hLSSKJr SSKrSSKrSSKrSSKrSSKJr SSKJ r SSK J r SSK J r SSKrSSKrSSKJr SSKJrJrJrJr SS KJrJrJrJrJr SS KJr "S S \5r g) ) annotationsN)deepcopy) ThreadPool)Path)Any)Dataset)FORMATS_HELP_MSGHELP_URL IMG_FORMATScheck_file_speeds) DEFAULT_CFG LOCAL_RANKLOGGER NUM_THREADSTQDM)imreadc  ^\rSrSrSrSSS\SSSSS SS S S 4 SU4S jjjrSSjrSSjrS S!Sjjr S"Sjr S#Sjr S$S%Sjjr S$S%Sjjr S"SjrS&SjrS&SjrS'SjrS(SjrS)S*SjjrS+SjrSrU=r$), BaseDatasetaj Base dataset class for loading and processing image data. This class provides core functionality for loading images, caching, and preparing data for training and inference in object detection tasks. Attributes: img_path (str): Path to the folder containing images. imgsz (int): Target image size for resizing. augment (bool): Whether to apply data augmentation. single_cls (bool): Whether to treat all objects as a single class. prefix (str): Prefix to print in log messages. fraction (float): Fraction of dataset to utilize. channels (int): Number of channels in the images (1 for grayscale, 3 for RGB). cv2_flag (int): OpenCV flag for reading images. im_files (list[str]): List of image file paths. labels (list[dict]): List of label data dictionaries. ni (int): Number of images in the dataset. rect (bool): Whether to use rectangular training. batch_size (int): Size of batches. stride (int): Stride used in the model. pad (float): Padding value. buffer (list): Buffer for mosaic images. max_buffer_length (int): Maximum buffer size. ims (list): List of loaded images. im_hw0 (list): List of original image dimensions (h, w). im_hw (list): List of resized image dimensions (h, w). npy_files (list[Path]): List of numpy file paths. cache (str): Cache images to RAM or disk during training. transforms (callable): Image transformation function. batch_shapes (np.ndarray): Batch shapes for rectangular training. batch (np.ndarray): Batch index of each image. Methods: get_img_files: Read image files from the specified path. update_labels: Update labels to include only specified classes. load_image: Load an image from the dataset. cache_images: Cache images to memory or disk. cache_images_to_disk: Save an image as an *.npy file for faster loading. check_cache_disk: Check image caching requirements vs available disk space. check_cache_ram: Check image caching requirements vs available memory. set_rectangle: Set the shape of bounding boxes as rectangles. get_image_and_label: Get and return label information from the dataset. update_labels_info: Custom label format method to be implemented by subclasses. build_transforms: Build transformation pipeline to be implemented by subclasses. get_labels: Get labels method to be implemented by subclasses. iFT ?Ng?cP>[TU]5 XlX lX@lXlX`lXlXlUS:Xa[RO[RUl URUR5UlUR5UlUR#U S9 [%UR 5UlXplXlXlXlUR((aUR*ceUR15 /UlUR(a%[5UR&UR*S-S45OSUlS/UR&-S/UR&-S/UR&-sUlUlUlURVs/sHn[?U5RAS5PM snUl![EU[F5(aURI5OUSLaS OSUl%URJS :XaMURM5(a8URN(a[PRR"S 5 URU5 O5URJS :Xa%URW5(aURU5 URYUS 9Ul-gs snf) au Initialize BaseDataset with given configuration and options. Args: img_path (str | list[str]): Path to the folder containing images or list of image paths. imgsz (int): Image size for resizing. cache (bool | str): Cache images to RAM or disk during training. augment (bool): If True, data augmentation is applied. hyp (dict[str, Any]): Hyperparameters to apply data augmentation. prefix (str): Prefix to print in log messages. rect (bool): If True, rectangular training is used. batch_size (int): Size of batches. stride (int): Stride used in the model. pad (float): Padding value. single_cls (bool): If True, single class training is used. classes (list[int], optional): List of included classes. fraction (float): Fraction of dataset to utilize. channels (int): Number of channels in the images (1 for grayscale, 3 for RGB). ) include_classNirz.npyTramzcache='ram' may produce non-deterministic training results. Consider cache='disk' as a deterministic alternative if your disk space allows.disk)hyp).super__init__img_pathimgszaugment single_clsprefixfractionchannelscv2IMREAD_GRAYSCALE IMREAD_COLORcv2_flag get_img_filesim_files get_labelslabels update_labelslennirect batch_sizestridepad set_rectanglebufferminmax_buffer_lengthimsim_hw0im_hwr with_suffix npy_files isinstancestrlowercachecheck_cache_ram deterministicrwarning cache_imagescheck_cache_diskbuild_transforms transforms)selfr$r%rFr&r!r(r6r7r8r9r'classesr)r*f __class__s O/home/james-whalen/.local/lib/python3.13/site-packages/ultralytics/data/base.pyr#BaseDataset.__init__HsH    $   08A ,,3CSCS **4==9 oo'  1dkk" $  99??. ..     NRlldggt/BD%I!J`a.2FTWW,NQUPVY]Y`Y`P`)$+tz?C}}M}!$q'--f5}M&0&<&,BaseDataset.get_img_files..sDpa<*5 KKN;7 /44Q7(+ Au%+19 Ax(NWXYl1bl72^a,(,l1bDKKN:.(2;,DKKN;/./ Au%ad+)2cs $ E5EcdURUURUURUpTnUGcjUR5(a[R "U5nO[X@RS9nUc[SU35eURSSupxU(aUR [#Xx5- n U S :wa|[%[&R("X-5UR 5[%[&R("Xy-5UR 5p[*R,"X:U 4[*R.S 9nOQXxs=:XaUR :Xd;O [*R,"X0R UR 4[*R.S 9nUR0S:XaUS nUR2(aX7U4URSSsURU'UR4U'UR6U'UR8R;U5 S [=UR85s=:aUR>:a[O OXUR8RAS 5n URBS :wa-SuURU 'UR4U 'UR6U 'X7U4URSS4$URUUR4UUR6U4$![ a^n[R"URSUSU35 [U5RSS9 [X@RS9nSnAGNSnAff=f)a Load an image from dataset index 'i'. Args: i (int): Index of the image to load. rect_mode (bool): Whether to use rectangular resizing. Returns: im (np.ndarray): Loaded image as a NumPy array. hw_original (tuple[int, int]): Original image dimensions in (height, width) format. hw_resized (tuple[int, int]): Resized image dimensions in (height, width) format. Raises: FileNotFoundError: If the image file is not found. Nz"Removing corrupt *.npy image file z due to: T) missing_ok)flagszImage Not Found r) interpolation).Nrr)NNN)"r>r0rBexistsr{loadrprrIr(runlinkrr.rnshaper%maxr<mathceilr+resize INTER_LINEARndimr&r?r@r;appendr4r=poprF) rNr rect_modeimrPfnrth0w0rwhrs rR load_imageBaseDataset.load_images} HHQKq!14>>!3Dr :yy{{8B A]]3z'*:1#(>??XXbq\FBJJR,6 "& 14::>DIIbfDUW[WaWa@bqBAc>N>NOB,$**,ZZZZ$r?r@nbytesr(descclose) rNbgbfcnstoragepoolresultspbarrr`s rRrJBaseDataset.cache_images s >BjjF>R116:Y]YhYhjoXp   $iiU477^4G '*$'':PQ>RD::'*//1999AAB>DHHQKQA!+++A#{{m+;AF3>E UAX% %A  77{Q!m*;< "113 -- 'DJ NN;;- 1#67MC/011J==2%c*!CIIN3+??_a  rc[R"[R"UR5UR- 5R [ 5nUSS-n[R"URVs/sHo3RS5PM sn5nUSS2S4USS2S4- nUR5nUVs/sHopRUPM snUl UVs/sHopRUPM snUlXVnSS//U-n[U5HHnXQU:Hn U R5U R5pU S:aU S/X'M7U S:dM?SSU - /X'MJ [R"[R"U5UR -UR"- UR$-5R [ 5UR"-UlXlgs snfs snfs snf)zBSet the shape of bounding boxes for YOLO detections as rectangles.rYrrNr)r{flooraranger5r7astyperr|r2rargsortr0r~r<rrr%r8r9 batch_shapesbatch) rNbinbr`sarirectrshapesariminimaxis rRr:BaseDataset.set_rectanglebs XXbii(4??: ; B B3 G VaZ HHdkk:keeGnk: ; q!tWqAw  3895aq)59 /45u!{{1~u5 Ya&BrA1W+CCGGI$ax!1I DM GGBHHV$4tzz$ADKK$ORVRZRZ$Z[bbcfgjnjujuu %;:5s;G'G,'G1cBURURU55$)z5Return transformed label information for given index.)rMget_image_and_label)rNindexs rR __getitem__BaseDataset.__getitem__{st77>??rc^[URU5nURSS5 URU5uUS'US'US'USSUSS- USSUSS- 4US'UR(aUR UR UUS 'URU5$) z Get and return label information from the dataset. Args: index (int): Index of the image to retrieve. Returns: (dict[str, Any]): Label dictionary with image and metadata. rNimg ori_shape resized_shaperr ratio_pad rect_shape)rr2rrr6rrupdate_labels_info)rNrlabels rRrBaseDataset.get_image_and_labelsU+, '4 CG??SXCY@e eK(%*@ / "1 %k(:1(= = / "1 %k(:1(= = k 99"&"3"3DJJu4E"FE, &&u--rc,[UR5$)z5Return the length of the labels list for the dataset.)r4r2rNs rR__len__BaseDataset.__len__s4;;rcU$)zCustom your label format here.)rNrs rRrBaseDataset.update_labels_infos rc[e)z Users can customize augmentations here. Examples: >>> if self.augment: ... # Training transforms ... return Compose([]) >>> else: ... # Val transforms ... return Compose([]) NotImplementedError)rNr!s rRrLBaseDataset.build_transformss "!rc[e)a Users can customize their own format here. Examples: Ensure output is a dictionary with the following keys: >>> dict( ... im_file=im_file, ... shape=shape, # format: (height, width) ... cls=cls, ... bboxes=bboxes, # xywh ... segments=segments, # xy ... keypoints=keypoints, # xy ... normalized=True, # or False ... bbox_format="xyxy", # or xywh, ltwh ... ) rrs rRr1BaseDataset.get_labelss ""!r)r&rrr7r;rFr*r.r)r0r@r?r$r%r>r2r=r5rBr9r(r6r'r8rM)r$str | list[str]r%rrFz bool | strr&boolr!dict[str, Any]r(rDr6rr7rr8rr9floatr'rrOlist[int] | Noner)r r*r)r$rreturnz list[str])rr r None)T)rrrrr z3tuple[np.ndarray, tuple[int, int], tuple[int, int]])r r )rrr r )r)rr r r)rrr r )r r)rr r r )N)r!zdict[str, Any] | None)r zlist[dict[str, Any]])__name__ __module__ __qualname____firstlineno____doc__r r#r/r3rrJrrKrGr:rrrrrLr1__static_attributes__ __classcell__)rQs@rRrrs'.f!) $(L9!L9L9 L9  L9  L9L9L9L9L9L9L9"L9L9L9L9\$L004:l P "H<2@.*  """rr)! __future__rrfrr]rcopyrmultiprocessing.poolrpathlibrtypingrr+numpyr{torch.utils.datarultralytics.data.utilsr r r r ultralytics.utilsr rrrrultralytics.utils.patchesrrrrrRrsH# + $]]PP,d"'d"r