o Ďim@sddlmZddlZddlZddlZddlZddlmZddlm Z ddl Z ddl Z ddl Z ddlmZmZddlmZddlmZdd gZgd ZeeZgd ZGd d d Z   dcddddZdedfddZdgd!d"Zdhd$d%Zdidjd*d+Zd,d-Zdkd0d1Z  dldmd9d:Z!        dndodIdJZ"         dpdqdLdMZ#drdPdQZ$dsdUdVZ%dtdXdYZ&dZd[Z'dud]d^Z(dvd_d`Z)dvdadbZ*dS)w) annotationsN) Generator)BytesIO)ImageImageOps)logger)Pathz.jpgz.jpeg).pngz.tifz.tiffz.bmp)z.mp4z.mkvz.flvz.aviz.tsz.mpgz.movwmvc@s,eZdZddZd d ddZedd Zd S) Colorscs,d}fdd|D_tj_dS)NzFF3838 2C99A8 FF701F 6473FF CFD231 48F90A 92CC17 3DDB86 1A9334 00D4BB FF9D97 00C2FF 344593 FFB21D 0018EC 8438FF 520085 CB38FF FF95C8 FF37C7csg|] }d|qS)#) hex_to_rgb).0cselfI/home/jeff/fluffinator/venv/lib/python3.10/site-packages/sahi/utils/cv.py sz#Colors.__init__..)splitpalettelenn)r hex_colorsrrr__init__szColors.__init__FbgrboolcCs2|jt||j}|r|d|d|dfS|S)a6Convert an index to a color code. Args: ind (int): The index to convert. bgr (bool, optional): Whether to return the color code in BGR format. Defaults to False. Returns: tuple: The color code in RGB or BGR format, depending on the value of `bgr`. r)rintr)rindr color_codesrrr__call__"s zColors.__call__cCs:g}dD]}|t|d|d|ddqt|S)zConverts a hexadecimal color code to RGB format. Args: hex_code (str): The hexadecimal color code to convert. Returns: tuple: A tuple representing the RGB values in the order (R, G, B). )rrrr)appendrtuple)hex_codergbirrrr /s &zColors.hex_to_rgbN)F)rr)__name__ __module__ __qualname__rr" staticmethodr rrrrr s   r prediction_visualpngimage np.ndarray output_dirstr file_name export_formatc Cst|jdddt|D]S\}}|}|j}|jj}t|t |dt |dt |dt |dddf} t j ||dt |d t |d |} t| t| tjq dS) auCrops bounding boxes over the source image and exports it to the output folder. Args: image (np.ndarray): The source image to crop bounding boxes from. object_prediction_list: A list of object predictions. output_dir (str): The directory where the resulting visualizations will be exported. Defaults to an empty string. file_name (str): The name of the exported file. The exported file will be saved as `output_dir + file_name + ".png"`. Defaults to "prediction_visual". export_format (str): The format of the exported file. Can be specified as 'jpg' or 'png'. Defaults to "png". Tparentsexist_okrrrN_box_class.)rmkdir enumeratedeepcopybboxto_xyxycategoryidcopyrospathjoinr4cv2imwritecvtColor COLOR_RGB2BGR) r1object_prediction_listr3r5r6r object_predictionrA category_id cropped_img save_pathrrrcrop_object_predictions?s& "rRjpgF extension grayscalercCsPt|}tj|\}}|rt|tj}|d}|d|}t||dS)aZReads an image from the given path and saves it with the specified extension. Args: read_path (str): The path to the image file. extension (str, optional): The desired file extension for the saved image. Defaults to "jpg". grayscale (bool, optional): Whether to convert the image to grayscale. Defaults to False. _grayr=N)rIimreadrFrGsplitextrKCOLOR_BGR2GRAYrJ) read_pathrTrUr1pre_rQrrrconvert_image_tohs  r] image_pathc Csd}zt|d}t|tj}W||fStyR}z2td|dzddl}Wn ty6tdw|j j|dd  t j }d}WYd}~||fSd}~ww) aReads a large image from the specified image path. Args: image_path (str): The path to the image file. Returns: tuple: A tuple containing the image data and a flag indicating whether cv2 was used to read the image. The image data is a numpy array representing the image in RGB format. The flag is True if cv2 was used, False otherwise. Trz'OpenCV failed reading image with error , trying skimage insteadrNz`Please run "pip install -U scikit-image" to install scikit-image first for large image handling.F)as_grey) rIrWrK COLOR_BGR2RGB Exceptionrerror skimage.io ImportErrorioastypenpuint8)r^use_cv2img_cv2image0eskimagerrrread_large_imageys&     roreturncCst|}t|tj}|S)zLoads image as a numpy array from the given path. Args: image_path (str): The path to the image file. Returns: numpy.ndarray: The loaded image as a numpy array. )rIrWrKra)r^r1rrr read_images rqTImage.Image | str | np.ndarrayexif_fix Image.Imagec Csdt_t|tjr |}|St|trz(tt|dr&ttj|ddj n| d}|r8t j |ddW|SW|St y}zrtd|dzd dl}Wn ty\td w|j|tj}t|jd krvtj|d d }n)|jd dkrtj|dd }n!|jd dkrtj|dd }ntd|jddWYd}~|SWYd}~|SWYd}~|Sd}~wwt|tjr|jd dkr|dddddddf}t|}|Std)aLoads an image as PIL.Image.Image. Args: image (Union[Image.Image, str, np.ndarray]): The image to be loaded. It can be an image path or URL (str), a numpy image (np.ndarray), or a PIL.Image object. exif_fix (bool, optional): Whether to apply an EXIF fix to the image. Defaults to False. Returns: PIL.Image.Image: The loaded image as a PIL.Image object. NhttpT)streamRGB)in_placez$PIL failed reading image with error r_rzNPlease run 'pip install -U scikit-image imagecodecs' for large image handling.r1)moder#RGBAr:zimage with shape: z is not supported.z-read image with 'pillow' using 'Image.open()')rMAX_IMAGE_PIXELS isinstancer4open startswithrrequestsgetcontentconvertrexif_transposerbrrcrdrerfrWrgrhrirshape fromarray TypeErrorndarray)r1rs image_pilrmrnimage_skrrrread_image_as_pilsZ  $        rc CsVgdgdgdgdgdgdgdgdgd gd gd g }|td d S)zSelects a random color from a predefined list of colors. Returns: list: A list representing the RGB values of the selected color. )rr)rrr)rrr)rrr)rrr)rrr)PF)r)2)rr)rrrr )random randrange)colorsrrrselect_random_colors rcolortuple[int, int, int]cCspt|tj}t|tj}t|tj}|\||dk<||dk<||dk<tj|||gdd}|S)aApplies color mask to given input image. Args: image (np.ndarray): The input image to apply the color mask to. color (tuple): The RGB color tuple to use for the mask. Returns: np.ndarray: The resulting image with the applied color mask. rraxis)rh zeros_likergristack)r1rrgb colored_maskrrrapply_color_masks  "rsourcesave_dirframe_skip_intervalr export_visual view_visual?tuple[Generator[Image.Image], cv2.VideoWriter | None, str, int]cstj|t|}t|tj}r||d}t|}d fdd }|re|dkr8|tj}||}n|tj}t|tj } t|tj } | | f} tj d} t tj || || } nd} |||| |fS) aCreates OpenCV video capture object from given video file path. Args: source: Video file path save_dir: Video export directory frame_skip_interval: Frame skip interval export_visual: Set True if you want to export visuals view_visual: Set True if you want to render visual Returns: iterator: Pillow Image video_writer: cv2.VideoWriter video_file_name: video name with extension rrpGenerator[Image.Image]c3s|rd}t|tjtjdtjd}t|||jr|tj }| tj ||t d}|tj }|dkrEt dt |dkrM|d7}|dkrU|d8}|d kr]|d7}|d kre|d8}| tj ||\}}|szt d dStt|tjV|js dSdS|jr|tj }| tj |||\}}|st d dStt|tjV|jsdSdS) NzPrediction of )iir:)dtypez> ===========================Closing===========================dagfzD =========================== Video Ended ===========================)rI namedWindowWINDOW_AUTOSIZErhzerosriimshowisOpenedrCAP_PROP_POS_FRAMESsetwaitKeyprintexitreadrrrKra) video_capturer window_name default_image frame_numkretframevideo_file_namerrrread_video_framesP        z*get_video_reader..read_video_framermp4vN)rpr)rFrGbasenamerI VideoCapturerrCAP_PROP_FRAME_COUNT CAP_PROP_FPSCAP_PROP_FRAME_WIDTHCAP_PROP_FRAME_HEIGHTVideoWriter_fourcc VideoWriterrH)rrrrrr num_framesrfpswhsizefourcc video_writerrrrget_video_readers(   .   rboxes list[list]classes list[str]maskslist[np.ndarray] | Nonerect_th int | None text_size float | Nonetext_th tuple | None hide_labels str | Nonec  Cs~t} t|}|durt} nd} |p"ttt|jddd}|p+t|dd}|p1|d}|dur@|dur@t dn#|durc|durc|D]} t| } t t | |}t |d|dd}qJtt|D]}t||}||}| dur| |}n |dur|}nt d qit|dt|dgt|dt|dg}}t j|||||d |s|}t j|d||d d\}}|d|ddk}|d||r|d|dn|d|df}t ||||pd d t jt j|||d|r|ddn|d|dfd|d|dqi| r4t| jdddtj| | p%dd}t |t |t jt| } || dS)aVisualizes prediction classes, bounding boxes over the source image and exports it to output folder. Args: image (np.ndarray): The source image. boxes (List[List]): List of bounding boxes coordinates. classes (List[str]): List of class labels corresponding to each bounding box. masks (Optional[List[np.ndarray]], optional): List of masks corresponding to each bounding box. Defaults to None. rect_th (int, optional): Thickness of the bounding box rectangle. Defaults to None. text_size (float, optional): Size of the text for class labels. Defaults to None. text_th (int, optional): Thickness of the text for class labels. Defaults to None. color (tuple, optional): Color of the bounding box and text. Defaults to None. hide_labels (bool, optional): Whether to hide the class labels. Defaults to False. output_dir (Optional[str], optional): Output directory to save the visualization. Defaults to None. file_name (Optional[str], optional): File name for the saved visualization. Defaults to "prediction_visual". Returns: dict: A dictionary containing the visualized image and the elapsed time for the visualization process. Nr~jth?rr:z%Cannot add mask, no color tuple given333333?rzcolor cannot be definedr thickness fontScalerrrrr}rrrrTr7unknownr r1 elapsed_time)timerEr@r maxroundsumrrrcrrhsqueezerI addWeightedrangerr rectangle getTextSizeLINE_AAputTextrr>rFrGrHrJrKrL)r1rrrrrrrrr3r5rrmaskrgb_mask box_indiceboxclass_mycolorpoint1point2label box_width box_heightoutsiderQrrrvisualize_predictioncsp       20(   r  hide_confc  Cst} t|}|durt} nd} |p"ttt|jddd}|p+t|dd}|p1|d}|D]r} | } | jj }|sL|d| j j d7}| durV| | jj }| j du}d}|r| j j}t|dkrtt|d d krtd }|rt|d tj}tj||gd |pd |d|s||dddddfd }tj|d ||dd \}}|d|dd k}|d |r|d|dn|ddf}|d ||df}tj||||pd dtjdtj|||d |r|ddn|d|dfd |d|dnt| j j|pd }t|d|dd }|s| j }t!|d t!|dft!|dt!|df}}tj||||pCd |d|stj|d ||dd \}}|d|dd k}|d ||rq|d|dn|d|df}t||||pd dtjtj|||d |r|ddn|d|dfd |d|dq4|durt"|j#d d dt$t"|| pdd| pd}t%|t&|tj't| } || dS)aVisualizes prediction category names, bounding boxes over the source image and exports it to output folder. Args: object_prediction_list: a list of prediction.ObjectPrediction rect_th: rectangle thickness text_size: size of the category name over box text_th: text thickness color: annotation color in the form: (0, 255, 0) hide_labels: hide labels hide_conf: hide confidence output_dir: directory for resulting visualization to be exported file_name: exported file will be saved as: output_dir+file_name+".png" export_format: can be specified as 'jpg' or 'png' Nrrrr: z.2fFrT)r}rrr)isClosedrrrr})rlineTyperrrrr7r.r=r)(rrEr@r rrrrrCnamescorevaluerDr segmentationrrharrayreshapergint32rI polylinesargmaxrrrrr bool_maskrrArBrrr>r4rJrKrL)r1rMrrrrrrr3r5r6rrrNr has_mask is_obb_predrpoints lowest_pointr r r text_bg_point1text_bg_point2rrArrrQrrrvisualize_object_predictionss       "&  22( $  r#rlist[list[float]]c Cst|}|tj}tj|ddddtjdd}tj|tjtj dd}t |dkr-|dn|d}g}|D]}| }t |dkrH| |q5|S) z Convert boolean mask to coco segmentation format [ [x1, y1, x2, y2, x3, y3, ...], [x1, y1, x2, y2, x3, y3, ...], ... ] rr)r)r}r})offsetrr)rhrrgrirIcopyMakeBorderBORDER_CONSTANT findContours RETR_LISTCHAIN_APPROX_SIMPLErflattentolistr%)rrpolygonscoco_segmentationpolygonrrrr$get_coco_segmentation_from_bool_mask]s    r1r/widthheightcCs<||g}dd|D}t|}t||d}|t|S)a?Convert coco segmentation to 2D boolean mask of given height and width. Parameters: - coco_segmentation: list of points representing the coco segmentation - width: width of the boolean mask - height: height of the boolean mask Returns: - bool_mask: 2D boolean mask of size (height, width) cSs(g|]}t|ddtqS)r}r)rhrrrrgr)rpointrrrrs(z8get_bool_mask_from_coco_segmentation..)g?)rhrrIfillPolyrgr)r/r2r3rrrrrr$get_bool_mask_from_coco_segmentationvs    r6list[int] | Nonec Cstj|dd}tj|dd}t|rt|sdSt|dddg\}}t|dddg\}}||}||}|dksD|dkrFdS||||gS)aGenerate VOC bounding box [xmin, ymin, xmax, ymax] from given boolean mask. Args: bool_mask (np.ndarray): 2D boolean mask. Returns: Optional[List[int]]: VOC bounding box [xmin, ymin, xmax, ymax] or None if no bounding box is found. rrrNr})rhanywhere) rrowscolsyminymaxxminxmaxr2r3rrrget_bbox_from_bool_masks  r@cCsg}g}|D]}||ddd||dddqt|dks)t|dkr+dSt|}t|}t|}t|}||||gS)zIGenerate voc box ([xmin, ymin, xmax, ymax]) from given coco segmentation.Nrrr)extendrminr)r/xsyssegmr>r?r<r=rrrget_bbox_from_coco_segmentations rF obb_pointscCs8|d}g}g||d|d}|||S)aConvert OBB (Oriented Bounding Box) points to COCO polygon format. Args: obb_points: np.ndarray OBB points tensor from ultralytics.engine.results.OBB Shape: (4, 2) containing 4 points with (x,y) coordinates each Returns: List[List[float]]: Polygon points in COCO format [[x1, y1, x2, y2, x3, y3, x4, y4], [...], ...] r}rr)rr-r%)rGrr.closed_polygonrrr%get_coco_segmentation_from_obb_pointss   rIcCs|t|S)zNormalizes numpy image.)rhr)r1rrrnormalize_numpy_imagesrJcCsDddl}t|tj}td|\}}|jj|d}|j|dS)zDisplays numpy image in notebook. If input image is in range 0..1, please first multiply img by 255 Assumes image is ndarray of shape [height, width, channels] where channels can be 1, 3 or 4 rNr )data)IPythonrIrKrLimencodedisplayr)r1rLr\rr)rrripython_displays rO)r.r/r0)r1r2r3r4r5r4r6r4)rSF)rTr4rUr)r^r4)r^r4rpr2)T)r1rrrsrrprt)r1r2rr)FF) rr4rr4rrrrrrrpr)NNNNNFNr/)r1r2rrrrrrrrrrrrrrrrr3rr5r) NNNNFFNr/r0)r1r2rrrrrrrrrrrrr3rr5rr6r)rr2rpr$)r/r$r2rr3rrpr2)rr2rpr7)rGr2rpr$)r1r2)+ __future__rrErFrrcollections.abcrrfrrInumpyrhrPILrr sahi.loggerrsahi.utils.filerIMAGE_EXTENSIONS_LOSSYIMAGE_EXTENSIONS_LOSSLESSIMAGE_EXTENSIONSVIDEO_EXTENSIONSr rRr]rorqrrrrr r#r1r6r@rFrIrJrOrrrrst     * )   0  g u