.hn2ddlmZddlZddlmZddlmZddlmZddlm Z ddl Z ddl Z ddl mZddlmZmZdd lmZdd lmZddd Zddd Z d dd ZdddZ d d dZ d! d"dZ d# d$dZ d# d$dZedk(reddeddyy)%) annotationsN)glob)ceil)Path)Any)Image) exif_sizeimg2label_paths)TQDM)check_requirementsc tdddlm}|jddd}t j |d}t j |d}t j||gd}t j|d d d d dfd d df}t j|d d d dd fd dd f}t j||z dtj} | d | d z} fd tdD\} } } }t j| | | | | || |gdjddd}|Dcgc] }|| }}|Dcgc] }|| }}t j| j}t!t j"| D],}||dj%||dj&||<.t j(|Dcgc]}|j&c}tj*}|d}t j||tj}||z }|j,dk(r|d}|Scc}wcc}wcc}w)a Calculate Intersection over Foreground (IoF) between polygons and bounding boxes. Args: polygon1 (np.ndarray): Polygon coordinates with shape (N, 8). bbox2 (np.ndarray): Bounding boxes with shape (N, 4). eps (float, optional): Small value to prevent division by zero. Returns: (np.ndarray): IoF scores with shape (N, 1) or (N, M) if bbox2 is (M, 4). Notes: Polygon format: [x1, y1, x2, y2, x3, y3, x4, y4]. Bounding box format: [x_min, y_min, x_max, y_max]. zshapely>=2.0.0r)PolygonaxisN.).r).c3,K|] }d|f yw.N).0ibbox2s Y/var/www/html/ai-service/venv/lib/python3.12/site-packages/ultralytics/data/split_dota.py zbbox_iof..1s@!c1f @sdtyperr)r shapely.geometryrreshapenpminmax concatenatemaximumminimumclipinfrangestackzerosshapezipnonzero intersectionareaarrayfloat32ndim)polygon1repsrlt_pointrb_pointbbox1ltrbwh h_overlapslefttoprightbottompolygon2p sg_polys1 sg_polys2overlapsunionsoutputss ` rbbox_iofrIs$ '((Aq)HvvhR(HvvhR(H NNHh/b 9E E!T2A2+&c2A2g 7B E!T12+&c12g 7B b!RVV $BFbj(J@uQx@D#ufxxsE3vtVLSUV^^_acdfghH%-..I.%-..I.xx (()H "**Z( )J!o229QrU3CDII J XXy1!qvv1 DF I F WWVS"&& )FG||q)$ N/.2s;I IIc|dvs Jd|dt|dz |z }|js Jd|dttt|dz |z dz }t |}g}t ||D]\}}t tj|\}} t|d 5} | jjjD cgc]} t| s| j } } tj| tj } d d d |j#t%| |f | |Scc} w#1swY1xYw) a1 Load DOTA dataset annotations and image information. Args: data_root (str): Data root directory. split (str, optional): The split data set, could be 'train' or 'val'. Returns: (list[dict[str, Any]]): List of annotation dictionaries containing image information. Notes: The directory structure assumed for the DOTA dataset: - data_root - images - train - val - labels - train - val >valtrainz$Split must be 'train' or 'val', not .images Can't find , please check your data root.*utf-8encodingrN)ori_sizelabelfilepath)rexistsrstrr r.r ropenreadstrip splitlineslensplitr"r2r3appenddict) data_rootr_im_dirim_fileslb_filesannosim_filelb_filewhfxlbs rload_yolo_dotarnCsJ* $ $U(LUGST&UU $ )_x '% /F ==?Pk&1OPP?CY(2U:S@ABHx(H E(3HG,-1 'G , 0%&VVX^^%5%@%@%BMc!f!'')MBM"BJJ/B 0  TAq6gFG H LN 0 0s$//EE/E'EEE! c |\}}g}t||D];\}} || kDsJd|d| d|| z } ||krdnt||z | z dz} t| D cgc]} | | z } } t| dkDr| d|z|kDr||z | d<||krdnt||z | z dz}t|D cgc]} | | z }} t|dkDr|d|z|kDr||z |d<t j t tj| |tj}||z}|jt j||gd>t j|d}|j}t j|d d dd d fd||d d dd d f<t j|d d dd d fd||d d dd d f<|d d d f|d d dfz |d d d f|d d dfz z}|d d d f|d d dfz |d d d f|d d dfz z}||z }||kDjs$|j}d|t!||z |k<|||kDScc} wcc} w) a6 Get the coordinates of sliding windows for image cropping. Args: im_size (tuple[int, int]): Original image size, (H, W). crop_sizes (tuple[int, ...], optional): Crop size of windows. gaps (tuple[int, ...], optional): Gap between crops. im_rate_thr (float, optional): Threshold of windows areas divided by image areas. eps (float, optional): Epsilon value for math operations. Returns: (np.ndarray): Array of window coordinates with shape (N, 4) where each row is [x_start, y_start, x_stop, y_stop]. zinvalid crop_size gap pair [ ]rrrrrNr)r.rr*r^r"r2list itertoolsproductint64r`r%copyr(anyr$abs)im_size crop_sizesgaps im_rate_thrr6rjriwindows crop_sizegapstepxnrxsynysstartstop im_in_winsim_areas win_areasim_ratesmax_rates r get_windowsrgs( DAqGj$/> 33Q">yk3%q QQ3y.QdA MT+AA+E&F %b *1dQh * * r7Q;2b6I-1]BrFy.QdA MT+AA+E&F %b *1dQh * * r7Q;2b6I-1]BrFi//B78Iy r~~udm!<=!>"nnW1-GJ''*Q1W"5q!34X()C/0 8k) **/+ +s  I"2 I'c|d\}}|d}t|rl|dddddfxx|zcc<|dddddfxx|zcc<t|ddddf|}tt|Dcgc]}||dd|f|k\c}Stt|Dcgc]'}tjdtj )c}Scc}wcc}w)z3Get objects for each window based on IoF threshold.rUrVNrr)r r)r^rIr*r"r,r3) annor~iof_thrrjrirViofsr_s rget_window_objrs  DAq ME 5z aAg! aAg!ae g.8=c'l8KL1tAqDzW,-LL F F 6FFF( c `t|dz |z }|jddt|dz |z }|jddt||}t|t ||D]?}t |d||} t || } t|| | t|t|Ay) a Split both images and labels for a given dataset split. Args: data_root (str): Root directory of the dataset. save_dir (str): Directory to save the split dataset. split (str, optional): The split data set, could be 'train' or 'val'. crop_sizes (tuple[int, ...], optional): Tuple of crop sizes. gaps (tuple[int, ...], optional): Tuple of gaps between crops. Notes: The directory structure assumed for the DOTA dataset: - data_root - images - split - labels - split and the output directory structure is: - save_dir - images - split - labels - split rNTparentsexist_oklabels)r_totaldescrUN) rmkdirrnr r^rrrrY) rbsave_dirr_r{r|rcrrfrr~rs rsplit_images_and_labelsrs>(^h & .F LLL- (^h & .F LLL- 9E 2EU#e*59Ld:. DA$T73 dG[#f+s6{KLcgg}}|D]<}|jt||z |jt||z >dD]}t|||||y)am Split train and val sets of DOTA dataset with multiple scaling rates. Args: data_root (str): Root directory of the dataset. save_dir (str): Directory to save the split dataset. crop_size (int, optional): Base crop size. gap (int, optional): Base gap between crops. rates (tuple[float, ...], optional): Scaling rates for crop_size and gap. Notes: The directory structure assumed for the DOTA dataset: - data_root - images - train - val - labels - train - val and the output directory structure is: - save_dir - images - train - val - labels - train - val >rKrLN)r`rr) rbrrrratesr{r|rr_s rsplit_trainvalr sj>2J "#i!m,- CaL!""N 8UJMNrc gg}}|D]<}|jt||z |jt||z >t|dz dz }|jddt|dz dz }|j s Jd|dt t |dz } t| t| dD]} ttj| \} } t| | f|| } tj| }t| j}| D]W}|j!\}}}}|d ||z d |d |}|||||f}tj"t ||d z |Yy )a Split test set of DOTA dataset, labels are not included within this set. Args: data_root (str): Root directory of the dataset. save_dir (str): Directory to save the split dataset. crop_size (int, optional): Base crop size. gap (int, optional): Base gap between crops. rates (tuple[float, ...], optional): Scaling rates for crop_size and gap. Notes: The directory structure assumed for the DOTA dataset: - data_root - images - test and the output directory structure is: - save_dir - images - test rNtestTrrOrPrQr)r{r|rrrN)r`rrrrXrrYr r^r rrZrrrrrr)rbrrrrr{r|rrcrdrgrirjr~rrrrrrrrrs r split_testr0s.2J "#i!m,- CaL!"H~(61H NN4$N/ )_x '& 0F ==?Pk&1OPP?C %&HH FC EG,-1q!f$G ZZ G}!! EF/5}} ,GWffr&7"2!32gYc'KH'&.'&.89H KKH(4'8898 D  E Er__main__DOTAv2z DOTAv2-split)rbr)gư>)r5 np.ndarrayrrr6floatreturnr)rL)rbrYr_rYrzlist[dict[str, Any]])g333333?g{Gz?) rzztuple[int, int]r{tuple[int, ...]r|rr}rr6rrr)gffffff?)rdict[str, Any]r~rrrrlist[np.ndarray])T)rrr~rrrrcrYrrYrboolrNone)rLrr) rbrYrrYr_rYr{rr|rrr)rr)g?) rbrYrrYrrrrrztuple[float, ...]rr) __future__rrtrmathrpathlibrtypingrrnumpyr"PILrultralytics.data.utilsr r ultralytics.utilsr ultralytics.utils.checksr rIrnrrrrrr__name__rrrrs# ="7,^!L#*" 2+ 2+2+ 2+ 2+  2+  2+j Q(%) 1L 1L 1L"1L  1L  1L " 1L 1Ln")" (L(L(L (L (L  (L  (LXfl$N$N!$N.1$N?B$NQb$N $NPfl*E*E!*E.1*E?B*EQb*E *EZ zX?N;r