import numpy as np


class DATConfig:
    img_scale_target_diagonal = 75
    search_win_padding = 2
    surr_win_factor = 1.9
    color_space = 'rgb'
    num_bins = 16
    prob_lut_update_rate = 0.05
    distractor_aware = True
    adapt_thresh_prob_bins = np.arange(0, 1.001, 0.05)
    motion_estimation_history_size = 5
    nms_scale = 1
    nms_overlap = 0.9
    nms_score_factor = 0.5
    nms_include_center_vote = True

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class DATConfig:
    <mask token>
    <mask token>
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    <mask token>
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    <mask token>
    <mask token>
    <mask token>
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    <mask token>
    <mask token>

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class DATConfig:
    img_scale_target_diagonal = 75
    search_win_padding = 2
    surr_win_factor = 1.9
    color_space = 'rgb'
    num_bins = 16
    prob_lut_update_rate = 0.05
    distractor_aware = True
    adapt_thresh_prob_bins = np.arange(0, 1.001, 0.05)
    motion_estimation_history_size = 5
    nms_scale = 1
    nms_overlap = 0.9
    nms_score_factor = 0.5
    nms_include_center_vote = True

import numpy as np


class DATConfig:
    img_scale_target_diagonal = 75
    search_win_padding = 2
    surr_win_factor = 1.9
    color_space = 'rgb'
    num_bins = 16
    prob_lut_update_rate = 0.05
    distractor_aware = True
    adapt_thresh_prob_bins = np.arange(0, 1.001, 0.05)
    motion_estimation_history_size = 5
    nms_scale = 1
    nms_overlap = 0.9
    nms_score_factor = 0.5
    nms_include_center_vote = True
