low Resolution', 'Aspect Ration Change']
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attr_list = ['IV', 'POC', 'DEF', 'MB', 'CM', 'ROT', 'BC', 'VC', 'SV', 'FOC', 'FM', 'OV', 'LR', 'ARC']
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# Iterate over the sequence and construct a valid_sequence for each attribute
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valid_sequence_attr = {}
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for attr in attr_list:
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valid_sequence_attr[attr] = torch.zeros(valid_sequence.shape[0], dtype=torch.bool)
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for seq_id, seq_obj in enumerate(dataset):
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seq_name = seq_obj.name
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attr_txt = osp.join(attr_folder, f'{seq_name}.txt')
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if osp.exists(attr_txt):
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# read the attribute file into a list of True and False
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# the attribute file looks like this: 0,0,0,0,0,1,0,1,1,0,0,0,0,0
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attr_anno = np.loadtxt(attr_txt, dtype=int, delimiter=',')
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# broadcast the valid_sequence to the attribute list
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for attr_id, attr in enumerate(attr_list):
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valid_sequence_attr[attr][seq_id] = attr_anno[attr_id]
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else:
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raise Exception(f'Attribute file not found for sequence {seq_name}')
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tracker_names = eval_data['trackers']
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if report_name == 'LaSOT-ext':
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ylim_success = (0, 95)
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ylim_precision = (0, 85)
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ylim_norm_precision = (0, 85)
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report_name = "LaSOT_{ext}"
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else:
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ylim_success = (0, 95)
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ylim_precision = (0, 100)
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ylim_norm_precision = (0, 88)
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threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap'])
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ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap'])
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ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center'])
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ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm'])
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for attr in attr_list:
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scores = {}
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print(f'{attr}: {valid_sequence_attr[attr].sum().item()}')
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|
|
valid_sequence_attr[attr] = valid_sequence_attr[attr] & valid_sequence
|
||
|
|
|
||
|
|
auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence_attr[attr])
|
||
|
|
scores['AUC'] = auc
|
||
|
|
|
||
|
|
prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence_attr[attr])
|
||
|
|
scores['Precision'] = prec_score
|
||
|
|
|
||
|
|
norm_prec_curve, norm_prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence_attr[attr])
|
||
|
|
scores['Norm Precision'] = norm_prec_score
|
||
|
|
|
||
|
|
tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names]
|
||
|
|
report_text = generate_formatted_report(tracker_disp_names, scores, table_name=attr)
|
||
|
|
print(report_text)
|
||
|
|
|
||
|
|
success_plot_opts = {'plot_type': 'success', 'legend_loc': 'lower left', 'xlabel': 'Overlap threshold', 'attr': attr,
|
||
|
|
'ylabel': 'Overlap Precision [%]', 'xlim': (0, 1.0), 'ylim': ylim_success, 'title': f'Success\ of\ {attr}\ ({report_name})'}
|
||
|
|
plot_draw_save(auc_curve, threshold_set_overlap, auc, tracker_names, plot_draw_styles, result_plot_path, success_plot_opts)
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
def plot_results(trackers, dataset, report_name, merge_results=False,
|
||
|
|
plot_types=('success'), force_evaluation=False, **kwargs):
|
||
|
|
"""
|
||
|
|
Plot results for the given trackers
|
||
|
|
|
||
|
|
args:
|
||
|
|
trackers - List of trackers to evaluate
|
||
|
|
dataset - List of sequences to evaluate
|
||
|
|
report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved
|
||
|
|
merge_results - If True, multiple random runs for a non-deterministic trackers are averaged
|
||
|
|
plot_types - List of scores to display. Can contain 'success',
|
||
|
|
'prec' (precision), and 'norm_prec' (normalized precision)
|
||
|
|
"""
|
||
|
|
# Load data
|
||
|
|
settings = env_settings()
|
||
|
|
|
||
|
|
plot_draw_styles = get_plot_draw_styles()
|
||
|
|
|
||
|
|
# Load pre-computed results
|
||
|
|
result_plot_path = os.path.join(settings.result_plot_path, report_name)
|
||
|
|
eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, force_evaluation, **kwargs)
|
||
|
|
|
||
|
|
# Merge results from multiple runs
|
||
|
|
if merge_results:
|
||
|
|
eval_data = merge_multiple_runs(eval_data)
|
||
|
|
|
||
|
|
tracker_names = eval_data['trackers']
|
||
|
|
|
||
|
|
valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool)
|
||
|
|
|
||
|
|
print('\nPlotting results over {} / {} sequences'.format(valid_sequence.long().sum().item(), valid_sequence.shape[0]))
|
||
|
|
|
||
|
|
print('\nGenerating plots for: {}'.format(report_name))
|
||
|
|
|
||
|
|
print(report_name)
|
||
|
|
if report_name == 'LaSOT':
|
||
|
|
ylim_success = (0, 95)
|
||
|
|
ylim_precision = (0, 95)
|
||
|
|
ylim_norm_precision = (0, 95)
|
||
|
|
elif report_name == 'LaSOT-ext':
|
||
|
|
ylim_success = (0, 85)
|
||
|
|
ylim_precision = (0, 85)
|
||
|
|
ylim_norm_precision = (0, 85)
|
||
|
|
else:
|
||
|
|
ylim_success = (0, 85)
|
||
|
|
ylim_precision = (0, 85)
|
||
|
|
ylim_norm_precision = (0, 85)
|
||
|
|
|
||
|
|
# ******************************** Success Plot **************************************
|
||
|
|
if 'success' in plot_types:
|
||
|
|
ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap'])
|
||
|
|
|
||
|
|
# Index out valid sequences
|
||
|
|
auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence)
|
||
|
|
threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap'])
|
||
|
|
|
||
|
|
success_plot_opts = {'plot_type': 'success', 'legend_loc': 'lower left', 'xlabel': 'Overlap threshold',
|
||
|
|
'ylabel': 'Overlap Precision [%]', 'xlim': (0, 1.0), 'ylim': ylim_success, 'title': f'Success\ ({report_name})'}
|
||
|
|
plot_draw_save(auc_curve, threshold_set_overlap, auc, tracker_names, plot_draw_styles, result_plot_path, success_plot_opts)
|
||
|
|
|
||
|
|
# ******************************** Precision Plot **************************************
|
||
|
|
if 'prec' in plot_types:
|
||
|
|
ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center'])
|
||
|
|
|
||
|
|
# Index out valid sequences
|
||
|
|
prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence)
|
||
|
|
threshold_set_center = torch.tensor(eval_data['threshold_set_center'])
|
||
|
|
|
||
|
|
precision_plot_opts = {'plot_type': 'precision', 'legend_loc': 'lower right',
|
||
|
|
'xlabel': 'Location error threshold [pixels]', 'ylabel': 'Distance Precision [%]',
|
||
|
|
'xlim': (0, 50), 'ylim': ylim_precision, 'title': f'Precision\ ({report_name})'}
|
||
|
|
plot_draw_save(prec_curve, threshold_set_center, prec_score, tracker_names, plot_draw_styles, result_plot_path,
|
||
|
|
precision_plot_opts)
|
||
|
|
|
||
|
|
# ******************************** Norm Precision Plot **************************************
|
||
|
|
if 'norm_prec' in plot_types:
|
||
|
|
ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm'])
|
||
|
|
|
||
|
|
# Index out valid sequences
|
||
|
|
prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence)
|
||
|
|
threshold_set_center_norm = torch.tensor(eval_data['threshold_set_center_norm'])
|
||
|
|
|
||
|
|
norm_precision_plot_opts = {'plot_type': 'norm_precision', 'legend_loc': 'lower right',
|
||
|
|
'xlabel': 'Location error threshold', 'ylabel': 'Distance Precision [%]',
|
||
|
|
'xlim': (0, 0.5), 'ylim': ylim_norm_precision, 'title': f'Normalized\ Precision\ ({report_name})'}
|
||
|
|
plot_draw_save(prec_curve, threshold_set_center_norm, prec_score, tracker_names, plot_draw_styles, result_plot_path,
|
||
|
|
norm_precision_plot_opts)
|
||
|
|
|
||
|
|
plt.show()
|
||
|
|
|
||
|
|
|
||
|
|
def generate_formatted_report(row_labels, scores, table_name=''):
|
||
|
|
name_width = max([len(d) for d in row_labels] + [len(table_name)]) + 5
|
||
|
|
min_score_width = 10
|
||
|
|
|
||
|
|
report_text = '\n{label: <{width}} |'.format(label=table_name, width=name_width)
|
||
|
|
|
||
|
|
score_widths = [max(min_score_width, len(k) + 3) for k in scores.keys()]
|
||
|
|
|
||
|
|
for s, s_w in zip(scores.keys(), score_widths):
|
||
|
|
report_text = '{prev} {s: <{width}} |'.format(prev=report_text, s=s, width=s_w)
|
||
|
|
|
||
|
|
report_text = '{prev}\n'.format(prev=report_text)
|
||
|
|
|
||
|
|
for trk_id, d_name in enumerate(row_labels):
|
||
|
|
# display name
|
||
|
|
report_text = '{prev}{tracker: <{width}} |'.format(prev=report_text, tracker=d_name,
|
||
|
|
width=name_width)
|
||
|
|
for (score_type, score_value), s_w in zip(scores.items(), score_widths):
|
||
|
|
report_text = '{prev} {score: <{width}} |'.format(prev=report_text,
|
||
|
|
score='{:0.2f}'.format(score_value[trk_id].item()),
|
||
|
|
width=s_w)
|
||
|
|
report_text = '{prev}\n'.format(prev=report_text)
|
||
|
|
|
||
|
|
return report_text
|
||
|
|
|
||
|
|
def print_per_attribute_results(trackers, dataset, report_name, merge_results=False,
|
||
|
|
plot_types=('success'), **kwargs):
|
||
|
|
# Load pre-computed results
|
||
|
|
eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, **kwargs)
|
||
|
|
|
||
|
|
tracker_names = eval_data['trackers']
|
||
|
|
valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool)
|
||
|
|
|
||
|
|
attr_folder = 'data/LaSOT/att'
|
||
|
|
|
||
|
|
attr_list = ['Illumination Variation', 'Partial Occlusion', 'Deformation', 'Motion Blur', 'Camera Motion', 'Rotation', 'Background Clutter', 'Viewpoint Change', 'Scale Variation', 'Full Occlusion', 'Fast Motion', 'Out-of-View', 'Low Resolution', 'Aspect Ration Change']
|
||
|
|
attr_list = ['IV', 'POC', 'DEF', 'MB', 'CM', 'ROT', 'BC', 'VC', 'SV', 'FOC', 'FM', 'OV', 'LR', 'ARC']
|
||
|
|
|
||
|
|
# Iterate over the sequence and construct a valid_sequence for each attribute
|
||
|
|
valid_sequence_attr = {}
|
||
|
|
for attr in attr_list:
|
||
|
|
valid_sequence_attr[attr] = torch.zeros(valid_sequence.shape[0], dtype=torch.bool)
|
||
|
|
for seq_id, seq_obj in enumerate(dataset):
|
||
|
|
seq_name = seq_obj.name
|
||
|
|
attr_txt = osp.join(attr_folder, f'{seq_name}.txt')
|
||
|
|
if osp.exists(attr_txt):
|
||
|
|
# read the attribute file into a list of True and False
|
||
|
|
# the attribute file looks like this: 0,0,0,0,0,1,0,1,1,0,0,0,0,0
|
||
|
|
attr_anno = np.loadtxt(attr_txt, dtype=int, delimiter=',')
|
||
|
|
# broadcast the valid_sequence to the attribute list
|
||
|
|
for attr_id, attr in enumerate(attr_list):
|
||
|
|
valid_sequence_attr[attr][seq_id] = attr_anno[attr_id]
|
||
|
|
else:
|
||
|
|
raise Exception(f'Attribute file not found for sequence {seq_name}')
|
||
|
|
|
||
|
|
tracker_names = eval_data['trackers']
|
||
|
|
|
||
|
|
threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap'])
|
||
|
|
ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap'])
|
||
|
|
ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center'])
|
||
|
|
ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm'])
|
||
|
|
for attr in attr_list:
|
||
|
|
scores = {}
|
||
|
|
|
||
|
|
print(f'{attr}: {valid_sequence_attr[attr].sum().item()}')
|
||
|
|
valid_sequence_attr[attr] = valid_sequence_attr[attr] & valid_sequence
|
||
|
|
|
||
|
|
auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence_attr[attr])
|
||
|
|
scores['AUC'] = auc
|
||
|
|
|
||
|
|
prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence_attr[attr])
|
||
|
|
scores['Precision'] = prec_score
|
||
|
|
|
||
|
|
norm_prec_curve, norm_prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence_attr[attr])
|
||
|
|
scores['Norm Precision'] = norm_prec_score
|
||
|
|
|
||
|
|
tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names]
|
||
|
|
report_text = generate_formatted_report(tracker_disp_names, scores, table_name=attr)
|
||
|
|
print(report_text)
|
||
|
|
|
||
|
|
|
||
|
|
def print_results(trackers, dataset, report_name, merge_results=False,
|
||
|
|
plot_types=('success'), **kwargs):
|
||
|
|
""" Print the results for the given trackers in a formatted table
|
||
|
|
args:
|
||
|
|
trackers - List of trackers to evaluate
|
||
|
|
dataset - List of sequences to evaluate
|
||
|
|
report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved
|
||
|
|
merge_results - If True, multiple random runs for a non-deterministic trackers are averaged
|
||
|
|
plot_types - List of scores to display. Can contain 'success' (prints AUC, OP50, and OP75 scores),
|
||
|
|
'prec' (prints precision score), and 'norm_prec' (prints normalized precision score)
|
||
|
|
"""
|
||
|
|
# Load pre-computed results
|
||
|
|
eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, **kwargs)
|
||
|
|
|
||
|
|
# Merge results from multiple runs
|
||
|
|
if merge_results:
|
||
|
|
eval_data = merge_multiple_runs(eval_data)
|
||
|
|
|
||
|
|
tracker_names = eval_data['trackers']
|
||
|
|
valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool)
|
||
|
|
|
||
|
|
print('\nReporting results over {} / {} sequences'.format(valid_sequence.long().sum().item(), valid_sequence.shape[0]))
|
||
|
|
|
||
|
|
scores = {}
|
||
|
|
|
||
|
|
# ******************************** Success Plot **************************************
|
||
|
|
if 'success' in plot_types:
|
||
|
|
threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap'])
|
||
|
|
ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap'])
|
||
|
|
|
||
|
|
# Index out valid sequences
|
||
|
|
auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence)
|
||
|
|
scores['AUC'] = auc
|
||
|
|
scores['OP50'] = auc_curve[:, threshold_set_overlap == 0.50]
|
||
|
|
scores['OP75'] = auc_curve[:, threshold_set_overlap == 0.75]
|
||
|
|
|
||
|
|
# ******************************** Precision Plot **************************************
|
||
|
|
if 'prec' in plot_types:
|
||
|
|
ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center'])
|
||
|
|
|
||
|
|
# Index out valid sequences
|
||
|
|
prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence)
|
||
|
|
scores['Precision'] = prec_score
|
||
|
|
|
||
|
|
# ******************************** Norm Precision Plot *********************************
|
||
|
|
if 'norm_prec' in plot_types:
|
||
|
|
ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm'])
|
||
|
|
|
||
|
|
# Index out valid sequences
|
||
|
|
norm_prec_curve, norm_prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence)
|
||
|
|
scores['Norm Precision'] = norm_prec_score
|
||
|
|
|
||
|
|
# Print
|
||
|
|
tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names]
|
||
|
|
report_text = generate_formatted_report(tracker_disp_names, scores, table_name=report_name)
|
||
|
|
print(report_text)
|
||
|
|
|
||
|
|
|
||
|
|
def plot_got_success(trackers, report_name):
|
||
|
|
""" Plot success plot for GOT-10k dataset using the json reports.
|
||
|
|
Save the json reports from http://got-10k.aitestunion.com/leaderboard in the directory set to
|
||
|
|
env_settings.got_reports_path
|
||
|
|
|
||
|
|
The tracker name in the experiment file should be set to the name of the report file for that tracker,
|
||
|
|
e.g. DiMP50_report_2019_09_02_15_44_25 if the report is name DiMP50_report_2019_09_02_15_44_25.json
|
||
|
|
|
||
|
|
args:
|
||
|
|
trackers - List of trackers to evaluate
|
||
|
|
report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved
|
||
|
|
"""
|
||
|
|
# Load data
|
||
|
|
settings = env_settings()
|
||
|
|
plot_draw_styles = get_plot_draw_styles()
|
||
|
|
|
||
|
|
result_plot_path = os.path.join(settings.result_plot_path, report_name)
|
||
|
|
|
||
|
|
auc_curve = torch.zeros((len(trackers), 101))
|
||
|
|
scores = torch.zeros(len(trackers))
|
||
|
|
|
||
|
|
# Load results
|
||
|
|
tracker_names = []
|
||
|
|
for trk_id, trk in enumerate(trackers):
|
||
|
|
json_path = '{}/{}.json'.format(settings.got_reports_path, trk.name)
|
||
|
|
|
||
|
|
if os.path.isfile(json_path):
|
||
|
|
with open(json_path, 'r') as f:
|
||
|
|
eval_data = json.load(f)
|
||
|
|
else:
|
||
|
|
raise Exception('Report not found {}'.format(json_path))
|
||
|
|
|
||
|
|
if len(eval_data.keys()) > 1:
|
||
|
|
raise Exception
|
||
|
|
|
||
|
|
# First field is the tracker name. Index it out
|
||
|
|
eval_data = eval_data[list(eval_data.keys())[0]]
|
||
|
|
if 'succ_curve' in eval_data.keys():
|
||
|
|
curve = eval_data['succ_curve']
|
||
|
|
ao = eval_data['ao']
|
||
|
|
elif 'overall' in eval_data.keys() and 'succ_curve' in eval_data['overall'].keys():
|
||
|
|
curve = eval_data['overall']['succ_curve']
|
||
|
|
ao = eval_data['overall']['ao']
|
||
|
|
else:
|
||
|
|
raise Exception('Invalid JSON file {}'.format(json_path))
|
||
|
|
|
||
|
|
auc_curve[trk_id, :] = torch.tensor(curve) * 100.0
|
||
|
|
scores[trk_id] = ao * 100.0
|
||
|
|
|
||
|
|
tracker_names.append({'name': trk.name, 'param': trk.parameter_name, 'run_id': trk.run_id,
|
||
|
|
'disp_name': trk.display_name})
|
||
|
|
|
||
|
|
threshold_set_overlap = torch.arange(0.0, 1.01, 0.01, dtype=torch.float64)
|
||
|
|
|
||
|
|
success_plot_opts = {'plot_type': 'success', 'legend_loc': 'lower left', 'xlabel': 'Overlap threshold',
|
||
|
|
'ylabel': 'Overlap Precision [%]', 'xlim': (0, 1.0), 'ylim': (0, 100), 'title': 'Success plot'}
|
||
|
|
plot_draw_save(auc_curve, threshold_set_overlap, scores, tracker_names, plot_draw_styles, result_plot_path,
|
||
|
|
success_plot_opts)
|
||
|
|
plt.show()
|
||
|
|
|
||
|
|
|
||
|
|
def print_per_sequence_results(trackers, dataset, report_name, merge_results=False,
|
||
|
|
filter_criteria=None, **kwargs):
|
||
|
|
""" Print per-sequence results for the given trackers. Additionally, the sequences to list can be filtered using
|
||
|
|
the filter criteria.
|
||
|
|
|
||
|
|
args:
|
||
|
|
trackers - List of trackers to evaluate
|
||
|
|
dataset - List of sequences to evaluate
|
||
|
|
report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved
|
||
|
|
merge_results - If True, multiple random runs for a non-deterministic trackers are averaged
|
||
|
|
filter_criteria - Filter sequence results which are reported. Following modes are supported
|
||
|
|
None: No filtering. Display results for all sequences in dataset
|
||
|
|
'ao_min': Only display sequences for which the minimum average overlap (AO) score over the
|
||
|
|
trackers is less than a threshold filter_criteria['threshold']. This mode can
|
||
|
|
be used to select sequences where at least one tracker performs poorly.
|
||
|
|
'ao_max': Only display sequences for which the maximum average overlap (AO) score over the
|
||
|
|
trackers is less than a threshold filter_criteria['threshold']. This mode can
|
||
|
|
be used to select sequences all tracker performs poorly.
|
||
|
|
'delta_ao': Only display sequences for which the performance of different trackers vary by at
|
||
|
|
least filter_criteria['threshold'] in average overlap (AO) score. This mode can
|
||
|
|
be used to select sequences where the behaviour of the trackers greatly differ
|
||
|
|
between each other.
|
||
|
|
"""
|
||
|
|
# Load pre-computed results
|
||
|
|
eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, **kwargs)
|
||
|
|
|
||
|
|
# Merge results from multiple runs
|
||
|
|
if merge_results:
|
||
|
|
eval_data = merge_multiple_runs(eval_data)
|
||
|
|
|
||
|
|
tracker_names = eval_data['trackers']
|
||
|
|
valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool)
|
||
|
|
sequence_names = eval_data['sequences']
|
||
|
|
avg_overlap_all = torch.tensor(eval_data['avg_overlap_all']) * 100.0
|
||
|
|
|
||
|
|
# Filter sequences
|
||
|
|
if filter_criteria is not None:
|
||
|
|
if filter_criteria['mode'] == 'ao_min':
|
||
|
|
min_ao = avg_overlap_all.min(dim=1)[0]
|
||
|
|
valid_sequence = valid_sequence & (min_ao < filter_criteria['threshold'])
|
||
|
|
elif filter_criteria['mode'] == 'ao_max':
|
||
|
|
max_ao = avg_overlap_all.max(dim=1)[0]
|
||
|
|
valid_sequence = valid_sequence & (max_ao < filter_criteria['threshold'])
|
||
|
|
elif filter_criteria['mode'] == 'delta_ao':
|
||
|
|
min_ao = avg_overlap_all.min(dim=1)[0]
|
||
|
|
max_ao = avg_overlap_all.max(dim=1)[0]
|
||
|
|
valid_sequence = valid_sequence & ((max_ao - min_ao) > filter_criteria['threshold'])
|
||
|
|
else:
|
||
|
|
raise Exception
|
||
|
|
|
||
|
|
avg_overlap_all = avg_overlap_all[valid_sequence, :]
|
||
|
|
sequence_names = [s + ' (ID={})'.format(i) for i, (s, v) in enumerate(zip(sequence_names, valid_sequence.tolist())) if v]
|
||
|
|
|
||
|
|
tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names]
|
||
|
|
|
||
|
|
scores_per_tracker = {k: avg_overlap_all[:, i] for i, k in enumerate(tracker_disp_names)}
|
||
|
|
report_text = generate_formatted_report(sequence_names, scores_per_tracker)
|
||
|
|
|
||
|
|
print(report_text)
|
||
|
|
|
||
|
|
|
||
|
|
def print_results_per_video(trackers, dataset, report_name, merge_results=False,
|
||
|
|
plot_types=('success'), per_video=False, **kwargs):
|
||
|
|
""" Print the results for the given trackers in a formatted table
|
||
|
|
args:
|
||
|
|
trackers - List of trackers to evaluate
|
||
|
|
dataset - List of sequences to evaluate
|
||
|
|
report_name - Name of the folder in env_settings.perm_mat_path where the computed results and plots are saved
|
||
|
|
merge_results - If True, multiple random runs for a non-deterministic trackers are averaged
|
||
|
|
plot_types - List of scores to display. Can contain 'success' (prints AUC, OP50, and OP75 scores),
|
||
|
|
'prec' (prints precision score), and 'norm_prec' (prints normalized precision score)
|
||
|
|
"""
|
||
|
|
# Load pre-computed results
|
||
|
|
eval_data = check_and_load_precomputed_results(trackers, dataset, report_name, **kwargs)
|
||
|
|
|
||
|
|
# Merge results from multiple runs
|
||
|
|
if merge_results:
|
||
|
|
eval_data = merge_multiple_runs(eval_data)
|
||
|
|
|
||
|
|
seq_lens = len(eval_data['sequences'])
|
||
|
|
eval_datas = [{} for _ in range(seq_lens)]
|
||
|
|
if per_video:
|
||
|
|
for key, value in eval_data.items():
|
||
|
|
if len(value) == seq_lens:
|
||
|
|
for i in range(seq_lens):
|
||
|
|
eval_datas[i][key] = [value[i]]
|
||
|
|
else:
|
||
|
|
for i in range(seq_lens):
|
||
|
|
eval_datas[i][key] = value
|
||
|
|
|
||
|
|
tracker_names = eval_data['trackers']
|
||
|
|
valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool)
|
||
|
|
|
||
|
|
print('\nReporting results over {} / {} sequences'.format(valid_sequence.long().sum().item(), valid_sequence.shape[0]))
|
||
|
|
|
||
|
|
scores = {}
|
||
|
|
|
||
|
|
# ******************************** Success Plot **************************************
|
||
|
|
if 'success' in plot_types:
|
||
|
|
threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap'])
|
||
|
|
ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap'])
|
||
|
|
|
||
|
|
# Index out valid sequences
|
||
|
|
auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence)
|
||
|
|
scores['AUC'] = auc
|
||
|
|
scores['OP50'] = auc_curve[:, threshold_set_overlap == 0.50]
|
||
|
|
scores['OP75'] = auc_curve[:, threshold_set_overlap == 0.75]
|
||
|
|
|
||
|
|
# ******************************** Precision Plot **************************************
|
||
|
|
if 'prec' in plot_types:
|
||
|
|
ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center'])
|
||
|
|
|
||
|
|
# Index out valid sequences
|
||
|
|
prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence)
|
||
|
|
scores['Precision'] = prec_score
|
||
|
|
|
||
|
|
# ******************************** Norm Precision Plot *********************************
|
||
|
|
if 'norm_prec' in plot_types:
|
||
|
|
ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm'])
|
||
|
|
|
||
|
|
# Index out valid sequences
|
||
|
|
norm_prec_curve, norm_prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence)
|
||
|
|
scores['Norm Precision'] = norm_prec_score
|
||
|
|
|
||
|
|
# Print
|
||
|
|
tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names]
|
||
|
|
report_text = generate_formatted_report(tracker_disp_names, scores, table_name=report_name)
|
||
|
|
print(report_text)
|
||
|
|
|
||
|
|
if per_video:
|
||
|
|
for i in range(seq_lens):
|
||
|
|
eval_data = eval_datas[i]
|
||
|
|
|
||
|
|
print('\n{} sequences'.format(eval_data['sequences'][0]))
|
||
|
|
|
||
|
|
scores = {}
|
||
|
|
valid_sequence = torch.tensor(eval_data['valid_sequence'], dtype=torch.bool)
|
||
|
|
|
||
|
|
# ******************************** Success Plot **************************************
|
||
|
|
if 'success' in plot_types:
|
||
|
|
threshold_set_overlap = torch.tensor(eval_data['threshold_set_overlap'])
|
||
|
|
ave_success_rate_plot_overlap = torch.tensor(eval_data['ave_success_rate_plot_overlap'])
|
||
|
|
|
||
|
|
# Index out valid sequences
|
||
|
|
auc_curve, auc = get_auc_curve(ave_success_rate_plot_overlap, valid_sequence)
|
||
|
|
scores['AUC'] = auc
|
||
|
|
scores['OP50'] = auc_curve[:, threshold_set_overlap == 0.50]
|
||
|
|
scores['OP75'] = auc_curve[:, threshold_set_overlap == 0.75]
|
||
|
|
|
||
|
|
# ******************************** Precision Plot **************************************
|
||
|
|
if 'prec' in plot_types:
|
||
|
|
ave_success_rate_plot_center = torch.tensor(eval_data['ave_success_rate_plot_center'])
|
||
|
|
|
||
|
|
# Index out valid sequences
|
||
|
|
prec_curve, prec_score = get_prec_curve(ave_success_rate_plot_center, valid_sequence)
|
||
|
|
scores['Precision'] = prec_score
|
||
|
|
|
||
|
|
# ******************************** Norm Precision Plot *********************************
|
||
|
|
if 'norm_prec' in plot_types:
|
||
|
|
ave_success_rate_plot_center_norm = torch.tensor(eval_data['ave_success_rate_plot_center_norm'])
|
||
|
|
|
||
|
|
# Index out valid sequences
|
||
|
|
norm_prec_curve, norm_prec_score = get_prec_curve(ave_success_rate_plot_center_norm, valid_sequence)
|
||
|
|
scores['Norm Precision'] = norm_prec_score
|
||
|
|
|
||
|
|
# Print
|
||
|
|
tracker_disp_names = [get_tracker_display_name(trk) for trk in tracker_names]
|
||
|
|
report_text = generate_formatted_report(tracker_disp_names, scores, table_name=report_name)
|
||
|
|
print(report_text)
|