.hibddlmZddlZddlZddlZddlZddlZddlZddl Z ddl m cm Z ddlmZGddej Zd"d#dZd$d%dZd&dZd Zd Zd'd Zd Zd Zd(d)dZd*d+dZdZdZdZdZdZ dZ!dZ"d,d-dZ#dZ$d.d/dZ%dZ&d0d1dZ'd2d3dZ(dZ)d4d5dZ*d6dZ+d Z,d!Z-y)7) annotationsN) NOT_MACOS14c2eZdZdZdd dZdZdZdZdZy) Profilea< Ultralytics Profile class for timing code execution. Use as a decorator with @Profile() or as a context manager with 'with Profile():'. Provides accurate timing measurements with CUDA synchronization support for GPU operations. Attributes: t (float): Accumulated time in seconds. device (torch.device): Device used for model inference. cuda (bool): Whether CUDA is being used for timing synchronization. Examples: Use as a context manager to time code execution >>> with Profile(device=device) as dt: ... pass # slow operation here >>> print(dt) # prints "Elapsed time is 9.5367431640625e-07 s" Use as a decorator to time function execution >>> @Profile() ... def slow_function(): ... time.sleep(0.1) Ncx||_||_t|xrt|j d|_y)z Initialize the Profile class. Args: t (float): Initial accumulated time in seconds. device (torch.device, optional): Device used for model inference to enable CUDA synchronization. cudaN)tdeviceboolstr startswithr)selfr r s S/var/www/html/ai-service/venv/lib/python3.12/site-packages/ultralytics/utils/ops.py__init__zProfile.__init__*s2 BCK$:$:6$BC c0|j|_|S)z Start timing.)timestartrs r __enter__zProfile.__enter__6sYY[  rc|j|jz |_|xj|jz c_y)z Stop timing.N)rrdtr )rtypevalue tracebacks r__exit__zProfile.__exit__;s*))+ * $''rc"d|jdS)zIReturn a human-readable string representing the accumulated elapsed time.zElapsed time is z s)r rs r__str__zProfile.__str__@s!$&&,,rc|jr)tjj|jt j S)z9Get current time with CUDA synchronization if applicable.)rtorch synchronizer r perf_counterrs rrz Profile.timeDs. 99 JJ " "4;; /  ""r)N)r floatr ztorch.device | None) __name__ __module__ __qualname____doc__rrrrrrrrrs!. D  -#rrc|j\}}tj|jdk|jdk|j |kD|j |kDgj dk\r$|j d|}|j d|}|dk\|dk\z||kz||kz}||}||}t|r]tj|j|j|j |j g|jStjd|jS)a+ Convert segment coordinates to bounding box coordinates. Converts a single segment label to a box label by finding the minimum and maximum x and y coordinates. Applies inside-image constraint and clips coordinates when necessary. Args: segment (torch.Tensor): Segment coordinates in format (N, 2) where N is number of points. width (int): Width of the image in pixels. height (int): Height of the image in pixels. Returns: (np.ndarray): Bounding box coordinates in xyxy format [x1, y1, x2, y2]. rdtype) Tnparrayminmaxsumclipanyr-zeros)segmentwidthheightxyinsides r segment2boxr>Ks 99DAq xx1aeegk1557U?AEEGfNN5RWR]R]^`RaehRhklRlsutjtjtj tj fn=tjtjtjtj f\}}}}dddf}fdtddD\}}}||||} } |dz | z|dz | zg} | dz | z|dz | zg} || d} || d} || z| z} || z| z }|| z | z }|| z | z}|| |||gdS)a Convert batched Oriented Bounding Boxes (OBB) from [xywh, rotation] to [xy1, xy2, xy3, xy4] format. Args: x (np.ndarray | torch.Tensor): Boxes in [cx, cy, w, h, rotation] format with shape (N, 5) or (B, N, 5). Rotation values should be in radians from 0 to pi/2. Returns: (np.ndarray | torch.Tensor): Converted corner points with shape (N, 4, 2) or (B, N, 4, 2). .NrAc36K|]}d||dzfyw).r@Nr)).0ir;s r z!xywhr2xyxyxyxy..s":1S!a!e)^$:srf) rTr rUcossincatstackr0rnrange)r;rrrrctrrar`r cos_value sin_valuevec1vec2pt1pt2pt3pt4s` rxywhr2xyxyxyxyrs2 a & EIIuyy%++6ffbffbnnbhh 7Cc5 C!G*C:eAqk:KAq%u:s5zyI EI q1uy0 1D BFY A 1 2D tR=D tR=D *t C *t C *t C *t C #sC%r **rct|tjr|jnt j |}|d|dz|d<|d|dz|d<|S)a Convert bounding box from [x1, y1, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right. Args: x (np.ndarray | torch.Tensor): Input bounding box coordinates. Returns: (np.ndarray | torch.Tensor): Bounding box coordinates in xyxy format. rErCrFrDrrs r ltwh2xyxyrrrcg}|D]^}|j\}}|j|j|j|j|jg`t t j |S)a% Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh). Args: segments (list): List of segments where each segment is a list of points, each point is [x, y] coordinates. Returns: (np.ndarray): Bounding box coordinates in xywh format. )r/rr2r3rr0r1)segmentsrJsr;r<s rsegments2boxesrsg E ;ss1 aeegquuw9:; RXXe_ %%rc t|D]D\}}t||k(rtj||ddddffd}tjdt|dz t||kr|t|z n|}tj t|}t||kr+tj |tj|||n|}tjtdDcgc] }tj|||dd|f"c}tjjddj|<G|Scc}w)a7 Resample segments to n points each using linear interpolation. Args: segments (list): List of (N, 2) arrays where N is the number of points in each segment. n (int): Number of points to resample each segment to. Returns: (list): Resampled segments with n points each. rr@NrgrAr,rf) enumeraterir0rnlinspacearangeinsert searchsortedrinterpfloat32rr/)rnrrr;xps rresample_segmentsrs(#  1 q6Q;  NNAq1ay> 2 KK3q6A:SVaZq3q6zQ G YYs1v 8;A BIIaB/ 4 NNuQxH!BIIaQq!tW5HPRPZPZ [ c cdegi j l l     OIs8%E c<|j\}}}|dkr`t|jjD]4\}\}}}} d||d|f<d||| df<d||ddd|f<d||dd|df<6|St j |dddddfdd\}}}} t j ||j|jddddf} t j ||j|jddddf} || |k\| |kz| |k\z| | kzzS)a Crop masks to bounding box regions. Args: masks (torch.Tensor): Masks with shape (N, H, W). boxes (torch.Tensor): Bounding box coordinates with shape (N, 4) in relative point form. Returns: (torch.Tensor): Cropped masks. 2rNr.r@r) r_rrGrVr chunkrr r-) rorJrr`rarrrrrrcs r crop_maskrs1kkGAq!2v#,U[[]->->-@#A ! ABBE!SbS&ME!RS&M E!Q)  E!Q)   !  U1a:%61=BB LL5<    rc|j\}}}||jj|dzjd||}t|d|d}t ||}|j dj S)a Apply masks to bounding boxes using mask head output with native upsampling. Args: protos (torch.Tensor): Mask prototypes with shape (mask_dim, mask_h, mask_w). masks_in (torch.Tensor): Mask coefficients with shape (N, mask_dim) where N is number of masks after NMS. bboxes (torch.Tensor): Bounding boxes with shape (N, 4) where N is number of masks after NMS. shape (tuple): Input image size as (height, width). Returns: (torch.Tensor): Binary mask tensor with shape (H, W, N). rfNrr#)r_r$r scale_masksrrr)rrrr_rrrros rprocess_mask_nativer3sw IAr2  ++Ar2 2 8 8R DE d U +A .E eV $E 99S>    rc|jdd\}}t||dz ||dz }||d|zz }||d|zz }|r |dz}|dz}|r.tt|dz tt|dz fnd\}} |tt|dzz } |tt|dzz } t j |d|| | | f|d S) aN Rescale segment masks to target shape. Args: masks (torch.Tensor): Masks with shape (N, C, H, W). shape (tuple): Target height and width as (height, width). padding (bool): Whether masks are based on YOLO-style augmented images with padding. Returns: (torch.Tensor): Rescaled masks. rANrr@rB)rr.rr)r_r2rVrGrr) ror_rMrrrOrwrxryrzr{r|s rrrGs[[_FB rE!H}b58m ,D qD E qD E    FMU53;'(#eECK.@*ABSYIC #eECK() )F U53;'( (E ==sCJU :;U TTrc^|dd\}}|1|dd\}} t||z | |z } | || zz dz ||| zz dz f} n |dd} |d} |r |dxx| dzcc<|dxx| dzcc<|dxx| zcc<|dxx| zcc<t||}|r|dxx|zcc<|dxx|zcc<|S)a{ Rescale segment coordinates from img1_shape to img0_shape. Args: img1_shape (tuple): Source image shape as HWC or HW (supports both). coords (torch.Tensor): Coordinates to scale with shape (N, 2). img0_shape (tuple): Image 0 shape as HWC or HW (supports both). ratio_pad (tuple, optional): Ratio and padding values as ((ratio_h, ratio_w), (pad_h, pad_w)). normalize (bool): Whether to normalize coordinates to range [0, 1]. padding (bool): Whether coordinates are based on YOLO-style augmented images with padding. Returns: (torch.Tensor): Scaled coordinates. NrArr@rCrD)r2rd) rIrcrKrL normalizerMimg0_himg0_wimg1_himg1_wrOrvs r scale_coordsr`s ^NFF#BQ6F?FVO4 %*Vftm-Cq,HH|Alv#a& v#a&  6NdN 6NdN  ,Fv& v&  Mrc@|jd\}}}}}|tjztjdz k\}tj|||}tj|||}|tjdz z}tj |||||gdS)z Regularize rotated bounding boxes to range [0, pi/2]. Args: rboxes (torch.Tensor): Input rotated boxes with shape (N, 5) in xywhr format. Returns: (torch.Tensor): Regularized rotated boxes. rf)dimrA)unbindrWrr wherer) rr;r<rar`r swapw_h_s rregularize_rboxesr sMMbM)MAq!Q tww;$''A+ %D T1a B T1a B TWWq[A ;;1b"a(b 11rc ddlm}g}|jjj D]8}t j |t jt jd}|r|dk(r]t|dkDr9tj||Dcgc]}|jddc}n|djdd}n{|dk(rvtj|tj|Dcgc] }t|c}jjdd}ntjd}|j!|j#d ;|Scc}wcc}w) a& Convert masks to segments using contour detection. Args: masks (torch.Tensor): Binary masks with shape (batch_size, 160, 160). strategy (str): Segmentation strategy, either 'all' or 'largest'. Returns: (list): List of segment masks as float32 arrays. r)merge_multi_segmentallr@rfrAlargest)rrAr)ultralytics.data.converterr rrrrl findContours RETR_EXTERNALCHAIN_APPROX_SIMPLErir0rnrr1argmaxr7rastype)rostrategyr rr;rs rmasks2segmentsrs4?H ZZ\    % % ' -   Q 1 133J3J KA N 5 1vzNN#6RS7TQ "a8H7T#UV1b!, Y&HHQrxx(;AQ(;<CCEFGOOPRTUV A+, - O8U )<+]ru)patternreplstring)resub)rs r clean_strr"s 66>SQR SSrct|tjr%tj|tjSt j|t jS)zSCreate empty torch.Tensor or np.ndarray with same shape as input and float32 dtype.r,)rTr rUrrr0)r;s rrrsG5?q%,,4O%--0UWUbUbcdlnlvlvUwr)r$)r9rVr:rV)NTF)rMr rNr )r;rV)N)r$r$rr)rarVr`rVrrVrrV)r$r$Fr#)rarVr`rVr5r rr$)i)rrV)F)rr )T)rMr )NFT)rr rMr )r )rr )rz torch.Tensorreturnz np.ndarray). __future__r contextlibrWr rrlrr0r torch.nn.functionalnn functionalrultralytics.utilsrContextDecoratorrr>rRrZrHrdr}rrrrrrrrrrrrrrrrrr rrr"rr)rrr-s#  )6#j))6#r  &"0E4!8!(U2!H2&> ] Tr