FPh0ddlZddlmZddlmZddlZddlZddlm Z m Z ddl m Z dZ dZ d dZd efd Zd Zd Zy)N) defaultdict)Path)LOGGERTQDM)increment_pathc gdS)z Converts 91-index COCO class IDs to 80-index COCO class IDs. Returns: (list): A list of 91 class IDs where the index represents the 80-index class ID and the value is the corresponding 91-index class ID. )[r N NNN !"#$%&'N()*+,-./0123456789:;N<NN=N>?@ABCDEFGHNIJKLMNONrXeC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\ultralytics/data/converter.pycoco91_to_coco80_classr[s  00rYc gdS)aB Converts 80-index (val2014) to 91-index (paper). For details see https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/. Example: ```python import numpy as np a = np.loadtxt('data/coco.names', dtype='str', delimiter=' ') b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter=' ') x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet ``` )Pr r r r r rrrrrrrrrrrrrrrrrr r!r#r$r'r(r)r*r+r,r-r.r/r0r1r2r3r4r6r7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIrKrNrPrQrRrSrTrUrVrWPQRTUVWXYZrXrXrYrZcoco80_to_coco91_classrgs ``rYc ~ t|}|dz |dz fD]}|jddt}tt |j j dD]}t |dz |jjddz }|jddt|5} tj| } ddd dD cic] } | d d |  } } tt} | d D]}| |d j|t| j!d |D]2\}}| |d }|d|d|d} }}g}g}g}|D]m}|dr t#j$|dt"j&}|ddxxx|dddz z ccc|ddgxx|zcc<|ddgxx|zcc<|ddks|ddkru|r ||ddz n|ddz }|g|j)z}||vr|j||r1|j+dt-|ddk(r|jgt-|ddkDr[t/|d}t#j0|dt#j$||gz j3dj)}nu|dDcgc] }|D]}| }}}t#j$|j3ddt#j$||gz j3dj)}|g|z}||vr|j||s|j+d|j|t#j$|dj3ddt#j$||dgz j3dj)zpt|| z j5dd5}t7t-|D]^}|r g||}n g|rt-||dkDr||n||}|j9d t-|zj;|zd!z` ddd5 t=j>d"|j y#1swYxYwcc} wcc}}w#1swYxYw)#al Converts COCO dataset annotations to a YOLO annotation format suitable for training YOLO models. Args: labels_dir (str, optional): Path to directory containing COCO dataset annotation files. save_dir (str, optional): Path to directory to save results to. use_segments (bool, optional): Whether to include segmentation masks in the output. use_keypoints (bool, optional): Whether to include keypoint annotations in the output. cls91to80 (bool, optional): Whether to map 91 COCO class IDs to the corresponding 80 COCO class IDs. Example: ```python from ultralytics.data.converter import convert_coco convert_coco('../datasets/coco/annotations/', use_segments=True, use_keypoints=False, cls91to80=True) ``` Output: Generates output files in the specified output directory. labelsimagesTparentsexist_okz*.json instances_Nidd annotationsimage_idz Annotations descheightwidth file_nameiscrowdbbox)dtyper rr r category_id segmentationaxis keypoints.txtaz%g  z3COCO data converted successfully. Results saved to ) rmkdirr[sortedrresolveglobstemreplaceopenjsonloadrlistappendritemsnparrayfloat64tolistgetlenmerge_multi_segment concatenatereshape with_suffixrangewriterstriprinfo) labels_dirsave_dir use_segments use_keypoints cls91to80pcoco80 json_filefnfdataxrj imgToAnnsannimg_idannsimghwbboxessegmentsrboxclssijfilelines rZ convert_cocor2s6h'H  (X"5 5 t,6$ %FD,446;;HEF (^h &)?)? b)Q Q - )_99Qhhs6{"**=BQ3qr7Q;&QF q QF q q6Q;#a&A+8AfS/!34s=GY\]G]ecjjl*f$MM#&CGGN$;$G3~./14 + S01A5/N0CD^^AA61a&9IIRRSUV]]_(+N(;G(;1QQQQ(;GXXa[00Q7"((Aq6:JJSSTVW^^` A( * SWW[%9%E$$SBHHS5E,F,N,NrST,U,.HHaAY,?-@AHVVX&VW;BrAv**62C8Ds6{+A$/1/ [$0S!5E5I"*!OUVWy [JJD 199;dBTIJ ,98QUG~ KKFxGWGWGYFZ[\y_=JH98s%6RR'>R, .A6R2R$ 2R< dota_root_pathc  t|}iddddddddd d d d d ddddddddddddddddddd d!d"d#d$i fd%}d&D]}|d'z |z }|d(z |d)z }|d(z |z }|jd*d*+t|j}t |d,|d-.D]Z}|j d/k7r|j }tjt|} | jd0d\} } ||| | ||\y0)1a Converts DOTA dataset annotations to YOLO OBB (Oriented Bounding Box) format. The function processes images in the 'train' and 'val' folders of the DOTA dataset. For each image, it reads the associated label from the original labels directory and writes new labels in YOLO OBB format to a new directory. Args: dota_root_path (str): The root directory path of the DOTA dataset. Example: ```python from ultralytics.data.converter import convert_dota_to_yolo_obb convert_dota_to_yolo_obb('path/to/DOTA') ``` Notes: The directory structure assumed for the DOTA dataset: - DOTA - images - train - val - labels - train_original - val_original After the function execution, the new labels will be saved in: - DOTA - labels - train - val planershipr z storage-tankr zbaseball-diamondr z tennis-courtr zbasketball-courtr zground-track-fieldrharborrbridgerz large-vehiclerz small-vehicler helicopterr roundaboutrzsoccer ball-fieldrz swimming-poolrzcontainer-cranerairportrhelipadrc ||dz }||dz }|jd5}|jd5}|j} | D]} | jj} t | dkr0| d} | } | ddDcgc] }t |}}t dDcgc]}|dzdk(r|||z n|||z }}|Dcgc]}d j|}}|j| d d j|d  ddddddycc}wcc}wcc}w#1swY!xYw#1swYyxYw) zcConverts a single image's DOTA annotation to YOLO OBB format and saves it to a specified directory.rrrrrNr rz{:.6g} r) r readlinesstripsplitrfloatrformatrjoin) image_name image_width image_heightorig_label_dirrorig_label_path save_pathrglinesrparts class_name class_idxrcoordsrnormalized_coordscoordformatted_coords class_mappings rZ convert_labelz/convert_dota_to_yolo_obb..convert_labels`(j\+>>*T22  ! !# &!Y^^C-@AKKME **,u:>"1X )*5 ,1"1I6Iq%(I6afghai%kai\]q1uzF1I +vayAD2 D# D2*D( D2D- '*D2D>#D22D; 7D>>E)trainvalrjri _originalTrkz Processing z imagesrtz.pngN) rrriterdirrsuffixrcv2imreadstrshape) rrphase image_dirrr image_paths image_pathimage_name_without_extrrrrs @rZconvert_dota_to_yolo_obbrsB.)N  A    A  a ! !   b b R  2!" 2#$ 2%M(G&""X-5 '(2wi5HH!H,u4td39,,./ {;ugW1MNJ  F*%/__ "**S_-C99Ra=DAq 0!Q Q O"rYc|dddddf|dddddfz dzjd}tjtj|d|jS)a Find a pair of indexes with the shortest distance between two arrays of 2D points. Args: arr1 (np.array): A NumPy array of shape (N, 2) representing N 2D points. arr2 (np.array): A NumPy array of shape (M, 2) representing M 2D points. Returns: (tuple): A tuple containing the indexes of the points with the shortest distance in arr1 and arr2 respectively. Nr rr~)sumr unravel_indexargminr)arr1arr2diss rZ min_indexrsV D! tD!QJ/ /A 5 : :2 >C  BIIc5syy AArYc.g}|Dcgc]'}tj|jdd)}}tt |Dcgc]}g}}tdt |D]E}t ||dz ||\}}||dz j |||j |GtdD]I}|dk(rt|D]\}}t |dk(r%|d|dkDr|ddd}||dddddf||<tj|||d d||<tj||||ddg||<|dt |dz fvr|j ||d|d|dz g}|j |||d|ddztt |dz ddD]E}|dt |dz fvs||}t|d|dz } |j ||| dGL|Scc}wcc}w)a Merge multiple segments into one list by connecting the coordinates with the minimum distance between each segment. This function connects these coordinates with a thin line to merge all segments into one. Args: segments (List[List]): Original segmentations in COCO's JSON file. Each element is a list of coordinates, like [segmentation1, segmentation2,...]. Returns: s (List[np.ndarray]): A list of connected segments represented as NumPy arrays. rr r rNr~) rrrrrrr enumeraterollrabs) rrr_idx_listidx1idx2kidxnidxs rZrrs4 A4<=Hq ##B*HH=!#h-010q0H11c(m $xA < dQt$ 4 % 1X 6#H-3s8q=SVc!f_dd)C"*1+ddAg"6HQK gghqkCF7C  nnhqk8A;r?-KL CMA-..HHXa[)c!fs1vo.CHHXa[QA ;<. 3x=1,b"5QH  122"1+Cs1vA/DHHXa[/0 6'0 HE>1s ,H  H)z../coco/annotations/zcoco_converted/FFT)r collectionsrpathlibrrnumpyrultralytics.utilsrrultralytics.utils.filesrr[rgrrrrrrXrYrZrs\ # *2 0`*3+#$ b]JYRSYRx B/ rY