output

(bob_env1) aqua@aquayx:~/Documents/_ia/_work/_class-imgs/_matching$ python3 main.py
lentrain 33
lentest 25
['data/1-roi/jersey/J64/S1_64_6.jpg', 'data/1-roi/jersey/J64/S1_64_7.jpg', 'data/1-roi/jersey/J64/S1_64_10.jpg', 'data/1-roi/jersey/J173/S1_173_2.jpg', 'data/1-roi/jersey/J173/S1_173_14.jpg', 'data/1-roi/jersey/J173/S1_173_20.jpg', 'data/1-roi/jersey/J99/S1_99_3.jpg', 'data/1-roi/jersey/J99/S1_99_4.jpg', 'data/1-roi/jersey/J99/S1_99_5.jpg', 'data/1-roi/jersey/J99/S1_99_11.jpg', 'data/1-roi/jersey/J99/S1_99_12.jpg', 'data/1-roi/jersey/J9/S1_9_12.jpg', 'data/1-roi/jersey/J9/S1_9_13.jpg', 'data/1-roi/jersey/J9/S1_9_19.jpg', 'data/1-roi/jersey/J128/S1_128_6.jpg', 'data/1-roi/jersey/J128/S1_128_7.jpg', 'data/1-roi/jersey/J128/S2_128_5.jpg', 'data/1-roi/jersey/J89/S1_89_3.jpg', 'data/1-roi/jersey/J89/S1_89_7.jpg', 'data/1-roi/jersey/J89/S1_89_11.jpg', 'data/1-roi/jersey/J73/S1_73_6.jpg', 'data/1-roi/jersey/J73/S1_73_8.jpg', 'data/1-roi/jersey/J73/S1_73_9.jpg', 'data/1-roi/jersey/J73/S1_73_10.jpg', 'data/1-roi/jersey/J25/S1_25_10.jpg', 'data/1-roi/jersey/J25/S1_25_11.jpg', 'data/1-roi/jersey/J25/S1_25_13.jpg', 'data/1-roi/jersey/J423/S1_423_1.jpg', 'data/1-roi/jersey/J423/S1_423_3.jpg', 'data/1-roi/jersey/J423/S1_423_4.jpg', 'data/1-roi/jersey/J7/S1_7_3.jpg', 'data/1-roi/jersey/J7/S1_7_7.jpg', 'data/1-roi/jersey/J7/S1_7_14.jpg']
['data/1-roi/jersey/J64/S2_64_1.jpg', 'data/1-roi/jersey/J64/S2_64_7.jpg', 'data/1-roi/jersey/J173/S2_173_9.jpg', 'data/1-roi/jersey/J173/S2_173_10.jpg', 'data/1-roi/jersey/J99/S1_99_16.jpg', 'data/1-roi/jersey/J99/S2_99_3.jpg', 'data/1-roi/jersey/J99/S2_99_5.jpg', 'data/1-roi/jersey/J99/S2_99_10.jpg', 'data/1-roi/jersey/J9/S2_09_12.jpg', 'data/1-roi/jersey/J9/S2_09_13.jpg', 'data/1-roi/jersey/J9/S2_09_15.jpg', 'data/1-roi/jersey/J128/S2_128_6.jpg', 'data/1-roi/jersey/J128/S2_128_10.jpg', 'data/1-roi/jersey/J89/S2_89_3.jpg', 'data/1-roi/jersey/J89/S2_89_7.jpg', 'data/1-roi/jersey/J89/S2_89_9.jpg', 'data/1-roi/jersey/J73/S2_73_0.jpg', 'data/1-roi/jersey/J73/S2_73_4.jpg', 'data/1-roi/jersey/J73/S2_73_10.jpg', 'data/1-roi/jersey/J25/S2_25_2.jpg', 'data/1-roi/jersey/J25/S2_25_7.jpg', 'data/1-roi/jersey/J423/S2_423_6.jpg', 'data/1-roi/jersey/J423/S2_423_8.jpg', 'data/1-roi/jersey/J7/S2_07_1.jpg', 'data/1-roi/jersey/J7/S2_07_3.jpg']

# 2.1. prepare segmentation model.
# 2.2. segment images.
# 2.2. segment images. - test
len train_seg 33
len test_seg 25

# 3. get graphs
train_jts type <class 'list'>
train_edges type <class 'list'>

# 4. get similarity
current animal: J64
0.739 J64
0.748 J64
0.767 J64
0.715 J173
0.706 J173
0.700 J173
0.740 J99
0.728 J99
0.741 J99
0.720 J99
0.690 J99
0.706 J9
0.709 J9
0.722 J9
0.704 J128
0.721 J128
0.743 J128
0.749 J89
0.760 J89
0.718 J89
0.630 J73
0.657 J73
0.662 J73
0.672 J73
0.683 J25
0.680 J25
0.701 J25
0.705 J423
0.680 J423
0.678 J423
0.714 J7
0.744 J7
0.685 J7
J64 J64
current animal: J64
0.745 J64
0.755 J64
0.771 J64
0.734 J173
0.724 J173
0.722 J173
0.755 J99
0.748 J99
0.758 J99
0.743 J99
0.713 J99
0.727 J9
0.731 J9
0.738 J9
0.722 J128
0.727 J128
0.752 J128
0.760 J89
0.767 J89
0.733 J89
0.663 J73
0.686 J73
0.686 J73
0.687 J73
0.704 J25
0.705 J25
0.715 J25
0.723 J423
0.706 J423
0.704 J423
0.728 J7
0.756 J7
0.708 J7
J64 J64
current animal: J173
0.748 J64
0.744 J64
0.753 J64
0.753 J173
0.750 J173
0.736 J173
0.767 J99
0.762 J99
0.761 J99
0.752 J99
0.736 J99
0.744 J9
0.747 J9
0.761 J9
0.750 J128
0.757 J128
0.753 J128
0.749 J89
0.761 J89
0.752 J89
0.711 J73
0.730 J73
0.724 J73
0.723 J73
0.728 J25
0.720 J25
0.740 J25
0.741 J423
0.731 J423
0.724 J423
0.756 J7
0.766 J7
0.746 J7
J173 J99
current animal: J173
0.758 J64
0.749 J64
0.756 J64
0.758 J173
0.755 J173
0.746 J173
0.774 J99
0.772 J99
0.773 J99
0.758 J99
0.745 J99
0.748 J9
0.751 J9
0.765 J9
0.758 J128
0.762 J128
0.756 J128
0.749 J89
0.770 J89
0.753 J89
0.719 J73
0.730 J73
0.735 J73
0.734 J73
0.739 J25
0.729 J25
0.741 J25
0.749 J423
0.742 J423
0.742 J423
0.760 J7
0.768 J7
0.744 J7
J173 J99
current animal: J99
0.747 J64
0.748 J64
0.738 J64
0.763 J173
0.762 J173
0.747 J173
0.788 J99
0.786 J99
0.783 J99
0.819 J99
0.798 J99
0.757 J9
0.758 J9
0.765 J9
0.759 J128
0.759 J128
0.748 J128
0.750 J89
0.764 J89
0.760 J89
0.731 J73
0.740 J73
0.740 J73
0.739 J73
0.746 J25
0.746 J25
0.758 J25
0.748 J423
0.757 J423
0.749 J423
0.742 J7
0.753 J7
0.734 J7
J99 J99
current animal: J99
0.752 J64
0.747 J64
0.748 J64
0.746 J173
0.744 J173
0.743 J173
0.767 J99
0.762 J99
0.756 J99
0.747 J99
0.739 J99
0.746 J9
0.739 J9
0.752 J9
0.749 J128
0.751 J128
0.752 J128
0.744 J89
0.750 J89
0.739 J89
0.708 J73
0.731 J73
0.718 J73
0.721 J73
0.732 J25
0.732 J25
0.739 J25
0.734 J423
0.731 J423
0.727 J423
0.736 J7
0.754 J7
0.725 J7
J99 J99
current animal: J99
0.754 J64
0.749 J64
0.752 J64
0.745 J173
0.748 J173
0.744 J173
0.765 J99
0.758 J99
0.751 J99
0.746 J99
0.738 J99
0.742 J9
0.740 J9
0.756 J9
0.745 J128
0.752 J128
0.757 J128
0.745 J89
0.749 J89
0.731 J89
0.709 J73
0.724 J73
0.717 J73
0.731 J73
0.736 J25
0.733 J25
0.734 J25
0.736 J423
0.730 J423
0.729 J423
0.729 J7
0.753 J7
0.727 J7
J99 J99
current animal: J99
0.748 J64
0.747 J64
0.746 J64
0.742 J173
0.742 J173
0.742 J173
0.767 J99
0.759 J99
0.756 J99
0.753 J99
0.738 J99
0.747 J9
0.743 J9
0.751 J9
0.742 J128
0.746 J128
0.758 J128
0.742 J89
0.749 J89
0.730 J89
0.707 J73
0.722 J73
0.717 J73
0.724 J73
0.734 J25
0.727 J25
0.734 J25
0.734 J423
0.729 J423
0.723 J423
0.732 J7
0.753 J7
0.728 J7
J99 J99
current animal: J9
0.722 J64
0.728 J64
0.737 J64
0.734 J173
0.742 J173
0.725 J173
0.765 J99
0.759 J99
0.761 J99
0.752 J99
0.734 J99
0.732 J9
0.738 J9
0.752 J9
0.736 J128
0.741 J128
0.730 J128
0.755 J89
0.760 J89
0.749 J89
0.709 J73
0.710 J73
0.708 J73
0.714 J73
0.726 J25
0.717 J25
0.743 J25
0.723 J423
0.711 J423
0.713 J423
0.738 J7
0.754 J7
0.735 J7
J9 J99
current animal: J9
0.730 J64
0.725 J64
0.734 J64
0.739 J173
0.744 J173
0.736 J173
0.766 J99
0.760 J99
0.760 J99
0.749 J99
0.735 J99
0.739 J9
0.743 J9
0.756 J9
0.739 J128
0.746 J128
0.732 J128
0.754 J89
0.762 J89
0.755 J89
0.708 J73
0.719 J73
0.714 J73
0.721 J73
0.731 J25
0.717 J25
0.745 J25
0.727 J423
0.717 J423
0.723 J423
0.739 J7
0.753 J7
0.739 J7
J9 J99
current animal: J9
0.722 J64
0.721 J64
0.732 J64
0.730 J173
0.740 J173
0.726 J173
0.767 J99
0.759 J99
0.760 J99
0.749 J99
0.731 J99
0.733 J9
0.736 J9
0.751 J9
0.730 J128
0.736 J128
0.728 J128
0.750 J89
0.758 J89
0.751 J89
0.704 J73
0.716 J73
0.712 J73
0.710 J73
0.725 J25
0.713 J25
0.743 J25
0.724 J423
0.712 J423
0.715 J423
0.739 J7
0.750 J7
0.732 J7
J9 J99
current animal: J128
0.761 J64
0.768 J64
0.757 J64
0.749 J173
0.741 J173
0.750 J173
0.763 J99
0.757 J99
0.760 J99
0.749 J99
0.738 J99
0.743 J9
0.746 J9
0.740 J9
0.744 J128
0.749 J128
0.808 J128
0.760 J89
0.769 J89
0.741 J89
0.696 J73
0.712 J73
0.703 J73
0.715 J73
0.720 J25
0.713 J25
0.717 J25
0.742 J423
0.739 J423
0.732 J423
0.745 J7
0.768 J7
0.737 J7
J128 J128
current animal: J128
0.759 J64
0.765 J64
0.758 J64
0.748 J173
0.740 J173
0.739 J173
0.761 J99
0.755 J99
0.759 J99
0.750 J99
0.738 J99
0.744 J9
0.739 J9
0.740 J9
0.747 J128
0.745 J128
0.908 J128
0.755 J89
0.766 J89
0.737 J89
0.691 J73
0.710 J73
0.711 J73
0.710 J73
0.716 J25
0.710 J25
0.707 J25
0.741 J423
0.732 J423
0.732 J423
0.745 J7
0.766 J7
0.719 J7
J128 J128
current animal: J89
0.761 J64
0.760 J64
0.751 J64
0.762 J173
0.750 J173
0.752 J173
0.774 J99
0.764 J99
0.765 J99
0.765 J99
0.749 J99
0.750 J9
0.750 J9
0.755 J9
0.752 J128
0.758 J128
0.766 J128
0.763 J89
0.767 J89
0.756 J89
0.724 J73
0.737 J73
0.732 J73
0.734 J73
0.730 J25
0.728 J25
0.740 J25
0.756 J423
0.754 J423
0.746 J423
0.757 J7
0.765 J7
0.732 J7
J89 J99
current animal: J89
0.761 J64
0.755 J64
0.744 J64
0.761 J173
0.750 J173
0.748 J173
0.771 J99
0.761 J99
0.762 J99
0.765 J99
0.743 J99
0.748 J9
0.750 J9
0.750 J9
0.743 J128
0.748 J128
0.761 J128
0.766 J89
0.766 J89
0.755 J89
0.720 J73
0.740 J73
0.733 J73
0.739 J73
0.727 J25
0.722 J25
0.745 J25
0.754 J423
0.749 J423
0.742 J423
0.755 J7
0.764 J7
0.732 J7
J89 J99
current animal: J89
0.760 J64
0.762 J64
0.759 J64
0.743 J173
0.739 J173
0.740 J173
0.757 J99
0.752 J99
0.761 J99
0.753 J99
0.754 J99
0.747 J9
0.749 J9
0.753 J9
0.742 J128
0.748 J128
0.758 J128
0.746 J89
0.754 J89
0.746 J89
0.713 J73
0.716 J73
0.714 J73
0.723 J73
0.709 J25
0.708 J25
0.730 J25
0.743 J423
0.742 J423
0.734 J423
0.740 J7
0.759 J7
0.726 J7
J89 J64
current animal: J73
0.717 J64
0.716 J64
0.734 J64
0.721 J173
0.732 J173
0.723 J173
0.749 J99
0.746 J99
0.748 J99
0.741 J99
0.719 J99
0.734 J9
0.714 J9
0.739 J9
0.732 J128
0.736 J128
0.723 J128
0.749 J89
0.754 J89
0.741 J89
0.683 J73
0.710 J73
0.690 J73
0.698 J73
0.701 J25
0.703 J25
0.727 J25
0.709 J423
0.701 J423
0.700 J423
0.743 J7
0.751 J7
0.725 J7
J73 J89
current animal: J73
0.715 J64
0.717 J64
0.734 J64
0.722 J173
0.740 J173
0.716 J173
0.749 J99
0.747 J99
0.746 J99
0.742 J99
0.718 J99
0.736 J9
0.713 J9
0.737 J9
0.727 J128
0.735 J128
0.723 J128
0.754 J89
0.759 J89
0.736 J89
0.674 J73
0.705 J73
0.690 J73
0.701 J73
0.699 J25
0.701 J25
0.724 J25
0.704 J423
0.699 J423
0.693 J423
0.728 J7
0.754 J7
0.721 J7
J73 J89
current animal: J73
0.718 J64
0.719 J64
0.740 J64
0.720 J173
0.729 J173
0.716 J173
0.747 J99
0.739 J99
0.743 J99
0.740 J99
0.713 J99
0.727 J9
0.710 J9
0.734 J9
0.727 J128
0.734 J128
0.725 J128
0.752 J89
0.759 J89
0.731 J89
0.663 J73
0.691 J73
0.682 J73
0.692 J73
0.691 J25
0.692 J25
0.715 J25
0.702 J423
0.691 J423
0.691 J423
0.726 J7
0.757 J7
0.714 J7
J73 J89
current animal: J25
0.739 J64
0.742 J64
0.746 J64
0.724 J173
0.731 J173
0.717 J173
0.751 J99
0.752 J99
0.750 J99
0.734 J99
0.709 J99
0.725 J9
0.712 J9
0.722 J9
0.729 J128
0.740 J128
0.759 J128
0.763 J89
0.758 J89
0.732 J89
0.669 J73
0.694 J73
0.684 J73
0.701 J73
0.711 J25
0.704 J25
0.730 J25
0.715 J423
0.706 J423
0.694 J423
0.732 J7
0.758 J7
0.716 J7
J25 J89
current animal: J25
0.734 J64
0.743 J64
0.752 J64
0.724 J173
0.736 J173
0.721 J173
0.754 J99
0.743 J99
0.753 J99
0.736 J99
0.706 J99
0.726 J9
0.711 J9
0.724 J9
0.727 J128
0.739 J128
0.759 J128
0.767 J89
0.764 J89
0.730 J89
0.657 J73
0.683 J73
0.672 J73
0.687 J73
0.711 J25
0.703 J25
0.721 J25
0.708 J423
0.700 J423
0.690 J423
0.729 J7
0.754 J7
0.713 J7
J25 J89
current animal: J423
0.763 J64
0.765 J64
0.748 J64
0.762 J173
0.757 J173
0.757 J173
0.772 J99
0.767 J99
0.769 J99
0.767 J99
0.754 J99
0.751 J9
0.758 J9
0.756 J9
0.746 J128
0.751 J128
0.754 J128
0.753 J89
0.761 J89
0.758 J89
0.722 J73
0.739 J73
0.732 J73
0.735 J73
0.722 J25
0.725 J25
0.739 J25
0.757 J423
0.751 J423
0.751 J423
0.748 J7
0.764 J7
0.734 J7
J423 J99
current animal: J423
0.765 J64
0.771 J64
0.756 J64
0.763 J173
0.749 J173
0.754 J173
0.764 J99
0.762 J99
0.763 J99
0.769 J99
0.751 J99
0.753 J9
0.756 J9
0.758 J9
0.746 J128
0.760 J128
0.758 J128
0.754 J89
0.761 J89
0.756 J89
0.713 J73
0.732 J73
0.729 J73
0.735 J73
0.724 J25
0.720 J25
0.731 J25
0.751 J423
0.749 J423
0.745 J423
0.746 J7
0.758 J7
0.736 J7
J423 J64
current animal: J7
0.738 J64
0.741 J64
0.747 J64
0.745 J173
0.748 J173
0.736 J173
0.760 J99
0.748 J99
0.755 J99
0.741 J99
0.726 J99
0.738 J9
0.734 J9
0.741 J9
0.731 J128
0.739 J128
0.756 J128
0.765 J89
0.772 J89
0.751 J89
0.681 J73
0.712 J73
0.699 J73
0.706 J73
0.713 J25
0.705 J25
0.729 J25
0.719 J423
0.718 J423
0.716 J423
0.749 J7
0.773 J7
0.743 J7
J7 J7
current animal: J7
0.742 J64
0.749 J64
0.748 J64
0.749 J173
0.751 J173
0.737 J173
0.759 J99
0.750 J99
0.756 J99
0.746 J99
0.730 J99
0.742 J9
0.737 J9
0.746 J9
0.728 J128
0.735 J128
0.758 J128
0.764 J89
0.771 J89
0.747 J89
0.688 J73
0.713 J73
0.705 J73
0.708 J73
0.715 J25
0.708 J25
0.726 J25
0.729 J423
0.727 J423
0.722 J423
0.750 J7
0.775 J7
0.745 J7
J7 J7

['J64', 'J64', 'J99', 'J99', 'J99', 'J99', 'J99', 'J99', 'J99', 'J99', 'J99', 'J128', 'J128', 'J99', 'J99', 'J64', 'J89', 'J89', 'J89', 'J89', 'J89', 'J99', 'J64', 'J7', 'J7'] ['J64', 'J64', 'J173', 'J173', 'J99', 'J99', 'J99', 'J99', 'J9', 'J9', 'J9', 'J128', 'J128', 'J89', 'J89', 'J89', 'J73', 'J73', 'J73', 'J25', 'J25', 'J423', 'J423', 'J7', 'J7']
0.4