a yf#%@sdZddlmZddlmZddlmZddlZddl Z ddl m Z ddl m Z mZmZddlmZdd lmZmZmZdd lmZGd d d eZGd ddeZGdddeZGdddeZGdddeZGdddeZGdddeZdS)z Ultralytics Results, Boxes and Masks classes for handling inference results. Usage: See https://docs.ultralytics.com/modes/predict/ )deepcopy) lru_cache)PathN) LetterBox)LOGGER SimpleClassops)check_requirements) Annotatorcolors save_one_box)smart_inference_modec@sZeZdZdZddddZeddZdd Zd d Zd d Z ddZ ddZ ddZ dS) BaseTensora Base tensor class with additional methods for easy manipulation and device handling. Attributes: data (torch.Tensor | np.ndarray): Prediction data such as bounding boxes, masks, or keypoints. orig_shape (Tuple[int, int]): Original shape of the image, typically in the format (height, width). Methods: cpu: Return a copy of the tensor stored in CPU memory. numpy: Returns a copy of the tensor as a numpy array. cuda: Moves the tensor to GPU memory, returning a new instance if necessary. to: Return a copy of the tensor with the specified device and dtype. Examples: >>> import torch >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]) >>> orig_shape = (720, 1280) >>> base_tensor = BaseTensor(data, orig_shape) >>> cpu_tensor = base_tensor.cpu() >>> numpy_array = base_tensor.numpy() >>> gpu_tensor = base_tensor.cuda() NreturncCs*t|tjtjfsJd||_||_dS)a  Initialize BaseTensor with prediction data and the original shape of the image. Args: data (torch.Tensor | np.ndarray): Prediction data such as bounding boxes, masks, or keypoints. orig_shape (Tuple[int, int]): Original shape of the image in (height, width) format. Examples: >>> import torch >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]) >>> orig_shape = (720, 1280) >>> base_tensor = BaseTensor(data, orig_shape) z'data must be torch.Tensor or np.ndarrayN) isinstancetorchTensornpndarraydata orig_shape)selfrrrV/var/www/html/django/DPS/env/lib/python3.9/site-packages/ultralytics/engine/results.py__init__.szBaseTensor.__init__cCs|jjS)aS Returns the shape of the underlying data tensor. Returns: (Tuple[int, ...]): The shape of the data tensor. Examples: >>> data = torch.rand(100, 4) >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280)) >>> print(base_tensor.shape) (100, 4) )rshaperrrrr@szBaseTensor.shapecCs&t|jtjr|S||j|jS)a Returns a copy of the tensor stored in CPU memory. Returns: (BaseTensor): A new BaseTensor object with the data tensor moved to CPU memory. Examples: >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]).cuda() >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280)) >>> cpu_tensor = base_tensor.cpu() >>> isinstance(cpu_tensor, BaseTensor) True >>> cpu_tensor.data.device device(type='cpu') )rrrr __class__cpurrrrrrPszBaseTensor.cpucCs&t|jtjr|S||j|jS)a Returns a copy of the tensor as a numpy array. Returns: (np.ndarray): A numpy array containing the same data as the original tensor. Examples: >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]) >>> orig_shape = (720, 1280) >>> base_tensor = BaseTensor(data, orig_shape) >>> numpy_array = base_tensor.numpy() >>> print(type(numpy_array)) )rrrrrnumpyrrrrrr bszBaseTensor.numpycCs|t|j|jS)aJ Moves the tensor to GPU memory. Returns: (BaseTensor): A new BaseTensor instance with the data moved to GPU memory if it's not already a numpy array, otherwise returns self. Examples: >>> import torch >>> from ultralytics.engine.results import BaseTensor >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]) >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280)) >>> gpu_tensor = base_tensor.cuda() >>> print(gpu_tensor.data.device) cuda:0 )rr as_tensorrcudarrrrrr"sszBaseTensor.cudacOs"|t|jj|i||jS)av Return a copy of the tensor with the specified device and dtype. Args: *args (Any): Variable length argument list to be passed to torch.Tensor.to(). **kwargs (Any): Arbitrary keyword arguments to be passed to torch.Tensor.to(). Returns: (BaseTensor): A new BaseTensor instance with the data moved to the specified device and/or dtype. Examples: >>> base_tensor = BaseTensor(torch.randn(3, 4), orig_shape=(480, 640)) >>> cuda_tensor = base_tensor.to("cuda") >>> float16_tensor = base_tensor.to(dtype=torch.float16) )rrr!rtorrargskwargsrrrr#sz BaseTensor.tocCs t|jS)ao Returns the length of the underlying data tensor. Returns: (int): The number of elements in the first dimension of the data tensor. Examples: >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]) >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280)) >>> len(base_tensor) 2 )lenrrrrr__len__s zBaseTensor.__len__cCs||j||jS)a] Returns a new BaseTensor instance containing the specified indexed elements of the data tensor. Args: idx (int | List[int] | torch.Tensor): Index or indices to select from the data tensor. Returns: (BaseTensor): A new BaseTensor instance containing the indexed data. Examples: >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]) >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280)) >>> result = base_tensor[0] # Select the first row >>> print(result.data) tensor([1, 2, 3]) )rrrridxrrr __getitem__szBaseTensor.__getitem__) __name__ __module__ __qualname____doc__rpropertyrrr r"r#r(r+rrrrrs rc@seZdZdZd6ddddZddZdd Zd7d d Zd d ZddZ ddZ ddZ ddZ ddZ d8ddZdd Zd9d!d"Zd#d$Zd:d%d&Zed'fd(d)Zd;d*d+Zdd0d1Zd?d2d3Zd@d4d5ZdS)AResultsa A class for storing and manipulating inference results. This class encapsulates the functionality for handling detection, segmentation, pose estimation, and classification results from YOLO models. Attributes: orig_img (numpy.ndarray): Original image as a numpy array. orig_shape (Tuple[int, int]): Original image shape in (height, width) format. boxes (Boxes | None): Object containing detection bounding boxes. masks (Masks | None): Object containing detection masks. probs (Probs | None): Object containing class probabilities for classification tasks. keypoints (Keypoints | None): Object containing detected keypoints for each object. obb (OBB | None): Object containing oriented bounding boxes. speed (Dict[str, float | None]): Dictionary of preprocess, inference, and postprocess speeds. names (Dict[int, str]): Dictionary mapping class IDs to class names. path (str): Path to the image file. _keys (Tuple[str, ...]): Tuple of attribute names for internal use. Methods: update: Updates object attributes with new detection results. cpu: Returns a copy of the Results object with all tensors on CPU memory. numpy: Returns a copy of the Results object with all tensors as numpy arrays. cuda: Returns a copy of the Results object with all tensors on GPU memory. to: Returns a copy of the Results object with tensors on a specified device and dtype. new: Returns a new Results object with the same image, path, and names. plot: Plots detection results on an input image, returning an annotated image. show: Shows annotated results on screen. save: Saves annotated results to file. verbose: Returns a log string for each task, detailing detections and classifications. save_txt: Saves detection results to a text file. save_crop: Saves cropped detection images. tojson: Converts detection results to JSON format. Examples: >>> results = model("path/to/image.jpg") >>> for result in results: ... print(result.boxes) # Print detection boxes ... result.show() # Display the annotated image ... result.save(filename="result.jpg") # Save annotated image Nrc Cs||_|jdd|_|dur*t||jnd|_|durDt||jnd|_|durZt|nd|_|durtt ||jnd|_ |durt ||jnd|_ | dur| n dddd|_ ||_||_d|_d|_dS)aX Initialize the Results class for storing and manipulating inference results. Args: orig_img (numpy.ndarray): The original image as a numpy array. path (str): The path to the image file. names (Dict): A dictionary of class names. boxes (torch.Tensor | None): A 2D tensor of bounding box coordinates for each detection. masks (torch.Tensor | None): A 3D tensor of detection masks, where each mask is a binary image. probs (torch.Tensor | None): A 1D tensor of probabilities of each class for classification task. keypoints (torch.Tensor | None): A 2D tensor of keypoint coordinates for each detection. obb (torch.Tensor | None): A 2D tensor of oriented bounding box coordinates for each detection. speed (Dict | None): A dictionary containing preprocess, inference, and postprocess speeds (ms/image). Examples: >>> results = model("path/to/image.jpg") >>> result = results[0] # Get the first result >>> boxes = result.boxes # Get the boxes for the first result >>> masks = result.masks # Get the masks for the first result Notes: For the default pose model, keypoint indices for human body pose estimation are: 0: Nose, 1: Left Eye, 2: Right Eye, 3: Left Ear, 4: Right Ear 5: Left Shoulder, 6: Right Shoulder, 7: Left Elbow, 8: Right Elbow 9: Left Wrist, 10: Right Wrist, 11: Left Hip, 12: Right Hip 13: Left Knee, 14: Right Knee, 15: Left Ankle, 16: Right Ankle N) preprocessZ inference postprocess)boxesmasksprobs keypointsobb)orig_imgrrBoxesr5Masksr6Probsr7 Keypointsr8OBBr9speednamespathsave_dir_keys) rr:rBrAr5r6r7r8r9r@rrrrszResults.__init__cCs |d|S)a Return a Results object for a specific index of inference results. Args: idx (int | slice): Index or slice to retrieve from the Results object. Returns: (Results): A new Results object containing the specified subset of inference results. Examples: >>> results = model("path/to/image.jpg") # Perform inference >>> single_result = results[0] # Get the first result >>> subset_results = results[1:4] # Get a slice of results r+_applyr)rrrr+szResults.__getitem__cCs.|jD]"}t||}|durt|SqdS)a Return the number of detections in the Results object. Returns: (int): The number of detections, determined by the length of the first non-empty attribute (boxes, masks, probs, keypoints, or obb). Examples: >>> results = Results(orig_img, path, names, boxes=torch.rand(5, 4)) >>> len(results) 5 N)rDgetattrr')rkvrrrr("s  zResults.__len__cCs^|dur tt||j|j|_|dur6t||j|_|durD||_|durZt||j|_ dS)a Updates the Results object with new detection data. This method allows updating the boxes, masks, probabilities, and oriented bounding boxes (OBB) of the Results object. It ensures that boxes are clipped to the original image shape. Args: boxes (torch.Tensor | None): A tensor of shape (N, 6) containing bounding box coordinates and confidence scores. The format is (x1, y1, x2, y2, conf, class). masks (torch.Tensor | None): A tensor of shape (N, H, W) containing segmentation masks. probs (torch.Tensor | None): A tensor of shape (num_classes,) containing class probabilities. obb (torch.Tensor | None): A tensor of shape (N, 5) containing oriented bounding box coordinates. Examples: >>> results = model("image.jpg") >>> new_boxes = torch.tensor([[100, 100, 200, 200, 0.9, 0]]) >>> results[0].update(boxes=new_boxes) N) r;rZ clip_boxesrr5r<r6r7r?r9)rr5r6r7r9rrrupdate4szResults.updatecOsF|}|jD]2}t||}|durt||t|||i|q|S)a Applies a function to all non-empty attributes and returns a new Results object with modified attributes. This method is internally called by methods like .to(), .cuda(), .cpu(), etc. Args: fn (str): The name of the function to apply. *args (Any): Variable length argument list to pass to the function. **kwargs (Any): Arbitrary keyword arguments to pass to the function. Returns: (Results): A new Results object with attributes modified by the applied function. Examples: >>> results = model("path/to/image.jpg") >>> for result in results: ... result_cuda = result.cuda() ... result_cpu = result.cpu() N)newrDrGsetattr)rfnr%r&rrHrIrrrrFPs   zResults._applycCs |dS)a Returns a copy of the Results object with all its tensors moved to CPU memory. This method creates a new Results object with all tensor attributes (boxes, masks, probs, keypoints, obb) transferred to CPU memory. It's useful for moving data from GPU to CPU for further processing or saving. Returns: (Results): A new Results object with all tensor attributes on CPU memory. Examples: >>> results = model("path/to/image.jpg") # Perform inference >>> cpu_result = results[0].cpu() # Move the first result to CPU >>> print(cpu_result.boxes.device) # Output: cpu rrErrrrrksz Results.cpucCs |dS)a\ Converts all tensors in the Results object to numpy arrays. Returns: (Results): A new Results object with all tensors converted to numpy arrays. Examples: >>> results = model("path/to/image.jpg") >>> numpy_result = results[0].numpy() >>> type(numpy_result.boxes.data) Notes: This method creates a new Results object, leaving the original unchanged. It's useful for interoperability with numpy-based libraries or when CPU-based operations are required. r rErrrrr |sz Results.numpycCs |dS)a Moves all tensors in the Results object to GPU memory. Returns: (Results): A new Results object with all tensors moved to CUDA device. Examples: >>> results = model("path/to/image.jpg") >>> cuda_results = results[0].cuda() # Move first result to GPU >>> for result in results: ... result_cuda = result.cuda() # Move each result to GPU r"rErrrrr"s z Results.cudacOs|jdg|Ri|S)a Moves all tensors in the Results object to the specified device and dtype. Args: *args (Any): Variable length argument list to be passed to torch.Tensor.to(). **kwargs (Any): Arbitrary keyword arguments to be passed to torch.Tensor.to(). Returns: (Results): A new Results object with all tensors moved to the specified device and dtype. Examples: >>> results = model("path/to/image.jpg") >>> result_cuda = results[0].to("cuda") # Move first result to GPU >>> result_cpu = results[0].to("cpu") # Move first result to CPU >>> result_half = results[0].to(dtype=torch.float16) # Convert first result to half precision r#rEr$rrrr#sz Results.tocCst|j|j|j|jdS)aM Creates a new Results object with the same image, path, names, and speed attributes. Returns: (Results): A new Results object with copied attributes from the original instance. Examples: >>> results = model("path/to/image.jpg") >>> new_result = results[0].new() )r:rBrAr@)r1r:rBrAr@rrrrrKs z Results.newT Arial.ttfFclassc$ sn|dvsJd|d|dur^t|jtjr^|jdddddtj }|j |j du}|rx|j n|j | }}|j| }}|j| }tt|dur|jn|||||pƈduo|d }|r|r|dur.t|jdd|d }tj|tj|jjd ddddd}|jdurJ|d krJ|jn$|r`|d kr`|jnttt|}|j|jdd|D|d|dur|rtt|D]\}}t |j|rt!|j"nd|jdurdn t |j#}}}|durdn d|d|}| r2|r.|d|dn|nd}|rP|j$%ddd&n|j'&} |j(| |t)|d krr|n|dur|n|d kr|ndd|dqdur|rd*fddj+D}!t,|j-dd}"|j.|"|"g|!dd|j/durDtt|j/jD]4\}}#|j0|#|j-|| |d kr8t)|dnddq|rV|1|j2|rf|3||S) a Plots detection results on an input RGB image. Args: conf (bool): Whether to plot detection confidence scores. line_width (float | None): Line width of bounding boxes. If None, scaled to image size. font_size (float | None): Font size for text. If None, scaled to image size. font (str): Font to use for text. pil (bool): Whether to return the image as a PIL Image. img (np.ndarray | None): Image to plot on. If None, uses original image. im_gpu (torch.Tensor | None): Normalized image on GPU for faster mask plotting. kpt_radius (int): Radius of drawn keypoints. kpt_line (bool): Whether to draw lines connecting keypoints. labels (bool): Whether to plot labels of bounding boxes. boxes (bool): Whether to plot bounding boxes. masks (bool): Whether to plot masks. probs (bool): Whether to plot classification probabilities. show (bool): Whether to display the annotated image. save (bool): Whether to save the annotated image. filename (str | None): Filename to save image if save is True. color_mode (bool): Specify the color mode, e.g., 'instance' or 'class'. Default to 'class'. Returns: (np.ndarray): Annotated image as a numpy array. Examples: >>> results = model("image.jpg") >>> for result in results: ... im = result.plot() ... im.show() >rQinstancez/Expected color_mode='instance' or 'class', not .Nrr2)Zexample)image)ZdtypedevicerRrQcSsg|]}t|dqS)T)r .0xrrr z Results.plot..)r im_gpuzid: .2fT)colorZrotatedz, c3s0|](}r|n|dj|dVqdSr_r`NrrYjrAZ pred_probsrr 0r\zResults.plot..gQ?)rUrUrU)Z txt_color)Zradiuskpt_lineZ kpt_color)4rr:rrdetachZpermute contiguousr#Zuint8rr rAr9r5r6r7r rrrresultr!Zfloat16rrWflipidclsreversedranger' enumerateintfloatconfitemxyxyxyxyreshapesqueezexyxyZ box_labelr jointop5roundrtextr8kptsshowrBsave)$rrv line_widthZ font_sizeZfontZpilZimgr]Z kpt_radiusrjlabelsr5r6r7rrfilenameZ color_modeis_obbZ pred_boxesZ show_boxesZ pred_masksZ show_masksZ show_probsZ annotatorr*idcronamelabelboxrrZrHrrhrplots30        @"$$      z Results.plotcOs|j|ddi|dS)a Display the image with annotated inference results. This method plots the detection results on the original image and displays it. It's a convenient way to visualize the model's predictions directly. Args: *args (Any): Variable length argument list to be passed to the `plot()` method. **kwargs (Any): Arbitrary keyword arguments to be passed to the `plot()` method. Examples: >>> results = model("path/to/image.jpg") >>> results[0].show() # Display the first result >>> for result in results: ... result.show() # Display all results rTN)rr$rrrrIsz Results.showcOs0|sdt|jj}|j|d|d||S)a Saves annotated inference results image to file. This method plots the detection results on the original image and saves the annotated image to a file. It utilizes the `plot` method to generate the annotated image and then saves it to the specified filename. Args: filename (str | Path | None): The filename to save the annotated image. If None, a default filename is generated based on the original image path. *args (Any): Variable length argument list to be passed to the `plot` method. **kwargs (Any): Arbitrary keyword arguments to be passed to the `plot` method. Examples: >>> results = model("path/to/image.jpg") >>> for result in results: ... result.save("annotated_image.jpg") >>> # Or with custom plot arguments >>> for result in results: ... result.save("annotated_image.jpg", conf=False, line_width=2) Zresults_T)rr)rrBrr)rrr%r&rrrr\sz Results.savecsd}jj}tdkr2dur(|S|dSdur`|dfddjDd7}|r|jD]<}|j|k}||djt |d |d kd7}qn|S) a Returns a log string for each task in the results, detailing detection and classification outcomes. This method generates a human-readable string summarizing the detection and classification results. It includes the number of detections for each class and the top probabilities for classification tasks. Returns: (str): A formatted string containing a summary of the results. For detection tasks, it includes the number of detections per class. For classification tasks, it includes the top 5 class probabilities. Examples: >>> results = model("path/to/image.jpg") >>> for result in results: ... print(result.verbose()) 2 persons, 1 car, 3 traffic lights, dog 0.92, cat 0.78, horse 0.64, Notes: - If there are no detections, the method returns "(no detections), " for detection tasks. - For classification tasks, it returns the top 5 class probabilities and their corresponding class names. - The returned string is comma-separated and ends with a comma and a space. r^rNz(no detections), z, c3s*|]"}j|dj|dVqdSrd)rArrfr7rrrrir\z"Results.verbose..r_srT) r7r5r'r|r}rpuniquesumrArt)rZ log_stringr5rnrrrverbosevs &,zResults.verbosecsjdu}|rjnj}j}jj}gdurTfddjDn.|rt|D]\}}t|jt |j |j durdn t|j } } } | g|r|j dn |jdR} |r||jdd} | g| R} |durD||jr"t||j||j dfdn||j}| g|dR7} | | f|| dur\dn| f7} d t| | qbrt|jjd d d t|d $}|d dDWdn1s0YdS)a Save detection results to a text file. Args: txt_file (str | Path): Path to the output text file. save_conf (bool): Whether to include confidence scores in the output. Returns: (str): Path to the saved text file. Examples: >>> from ultralytics import YOLO >>> model = YOLO("yolov8n.pt") >>> results = model("path/to/image.jpg") >>> for result in results: ... result.save_txt("output.txt") Notes: - The file will contain one line per detection or classification with the following structure: - For detections: `class confidence x_center y_center width height` - For classifications: `confidence class_name` - For masks and keypoints, the specific formats will vary accordingly. - The function will create the output directory if it does not exist. - If save_conf is False, the confidence scores will be excluded from the output. - Existing contents of the file will not be overwritten; new results will be appended. Ncs.g|]&}j|ddj|qS)r`r_)appendrrArfr7rZtextsrrr[r\z$Results.save_txt..rar).Nr2rz%g T)parentsexist_okacss|]}|dVqdS) Nr)rYrrrrrir\z#Results.save_txt..)r9r5r6r7r8r}rsrtrprurvrorw xyxyxyxynviewxywhnxyncopyry has_visiblercattolistrr'rstriprparentmkdiropen writelines)rZtxt_fileZ save_confrr5r6rrgrrrvrolinesegZkptfrrrsave_txts0 4$  6" zResults.save_txtzim.jpgcCsz|jdurtddS|jdur0tddS|jD]>}t|j|jt ||j t |j t |dddq6dS)a  Saves cropped detection images to specified directory. This method saves cropped images of detected objects to a specified directory. Each crop is saved in a subdirectory named after the object's class, with the filename based on the input file_name. Args: save_dir (str | Path): Directory path where cropped images will be saved. file_name (str | Path): Base filename for the saved cropped images. Default is Path("im.jpg"). Notes: - This method does not support Classify or Oriented Bounding Box (OBB) tasks. - Crops are saved as 'save_dir/class_name/file_name.jpg'. - The method will create necessary subdirectories if they don't exist. - Original image is copied before cropping to avoid modifying the original. Examples: >>> results = model("path/to/image.jpg") >>> for result in results: ... result.save_crop(save_dir="path/to/crops", file_name="detection") Nu8WARNING ⚠️ Classify task do not support `save_crop`.u3WARNING ⚠️ OBB task do not support `save_crop`.z.jpgT)fileZBGR) r7rwarningr9r5r r{r:rrrArtrp)rrC file_namerrrr save_crops     $zResults.save_cropcCsg}|jdur@|jj}||j||t|jj|d|S|jdu}|rT|jn|j}|rd|j nd\}}t |D]\} } t | j t| j |}} |r| jn| jdd} i} t | D]H\}}t|d||| d|d<t|d||| d |d<q|j||| | d }|jr>t | j|d <|jr|jj| dddf|||jj| dddf||d |d <|jdur|j| jdjdd\}}}||||||||d|d<||qt|S)a Converts inference results to a summarized dictionary with optional normalization for box coordinates. This method creates a list of detection dictionaries, each containing information about a single detection or classification result. For classification tasks, it returns the top class and its confidence. For detection tasks, it includes class information, bounding box coordinates, and optionally mask segments and keypoints. Args: normalize (bool): Whether to normalize bounding box coordinates by image dimensions. Defaults to False. decimals (int): Number of decimal places to round the output values to. Defaults to 5. Returns: (List[Dict]): A list of dictionaries, each containing summarized information for a single detection or classification result. The structure of each dictionary varies based on the task type (classification or detection) and available information (boxes, masks, keypoints). Examples: >>> results = model("image.jpg") >>> summary = results[0].summary() >>> print(summary) N)rrQ confidence)rTrTrar2rrZrTy)rrQrrZtrack_id)rZrsegments)dim)rZrvisibler8)r7top1rrAr~top1confrwr9r5rrsrtrprvrxr{rzryris_trackror6xyr8rrZunbindr )r normalizedecimalsresultsZclass_idrrhwrrowrvrrrgbrmrZrrrrrsummarysF    "$$  "  zResults.summarycCsddl}||j||dS)a Converts detection results to a Pandas Dataframe. This method converts the detection results into Pandas Dataframe format. It includes information about detected objects such as bounding boxes, class names, confidence scores, and optionally segmentation masks and keypoints. Args: normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions. If True, coordinates will be returned as float values between 0 and 1. Defaults to False. decimals (int): Number of decimal places to round the output values to. Defaults to 5. Returns: (DataFrame): A Pandas Dataframe containing all the information in results in an organized way. Examples: >>> results = model("path/to/image.jpg") >>> df_result = results[0].to_df() >>> print(df_result) rNrr)ZpandasZ DataFramer)rrrpdrrrto_df6sz Results.to_dfcOs|j||dj|i|S)a< Converts detection results to a CSV format. This method serializes the detection results into a CSV format. It includes information about detected objects such as bounding boxes, class names, confidence scores, and optionally segmentation masks and keypoints. Args: normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions. If True, coordinates will be returned as float values between 0 and 1. Defaults to False. decimals (int): Number of decimal places to round the output values to. Defaults to 5. *args (Any): Variable length argument list to be passed to pandas.DataFrame.to_csv(). **kwargs (Any): Arbitrary keyword arguments to be passed to pandas.DataFrame.to_csv(). Returns: (str): CSV containing all the information in results in an organized way. Examples: >>> results = model("path/to/image.jpg") >>> csv_result = results[0].to_csv() >>> print(csv_result) r)rto_csv)rrrr%r&rrrrOszResults.to_csvcOs0td|j||d}|jr dS|j|i|S)aD Converts detection results to XML format. This method serializes the detection results into an XML format. It includes information about detected objects such as bounding boxes, class names, confidence scores, and optionally segmentation masks and keypoints. Args: normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions. If True, coordinates will be returned as float values between 0 and 1. Defaults to False. decimals (int): Number of decimal places to round the output values to. Defaults to 5. *args (Any): Variable length argument list to be passed to pandas.DataFrame.to_xml(). **kwargs (Any): Arbitrary keyword arguments to be passed to pandas.DataFrame.to_xml(). Returns: (str): An XML string containing all the information in results in an organized way. Examples: >>> results = model("path/to/image.jpg") >>> xml_result = results[0].to_xml() >>> print(xml_result) Zlxmlrz4 )r remptyto_xml)rrrr%r&dfrrrriszResults.to_xmlcCstd|||S)z Deprecated version of to_json().uPWARNING ⚠️ 'result.tojson()' is deprecated, replace with 'result.to_json()'.)rrto_json)rrrrrrtojsons zResults.tojsoncCs ddl}|j|j||dddS)au Converts detection results to JSON format. This method serializes the detection results into a JSON-compatible format. It includes information about detected objects such as bounding boxes, class names, confidence scores, and optionally segmentation masks and keypoints. Args: normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions. If True, coordinates will be returned as float values between 0 and 1. Defaults to False. decimals (int): Number of decimal places to round the output values to. Defaults to 5. Returns: (str): A JSON string containing the serialized detection results. Examples: >>> results = model("path/to/image.jpg") >>> json_result = results[0].to_json() >>> print(json_result) Notes: - For classification tasks, the JSON will contain class probabilities instead of bounding boxes. - For object detection tasks, the JSON will include bounding box coordinates, class names, and confidence scores. - If available, segmentation masks and keypoints will also be included in the JSON output. - The method uses the `summary` method internally to generate the data structure before converting it to JSON. rNrr2)indent)jsondumpsr)rrrrrrrrszResults.to_json)NNNNNN)NNNN)TNNrOFNNrPTTTTTFFNrQ)N)F)FrP)FrP)FrP)FrP)FrP)FrP)r,r-r.r/rr+r(rJrFrr r"r#rKrrrrrrrrrrrrrrrrrr1sX+ +    $ 7$ A    r1cseZdZdZddfdd ZeddZedd Zed d Zed d Z ee ddddZ ee ddddZ ee ddddZ ZS)r;a A class for managing and manipulating detection boxes. This class provides functionality for handling detection boxes, including their coordinates, confidence scores, class labels, and optional tracking IDs. It supports various box formats and offers methods for easy manipulation and conversion between different coordinate systems. Attributes: data (torch.Tensor | numpy.ndarray): The raw tensor containing detection boxes and associated data. orig_shape (Tuple[int, int]): The original image dimensions (height, width). is_track (bool): Indicates whether tracking IDs are included in the box data. xyxy (torch.Tensor | numpy.ndarray): Boxes in [x1, y1, x2, y2] format. conf (torch.Tensor | numpy.ndarray): Confidence scores for each box. cls (torch.Tensor | numpy.ndarray): Class labels for each box. id (torch.Tensor | numpy.ndarray): Tracking IDs for each box (if available). xywh (torch.Tensor | numpy.ndarray): Boxes in [x, y, width, height] format. xyxyn (torch.Tensor | numpy.ndarray): Normalized [x1, y1, x2, y2] boxes relative to orig_shape. xywhn (torch.Tensor | numpy.ndarray): Normalized [x, y, width, height] boxes relative to orig_shape. Methods: cpu(): Returns a copy of the object with all tensors on CPU memory. numpy(): Returns a copy of the object with all tensors as numpy arrays. cuda(): Returns a copy of the object with all tensors on GPU memory. to(*args, **kwargs): Returns a copy of the object with tensors on specified device and dtype. Examples: >>> import torch >>> boxes_data = torch.tensor([[100, 50, 150, 100, 0.9, 0], [200, 150, 300, 250, 0.8, 1]]) >>> orig_shape = (480, 640) # height, width >>> boxes = Boxes(boxes_data, orig_shape) >>> print(boxes.xyxy) >>> print(boxes.conf) >>> print(boxes.cls) >>> print(boxes.xywhn) Nrcs\|jdkr|dddf}|jd}|dvs:Jd|t|||dk|_||_dS)aS Initialize the Boxes class with detection box data and the original image shape. This class manages detection boxes, providing easy access and manipulation of box coordinates, confidence scores, class identifiers, and optional tracking IDs. It supports multiple formats for box coordinates, including both absolute and normalized forms. Args: boxes (torch.Tensor | np.ndarray): A tensor or numpy array with detection boxes of shape (num_boxes, 6) or (num_boxes, 7). Columns should contain [x1, y1, x2, y2, confidence, class, (optional) track_id]. orig_shape (Tuple[int, int]): The original image shape as (height, width). Used for normalization. Attributes: data (torch.Tensor): The raw tensor containing detection boxes and their associated data. orig_shape (Tuple[int, int]): The original image size, used for normalization. is_track (bool): Indicates whether tracking IDs are included in the box data. Examples: >>> import torch >>> boxes = torch.tensor([[100, 50, 150, 100, 0.9, 0]]) >>> orig_shape = (480, 640) >>> detection_boxes = Boxes(boxes, orig_shape) >>> print(detection_boxes.xyxy) tensor([[100., 50., 150., 100.]]) rTNra>zexpected 6 or 7 values but got rndimrsuperrrrrr5rrrrrrs   zBoxes.__init__cCs|jddddfS)a Returns bounding boxes in [x1, y1, x2, y2] format. Returns: (torch.Tensor | numpy.ndarray): A tensor or numpy array of shape (n, 4) containing bounding box coordinates in [x1, y1, x2, y2] format, where n is the number of boxes. Examples: >>> results = model("image.jpg") >>> boxes = results[0].boxes >>> xyxy = boxes.xyxy >>> print(xyxy) Nrbrerrrrr{sz Boxes.xyxycCs|jdddfS)a Returns the confidence scores for each detection box. Returns: (torch.Tensor | numpy.ndarray): A 1D tensor or array containing confidence scores for each detection, with shape (N,) where N is the number of detections. Examples: >>> boxes = Boxes(torch.tensor([[10, 20, 30, 40, 0.9, 0]]), orig_shape=(100, 100)) >>> conf_scores = boxes.conf >>> print(conf_scores) tensor([0.9000]) Nrerrrrrvsz Boxes.confcCs|jdddfS)a Returns the class ID tensor representing category predictions for each bounding box. Returns: (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the class IDs for each detection box. The shape is (N,), where N is the number of boxes. Examples: >>> results = model("image.jpg") >>> boxes = results[0].boxes >>> class_ids = boxes.cls >>> print(class_ids) # tensor([0., 2., 1.]) Nrarerrrrrpsz Boxes.clscCs|jr|jdddfSdS)a Returns the tracking IDs for each detection box if available. Returns: (torch.Tensor | None): A tensor containing tracking IDs for each box if tracking is enabled, otherwise None. Shape is (N,) where N is the number of boxes. Examples: >>> results = model.track("path/to/video.mp4") >>> for result in results: ... boxes = result.boxes ... if boxes.is_track: ... track_ids = boxes.id ... print(f"Tracking IDs: {track_ids}") ... else: ... print("Tracking is not enabled for these boxes.") Notes: - This property is only available when tracking is enabled (i.e., when `is_track` is True). - The tracking IDs are typically used to associate detections across multiple frames in video analysis. Nrrrrrrro&szBoxes.idr2maxsizecCs t|jS)a Convert bounding boxes from [x1, y1, x2, y2] format to [x, y, width, height] format. Returns: (torch.Tensor | numpy.ndarray): Boxes in [x_center, y_center, width, height] format, where x_center, y_center are the coordinates of the center point of the bounding box, width, height are the dimensions of the bounding box and the shape of the returned tensor is (N, 4), where N is the number of boxes. Examples: >>> boxes = Boxes(torch.tensor([[100, 50, 150, 100], [200, 150, 300, 250]]), orig_shape=(480, 640)) >>> xywh = boxes.xywh >>> print(xywh) tensor([[100.0000, 50.0000, 50.0000, 50.0000], [200.0000, 150.0000, 100.0000, 100.0000]]) )r xyxy2xywhr{rrrrxywh?sz Boxes.xywhcCsdt|jtjr|jn t|j}|dddgf|jd<|dddgf|jd<|S)a Returns normalized bounding box coordinates relative to the original image size. This property calculates and returns the bounding box coordinates in [x1, y1, x2, y2] format, normalized to the range [0, 1] based on the original image dimensions. Returns: (torch.Tensor | numpy.ndarray): Normalized bounding box coordinates with shape (N, 4), where N is the number of boxes. Each row contains [x1, y1, x2, y2] values normalized to [0, 1]. Examples: >>> boxes = Boxes(torch.tensor([[100, 50, 300, 400, 0.9, 0]]), orig_shape=(480, 640)) >>> normalized = boxes.xyxyn >>> print(normalized) tensor([[0.1562, 0.1042, 0.4688, 0.8333]]) .rr2rT)rr{rrclonerrr)rr{rrrxyxynSs$z Boxes.xyxyncCsLt|j}|dddgf|jd<|dddgf|jd<|S)aA Returns normalized bounding boxes in [x, y, width, height] format. This property calculates and returns the normalized bounding box coordinates in the format [x_center, y_center, width, height], where all values are relative to the original image dimensions. Returns: (torch.Tensor | numpy.ndarray): Normalized bounding boxes with shape (N, 4), where N is the number of boxes. Each row contains [x_center, y_center, width, height] values normalized to [0, 1] based on the original image dimensions. Examples: >>> boxes = Boxes(torch.tensor([[100, 50, 150, 100, 0.9, 0]]), orig_shape=(480, 640)) >>> normalized = boxes.xywhn >>> print(normalized) tensor([[0.1953, 0.1562, 0.0781, 0.1042]]) .rr2rTr)rrr{r)rrrrrrks z Boxes.xywhn)r,r-r.r/rr0r{rvrprorrrr __classcell__rrrrr;s&$#      r;csReZdZdZddfdd Zeedddd Zeeddd d ZZ S) r<a A class for storing and manipulating detection masks. This class extends BaseTensor and provides functionality for handling segmentation masks, including methods for converting between pixel and normalized coordinates. Attributes: data (torch.Tensor | numpy.ndarray): The raw tensor or array containing mask data. orig_shape (tuple): Original image shape in (height, width) format. xy (List[numpy.ndarray]): A list of segments in pixel coordinates. xyn (List[numpy.ndarray]): A list of normalized segments. Methods: cpu(): Returns a copy of the Masks object with the mask tensor on CPU memory. numpy(): Returns a copy of the Masks object with the mask tensor as a numpy array. cuda(): Returns a copy of the Masks object with the mask tensor on GPU memory. to(*args, **kwargs): Returns a copy of the Masks object with the mask tensor on specified device and dtype. Examples: >>> masks_data = torch.rand(1, 160, 160) >>> orig_shape = (720, 1280) >>> masks = Masks(masks_data, orig_shape) >>> pixel_coords = masks.xy >>> normalized_coords = masks.xyn Nrcs,|jdkr|dddf}t||dS)ar Initialize the Masks class with detection mask data and the original image shape. Args: masks (torch.Tensor | np.ndarray): Detection masks with shape (num_masks, height, width). orig_shape (tuple): The original image shape as (height, width). Used for normalization. Examples: >>> import torch >>> from ultralytics.engine.results import Masks >>> masks = torch.rand(10, 160, 160) # 10 masks of 160x160 resolution >>> orig_shape = (720, 1280) # Original image shape >>> mask_obj = Masks(masks, orig_shape) r2N)rrr)rr6rrrrrs zMasks.__init__rTrcsfddtjDS)a Returns normalized xy-coordinates of the segmentation masks. This property calculates and caches the normalized xy-coordinates of the segmentation masks. The coordinates are normalized relative to the original image shape. Returns: (List[numpy.ndarray]): A list of numpy arrays, where each array contains the normalized xy-coordinates of a single segmentation mask. Each array has shape (N, 2), where N is the number of points in the mask contour. Examples: >>> results = model("image.jpg") >>> masks = results[0].masks >>> normalized_coords = masks.xyn >>> print(normalized_coords[0]) # Normalized coordinates of the first mask cs,g|]$}tjjjdd|jddqS)rTNTrrZ scale_coordsrrrrXrrrr[szMasks.xyn..rZmasks2segmentsrrrrrrs  z Masks.xyncsfddtjDS)aG Returns the [x, y] pixel coordinates for each segment in the mask tensor. This property calculates and returns a list of pixel coordinates for each segmentation mask in the Masks object. The coordinates are scaled to match the original image dimensions. Returns: (List[numpy.ndarray]): A list of numpy arrays, where each array contains the [x, y] pixel coordinates for a single segmentation mask. Each array has shape (N, 2), where N is the number of points in the segment. Examples: >>> results = model("image.jpg") >>> masks = results[0].masks >>> xy_coords = masks.xy >>> print(len(xy_coords)) # Number of masks >>> print(xy_coords[0].shape) # Shape of first mask's coordinates cs,g|]$}tjjjdd|jddqS)rTNFrrrXrrrr[szMasks.xy..rrrrrrs  zMasks.xy) r,r-r.r/rr0rrrrrrrrr<s r<csneZdZdZeddfdd Zeedddd Zeeddd d Z eeddd d Z Z S)r>a2 A class for storing and manipulating detection keypoints. This class encapsulates functionality for handling keypoint data, including coordinate manipulation, normalization, and confidence values. Attributes: data (torch.Tensor): The raw tensor containing keypoint data. orig_shape (Tuple[int, int]): The original image dimensions (height, width). has_visible (bool): Indicates whether visibility information is available for keypoints. xy (torch.Tensor): Keypoint coordinates in [x, y] format. xyn (torch.Tensor): Normalized keypoint coordinates in [x, y] format, relative to orig_shape. conf (torch.Tensor): Confidence values for each keypoint, if available. Methods: cpu(): Returns a copy of the keypoints tensor on CPU memory. numpy(): Returns a copy of the keypoints tensor as a numpy array. cuda(): Returns a copy of the keypoints tensor on GPU memory. to(*args, **kwargs): Returns a copy of the keypoints tensor with specified device and dtype. Examples: >>> import torch >>> from ultralytics.engine.results import Keypoints >>> keypoints_data = torch.rand(1, 17, 3) # 1 detection, 17 keypoints, (x, y, conf) >>> orig_shape = (480, 640) # Original image shape (height, width) >>> keypoints = Keypoints(keypoints_data, orig_shape) >>> print(keypoints.xy.shape) # Access xy coordinates >>> print(keypoints.conf) # Access confidence values >>> keypoints_cpu = keypoints.cpu() # Move keypoints to CPU Nrcsl|jdkr|dddf}|jddkrH|ddk}d|dddf|<t|||jjddk|_dS) a Initializes the Keypoints object with detection keypoints and original image dimensions. This method processes the input keypoints tensor, handling both 2D and 3D formats. For 3D tensors (x, y, confidence), it masks out low-confidence keypoints by setting their coordinates to zero. Args: keypoints (torch.Tensor): A tensor containing keypoint data. Shape can be either: - (num_objects, num_keypoints, 2) for x, y coordinates only - (num_objects, num_keypoints, 3) for x, y coordinates and confidence scores orig_shape (Tuple[int, int]): The original image dimensions (height, width). Examples: >>> kpts = torch.rand(1, 17, 3) # 1 object, 17 keypoints (COCO format), x,y,conf >>> orig_shape = (720, 1280) # Original image height, width >>> keypoints = Keypoints(kpts, orig_shape) r2Nr.r2g?r.ra)rrrrrr)rr8rmaskrrrrs  zKeypoints.__init__rTrcCs|jdddfS)am Returns x, y coordinates of keypoints. Returns: (torch.Tensor): A tensor containing the x, y coordinates of keypoints with shape (N, K, 2), where N is the number of detections and K is the number of keypoints per detection. Examples: >>> results = model("image.jpg") >>> keypoints = results[0].keypoints >>> xy = keypoints.xy >>> print(xy.shape) # (N, K, 2) >>> print(xy[0]) # x, y coordinates of keypoints for first detection Notes: - The returned coordinates are in pixel units relative to the original image dimensions. - If keypoints were initialized with confidence values, only keypoints with confidence >= 0.5 are returned. - This property uses LRU caching to improve performance on repeated access. .Nr2rerrrrr"sz Keypoints.xycCsTt|jtjr|jn t|j}|d|jd<|d|jd<|S)a Returns normalized coordinates (x, y) of keypoints relative to the original image size. Returns: (torch.Tensor | numpy.ndarray): A tensor or array of shape (N, K, 2) containing normalized keypoint coordinates, where N is the number of instances, K is the number of keypoints, and the last dimension contains [x, y] values in the range [0, 1]. Examples: >>> keypoints = Keypoints(torch.rand(1, 17, 2), orig_shape=(480, 640)) >>> normalized_kpts = keypoints.xyn >>> print(normalized_kpts.shape) torch.Size([1, 17, 2]) .rrT.rTr)rrrrrrrr)rrrrrr:s$z Keypoints.xyncCs|jr|jdSdS)a/ Returns confidence values for each keypoint. Returns: (torch.Tensor | None): A tensor containing confidence scores for each keypoint if available, otherwise None. Shape is (num_detections, num_keypoints) for batched data or (num_keypoints,) for single detection. Examples: >>> keypoints = Keypoints(torch.rand(1, 17, 3), orig_shape=(640, 640)) # 1 detection, 17 keypoints >>> conf = keypoints.conf >>> print(conf.shape) # torch.Size([1, 17]) rN)rrrrrrrvPszKeypoints.conf) r,r-r.r/r rr0rrrrvrrrrrr>s  r>cseZdZdZdddfdd Zeedddd Zeeddd d Zeeddd d Z eeddddZ Z S)r=a A class for storing and manipulating classification probabilities. This class extends BaseTensor and provides methods for accessing and manipulating classification probabilities, including top-1 and top-5 predictions. Attributes: data (torch.Tensor | numpy.ndarray): The raw tensor or array containing classification probabilities. orig_shape (tuple | None): The original image shape as (height, width). Not used in this class. top1 (int): Index of the class with the highest probability. top5 (List[int]): Indices of the top 5 classes by probability. top1conf (torch.Tensor | numpy.ndarray): Confidence score of the top 1 class. top5conf (torch.Tensor | numpy.ndarray): Confidence scores of the top 5 classes. Methods: cpu(): Returns a copy of the probabilities tensor on CPU memory. numpy(): Returns a copy of the probabilities tensor as a numpy array. cuda(): Returns a copy of the probabilities tensor on GPU memory. to(*args, **kwargs): Returns a copy of the probabilities tensor with specified device and dtype. Examples: >>> probs = torch.tensor([0.1, 0.3, 0.6]) >>> p = Probs(probs) >>> print(p.top1) 2 >>> print(p.top5) [2, 1, 0] >>> print(p.top1conf) tensor(0.6000) >>> print(p.top5conf) tensor([0.6000, 0.3000, 0.1000]) Nrcst||dS)a Initialize the Probs class with classification probabilities. This class stores and manages classification probabilities, providing easy access to top predictions and their confidences. Args: probs (torch.Tensor | np.ndarray): A 1D tensor or array of classification probabilities. orig_shape (tuple | None): The original image shape as (height, width). Not used in this class but kept for consistency with other result classes. Attributes: data (torch.Tensor | np.ndarray): The raw tensor or array containing classification probabilities. top1 (int): Index of the top 1 class. top5 (List[int]): Indices of the top 5 classes. top1conf (torch.Tensor | np.ndarray): Confidence of the top 1 class. top5conf (torch.Tensor | np.ndarray): Confidences of the top 5 classes. Examples: >>> import torch >>> probs = torch.tensor([0.1, 0.3, 0.2, 0.4]) >>> p = Probs(probs) >>> print(p.top1) 3 >>> print(p.top1conf) tensor(0.4000) >>> print(p.top5) [3, 1, 2, 0] N)rr)rr7rrrrrszProbs.__init__rTrcCst|jS)a Returns the index of the class with the highest probability. Returns: (int): Index of the class with the highest probability. Examples: >>> probs = Probs(torch.tensor([0.1, 0.3, 0.6])) >>> probs.top1 2 )rtrZargmaxrrrrrsz Probs.top1cCs|j dddS)af Returns the indices of the top 5 class probabilities. Returns: (List[int]): A list containing the indices of the top 5 class probabilities, sorted in descending order. Examples: >>> probs = Probs(torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5])) >>> print(probs.top5) [4, 3, 2, 1, 0] rNrP)rZargsortrrrrrr}sz Probs.top5cCs |j|jS)a Returns the confidence score of the highest probability class. This property retrieves the confidence score (probability) of the class with the highest predicted probability from the classification results. Returns: (torch.Tensor | numpy.ndarray): A tensor containing the confidence score of the top 1 class. Examples: >>> results = model("image.jpg") # classify an image >>> probs = results[0].probs # get classification probabilities >>> top1_confidence = probs.top1conf # get confidence of top 1 class >>> print(f"Top 1 class confidence: {top1_confidence.item():.4f}") )rrrrrrrszProbs.top1confcCs |j|jS)a Returns confidence scores for the top 5 classification predictions. This property retrieves the confidence scores corresponding to the top 5 class probabilities predicted by the model. It provides a quick way to access the most likely class predictions along with their associated confidence levels. Returns: (torch.Tensor | numpy.ndarray): A tensor or array containing the confidence scores for the top 5 predicted classes, sorted in descending order of probability. Examples: >>> results = model("image.jpg") >>> probs = results[0].probs >>> top5_conf = probs.top5conf >>> print(top5_conf) # Prints confidence scores for top 5 classes )rr}rrrrtop5confszProbs.top5conf)N) r,r-r.r/rr0rrr}rrrrrrrr=cs!    r=cseZdZdZddfdd ZeddZedd Zed d Zed d Z ee ddddZ ee ddddZ ee ddddZ ZS)r?a A class for storing and manipulating Oriented Bounding Boxes (OBB). This class provides functionality to handle oriented bounding boxes, including conversion between different formats, normalization, and access to various properties of the boxes. Attributes: data (torch.Tensor): The raw OBB tensor containing box coordinates and associated data. orig_shape (tuple): Original image size as (height, width). is_track (bool): Indicates whether tracking IDs are included in the box data. xywhr (torch.Tensor | numpy.ndarray): Boxes in [x_center, y_center, width, height, rotation] format. conf (torch.Tensor | numpy.ndarray): Confidence scores for each box. cls (torch.Tensor | numpy.ndarray): Class labels for each box. id (torch.Tensor | numpy.ndarray): Tracking IDs for each box, if available. xyxyxyxy (torch.Tensor | numpy.ndarray): Boxes in 8-point [x1, y1, x2, y2, x3, y3, x4, y4] format. xyxyxyxyn (torch.Tensor | numpy.ndarray): Normalized 8-point coordinates relative to orig_shape. xyxy (torch.Tensor | numpy.ndarray): Axis-aligned bounding boxes in [x1, y1, x2, y2] format. Methods: cpu(): Returns a copy of the OBB object with all tensors on CPU memory. numpy(): Returns a copy of the OBB object with all tensors as numpy arrays. cuda(): Returns a copy of the OBB object with all tensors on GPU memory. to(*args, **kwargs): Returns a copy of the OBB object with tensors on specified device and dtype. Examples: >>> boxes = torch.tensor([[100, 50, 150, 100, 30, 0.9, 0]]) # xywhr, conf, cls >>> obb = OBB(boxes, orig_shape=(480, 640)) >>> print(obb.xyxyxyxy) >>> print(obb.conf) >>> print(obb.cls) Nrcs\|jdkr|dddf}|jd}|dvs:Jd|t|||dk|_||_dS)aH Initialize an OBB (Oriented Bounding Box) instance with oriented bounding box data and original image shape. This class stores and manipulates Oriented Bounding Boxes (OBB) for object detection tasks. It provides various properties and methods to access and transform the OBB data. Args: boxes (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the detection boxes, with shape (num_boxes, 7) or (num_boxes, 8). The last two columns contain confidence and class values. If present, the third last column contains track IDs, and the fifth column contains rotation. orig_shape (Tuple[int, int]): Original image size, in the format (height, width). Attributes: data (torch.Tensor | numpy.ndarray): The raw OBB tensor. orig_shape (Tuple[int, int]): The original image shape. is_track (bool): Whether the boxes include tracking IDs. Raises: AssertionError: If the number of values per box is not 7 or 8. Examples: >>> import torch >>> boxes = torch.rand(3, 7) # 3 boxes with 7 values each >>> orig_shape = (640, 480) >>> obb = OBB(boxes, orig_shape) >>> print(obb.xywhr) # Access the boxes in xywhr format rTNra>rzexpected 7 or 8 values but got rrrrrrrs   z OBB.__init__cCs|jddddfS)a Returns boxes in [x_center, y_center, width, height, rotation] format. Returns: (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the oriented bounding boxes with format [x_center, y_center, width, height, rotation]. The shape is (N, 5) where N is the number of boxes. Examples: >>> results = model("image.jpg") >>> obb = results[0].obb >>> xywhr = obb.xywhr >>> print(xywhr.shape) torch.Size([3, 5]) NrPrerrrrxywhr5sz OBB.xywhrcCs|jdddfS)a Returns the confidence scores for Oriented Bounding Boxes (OBBs). This property retrieves the confidence values associated with each OBB detection. The confidence score represents the model's certainty in the detection. Returns: (torch.Tensor | numpy.ndarray): A tensor or numpy array of shape (N,) containing confidence scores for N detections, where each score is in the range [0, 1]. Examples: >>> results = model("image.jpg") >>> obb_result = results[0].obb >>> confidence_scores = obb_result.conf >>> print(confidence_scores) NrrerrrrrvGszOBB.confcCs|jdddfS)a Returns the class values of the oriented bounding boxes. Returns: (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the class values for each oriented bounding box. The shape is (N,), where N is the number of boxes. Examples: >>> results = model("image.jpg") >>> result = results[0] >>> obb = result.obb >>> class_values = obb.cls >>> print(class_values) Nrarerrrrrp[szOBB.clscCs|jr|jdddfSdS)a Returns the tracking IDs of the oriented bounding boxes (if available). Returns: (torch.Tensor | numpy.ndarray | None): A tensor or numpy array containing the tracking IDs for each oriented bounding box. Returns None if tracking IDs are not available. Examples: >>> results = model("image.jpg", tracker=True) # Run inference with tracking >>> for result in results: ... if result.obb is not None: ... track_ids = result.obb.id ... if track_ids is not None: ... print(f"Tracking IDs: {track_ids}") NrrrrrrromszOBB.idr2rcCs t|jS)ap Converts OBB format to 8-point (xyxyxyxy) coordinate format for rotated bounding boxes. Returns: (torch.Tensor | numpy.ndarray): Rotated bounding boxes in xyxyxyxy format with shape (N, 4, 2), where N is the number of boxes. Each box is represented by 4 points (x, y), starting from the top-left corner and moving clockwise. Examples: >>> obb = OBB(torch.tensor([[100, 100, 50, 30, 0.5, 0.9, 0]]), orig_shape=(640, 640)) >>> xyxyxyxy = obb.xyxyxyxy >>> print(xyxyxyxy.shape) torch.Size([1, 4, 2]) )rZxywhr2xyxyxyxyrrrrrrxsz OBB.xyxyxyxycCsTt|jtjr|jn t|j}|d|jd<|d|jd<|S)af Converts rotated bounding boxes to normalized xyxyxyxy format. Returns: (torch.Tensor | numpy.ndarray): Normalized rotated bounding boxes in xyxyxyxy format with shape (N, 4, 2), where N is the number of boxes. Each box is represented by 4 points (x, y), normalized relative to the original image dimensions. Examples: >>> obb = OBB(torch.rand(10, 7), orig_shape=(640, 480)) # 10 random OBBs >>> normalized_boxes = obb.xyxyxyxyn >>> print(normalized_boxes.shape) torch.Size([10, 4, 2]) rrTrr)rrxrrrrrr)rrrrrrs$z OBB.xyxyxyxyncCsx|jd}|jd}t|tjrLt|d|d|d|dgdSt|d|d| d| dgdS)a  Converts oriented bounding boxes (OBB) to axis-aligned bounding boxes in xyxy format. This property calculates the minimal enclosing rectangle for each oriented bounding box and returns it in xyxy format (x1, y1, x2, y2). This is useful for operations that require axis-aligned bounding boxes, such as IoU calculation with non-rotated boxes. Returns: (torch.Tensor | numpy.ndarray): Axis-aligned bounding boxes in xyxy format with shape (N, 4), where N is the number of boxes. Each row contains [x1, y1, x2, y2] coordinates. Examples: >>> import torch >>> from ultralytics import YOLO >>> model = YOLO("yolov8n-obb.pt") >>> results = model("path/to/image.jpg") >>> for result in results: ... obb = result.obb ... if obb is not None: ... xyxy_boxes = obb.xyxy ... print(xyxy_boxes.shape) # (N, 4) Notes: - This method approximates the OBB by its minimal enclosing rectangle. - The returned format is compatible with standard object detection metrics and visualization tools. - The property uses caching to improve performance for repeated access. rrrTra) rxrrrstackZaminZamaxrminmax)rrZrrrrr{s    .*zOBB.xyxy)r,r-r.r/rr0rrvrprorrxrr{rrrrrr?s& $      r?)r/rr functoolsrpathlibrr rrZultralytics.data.augmentrZultralytics.utilsrrrZultralytics.utils.checksr Zultralytics.utils.plottingr r r Zultralytics.utils.torch_utilsr rr1r;r<r>r=r?rrrrs2      &u[b|