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.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+annotations/tpc268_annotations.json filter=lfs diff=lfs merge=lfs -text

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+}

annotations/tpc268_taxonomy_vectors.json

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+  "Sohmaea laxiflora": [
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+  "Solanum lycopersicum": [
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+  "Solanum melongena": [
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+  "Solanum torvum": [
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+  "Solanum tuberosum": [
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+  "Sorghum bicolor": [
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+  "Spondias dulcis": [
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+  "Stauntonia chinensis": [
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+  "Vaccinium stamineum": [
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+  "Vaccinium vitis-idaea": [
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+  "Vatica mangachapoi": [
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+  "Vernicia fordii": [
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+  "Vincetoxicum koi": [
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+  "Vitis vinifera": [
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+  "Walsholaria magniflora": [
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+  "Walsholaria muelleri": [
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+  "Pascalia mexicana": [
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+  "Wissadula excelsior": [
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+  ],
+  "Ximenia caffra": [
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+  ],
+  "Zea mays": [
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+  "Ziziphus jujuba": [
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+    241
+  ],
+  "Ziziphus mauritiana": [
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+    192,
+    242
+  ]
+}

splits/tpc268_split.json

@@ -0,0 +1,276 @@
+{
+    "train": [
+        "Abutilon_indicum_fruit",
+        "Acacia_aspera_flower",
+        "Acacia_tortilis_fruit",
+        "Acer_rubrum_stoma",
+        "Achyranthes_affinis_flower",
+        "Achyranthes_argentea_flower",
+        "Achyranthes_mucronata_flower",
+        "Achyranthes_sordescens_flower",
+        "Acmispon_decumbens_flower",
+        "Actinidia_deliciosa_fruit",
+        "Adelinia_grandis_flower",
+        "Adenanthera_pavonina_seed",
+        "Agrocybe_aegerita_fruit",
+        "Allium_cepa_stem",
+        "Allium_sativum_stem",
+        "Anacardium_occidentale_seed",
+        "Anarthrophyllum_desideratum_flower",
+        "Andesanthus_lepidotus_flower",
+        "Anemonastrum_deltoideum_flower",
+        "Arachis_hypogaea_flower",
+        "Asparagus_officinalis_stem",
+        "Averrhoa_carambola_fruit",
+        "Azadirachta_indica_fruit",
+        "Benincasa_hispida_fruit",
+        "Beta_vulgaris_plant",
+        "Boswellia_sacra_resin",
+        "Brucea_javanica_fruit",
+        "Bupleurum_ranunculoides_flower",
+        "Cannabis_sativa_seed",
+        "Carica_papaya_fruit",
+        "Carya_cathayensis_fruit",
+        "Carya_illinoinensis_seed",
+        "Carya_ovata_stoma",
+        "Ceratonia_siliqua_fruit",
+        "Chaetogastra_paratropica_flower",
+        "Chrysanthemum_morifolium_flower",
+        "Citrus_limon_fruit",
+        "Citrus_sinensis_fruit",
+        "Clausena_lansium_fruit",
+        "Cocos_nucifera_fruit",
+        "Coffea_arabica_seed",
+        "Cordia_sinensis_fruit",
+        "Crataegus_pinnatifida_flower",
+        "Crataegus_pinnatifida_fruit",
+        "Cucumis_melo_fruit",
+        "Cucurbita_pepo_fruit",
+        "Cyanothamnus_coerulescens_flower",
+        "Cyanothamnus_nanus_flower",
+        "Cyclamen_hederifolium_flower",
+        "Daucus_carota_root",
+        "Deutzia_rugulosa_flower",
+        "Didymanthus_littoreus_flower",
+        "Didymanthus_trilobus_flower",
+        "Diospyros_kaki_fruit",
+        "Dirca_palustris_stoma",
+        "Doellingeria_breweri_flower",
+        "Doellingeria_engelmannii_flower",
+        "Doellingeria_ledophylla_flower",
+        "Drosanthemum_calcareum_flower",
+        "Drynaria_roosii_root",
+        "Durio_zibethinus_fruit",
+        "Eryngium_carlinae_flower",
+        "Fenestraria_rhopalophylla_flower",
+        "Ficus_carica_fruit",
+        "Ficus_carica_plant",
+        "Fragaria_ananassa_fruit",
+        "Frankenia_salsoloides_flower",
+        "Fraxinus_excelsior_stoma",
+        "Gelasia_cretica_flower",
+        "Gelasia_ensifolia_flower",
+        "Glycine_max_fruit",
+        "Holboellia_latifolia_fruit",
+        "Ilex_opaca_stoma",
+        "Ipomoea_batatas_root",
+        "Lactuca_sativa_plant",
+        "Leptosiphon_nuttallii_flower",
+        "Lordhowea_amygdalifolia_flower",
+        "Lordhowea_velleioides_flower",
+        "Malus_domestica_fruit",
+        "Manilkara_zapota_fruit",
+        "Mitella_formosana_fruit",
+        "Mononeuria_caroliniana_flower",
+        "Musa_sapientum_fruit",
+        "Myrica_pensylvanica_fruit",
+        "Narcissus_pseudonarcissus_flower",
+        "Nelumbo_nucifera_flower",
+        "Nepenthes_ventrata_fruit",
+        "Nyssa_sylvatica_stoma",
+        "Opuntia_ficus-indica_fruit",
+        "Ormosia_ormondii_fruit",
+        "Pentanema_britannicum_flower",
+        "Pentanema_conyzae_flower",
+        "Pentanema_orientale_flower",
+        "Pentanema_spiraeifolium_flower",
+        "Pilosocereus_curtisii_stem",
+        "Populus_tomentosa_stoma",
+        "Prunus_armeniaca_flower",
+        "Prunus_armeniaca_fruit",
+        "Prunus_davidiana_fruit",
+        "Prunus_domestica_fruit",
+        "Prunus_persica_fruit",
+        "Pyrus_communis_fruit",
+        "Quercus_michauxii_stoma",
+        "Quercus_nigra_stoma",
+        "Quercus_pagoda_stoma",
+        "Quercus_stellata_stoma",
+        "Rabelera_holostea_flower",
+        "Raphanus_sativus_root",
+        "Ricinus_communis_leaf",
+        "Rubus_idaeus_flower",
+        "Rubus_idaeus_fruit",
+        "Salix_babylonica_stoma",
+        "Senegalia_senegal_resin",
+        "Solanum_lycopersicum_fruit",
+        "Solanum_tuberosum_stem",
+        "Spondias_dulcis_fruit",
+        "Syzygium_jambos_fruit",
+        "Syzygium_samarangense_fruit",
+        "Tamarindus_indica_fruit",
+        "Tanaecium_dichotomum_flower",
+        "Theobroma_cacao_fruit",
+        "Tribulus_terrestris_fruit",
+        "Triticum_aestivum_flower",
+        "Triticum_aestivum_plant",
+        "Triticum_aestivum_stoma",
+        "Tulipa_gesneriana_flower",
+        "Ulmus_alata_stoma",
+        "Ulmus_americana_stoma",
+        "Vaccinium_corymbosum_fruit",
+        "Vaccinium_stamineum_stoma",
+        "Vaccinium_vitis-idaea_fruit",
+        "Vitis_vinifera_fruit",
+        "Walsholaria_muelleri_flower",
+        "Wissadula_excelsior_flower",
+        "Ximenia_caffra_fruit",
+        "Zea_mays_flower",
+        "Ziziphus_jujuba_fruit",
+        "Ziziphus_mauritiana_fruit"
+    ],
+    "val": [
+        "Acmispon_neomexicanus_flower",
+        "Agaricus_blazei_fruit",
+        "Arachis_hypogaea_fruit",
+        "Brassica_napus_flower",
+        "Capsicum_annuum_fruit",
+        "Cheniella_glauca_flower",
+        "Citrullus_lanatus_fruit",
+        "Cordeauxia_edulis_seed",
+        "Cucurbita_maxima_fruit",
+        "Cyclamen_creticum_flower",
+        "Dalbergia_brownei_flower",
+        "Donnellsmithia_juncea_flower",
+        "Eleocharis_dulcis_fruit",
+        "Epiphyllum_oxypetalum_flower",
+        "Eriolarynx_australis_flower",
+        "Euploca_foliosissima_flower",
+        "Gelasia_filifolia_flower",
+        "Lagenaria_siceraria_fruit",
+        "Laserpitium_gallicum_flower",
+        "Lycium_barbarum_flower",
+        "Mangifera_indica_fruit",
+        "Manihot_esculenta_leaf",
+        "Mentha_haplocalyx_flower",
+        "Mentha_haplocalyx_leaf",
+        "Musa_sapientum_plant",
+        "Nelumbo_nucifera_fruit",
+        "Nicotiana_tabacum_plant",
+        "Oryza_sativa_plant",
+        "Pisum_sativum_fruit",
+        "Potentilla_indica_fruit",
+        "Prunus_dulcis_fruit",
+        "Punica_granatum_fruit",
+        "Quercus_shumardii_stoma",
+        "Salvadora_persica_fruit",
+        "Schinus_molle_fruit",
+        "Selenicereus_undatus_fruit",
+        "Silene_stellata_flower",
+        "Siraitia_grosvenorii_fruit",
+        "Solanum_torvum_fruit",
+        "Toiyabea_peirsonii_flower",
+        "Trichosanthes_cucumerina_fruit"
+    ],
+    "test": [
+        "Abelmoschus_esculentus_fruit",
+        "Achyranthes_recurvata_flower",
+        "Achyranthes_stricta_flower",
+        "Acmispon_tomentosus_flower",
+        "Agaricus_bisporus_fruit",
+        "Amphicarpaea_elliptica_flower",
+        "Ananas_comosus_fruit",
+        "Annona_reticulata_fruit",
+        "Annona_squamosa_fruit",
+        "Arenaria_paludicola_flower",
+        "Arrowsmithia_tenuifolia_flower",
+        "Artocarpus_heterophyllus_fruit",
+        "Aylostera_pygmaea_flower",
+        "Brassica_napus_fruit",
+        "Canarium_album_fruit",
+        "Casearia_yucatanensis_flower",
+        "Castanea_mollissima_fruit",
+        "Chaetogastra_herbacea_flower",
+        "Cheniella_corymbosa_flower",
+        "Cicer_arietinum_seed",
+        "Citrus_tangerina_fruit",
+        "Conopodium_majus_flower",
+        "Coprinus_comatus_fruit",
+        "Corylus_heterophylla_fruit",
+        "Crotalaria_magaliesbergensis_flower",
+        "Cucumis_anguria_fruit",
+        "Cucumis_sativus_fruit",
+        "Cucurbita_pepo_flower",
+        "Cyanothamnus_polygalifolius_flower",
+        "Dianthus_caryophyllus_flower",
+        "Garcinia_mangostana_fruit",
+        "Giliastrum_rigidulum_flower",
+        "Helianthus_annuus_plant",
+        "Holboellia_latifolia_flower",
+        "Hypochaeris_valdesii_flower",
+        "Ipomoea_batatas_leaf",
+        "Jacobaea_taitungensis_flower",
+        "Jaumea_ciliata_flower",
+        "Juglans_regia_flower",
+        "Juglans_regia_fruit",
+        "Lablab_purpureus_fruit",
+        "Lablab_purpureus_seed",
+        "Lansium_parasiticum_fruit",
+        "Litchi_chinensis_fruit",
+        "Luffa_aegyptiaca_fruit",
+        "Lycium_barbarum_fruit",
+        "Magnolia_denudata_flower",
+        "Manihot_esculenta_fruit",
+        "Matourea_azurea_flower",
+        "Momordica_charantia_fruit",
+        "Moringa_oleifera_fruit",
+        "Morus_alba_fruit",
+        "Myrica_pensylvanica_plant",
+        "Nelumbo_nucifera_leaf",
+        "Nephelium_lappaceum_fruit",
+        "Nubelaria_arisanensis_flower",
+        "Passiflora_edulis_fruit",
+        "Peniocereus_viperinus_flower",
+        "Pentanema_helenioides_flower",
+        "Persea_americana_fruit",
+        "Phaseolus_vulgaris_seed",
+        "Phoenix_dactylifera_fruit",
+        "Pleurotus_eryngii_fruit",
+        "Prunus_avium_fruit",
+        "Pseudopodospermum_hispanicum_fruit",
+        "Quercus_phellos_stoma",
+        "Quercus_texana_stoma",
+        "Raphanus_raphanistrum_fruit",
+        "Ricinus_communis_fruit",
+        "Rosa_rugosa_flower",
+        "Sandoricum_koetjape_fruit",
+        "Sapindus_saponaria_fruit",
+        "Sapindus_saponaria_seed",
+        "Sohmaea_laxiflora_flower",
+        "Solanum_melongena_fruit",
+        "Sorghum_bicolor_flower",
+        "Stauntonia_chinensis_flower",
+        "Stauntonia_chinensis_fruit",
+        "Tibetiodes_asteroides_flower",
+        "Tibetiodes_flaccida_flower",
+        "Vaccinium_myrtillus_fruit",
+        "Vatica_mangachapoi_fruit",
+        "Vernicia_fordii_flower",
+        "Vernicia_fordii_fruit",
+        "Vincetoxicum_koi_flower",
+        "Walsholaria_magniflora_flower",
+        "Wedelia_mexicana_flower",
+        "Zea_mays_fruit",
+        "Zea_mays_plant"
+    ]
+}

splits/tpc268_val.txt

@@ -0,0 +1,1000 @@
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+Silene_stellata\flower\Silene_stellata_flower_14.jpg
+Silene_stellata\flower\Silene_stellata_flower_15.jpg
+Silene_stellata\flower\Silene_stellata_flower_16.jpg
+Silene_stellata\flower\Silene_stellata_flower_17.jpg
+Silene_stellata\flower\Silene_stellata_flower_18.jpg
+Silene_stellata\flower\Silene_stellata_flower_19.jpg
+Silene_stellata\flower\Silene_stellata_flower_2.jpg
+Silene_stellata\flower\Silene_stellata_flower_20.jpg
+Silene_stellata\flower\Silene_stellata_flower_21.jpg
+Silene_stellata\flower\Silene_stellata_flower_22.jpg
+Silene_stellata\flower\Silene_stellata_flower_23.jpg
+Silene_stellata\flower\Silene_stellata_flower_24.jpg
+Silene_stellata\flower\Silene_stellata_flower_25.jpg
+Silene_stellata\flower\Silene_stellata_flower_26.jpg
+Silene_stellata\flower\Silene_stellata_flower_27.jpg
+Silene_stellata\flower\Silene_stellata_flower_28.jpg
+Silene_stellata\flower\Silene_stellata_flower_29.jpg
+Silene_stellata\flower\Silene_stellata_flower_3.jpg
+Silene_stellata\flower\Silene_stellata_flower_4.jpg
+Silene_stellata\flower\Silene_stellata_flower_5.jpg
+Silene_stellata\flower\Silene_stellata_flower_6.jpg
+Silene_stellata\flower\Silene_stellata_flower_7.jpg
+Silene_stellata\flower\Silene_stellata_flower_8.jpg
+Silene_stellata\flower\Silene_stellata_flower_9.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_1.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_10.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_11.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_12.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_13.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_14.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_15.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_16.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_17.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_18.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_19.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_2.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_20.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_3.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_4.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_5.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_6.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_7.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_8.jpg
+Siraitia_grosvenorii\fruit\Siraitia_grosvenorii_fruit_9.jpg
+Solanum_torvum\fruit\Solanum_torvum_fruit_1.jpg
+Solanum_torvum\fruit\Solanum_torvum_fruit_2.jpg
+Solanum_torvum\fruit\Solanum_torvum_fruit_3.jpg
+Solanum_torvum\fruit\Solanum_torvum_fruit_4.jpg
+Solanum_torvum\fruit\Solanum_torvum_fruit_5.jpg
+Solanum_torvum\fruit\Solanum_torvum_fruit_6.jpg
+Toiyabea_peirsonii\flower\Toiyabea_peirsonii_flower_1.jpg
+Toiyabea_peirsonii\flower\Toiyabea_peirsonii_flower_2.jpg
+Toiyabea_peirsonii\flower\Toiyabea_peirsonii_flower_3.jpg
+Toiyabea_peirsonii\flower\Toiyabea_peirsonii_flower_4.jpg
+Toiyabea_peirsonii\flower\Toiyabea_peirsonii_flower_5.jpg
+Toiyabea_peirsonii\flower\Toiyabea_peirsonii_flower_6.jpg
+Toiyabea_peirsonii\flower\Toiyabea_peirsonii_flower_7.jpg
+Toiyabea_peirsonii\flower\Toiyabea_peirsonii_flower_8.jpg
+Toiyabea_peirsonii\flower\Toiyabea_peirsonii_flower_9.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_1.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_10.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_11.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_12.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_13.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_14.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_15.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_16.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_17.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_18.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_19.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_2.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_20.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_21.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_22.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_23.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_24.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_25.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_26.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_27.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_28.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_29.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_3.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_30.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_31.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_32.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_33.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_34.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_35.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_36.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_37.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_38.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_39.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_4.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_40.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_41.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_42.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_43.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_44.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_45.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_46.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_47.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_48.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_49.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_5.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_50.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_51.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_52.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_53.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_54.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_55.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_56.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_57.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_58.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_59.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_6.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_60.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_61.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_62.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_63.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_7.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_8.jpg
+Trichosanthes_cucumerina\fruit\Trichosanthes_cucumerina_fruit_9.jpg

tools/TPC268_Annotator.html

@@ -0,0 +1,731 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>TPC-268 Annotator</title>
+    <style>
+        :root {
+            --bg-color: #1e1e1e;
+            --panel-bg: #252526;
+            --border-color: #3e3e42;
+            --text-main: #cccccc;
+            --text-muted: #858585;
+            --accent-color: #007acc;
+            --accent-hover: #005f9e;
+            --danger-color: #d16969;
+            --box-color: #f44336;
+            --point-color: #ffeb3b;
+        }
+
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            background-color: var(--bg-color);
+            color: var(--text-main);
+            height: 100vh;
+            display: flex;
+            flex-direction: column;
+            overflow: hidden;
+        }
+
+        /* Top Navigation Bar */
+        .navbar {
+            height: 50px;
+            background-color: var(--panel-bg);
+            border-bottom: 1px solid var(--border-color);
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            padding: 0 20px;
+        }
+
+        .nav-brand {
+            font-size: 1.2rem;
+            font-weight: 600;
+            color: #fff;
+        }
+
+        .nav-controls {
+            display: flex;
+            gap: 10px;
+            align-items: center;
+        }
+
+        .btn {
+            background-color: var(--border-color);
+            color: var(--text-main);
+            border: none;
+            padding: 6px 12px;
+            border-radius: 4px;
+            cursor: pointer;
+            font-size: 0.85rem;
+            transition: background 0.2s;
+            display: flex;
+            align-items: center;
+            gap: 5px;
+        }
+
+        .btn:hover {
+            background-color: #4a4a4d;
+        }
+
+        .btn-primary {
+            background-color: var(--accent-color);
+            color: #fff;
+        }
+
+        .btn-primary:hover {
+            background-color: var(--accent-hover);
+        }
+
+        /* Main Workspace */
+        .workspace {
+            display: flex;
+            flex: 1;
+            height: calc(100vh - 50px);
+        }
+
+        /* Left Toolbar */
+        .toolbar {
+            width: 200px;
+            background-color: var(--panel-bg);
+            border-right: 1px solid var(--border-color);
+            padding: 15px;
+            display: flex;
+            flex-direction: column;
+            gap: 20px;
+        }
+
+        .tool-group-title {
+            font-size: 0.75rem;
+            text-transform: uppercase;
+            color: var(--text-muted);
+            margin-bottom: 8px;
+            letter-spacing: 1px;
+        }
+
+        .tool-btn {
+            width: 100%;
+            padding: 8px 10px;
+            background: transparent;
+            border: 1px solid transparent;
+            color: var(--text-main);
+            text-align: left;
+            cursor: pointer;
+            border-radius: 4px;
+            margin-bottom: 5px;
+            transition: all 0.2s;
+        }
+
+        .tool-btn:hover {
+            background-color: rgba(255, 255, 255, 0.05);
+        }
+
+        .tool-btn.active {
+            background-color: rgba(0, 122, 204, 0.2);
+            border-color: var(--accent-color);
+            color: #fff;
+        }
+
+        .stats-box {
+            background-color: rgba(0, 0, 0, 0.2);
+            padding: 10px;
+            border-radius: 4px;
+            font-size: 0.85rem;
+        }
+
+        .stats-row {
+            display: flex;
+            justify-content: space-between;
+            margin-bottom: 5px;
+        }
+
+        /* Center Canvas Area */
+        .canvas-container {
+            flex: 1;
+            position: relative;
+            background-color: #1a1a1a;
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            overflow: hidden;
+            background-image: linear-gradient(45deg, #222 25%, transparent 25%, transparent 75%, #222 75%, #222),
+                linear-gradient(45deg, #222 25%, transparent 25%, transparent 75%, #222 75%, #222);
+            background-size: 20px 20px;
+            background-position: 0 0, 10px 10px;
+        }
+
+        canvas {
+            box-shadow: 0 0 20px rgba(0, 0, 0, 0.5);
+            cursor: crosshair;
+            max-width: 95%;
+            max-height: 95%;
+            object-fit: contain;
+        }
+
+        /* Right File List */
+        .file-sidebar {
+            width: 250px;
+            background-color: var(--panel-bg);
+            border-left: 1px solid var(--border-color);
+            display: flex;
+            flex-direction: column;
+        }
+
+        .file-header {
+            padding: 15px;
+            border-bottom: 1px solid var(--border-color);
+            font-size: 0.85rem;
+            color: var(--text-muted);
+        }
+
+        .file-list {
+            flex: 1;
+            overflow-y: auto;
+            padding: 10px;
+        }
+
+        .file-item {
+            padding: 8px 10px;
+            cursor: pointer;
+            font-size: 0.85rem;
+            border-radius: 4px;
+            margin-bottom: 2px;
+            white-space: nowrap;
+            overflow: hidden;
+            text-overflow: ellipsis;
+        }
+
+        .file-item:hover {
+            background-color: rgba(255, 255, 255, 0.05);
+        }
+
+        .file-item.active {
+            background-color: var(--accent-color);
+            color: #fff;
+        }
+
+        /* Hidden Inputs */
+        input[type="file"] {
+            display: none;
+        }
+
+        /* Loading Overlay */
+        #loadingOverlay {
+            position: absolute;
+            top: 0;
+            left: 0;
+            right: 0;
+            bottom: 0;
+            background: rgba(0, 0, 0, 0.7);
+            color: #fff;
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            z-index: 100;
+            font-size: 1.2rem;
+            display: none;
+        }
+    </style>
+</head>
+
+<body>
+
+    <div class="navbar">
+        <div class="nav-brand">TPC-268 Annotator</div>
+        <div class="nav-controls">
+            <span id="imgCounter" style="color: var(--text-muted); font-size: 0.85rem; margin-right: 15px;">0 / 0</span>
+            <button class="btn" onclick="document.getElementById('imgUpload').click()">📁 Load Images</button>
+            <input type="file" id="imgUpload" multiple accept="image/*" onchange="handleImageUpload(event)">
+
+            <button class="btn" onclick="document.getElementById('jsonUpload').click()">📄 Load JSON</button>
+            <input type="file" id="jsonUpload" accept=".json" onchange="handleJsonUpload(event)">
+
+            <button class="btn btn-primary" onclick="exportAnnotations()">💾 Export JSON</button>
+        </div>
+    </div>
+
+    <div class="workspace">
+
+        <div class="toolbar">
+            <div>
+                <div class="tool-group-title">Modes</div>
+                <button class="tool-btn active" id="modePoint" onclick="setMode('point')">⊙ Add Point
+                    (Instance)</button>
+                <button class="tool-btn" id="modeBox" onclick="setMode('box')">□ Draw Box (Exemplar)</button>
+                <button class="tool-btn" id="modeEdit" onclick="setMode('edit')">↖ Edit / Delete</button>
+            </div>
+
+            <div>
+                <div class="tool-group-title">Statistics</div>
+                <div class="stats-box">
+                    <div class="stats-row"><span>Points:</span> <strong id="statPoints"
+                            style="color: var(--point-color)">0</strong></div>
+                    <div class="stats-row"><span>Boxes:</span> <strong id="statBoxes"
+                            style="color: var(--box-color)">0</strong></div>
+                </div>
+            </div>
+
+            <div style="margin-top: auto; font-size: 0.75rem; color: var(--text-muted); line-height: 1.5;">
+                <strong>Shortcuts:</strong><br>
+                [W] / [S] : Prev / Next Image<br>
+                [1] : Point Mode<br>
+                [2] : Box Mode<br>
+                [3] : Edit Mode<br>
+                [Right-Click] : Delete (in Edit Mode)
+            </div>
+        </div>
+
+        <div class="canvas-container" id="canvasContainer">
+            <div id="loadingOverlay">Loading...</div>
+            <canvas id="mainCanvas"></canvas>
+        </div>
+
+        <div class="file-sidebar">
+            <div class="file-header">IMAGE LIST</div>
+            <div class="file-list" id="fileList">
+                <div style="color: var(--text-muted); font-size: 0.8rem; text-align: center; margin-top: 20px;">
+                    Please load images to start.
+                </div>
+            </div>
+        </div>
+
+    </div>
+
+    <script>
+        // --- Global States ---
+        let annotations = {};
+        let uploadedImages = {}; // Format: { "filename.jpg": "data:image/..." }
+        let imageNames = [];
+        let currentIndex = -1;
+
+        let currentMode = 'point'; // 'point', 'box', 'edit'
+        let isDrawingBox = false;
+        let startX = 0, startY = 0;
+        let tempBox = null;
+
+        let isDraggingPoint = false;
+        let draggingPointIdx = -1;
+
+        // --- Canvas Setup ---
+        const canvas = document.getElementById('mainCanvas');
+        const ctx = canvas.getContext('2d');
+        let currentImageObj = null;
+
+        // --- Core Functions ---
+
+        function setMode(mode) {
+            currentMode = mode;
+            document.getElementById('modePoint').classList.remove('active');
+            document.getElementById('modeBox').classList.remove('active');
+            document.getElementById('modeEdit').classList.remove('active');
+
+            if (mode === 'point') document.getElementById('modePoint').classList.add('active');
+            if (mode === 'box') document.getElementById('modeBox').classList.add('active');
+            if (mode === 'edit') document.getElementById('modeEdit').classList.add('active');
+
+            canvas.style.cursor = mode === 'edit' ? 'default' : 'crosshair';
+        }
+
+        async function handleImageUpload(e) {
+            const files = Array.from(e.target.files);
+            if (files.length === 0) return;
+
+            showLoading(`Loading ${files.length} images...`);
+
+            for (let file of files) {
+                const dataUrl = await readFileAsDataURL(file);
+                uploadedImages[file.name] = dataUrl;
+                if (!annotations[file.name]) {
+                    annotations[file.name] = { box_examples_coordinates: [], points: [] };
+                }
+                if (!imageNames.includes(file.name)) {
+                    imageNames.push(file.name);
+                }
+            }
+
+            updateFileList();
+            if (currentIndex === -1 && imageNames.length > 0) {
+                loadImage(0);
+            }
+            hideLoading();
+            e.target.value = ''; // Reset input
+        }
+
+        function handleJsonUpload(e) {
+            const file = e.target.files[0];
+            if (!file) return;
+
+            const reader = new FileReader();
+            reader.onload = function (evt) {
+                try {
+                    const parsed = JSON.parse(evt.target.result);
+                    // Merge annotations
+                    for (let key in parsed) {
+                        annotations[key] = parsed[key];
+                        // If image doesn't exist in sidebar yet, add it to list
+                        if (!imageNames.includes(key) && !uploadedImages[key]) {
+                            imageNames.push(key);
+                            uploadedImages[key] = null; // Placeholder indicating no image data yet
+                        }
+                    }
+                    updateFileList();
+                    if (currentIndex !== -1) renderCanvas();
+                    alert("Annotations loaded successfully!");
+                } catch (err) {
+                    alert("Invalid JSON format.");
+                }
+            };
+            reader.readAsText(file);
+            e.target.value = ''; // Reset input
+        }
+
+        function exportAnnotations() {
+            // Clean up empty entries before export
+            const cleanAnnotations = {};
+            for (let key in annotations) {
+                const hasPoints = annotations[key].points && annotations[key].points.length > 0;
+                const hasBoxes = annotations[key].box_examples_coordinates && annotations[key].box_examples_coordinates.length > 0;
+                if (hasPoints || hasBoxes) {
+                    cleanAnnotations[key] = annotations[key];
+                }
+            }
+
+            const dataStr = JSON.stringify(cleanAnnotations, null, 4);
+            const blob = new Blob([dataStr], { type: 'application/json' });
+            const url = URL.createObjectURL(blob);
+            const a = document.createElement('a');
+            a.href = url;
+            a.download = 'tpc268_annotations.json';
+            a.click();
+            URL.revokeObjectURL(url);
+        }
+
+        function loadImage(index) {
+            if (index < 0 || index >= imageNames.length) return;
+            currentIndex = index;
+
+            const filename = imageNames[index];
+            const imgData = uploadedImages[filename];
+
+            updateFileListUI();
+            document.getElementById('imgCounter').innerText = `${index + 1} / ${imageNames.length}`;
+
+            if (!imgData) {
+                // Image not uploaded yet (only JSON loaded)
+                ctx.clearRect(0, 0, canvas.width, canvas.height);
+                currentImageObj = null;
+                updateStats();
+                return;
+            }
+
+            const img = new Image();
+            img.onload = () => {
+                currentImageObj = img;
+                // Fit canvas to container while maintaining aspect ratio
+                const container = document.getElementById('canvasContainer');
+                const ratio = Math.min((container.clientWidth - 40) / img.width, (container.clientHeight - 40) / img.height, 1);
+
+                canvas.width = img.width * ratio;
+                canvas.height = img.height * ratio;
+
+                // Ensure annotation structure exists
+                if (!annotations[filename]) {
+                    annotations[filename] = { box_examples_coordinates: [], points: [] };
+                }
+
+                renderCanvas();
+            };
+            img.src = imgData;
+        }
+
+        function renderCanvas() {
+            if (!currentImageObj) return;
+
+            ctx.clearRect(0, 0, canvas.width, canvas.height);
+            ctx.drawImage(currentImageObj, 0, 0, canvas.width, canvas.height);
+
+            const filename = imageNames[currentIndex];
+            const anno = annotations[filename];
+            if (!anno) return;
+
+            const scaleX = canvas.width / currentImageObj.width;
+            const scaleY = canvas.height / currentImageObj.height;
+
+            // Draw Boxes
+            if (anno.box_examples_coordinates) {
+                ctx.strokeStyle = '#f44336'; // Red
+                ctx.lineWidth = 2;
+                anno.box_examples_coordinates.forEach(box => {
+                    ctx.beginPath();
+                    ctx.moveTo(box[0][0] * scaleX, box[0][1] * scaleY);
+                    ctx.lineTo(box[1][0] * scaleX, box[1][1] * scaleY);
+                    ctx.lineTo(box[2][0] * scaleX, box[2][1] * scaleY);
+                    ctx.lineTo(box[3][0] * scaleX, box[3][1] * scaleY);
+                    ctx.closePath();
+                    ctx.stroke();
+                });
+            }
+
+            // Draw Points
+            if (anno.points) {
+                anno.points.forEach((pt, idx) => {
+                    const cx = pt[0] * scaleX;
+                    const cy = pt[1] * scaleY;
+                    ctx.beginPath();
+                    ctx.arc(cx, cy, 3, 0, 2 * Math.PI);
+                    ctx.fillStyle = (isDraggingPoint && draggingPointIdx === idx) ? '#ffffff' : '#ffeb3b';
+                    ctx.fill();
+                    ctx.strokeStyle = '#000';
+                    ctx.lineWidth = 1;
+                    ctx.stroke();
+                });
+            }
+
+            // Draw Temp Box (if dragging)
+            if (isDrawingBox && tempBox) {
+                ctx.strokeStyle = '#4CAF50';
+                ctx.setLineDash([5, 5]);
+                ctx.lineWidth = 2;
+                ctx.strokeRect(tempBox.x, tempBox.y, tempBox.w, tempBox.h);
+                ctx.setLineDash([]);
+            }
+
+            updateStats();
+        }
+
+        // --- Event Listeners (Mouse Interaction) ---
+
+        function getMousePos(evt) {
+            const rect = canvas.getBoundingClientRect();
+            return {
+                x: evt.clientX - rect.left,
+                y: evt.clientY - rect.top
+            };
+        }
+
+        function getOriginalCoords(canvasX, canvasY) {
+            const scaleX = currentImageObj.width / canvas.width;
+            const scaleY = currentImageObj.height / canvas.height;
+            return {
+                x: Math.round(canvasX * scaleX),
+                y: Math.round(canvasY * scaleY)
+            };
+        }
+
+        canvas.addEventListener('mousedown', (e) => {
+            if (!currentImageObj) return;
+            const pos = getMousePos(e);
+            const orig = getOriginalCoords(pos.x, pos.y);
+            const filename = imageNames[currentIndex];
+            const anno = annotations[filename];
+
+            // Right click (Delete in Edit mode)
+            if (e.button === 2 && currentMode === 'edit') {
+                // Check points first
+                if (anno.points) {
+                    for (let i = anno.points.length - 1; i >= 0; i--) {
+                        const pt = anno.points[i];
+                        const dist = Math.hypot(pt[0] - orig.x, pt[1] - orig.y);
+                        if (dist < 10) { // Tolerance in original pixels
+                            anno.points.splice(i, 1);
+                            renderCanvas();
+                            return;
+                        }
+                    }
+                }
+                // Check boxes
+                if (anno.box_examples_coordinates) {
+                    for (let i = anno.box_examples_coordinates.length - 1; i >= 0; i--) {
+                        const box = anno.box_examples_coordinates[i];
+                        const minX = Math.min(box[0][0], box[2][0]);
+                        const maxX = Math.max(box[0][0], box[2][0]);
+                        const minY = Math.min(box[0][1], box[2][1]);
+                        const maxY = Math.max(box[0][1], box[2][1]);
+                        if (orig.x >= minX && orig.x <= maxX && orig.y >= minY && orig.y <= maxY) {
+                            anno.box_examples_coordinates.splice(i, 1);
+                            renderCanvas();
+                            return;
+                        }
+                    }
+                }
+                return;
+            }
+
+            // Left click interactions
+            if (e.button === 0) {
+                if (currentMode === 'point') {
+                    if (!anno.points) anno.points = [];
+                    anno.points.push([orig.x, orig.y]);
+                    renderCanvas();
+                }
+                else if (currentMode === 'box') {
+                    isDrawingBox = true;
+                    startX = pos.x;
+                    startY = pos.y;
+                }
+                else if (currentMode === 'edit') {
+                    // Start dragging point
+                    if (anno.points) {
+                        for (let i = 0; i < anno.points.length; i++) {
+                            const pt = anno.points[i];
+                            const dist = Math.hypot(pt[0] - orig.x, pt[1] - orig.y);
+                            if (dist < 10) {
+                                isDraggingPoint = true;
+                                draggingPointIdx = i;
+                                return;
+                            }
+                        }
+                    }
+                }
+            }
+        });
+
+        canvas.addEventListener('mousemove', (e) => {
+            if (!currentImageObj) return;
+            const pos = getMousePos(e);
+
+            if (isDrawingBox) {
+                tempBox = {
+                    x: Math.min(startX, pos.x),
+                    y: Math.min(startY, pos.y),
+                    w: Math.abs(pos.x - startX),
+                    h: Math.abs(pos.y - startY)
+                };
+                renderCanvas();
+            }
+
+            if (isDraggingPoint) {
+                const orig = getOriginalCoords(pos.x, pos.y);
+                annotations[imageNames[currentIndex]].points[draggingPointIdx] = [orig.x, orig.y];
+                renderCanvas();
+            }
+
+            // Change cursor dynamically in edit mode
+            if (currentMode === 'edit' && !isDraggingPoint) {
+                const orig = getOriginalCoords(pos.x, pos.y);
+                let hover = false;
+                const pts = annotations[imageNames[currentIndex]].points || [];
+                for (let pt of pts) {
+                    if (Math.hypot(pt[0] - orig.x, pt[1] - orig.y) < 10) {
+                        hover = true; break;
+                    }
+                }
+                canvas.style.cursor = hover ? 'move' : 'default';
+            }
+        });
+
+        canvas.addEventListener('mouseup', (e) => {
+            if (e.button !== 0) return;
+
+            if (isDrawingBox && tempBox) {
+                if (tempBox.w > 5 && tempBox.h > 5) {
+                    const tl = getOriginalCoords(tempBox.x, tempBox.y);
+                    const br = getOriginalCoords(tempBox.x + tempBox.w, tempBox.y + tempBox.h);
+
+                    const filename = imageNames[currentIndex];
+                    if (!annotations[filename].box_examples_coordinates) {
+                        annotations[filename].box_examples_coordinates = [];
+                    }
+
+                    // Format: [[x1, y1], [x2, y1], [x2, y2], [x1, y2]]
+                    annotations[filename].box_examples_coordinates.push([
+                        [tl.x, tl.y],
+                        [br.x, tl.y],
+                        [br.x, br.y],
+                        [tl.x, br.y]
+                    ]);
+                }
+                isDrawingBox = false;
+                tempBox = null;
+                renderCanvas();
+            }
+
+            if (isDraggingPoint) {
+                isDraggingPoint = false;
+                draggingPointIdx = -1;
+                renderCanvas();
+            }
+        });
+
+        // Prevent context menu on canvas (used for right-click delete)
+        canvas.addEventListener('contextmenu', e => e.preventDefault());
+
+        // --- Keyboard Shortcuts ---
+        document.addEventListener('keydown', (e) => {
+            if (e.key.toLowerCase() === 'w') { loadImage(currentIndex - 1); }
+            if (e.key.toLowerCase() === 's') { loadImage(currentIndex + 1); }
+            if (e.key === '1') setMode('point');
+            if (e.key === '2') setMode('box');
+            if (e.key === '3') setMode('edit');
+        });
+
+        // --- Helpers ---
+
+        function updateFileList() {
+            const listDiv = document.getElementById('fileList');
+            listDiv.innerHTML = '';
+            imageNames.forEach((name, idx) => {
+                const div = document.createElement('div');
+                div.className = 'file-item';
+                div.innerText = name;
+                div.onclick = () => loadImage(idx);
+                // Highlight red if image data is missing
+                if (!uploadedImages[name]) div.style.color = 'var(--danger-color)';
+                listDiv.appendChild(div);
+            });
+            updateFileListUI();
+        }
+
+        function updateFileListUI() {
+            const items = document.querySelectorAll('.file-item');
+            items.forEach((item, idx) => {
+                if (idx === currentIndex) item.classList.add('active');
+                else item.classList.remove('active');
+
+                if (idx === currentIndex) item.scrollIntoView({ block: 'nearest' });
+            });
+        }
+
+        function updateStats() {
+            if (currentIndex === -1) return;
+            const anno = annotations[imageNames[currentIndex]];
+            const pts = anno?.points?.length || 0;
+            const boxes = anno?.box_examples_coordinates?.length || 0;
+            document.getElementById('statPoints').innerText = pts;
+            document.getElementById('statBoxes').innerText = boxes;
+        }
+
+        function readFileAsDataURL(file) {
+            return new Promise((resolve) => {
+                const reader = new FileReader();
+                reader.onload = e => resolve(e.target.result);
+                reader.readAsDataURL(file);
+            });
+        }
+
+        function showLoading(msg) {
+            const loader = document.getElementById('loadingOverlay');
+            loader.innerText = msg;
+            loader.style.display = 'flex';
+        }
+        function hideLoading() {
+            document.getElementById('loadingOverlay').style.display = 'none';
+        }
+
+        // Handle window resize
+        window.addEventListener('resize', () => {
+            if (currentIndex !== -1) loadImage(currentIndex);
+        });
+
+    </script>
+</body>
+
+</html>

tools/tpc268_dataset.py

@@ -0,0 +1,132 @@
+"""
+Official PyTorch Dataset implementation for the TPC-268 Dataset.
+
+This script provides the `TPC268Dataset` class, which handles:
+1. Loading image paths from dataset split files (e.g., train.txt, test.txt).
+2. Parsing the global JSON annotation file to extract instances (points) and exemplars (4-point polygons).
+3. Automatically converting 4-point polygons into standard horizontal bounding boxes [x_min, y_min, x_max, y_max]
+   for maximum compatibility with mainstream object detection and counting frameworks.
+4. Returning standardized PyTorch Tensors.
+
+Usage Example:
+    from tools.dataset import TPC268Dataset
+
+    dataset = TPC268Dataset(
+        data_dir='./data',
+        split_txt='./splits/train.txt',
+        anno_json='./annotations/tpc268_annotations.json'
+    )
+    img, target = dataset[0]
+    print("Points shape:", target['points'].shape) # torch.Size([N, 2])
+    print("Boxes shape:", target['boxes'].shape)   # torch.Size([M, 4])
+"""
+
+import os
+import json
+import torch
+from torch.utils.data import Dataset
+from PIL import Image
+
+
+class TPC268Dataset(Dataset):
+    def __init__(self, data_dir, split_txt, anno_json, transform=None):
+        """
+        Args:
+            data_dir (str): Root directory where the unzipped images are stored (e.g., './data').
+            split_txt (str): Path to the txt file containing relative image paths (e.g., './splits/train.txt').
+            anno_json (str): Path to the global annotation JSON file.
+            transform (callable, optional): Optional transform to be applied on a sample (image and target).
+        """
+        self.data_dir = data_dir
+        self.transform = transform
+
+        # 1. Load relative image paths from the split text file
+        if not os.path.exists(split_txt):
+            raise FileNotFoundError(f"Split file not found: {split_txt}")
+
+        with open(split_txt, "r") as f:
+            # e.g., "Abelmoschus_esculentus/fruit/Abelmoschus_esculentus_fruit_1.jpg"
+            self.img_paths = [line.strip() for line in f.readlines() if line.strip()]
+
+        # 2. Load the global annotation JSON
+        if not os.path.exists(anno_json):
+            raise FileNotFoundError(f"Annotation JSON not found: {anno_json}")
+
+        with open(anno_json, "r") as f:
+            self.annotations = json.load(f)
+
+    def __len__(self):
+        """Returns the total number of images in this dataset split."""
+        return len(self.img_paths)
+
+    def __getitem__(self, idx):
+        """
+        Fetches the image and its corresponding annotations at the given index.
+
+        Returns:
+            image (PIL.Image or Tensor): The input image (RGB).
+            target (dict): A dictionary containing:
+                - 'points' (Tensor): Instance point coordinates of shape [N, 2].
+                - 'boxes' (Tensor): Standard bounding boxes [x_min, y_min, x_max, y_max] of shape [M, 4].
+                - 'polygons' (Tensor): Original 4-point polygons of shape [M, 4, 2].
+                - 'image_id' (str): The pure filename of the image.
+        """
+        img_rel_path = self.img_paths[idx]
+        img_path = os.path.join(self.data_dir, img_rel_path)
+
+        # 1. Load image (convert to RGB to ensure 3 channels consistently)
+        try:
+            image = Image.open(img_path).convert("RGB")
+        except Exception as e:
+            raise IOError(f"Failed to load image at {img_path}. Error: {e}")
+
+        # 2. Retrieve annotations using the pure image filename as the key
+        img_filename = os.path.basename(img_rel_path)
+        anno = self.annotations.get(img_filename, {})
+
+        # 3. Process instance points (Instances)
+        points = anno.get("points", [])
+        if len(points) > 0:
+            points_tensor = torch.as_tensor(points, dtype=torch.float32)
+        else:
+            # Handle empty points safely to prevent shape mismatch errors in PyTorch
+            points_tensor = torch.empty((0, 2), dtype=torch.float32)
+
+        # 4. Process exemplar bounding boxes (Exemplars)
+        polygons = anno.get("box_examples_coordinates", [])
+        boxes = []
+        for poly in polygons:
+            # Parse the 4-point polygon format: [[x1,y1], [x2,y1], [x2,y2], [x1,y2]]
+            xs = [pt[0] for pt in poly]
+            ys = [pt[1] for pt in poly]
+            # Convert to standard horizontal bounding box: [x_min, y_min, x_max, y_max]
+            boxes.append([min(xs), min(ys), max(xs), max(ys)])
+
+        if len(boxes) > 0:
+            boxes_tensor = torch.as_tensor(boxes, dtype=torch.float32)
+            polygons_tensor = torch.as_tensor(polygons, dtype=torch.float32)
+        else:
+            # Handle empty boxes safely
+            boxes_tensor = torch.empty((0, 4), dtype=torch.float32)
+            polygons_tensor = torch.empty((0, 4, 2), dtype=torch.float32)
+
+        # 5. Assemble the standardized target dictionary
+        target = {
+            "points": points_tensor,
+            "boxes": boxes_tensor,
+            "polygons": polygons_tensor,
+            "image_id": img_filename,
+        }
+
+        # 6. Apply transforms (Data Augmentation) if specified
+        if self.transform is not None:
+            # Note: A custom transform function must handle both the PIL image
+            # and the target dictionary (updating point/box coordinates accordingly).
+            image, target = self.transform(image, target)
+
+        return image, target
+
+
+if __name__ == "__main__":
+    # Simple sanity check message
+    print("TPC268Dataset module loaded successfully. Ready for integration.")

tools/tpc268_generate_benchmark_split.py

@@ -0,0 +1,266 @@
+"""
+=============================================================================
+Disclaimer for Reproducibility:
+Due to the inherent randomness in the MILP solver's heuristics and the use of 
+random seeds for coverage verification, re-running this script from scratch 
+may yield slightly different data splits. This behavior is expected in 
+heuristic search.
+
+For strictly reproducible benchmarking and fair comparisons, please directly 
+use the provided `split.json` and `.txt` files in this repository.
+=============================================================================
+
+This script provides the end-to-end pipeline for generating the TPC-268 
+benchmark splits. It dynamically reads the annotation JSON and physical 
+directory structure, formulates the Mixed-Integer Linear Programming (MILP) 
+problem to ensure taxonomic independence and density balance, and outputs 
+the final file lists.
+"""
+
+import os
+import json
+import random
+import argparse
+import pandas as pd
+import pulp
+from collections import defaultdict
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Generate Taxonomic Dataset Splits via MILP")
+    parser.add_argument("--data_dir", type=str, default="data", 
+                        help="Path to the root directory containing Species/Organ images.")
+    parser.add_argument("--anno_json", type=str, default="annotations.json", 
+                        help="Path to the instance annotation JSON file.")
+    parser.add_argument("--output_dir", type=str, default="split", 
+                        help="Directory to save the generated split txt and json files.")
+    parser.add_argument("--max_retries", type=int, default=50, 
+                        help="Maximum attempts for heuristic optimization.")
+    return parser.parse_args()
+
+
+def build_statistics_from_data(data_dir, anno_path):
+    """
+    Scans the physical directory and parses the annotation JSON to compute 
+    image counts and total instance points for each Species-Organ unit.
+    """
+    print(f"Loading annotations from {anno_path}...")
+    try:
+        with open(anno_path, "r") as f:
+            anno_data = json.load(f)
+    except FileNotFoundError:
+        print(f"Error: Annotation file {anno_path} not found.")
+        return pd.DataFrame()
+
+    stats = defaultdict(lambda: {"image_count": 0, "total_points": 0})
+
+    print(f"Scanning physical directories in {data_dir}...")
+    if not os.path.exists(data_dir):
+        print(f"Error: Data directory {data_dir} not found.")
+        return pd.DataFrame()
+
+    for species in os.listdir(data_dir):
+        species_path = os.path.join(data_dir, species)
+        if not os.path.isdir(species_path):
+            continue
+            
+        for organ in os.listdir(species_path):
+            organ_path = os.path.join(species_path, organ)
+            if not os.path.isdir(organ_path):
+                continue
+                
+            unit_key = f"{species}_{organ}"
+            
+            for img_file in os.listdir(organ_path):
+                if img_file.endswith(".jpg") or img_file.endswith(".png"):
+                    if img_file in anno_data:
+                        stats[unit_key]["image_count"] += 1
+                        points = anno_data[img_file].get("points", [])
+                        stats[unit_key]["total_points"] += len(points)
+                    else:
+                        # Silently skip images without annotations to keep output clean
+                        pass
+
+    records = []
+    for unit, data in stats.items():
+        if data["image_count"] > 0:
+            species, organ = unit.rsplit("_", 1)
+            records.append({
+                "Unit": unit,
+                "Species": species,
+                "Organ": organ,
+                "Image Count": data["image_count"],
+                "Total Points": data["total_points"]
+            })
+            
+    df = pd.DataFrame(records)
+    print(f"Successfully built statistics for {len(df)} taxonomic units.")
+    return df
+
+
+def verify_scale_coverage(assignments_dict):
+    """
+    Verification step: Ensure all subsets (train, val, test) cover the full 
+    spectrum of observation scales (Microscopy, Close-range, Remote Sensing).
+    
+    NOTE: In the practical curation of TPC-268, this step was verified manually 
+    by researchers after the solver generated candidate splits, as scale metadata 
+    is not hardcoded into the instance-level JSON. 
+    
+    For automated pipelines, users can inject a dictionary mapping 'Units' 
+    to their 'Scale' here. This function serves as the programmatic hook.
+    """
+    return True
+
+
+def solve_milp_partition(df, max_retries):
+    """
+    Formulates and solves the MILP problem to assign each Species-Organ unit 
+    to train/val/test splits while balancing image proportions and point density.
+    """
+    splits = ["train", "val", "test"]
+    target_prop = {"train": 0.7, "val": 0.1, "test": 0.2}
+
+    total_imgs = df["Image Count"].sum()
+    total_pts = df["Total Points"].sum()
+
+    target_imgs = {k: target_prop[k] * total_imgs for k in splits}
+    target_pts = {k: target_prop[k] * total_pts for k in splits}
+
+    attempt = 0
+    final_assignments = {}
+
+    while attempt < max_retries:
+        attempt += 1
+        current_seed = random.randint(1, 10000)
+        print(f"\n--- Attempt {attempt}: MILP optimization (Seed {current_seed}) ---")
+
+        prob = pulp.LpProblem("Dataset_Taxonomic_Split", pulp.LpMinimize)
+
+        # Decision variables: z[(i,k)] = 1 if unit i is in split k
+        z = {(i, k): pulp.LpVariable(f"z_{i}_{k}", cat="Binary")
+             for i in df.index for k in splits}
+
+        # Constraint 1: Unique assignment (Zero-shot taxonomic evaluation)
+        for i in df.index:
+            prob += sum(z[i, k] for k in splits) == 1, f"one_split_{i}"
+
+        # Constraint 2: Biological Organization Coverage
+        # Ensure the training set contains at least one of every 'Organ' type
+        organs = df["Organ"].unique()
+        for organ in organs:
+            species_in_organ = df.index[df["Organ"] == organ]
+            prob += sum(z[i, "train"] for i in species_in_organ) >= 1, f"organ_cover_{organ}"
+
+        imgs = {k: sum(df.loc[i, "Image Count"] * z[i, k] for i in df.index) for k in splits}
+        pts = {k: sum(df.loc[i, "Total Points"] * z[i, k] for i in df.index) for k in splits}
+
+        u_img = {k: pulp.LpVariable(f"u_img_{k}", lowBound=0) for k in splits}
+        u_pts = {k: pulp.LpVariable(f"u_pts_{k}", lowBound=0) for k in splits}
+
+        for k in splits:
+            prob += imgs[k] - target_imgs[k] <= u_img[k]
+            prob += target_imgs[k] - imgs[k] <= u_img[k]
+            prob += pts[k] - target_pts[k] <= u_pts[k]
+            prob += target_pts[k] - pts[k] <= u_pts[k]
+
+        # Objective: Minimize weighted sum of deviations (Images >> Points)
+        w_img, w_pts = 100.0, 1.0
+        prob += sum(u_img[k] for k in splits) * w_img + sum(u_pts[k] for k in splits) * w_pts
+
+        # Solve silently
+        prob.solve(pulp.PULP_CBC_CMD(msg=0, gapRel=0.01, timeLimit=60, randomSeed=current_seed))
+
+        current_assignments = {}
+        for i in df.index:
+            for k in splits:
+                if pulp.value(z[i, k]) is not None and pulp.value(z[i, k]) > 0.5:
+                    current_assignments[df.loc[i, "Unit"]] = k
+
+        # Constraint 3: Scale Verification Hook
+        if verify_scale_coverage(current_assignments):
+            print("Success! Found a taxonomically strict split covering all constraints.")
+            final_assignments = current_assignments
+            break
+        else:
+            print("Verification failed: Scales not fully covered. Retrying...")
+
+    if not final_assignments:
+        print("Warning: Reached maximum retries. Please check if constraints are too strict.")
+        
+    return final_assignments
+
+
+def generate_output_files(data_dir, assignments, output_dir):
+    """
+    Scans the physical directory again and writes the exact image paths 
+    into txt files, and taxonomic categories into a json file.
+    """
+    os.makedirs(output_dir, exist_ok=True)
+    
+    paths_dict = {"train": [], "val": [], "test": []}
+    sets_dict = {"train": set(), "val": set(), "test": set()}
+    
+    for species in os.listdir(data_dir):
+        species_path = os.path.join(data_dir, species)
+        if not os.path.isdir(species_path):
+            continue
+            
+        for organ in os.listdir(species_path):
+            organ_path = os.path.join(species_path, organ)
+            if not os.path.isdir(organ_path):
+                continue
+                
+            unit_key = f"{species}_{organ}"
+            if unit_key not in assignments:
+                continue
+                
+            split = assignments[unit_key]
+            
+            variety_organ = unit_key.replace("_", " ")
+            sets_dict[split].add(variety_organ)
+            
+            for file in os.listdir(organ_path):
+                if file.endswith(".jpg") or file.endswith(".png"):
+                    full_path = os.path.join(species, organ, file)
+                    paths_dict[split].append(full_path)
+                    
+    # Write .txt files
+    for split_name, paths in paths_dict.items():
+        output_file = os.path.join(output_dir, f"{split_name}.txt")
+        with open(output_file, "w") as f:
+            for p in sorted(paths):
+                f.write(p + "\n")
+        print(f"Generated {output_file}: {len(paths)} images.")
+
+    # Write .json file
+    split_json = {
+        "train": sorted(list(sets_dict["train"])),
+        "val": sorted(list(sets_dict["val"])),
+        "test": sorted(list(sets_dict["test"])),
+    }
+    json_path = os.path.join(output_dir, "split.json")
+    with open(json_path, "w") as f:
+        json.dump(split_json, f, indent=4)
+    print(f"Generated {json_path} successfully.")
+
+
+def main():
+    args = parse_args()
+
+    # 1. Parse JSON and folders
+    df = build_statistics_from_data(args.data_dir, args.anno_json)
+    if df.empty:
+        return
+
+    # 2. Run the MILP optimization
+    assignments = solve_milp_partition(df, args.max_retries)
+    if not assignments:
+        return
+
+    # 3. Route files and generate outputs
+    print("\nGenerating final split files...")
+    generate_output_files(args.data_dir, assignments, args.output_dir)
+    print("\nBenchmark split generation completed!")
+
+if __name__ == "__main__":
+    main()

tools/tpc268_visualize_dataset.py

@@ -0,0 +1,116 @@
+"""
+=============================================================================
+TPC-268 Dataset Visualization Tool
+=============================================================================
+
+This script visualizes the point and bounding box annotations for a given 
+image in the TPC-268 dataset. It reads the annotation JSON, overlays the 
+instances (points) and exemplars (boxes) onto the image, and saves the 
+result to an output directory.
+
+Usage Example:
+    python visualize_dataset.py \
+        --img_path data_final/Abelmoschus_esculentus/fruit/Abelmoschus_esculentus_fruit_1.jpg \
+        --anno_json annotations/tpc268_annotations.json \
+        --output_dir vis_results
+
+Arguments:
+    --img_path    : Path to the specific image you want to visualize.
+    --anno_json   : Path to the TPC-268 annotation JSON file.
+    --output_dir  : Directory where the visualized image will be saved.
+    --point_color : Color of the point annotations (B, G, R). Default is Yellow (0, 255, 255).
+    --box_color   : Color of the bounding boxes (B, G, R). Default is Red (0, 0, 255).
+=============================================================================
+"""
+
+import os
+import json
+import argparse
+import cv2
+import numpy as np
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Visualize annotations for TPC-268 Dataset")
+    parser.add_argument("--img_path", type=str, required=True,
+                        help="Path to the input image.")
+    parser.add_argument("--anno_json", type=str, default="annotations/tpc268_annotations.json",
+                        help="Path to the dataset annotation JSON.")
+    parser.add_argument("--output_dir", type=str, default="vis_results",
+                        help="Directory to save the visualization results.")
+    
+    # Optional arguments for customizable drawing aesthetics (OpenCV uses BGR format)
+    parser.add_argument("--point_color", type=int, nargs=3, default=[0, 255, 255],
+                        help="Point color in BGR format (default: Yellow).")
+    parser.add_argument("--box_color", type=int, nargs=3, default=[0, 0, 255],
+                        help="Bounding box color in BGR format (default: Red).")
+    parser.add_argument("--radius", type=int, default=4,
+                        help="Radius of the annotation points.")
+    parser.add_argument("--thickness", type=int, default=3,
+                        help="Line thickness for the bounding boxes.")
+    
+    return parser.parse_args()
+
+
+def visualize(args):
+    # 1. Ensure the output directory exists
+    os.makedirs(args.output_dir, exist_ok=True)
+    
+    # 2. Check if the input image exists
+    if not os.path.exists(args.img_path):
+        print(f"Error: Image not found at {args.img_path}")
+        return
+
+    # 3. Load the annotation JSON
+    print(f"Loading annotations from {args.anno_json}...")
+    try:
+        with open(args.anno_json, "r") as f:
+            anno_data = json.load(f)
+    except FileNotFoundError:
+        print(f"Error: Annotation file not found at {args.anno_json}")
+        return
+
+    # Extract the filename as the key to search in the JSON
+    img_filename = os.path.basename(args.img_path)
+    if img_filename not in anno_data:
+        print(f"Error: No annotations found for '{img_filename}' in the JSON file.")
+        return
+
+    img_annos = anno_data[img_filename]
+    points = img_annos.get("points", [])
+    boxes = img_annos.get("box_examples_coordinates", [])
+
+    print(f"Found {len(points)} points and {len(boxes)} exemplar boxes for {img_filename}.")
+
+    # 4. Read the image using OpenCV
+    img = cv2.imread(args.img_path)
+    if img is None:
+        print(f"Error: Failed to read the image at {args.img_path}. It might be corrupted.")
+        return
+
+    # Convert color arguments to tuples
+    point_color = tuple(args.point_color)
+    box_color = tuple(args.box_color)
+
+    # 5. Draw bounding boxes (Exemplars)
+    # The JSON provides 4 corners for each box: [[x1, y1], [x2, y1], [x2, y2], [x1, y2]]
+    for box in boxes:
+        # Convert the list of coordinates into a NumPy array of shape (4, 1, 2) for OpenCV
+        pts_array = np.array(box, np.int32).reshape((-1, 1, 2))
+        cv2.polylines(img, [pts_array], isClosed=True, color=box_color, thickness=args.thickness)
+
+    # 6. Draw point annotations (Instances)
+    for pt in points:
+        x, y = int(pt[0]), int(pt[1])
+        cv2.circle(img, (x, y), radius=args.radius, color=point_color, thickness=-1)
+
+    # 7. Save the visualized image
+    output_filename = f"vis_{img_filename}"
+    output_path = os.path.join(args.output_dir, output_filename)
+    
+    cv2.imwrite(output_path, img)
+    print(f"Visualization saved successfully to: {output_path}")
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    visualize(args)