① import _init_paths
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [8, 8]
import argparse

from lib.test.analysis.plot_results import plot_results, print_results, print_per_sequence_results
from lib.test.evaluation import get_dataset, trackerlist


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Run tracker on sequence or dataset.')
    parser.add_argument('--dataset', type=str, default='coesot', help='coesot or fe108 or visevent or eed.')
    parser.add_argument('--parameter_name', type=str, default='ceutrack_coesot', help='coesot or fe108 or visevent or eed.')
    args = parser.parse_args()

    trackers = []
    dataset_name = args.dataset      # coesot  fe108
    """pytracking"""
    # trackers.extend(trackerlist('atom', 'default', None, range(0,5), 'ATOM'))
    """ceutrack"""
    trackers.extend(trackerlist(name='ceutrack', parameter_name=args.parameter_name, dataset_name=dataset_name,
                                run_ids=None, display_name='CEUTrack256'))
    dataset = get_dataset(dataset_name)
    print_results(trackers, dataset, dataset_name, merge_results=True, plot_types=('success', 'norm_prec', 'prec')) 和 ② import importlib
import os
from collections import OrderedDict
from lib.test.evaluation.environment import env_settings
import time
import cv2 as cv

from lib.utils.lmdb_utils import decode_img
from pathlib import Path
import numpy as np
import scipy.io as scio
import torch.nn.functional as F
import torch

def trackerlist(name: str, parameter_name: str, dataset_name: str, run_ids = None, display_name: str = None,
                result_only=False):
    """Generate list of trackers.
    args:
        name: Name of tracking method.
        parameter_name: Name of parameter file.
        run_ids: A single or list of run_ids.
        display_name: Name to be displayed in the result plots.
    """
    if run_ids is None or isinstance(run_ids, int):
        run_ids = [run_ids]
    return [Tracker(name, parameter_name, dataset_name, run_id, display_name, result_only) for run_id in run_ids]


class Tracker:
    """Wraps the tracker for evaluation and running purposes.
    args:
        name: Name of tracking method.
        parameter_name: Name of parameter file.
        run_id: The run id.
        display_name: Name to be displayed in the result plots.
    """

    def __init__(self, name: str, parameter_name: str, dataset_name: str, run_id: int = None, display_name: str = None,
                 result_only=False):
        assert run_id is None or isinstance(run_id, int)

        self.name = name
        self.parameter_name = parameter_name
        self.dataset_name = dataset_name
        self.run_id = run_id
        self.display_name = display_name

        env = env_settings()
        if self.run_id is None:
            self.results_dir = '{}/{}/{}'.format(env.results_path, self.name, self.parameter_name)
        else:
            self.results_dir = '{}/{}/{}_{:03d}'.format(env.results_path, self.name, self.parameter_name, self.run_id)
        if result_only:
            self.results_dir = '{}/{}'.format(env.results_path, self.name)

        tracker_module_abspath = os.path.abspath(os.path.join(os.path.dirname(__file__),
                                                              '..', 'tracker', '%s.py' % self.name))
        if os.path.isfile(tracker_module_abspath):
            tracker_module = importlib.import_module('lib.test.tracker.{}'.format(self.name))
            self.tracker_class = tracker_module.get_tracker_class()
        else:
            self.tracker_class = None

    def create_tracker(self, params):
        tracker = self.tracker_class(params, self.dataset_name)
        return tracker

    def run_sequence(self, seq, debug=None):
        """Run tracker on sequence.
        args:
            seq: Sequence to run the tracker on.
            visualization: Set visualization flag (None means default value specified in the parameters).
            debug: Set debug level (None means default value specified in the parameters).
            multiobj_mode: Which mode to use for multiple objects.
        """
        params = self.get_parameters()

        debug_ = debug
        if debug is None:
            debug_ = getattr(params, 'debug', 0)

        params.debug = debug_

        # Get init information
        init_info = seq.init_info(0)

        tracker = self.create_tracker(params)

        output = self._track_sequence(tracker, seq, init_info)
        return output

    def _track_sequence(self, tracker, seq, init_info):
        # Define outputs
        # Each field in output is a list containing tracker prediction for each frame.

        # In case of single object tracking mode:
        # target_bbox[i] is the predicted bounding box for frame i
        # time[i] is the processing time for frame i

        # In case of multi object tracking mode:
        # target_bbox[i] is an OrderedDict, where target_bbox[i][obj_id] is the predicted box for target obj_id in
        # frame i
        # time[i] is either the processing time for frame i, or an OrderedDict containing processing times for each
        # object in frame i

        output = {'target_bbox': [],
                  'time': []}
        if tracker.params.save_all_boxes:
            output['all_boxes'] = []
            output['all_scores'] = []

        def _store_outputs(tracker_out: dict, defaults=None):
            defaults = {} if defaults is None else defaults
            for key in output.keys():
                val = tracker_out.get(key, defaults.get(key, None))
                if key in tracker_out or val is not None:
                    output[key].append(val)

        right_template = False  # flag for while
        i = 0  # template  index,
        # Initialize
        while not right_template:
            image = self._read_image(seq.frames[i])
            # event_image = self._read_image(seq.event_img_list[i])
            # image = cv.addWeighted(image, 1, event_image, 0.2, 0)

            # event_template
            event_template = scio.loadmat(seq.event_frames[i])
            gt_bbox = seq.ground_truth_rect[i]
            if (os.path.getsize(seq.event_frames[i]) == 0) or (np.isnan(event_template['features']).any()):
                event_template = torch.zeros([1024, 19])
                right_template = True
                print(seq.event_frames[i], i, 'template voxel is empty/nan, only based on rgb frames  for tracking.')
            elif gt_bbox[2]*gt_bbox[3] < 1:
                right_template = False
                print(seq.event_frames[i], 'idx bbox zero, without any target or too small.')
                i = i+1
            else:
                event_template = np.concatenate((event_template['coor'], event_template['features']), axis=1)
                event_template = torch.from_numpy(event_template)
                right_template = True
                init_info['init_bbox'] = gt_bbox

        start_time = time.time()
        out = tracker.initialize(image, event_template, init_info, idx=i)
        if out is None:
            out = {}
        prev_output = OrderedDict(out)
        if i != 0:
            for idx in range(0, i+1):
                target_box_template = seq.ground_truth_rect[idx]
                init_info = {'target_bbox': target_box_template, 'time': 0}
                _store_outputs(out, init_info)
        else:
            target_box_template = init_info.get('init_bbox')
            init_default = {'target_bbox': target_box_template,
                            'time': time.time() - start_time}
            _store_outputs(out, init_default)
        if tracker.params.save_all_boxes:
            init_default['all_boxes'] = out['all_boxes']
            init_default['all_scores'] = out['all_scores']
            _store_outputs(out, init_default)

        for frame_num, (frame_path, frame_event_path) in enumerate(zip(seq.frames[i+1:], seq.event_frames[i+1:]), start=1):
        # for frame_num, (frame_path, frame_event_path, event_img_path) in enumerate(zip(seq.frames[i+1:], seq.event_frames[i+1:], seq.event_img_list[i+1:]), start=1):
            image = self._read_image(frame_path)
            # event_img = self._read_image(event_img_path)
            # image = cv.addWeighted(image, 1, event_img, 0.2, 0)

            # event_search
            if os.path.getsize(frame_event_path) == 0:
                event_search = torch.zeros([4096, 19])
                print(frame_event_path, 'idx frame of search voxel is empty. only based rgb')
            else:
                event_search = scio.loadmat(frame_event_path)
                event_search = np.concatenate((event_search['coor'], event_search['features']), axis=1)
                event_search = torch.from_numpy(event_search)
                if np.isnan(event_search).any():
                    event_search = torch.zeros([4096, 19])
                    print(frame_event_path, 'idx frame of search voxel exist  nan, only based on rgb.')

            start_time = time.time()

            info = seq.frame_info(frame_num)
            info['previous_output'] = prev_output

            if len(seq.ground_truth_rect) > 1:
                info['gt_bbox'] = seq.ground_truth_rect[frame_num]
            out = tracker.track(image, event_search, info)
            prev_output = OrderedDict(out)
            _store_outputs(out, {'time': time.time() - start_time})

        for key in ['target_bbox', 'all_boxes', 'all_scores']:
            if key in output and len(output[key]) <= 1:
                output.pop(key)

        return output

    def run_video(self, videofilepath, optional_box=None, debug=None, visdom_info=None, save_results=False):
        """Run the tracker with the vieofile.
        args:
            debug: Debug level.
        """

        params = self.get_parameters()

        debug_ = debug
        if debug is None:
            debug_ = getattr(params, 'debug', 0)
        params.debug = debug_

        params.tracker_name = self.name
        params.param_name = self.parameter_name
        # self._init_visdom(visdom_info, debug_)

        multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default'))

        if multiobj_mode == 'default':
            tracker = self.create_tracker(params)

        elif multiobj_mode == 'parallel':
            tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom, fast_load=True)
        else:
            raise ValueError('Unknown multi object mode {}'.format(multiobj_mode))

        assert os.path.isfile(videofilepath), "Invalid param {}".format(videofilepath)
        ", videofilepath must be a valid videofile"

        output_boxes = []

        cap = cv.VideoCapture(videofilepath)
        display_name = 'Display: ' + tracker.params.tracker_name
        cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
        cv.resizeWindow(display_name, 960, 720)
        success, frame = cap.read()
        cv.imshow(display_name, frame)

        def _build_init_info(box):
            return {'init_bbox': box}

        if success is not True:
            print("Read frame from {} failed.".format(videofilepath))
            exit(-1)
        if optional_box is not None:
            assert isinstance(optional_box, (list, tuple))
            assert len(optional_box) == 4, "valid box's foramt is [x,y,w,h]"
            tracker.initialize(frame, _build_init_info(optional_box))
            output_boxes.append(optional_box)
        else:
            while True:
                # cv.waitKey()
                frame_disp = frame.copy()

                cv.putText(frame_disp, 'Select target ROI and press ENTER', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL,
                           1.5, (0, 0, 0), 1)

                x, y, w, h = cv.selectROI(display_name, frame_disp, fromCenter=False)
                init_state = [x, y, w, h]
                tracker.initialize(frame, _build_init_info(init_state))
                output_boxes.append(init_state)
                break

        while True:
            ret, frame = cap.read()

            if frame is None:
                break

            frame_disp = frame.copy()

            # Draw box
            out = tracker.track(frame)
            state = [int(s) for s in out['target_bbox']]
            output_boxes.append(state)

            cv.rectangle(frame_disp, (state[0], state[1]), (state[2] + state[0], state[3] + state[1]),
                         (0, 255, 0), 5)

            font_color = (0, 0, 0)
            cv.putText(frame_disp, 'Tracking!', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1,
                       font_color, 1)
            cv.putText(frame_disp, 'Press r to reset', (20, 55), cv.FONT_HERSHEY_COMPLEX_SMALL, 1,
                       font_color, 1)
            cv.putText(frame_disp, 'Press q to quit', (20, 80), cv.FONT_HERSHEY_COMPLEX_SMALL, 1,
                       font_color, 1)

            # Display the resulting frame
            cv.imshow(display_name, frame_disp)
            key = cv.waitKey(1)
            if key == ord('q'):
                break
            elif key == ord('r'):
                ret, frame = cap.read()
                frame_disp = frame.copy()

                cv.putText(frame_disp, 'Select target ROI and press ENTER', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5,
                           (0, 0, 0), 1)

                cv.imshow(display_name, frame_disp)
                x, y, w, h = cv.selectROI(display_name, frame_disp, fromCenter=False)
                init_state = [x, y, w, h]
                tracker.initialize(frame, _build_init_info(init_state))
                output_boxes.append(init_state)

        # When everything done, release the capture
        cap.release()
        cv.destroyAllWindows()

        if save_results:
            if not os.path.exists(self.results_dir):
                os.makedirs(self.results_dir)
            video_name = Path(videofilepath).stem
            base_results_path = os.path.join(self.results_dir, 'video_{}'.format(video_name))

            tracked_bb = np.array(output_boxes).astype(int)
            bbox_file = '{}.txt'.format(base_results_path)
            np.savetxt(bbox_file, tracked_bb, delimiter='\t', fmt='%d')


    def get_parameters(self):
        """Get parameters."""
        param_module = importlib.import_module('lib.test.parameter.{}'.format(self.name))
        params = param_module.parameters(self.parameter_name)
        return params

    def _read_image(self, image_file: str):
        if isinstance(image_file, str):
            im = cv.imread(image_file)
            return cv.cvtColor(im, cv.COLOR_BGR2RGB)
        elif isinstance(image_file, list) and len(image_file) == 2:
            return decode_img(image_file[0], image_file[1])
        else:
            raise ValueError("type of image_file should be str or list")
③ import tikzplotlib
import matplotlib
import matplotlib.pyplot as plt
import os
import torch
import pickle
import json
from lib.test.evaluation.environment import env_settings
from lib.test.analysis.extract_results import extract_results


def get_plot_draw_styles():
    plot_draw_style = [{'color': (1.0, 0.0, 0.0), 'line_style': '-'},
                       {'color': (0.0, 1.0, 0.0), 'line_style': '-'},
                       {'color': (0.0, 0.0, 1.0), 'line_style': '-'},
                       {'color': (0.0, 0.0, 0.0), 'line_style': '-'},
                       {'color': (1.0, 0.0, 1.0), 'line_style': '-'},
                       {'color': (0.0, 1.0, 1.0), 'line_style': '-'},
                       {'color': (0.5, 0.5, 0.5), 'line_style': '-'},
                       {'color': (136.0 / 255.0, 0.0, 21.0 / 255.0), 'line_style': '-'},
                       {'color': (1.0, 127.0 / 255.0, 39.0 / 255.0), 'line_style': '-'},
                       {'color': (0.0, 162.0 / 255.0, 232.0 / 255.0), 'line_style': '-'},
                       {'color': (0.0, 0.5, 0.0), 'line_style': '-'},
                       {'color': (1.0, 0.5, 0.2), 'line_style': '-'},
                       {'color': (0.1, 0.4, 0.0), 'line_style': '-'},
                       {'color': (0.6, 0.3, 0.9), 'line_style': '-'},
                       {'color': (0.4, 0.7, 0.1), 'line_style': '-'},
                       {'color': (0.2, 0.1, 0.7), 'line_style': '-'},
                       {'color': (0.7, 0.6, 0.2), 'line_style': '-'}]

    return plot_draw_style


def check_eval_data_is_valid(eval_data, trackers, dataset):
    """ Checks if the pre-computed results are valid"""
    seq_names = [s.name for s in dataset]
    seq_names_saved = eval_data['sequences']

    tracker_names_f = [(t.name, t.parameter_name, t.run_id) for t in trackers]
    tracker_names_f_saved = [(t['name'], t['param'], t['run_id']) for t in eval_data['trackers']]

    return seq_names == seq_names_saved and tracker_names_f == tracker_names_f_saved


def merge_multiple_runs(eval_data):
    new_tracker_names = []
    ave_success_rate_plot_overlap_merged = []
    ave_success_rate_plot_center_merged = []
    ave_success_rate_plot_center_norm_merged = []
    avg_overlap_all_merged = []

    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'])
    avg_overlap_all = torch.tensor(eval_data['avg_overlap_all'])

    trackers = eval_data['trackers']
    merged = torch.zeros(len(trackers), dtype=torch.uint8)
    for i in range(len(trackers)):
        if merged[i]:
            continue
        base_tracker = trackers[i]
        new_tracker_names.append(base_tracker)

        match = [t['name'] == base_tracker['name'] and t['param'] == base_tracker['param'] for t in trackers]
        match = torch.tensor(match)

        ave_success_rate_plot_overlap_merged.append(ave_success_rate_plot_overlap[:, match, :].mean(1))
        ave_success_rate_plot_center_merged.append(ave_success_rate_plot_center[:, match, :].mean(1))
        ave_success_rate_plot_center_norm_merged.append(ave_success_rate_plot_center_norm[:, match, :].mean(1))
        avg_overlap_all_merged.append(avg_overlap_all[:, match].mean(1))

        merged[match] = 1

    ave_success_rate_plot_overlap_merged = torch.stack(ave_success_rate_plot_overlap_merged, dim=1)
    ave_success_rate_plot_center_merged = torch.stack(ave_success_rate_plot_center_merged, dim=1)
    ave_success_rate_plot_center_norm_merged = torch.stack(ave_success_rate_plot_center_norm_merged, dim=1)
    avg_overlap_all_merged = torch.stack(avg_overlap_all_merged, dim=1)

    eval_data['trackers'] = new_tracker_names
    eval_data['ave_success_rate_plot_overlap'] = ave_success_rate_plot_overlap_merged.tolist()
    eval_data['ave_success_rate_plot_center'] = ave_success_rate_plot_center_merged.tolist()
    eval_data['ave_success_rate_plot_center_norm'] = ave_success_rate_plot_center_norm_merged.tolist()
    eval_data['avg_overlap_all'] = avg_overlap_all_merged.tolist()

    return eval_data


def get_tracker_display_name(tracker):
    if tracker['disp_name'] is None:
        if tracker['run_id'] is None:
            disp_name = '{}_{}'.format(tracker['name'], tracker['param'])
        else:
            disp_name = '{}_{}_{:03d}'.format(tracker['name'], tracker['param'],
                                              tracker['run_id'])
    else:
        disp_name = tracker['disp_name']

    return  disp_name


def plot_draw_save(y, x, scores, trackers, plot_draw_styles, result_plot_path, plot_opts):
    plt.rcParams['text.usetex']=True
    plt.rcParams["font.family"] = "Times New Roman"
    # Plot settings
    font_size = plot_opts.get('font_size', 20)
    font_size_axis = plot_opts.get('font_size_axis', 20)
    line_width = plot_opts.get('line_width', 2)
    font_size_legend = plot_opts.get('font_size_legend', 20)

    plot_type = plot_opts['plot_type']
    legend_loc = plot_opts['legend_loc']

    xlabel = plot_opts['xlabel']
    ylabel = plot_opts['ylabel']
    ylabel = "%s"%(ylabel.replace('%','\%'))
    xlim = plot_opts['xlim']
    ylim = plot_opts['ylim']

    title = r"$\bf{%s}$" %(plot_opts['title'])

    matplotlib.rcParams.update({'font.size': font_size})
    matplotlib.rcParams.update({'axes.titlesize': font_size_axis})
    matplotlib.rcParams.update({'axes.titleweight': 'black'})
    matplotlib.rcParams.update({'axes.labelsize': font_size_axis})

    fig, ax = plt.subplots()

    index_sort = scores.argsort(descending=False)

    plotted_lines = []
    legend_text = []

    for id, id_sort in enumerate(index_sort):
        line = ax.plot(x.tolist(), y[id_sort, :].tolist(),
                       linewidth=line_width,
                       color=plot_draw_styles[index_sort.numel() - id - 1]['color'],
                       linestyle=plot_draw_styles[index_sort.numel() - id - 1]['line_style'])

        plotted_lines.append(line[0])

        tracker = trackers[id_sort]
        disp_name = get_tracker_display_name(tracker)

        legend_text.append('{} [{:.1f}]'.format(disp_name, scores[id_sort]))

    try:
        # add bold to our method
        for i in range(1,2):
            legend_text[-i] = r'\textbf{%s}'%(legend_text[-i])

        ax.legend(plotted_lines[::-1], legend_text[::-1], loc=legend_loc, fancybox=False, edgecolor='black',
                  fontsize=font_size_legend, framealpha=1.0)
    except:
        pass

    ax.set(xlabel=xlabel,
           ylabel=ylabel,
           xlim=xlim, ylim=ylim,
           title=title)

    ax.grid(True, linestyle='-.')
    fig.tight_layout()

    tikzplotlib.save('{}/{}_plot.tex'.format(result_plot_path, plot_type))
    fig.savefig('{}/{}_plot.pdf'.format(result_plot_path, plot_type), dpi=300, format='pdf', transparent=True)
    plt.draw()


def check_and_load_precomputed_results(trackers, dataset, report_name, force_evaluation=False, **kwargs):
    # Load data
    settings = env_settings()

    # Load pre-computed results
    result_plot_path = os.path.join(settings.result_plot_path, report_name)
    eval_data_path = os.path.join(result_plot_path, 'eval_data.pkl')

    if os.path.isfile(eval_data_path) and not force_evaluation:
        with open(eval_data_path, 'rb') as fh:
            eval_data = pickle.load(fh)
    else:
        # print('Pre-computed evaluation data not found. Computing results!')
        eval_data = extract_results(trackers, dataset, report_name, **kwargs)

    if not check_eval_data_is_valid(eval_data, trackers, dataset):
        # print('Pre-computed evaluation data invalid. Re-computing results!')
        eval_data = extract_results(trackers, dataset, report_name, **kwargs)
        # pass
    else:
        # Update display names
        tracker_names = [{'name': t.name, 'param': t.parameter_name, 'run_id': t.run_id, 'disp_name': t.display_name}
                         for t in trackers]
        eval_data['trackers'] = tracker_names
    with open(eval_data_path, 'wb') as fh:
        pickle.dump(eval_data, fh)
    return eval_data


def get_auc_curve(ave_success_rate_plot_overlap, valid_sequence):
    ave_success_rate_plot_overlap = ave_success_rate_plot_overlap[valid_sequence, :, :]
    auc_curve = ave_success_rate_plot_overlap.mean(0) * 100.0
    auc = auc_curve.mean(-1)

    return auc_curve, auc


def get_prec_curve(ave_success_rate_plot_center, valid_sequence):
    ave_success_rate_plot_center = ave_success_rate_plot_center[valid_sequence, :, :]
    prec_curve = ave_success_rate_plot_center.mean(0) * 100.0
    prec_score = prec_curve[:, 20]

    return prec_curve, prec_score


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))

    # ********************************  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': (0, 88), 'title': 'Success'}
        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': (0, 100), 'title': 'Precision plot'}
        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': (0, 85), 'title': 'Normalized Precision'}
        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_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)
，当前运行命令：python tracking/vis_results.py --dataset coesot --parameter_name ceutrack_coesot，那么现在我们期望同时评估两个跟踪器，ceutrack和atom，那么应该如何调整，直接将需要调整的部分标出来。