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YOLOv8 Base

YoloV8 base detector class.

YOLOV8Base

Bases: BaseDetector

Base detector class for the new ultralytics YOLOV8 framework. This class provides utility methods for loading the model, generating results, and performing single and batch image detections. This base detector class is also compatible with all the new ultralytics models including YOLOV9, RTDetr, and more.

Source code in PytorchWildlife/models/detection/ultralytics_based/yolov8_base.py 
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class YOLOV8Base(BaseDetector):
    """
    Base detector class for the new ultralytics YOLOV8 framework. This class provides utility methods for
    loading the model, generating results, and performing single and batch image detections.
    This base detector class is also compatible with all the new ultralytics models including YOLOV9, 
    RTDetr, and more.
    """
    def __init__(self, weights=None, device="cpu", url=None, transform=None):
        """
        Initialize the YOLOV8 detector.

        Args:
            weights (str, optional): 
                Path to the model weights. Defaults to None.
            device (str, optional): 
                Device for model inference. Defaults to "cpu".
            url (str, optional): 
                URL to fetch the model weights. Defaults to None.
        """
        super(YOLOV8Base, self).__init__(weights=weights, device=device, url=url)
        self.transform = transform
        self._load_model(weights, self.device, url)

    def _load_model(self, weights=None, device="cpu", url=None):
        """
        Load the YOLOV8 model weights.

        Args:
            weights (str, optional): 
                Path to the model weights. Defaults to None.
            device (str, optional): 
                Device for model inference. Defaults to "cpu".
            url (str, optional): 
                URL to fetch the model weights. Defaults to None.
        Raises:
            Exception: If weights are not provided.
        """

        if self.MODEL_NAME == 'MDV6b-rtdetrl.pt':
            self.predictor = rtdetr.RTDETRPredictor()
        else:
            self.predictor = yolo.detect.DetectionPredictor()
        # self.predictor.args.device = device # Will uncomment later
        self.predictor.args.imgsz = self.IMAGE_SIZE
        self.predictor.args.save = False # Will see if we want to use ultralytics native inference saving functions.

        if weights:
            self.predictor.setup_model(weights)
        elif url:
            if not os.path.exists(os.path.join(torch.hub.get_dir(), "checkpoints", self.MODEL_NAME)):
                os.makedirs(os.path.join(torch.hub.get_dir(), "checkpoints"), exist_ok=True)
                weights = wget.download(url, out=os.path.join(torch.hub.get_dir(), "checkpoints"))
            else:
                weights = os.path.join(torch.hub.get_dir(), "checkpoints", self.MODEL_NAME)
            self.predictor.setup_model(weights)
        else:
            raise Exception("Need weights for inference.")

        if not self.transform:
            self.transform = pw_trans.MegaDetector_v5_Transform(target_size=self.IMAGE_SIZE,
                                                                stride=self.STRIDE)

    def results_generation(self, preds, img_id, id_strip=None) -> dict:
        """
        Generate results for detection based on model predictions.

        Args:
            preds (ultralytics.engine.results.Results): 
                Model predictions.
            img_id (str): 
                Image identifier.
            id_strip (str, optional): 
                Strip specific characters from img_id. Defaults to None.

        Returns:
            dict: Dictionary containing image ID, detections, and labels.
        """
        xyxy = preds.boxes.xyxy.cpu().numpy()
        confidence = preds.boxes.conf.cpu().numpy()
        class_id = preds.boxes.cls.cpu().numpy().astype(int)

        results = {"img_id": str(img_id).strip(id_strip)}
        results["detections"] = sv.Detections(
            xyxy=xyxy,
            confidence=confidence,
            class_id=class_id
        )

        results["labels"] = [
            f"{self.CLASS_NAMES[class_id]} {confidence:0.2f}"  
            for _, _, confidence, class_id, _, _ in results["detections"] 
        ]

        return results


    def single_image_detection(self, img, img_path=None, det_conf_thres=0.2, id_strip=None) -> dict:
        """
        Perform detection on a single image.

        Args:
            img (str or ndarray): 
                Image path or ndarray of images.
            img_path (str, optional): 
                Image path or identifier.
            det_conf_thres (float, optional): 
                Confidence threshold for predictions. Defaults to 0.2.
            id_strip (str, optional): 
                Characters to strip from img_id. Defaults to None.

        Returns:
            dict: Detection results.
        """

        if type(img) == str:
            if img_path is None:
                img_path = img
            img = np.array(Image.open(img_path).convert("RGB"))
        img_size = img.shape

        self.predictor.args.batch = 1
        self.predictor.args.conf = det_conf_thres

        det_results = list(self.predictor.stream_inference([img]))

        res = self.results_generation(det_results[0], img_path, id_strip)

        normalized_coords = [[x1 / img_size[1], y1 / img_size[0], x2 / img_size[1], y2 / img_size[0]] 
                             for x1, y1, x2, y2 in res["detections"].xyxy]
        res["normalized_coords"] = normalized_coords

        return res

    def batch_image_detection(self, data_source, batch_size: int = 16, det_conf_thres: float = 0.2, id_strip: str = None) -> list[dict]:
        """
        Perform detection on a batch of images.

        Args:
            data_source (str or List[np.ndarray]): Either path containing images for inference or list of numpy arrays (RGB format, shape: H×W×3).
            batch_size (int, optional): Batch size for inference. Defaults to 16.
            det_conf_thres (float, optional): Confidence threshold for predictions. Defaults to 0.2.
            id_strip (str, optional): Characters to strip from img_id. Defaults to None.

        Returns:
            list[dict]: List of detection results for all images.
        """
        self.predictor.args.batch = batch_size
        self.predictor.args.conf = det_conf_thres

        # Handle numpy array input
        if isinstance(data_source, (list, np.ndarray)):
            results = []
            num_batches = (len(data_source) + batch_size - 1) // batch_size  # Calculate total batches

            with tqdm(total=num_batches) as pbar:
                for start_idx in range(0, len(data_source), batch_size):
                    batch_arrays = data_source[start_idx:start_idx + batch_size]
                    det_results = self.predictor.stream_inference(batch_arrays)

                    for idx, preds in enumerate(det_results):
                        res = self.results_generation(preds, f"{start_idx + idx}", id_strip)
                        # Get size directly from numpy array
                        img_height, img_width = batch_arrays[idx].shape[:2]
                        normalized_coords = [[x1/img_width, y1/img_height, x2/img_width, y2/img_height] 
                                        for x1, y1, x2, y2 in res["detections"].xyxy]
                        res["normalized_coords"] = normalized_coords
                        results.append(res)
                    pbar.update(1)
            return results

        # Handle image directory input
        dataset = pw_data.DetectionImageFolder(
            data_source,
            transform=self.transform,
        )

        # Creating a DataLoader for batching and parallel processing of the images
        loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, 
                            pin_memory=True, num_workers=0, drop_last=False
                            )

        results = []
        with tqdm(total=len(loader)) as pbar:
            for batch_index, (imgs, paths, sizes) in enumerate(loader):
                det_results = self.predictor.stream_inference(paths)
                batch_results = []
                for idx, preds in enumerate(det_results):
                    res = self.results_generation(preds, paths[idx], id_strip)
                    size = preds.orig_shape
                    # Normalize the coordinates for timelapse compatibility
                    normalized_coords = [[x1 / size[1], y1 / size[0], x2 / size[1], y2 / size[0]] for x1, y1, x2, y2 in res["detections"].xyxy]
                    res["normalized_coords"] = normalized_coords
                    results.append(res)
                pbar.update(1)
                results.extend(batch_results)
        return results

__init__(weights=None, device='cpu', url=None, transform=None)

Initialize the YOLOV8 detector.

Parameters:

Name Type Description Default
weights str

Path to the model weights. Defaults to None.

 None
device str

Device for model inference. Defaults to "cpu".

 'cpu'
url str

URL to fetch the model weights. Defaults to None.

 None
Source code in PytorchWildlife/models/detection/ultralytics_based/yolov8_base.py 
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def __init__(self, weights=None, device="cpu", url=None, transform=None):
    """
    Initialize the YOLOV8 detector.

    Args:
        weights (str, optional): 
            Path to the model weights. Defaults to None.
        device (str, optional): 
            Device for model inference. Defaults to "cpu".
        url (str, optional): 
            URL to fetch the model weights. Defaults to None.
    """
    super(YOLOV8Base, self).__init__(weights=weights, device=device, url=url)
    self.transform = transform
    self._load_model(weights, self.device, url)

batch_image_detection(data_source, batch_size=16, det_conf_thres=0.2, id_strip=None)

Perform detection on a batch of images.

Parameters:

Name Type Description Default
data_source str or List[ndarray]

Either path containing images for inference or list of numpy arrays (RGB format, shape: H×W×3).

 required
batch_size int

Batch size for inference. Defaults to 16.

 16
det_conf_thres float

Confidence threshold for predictions. Defaults to 0.2.

 0.2
id_strip str

Characters to strip from img_id. Defaults to None.

 None

Returns:

Type Description
list[dict]

list[dict]: List of detection results for all images.
Source code in PytorchWildlife/models/detection/ultralytics_based/yolov8_base.py 
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def batch_image_detection(self, data_source, batch_size: int = 16, det_conf_thres: float = 0.2, id_strip: str = None) -> list[dict]:
    """
    Perform detection on a batch of images.

    Args:
        data_source (str or List[np.ndarray]): Either path containing images for inference or list of numpy arrays (RGB format, shape: H×W×3).
        batch_size (int, optional): Batch size for inference. Defaults to 16.
        det_conf_thres (float, optional): Confidence threshold for predictions. Defaults to 0.2.
        id_strip (str, optional): Characters to strip from img_id. Defaults to None.

    Returns:
        list[dict]: List of detection results for all images.
    """
    self.predictor.args.batch = batch_size
    self.predictor.args.conf = det_conf_thres

    # Handle numpy array input
    if isinstance(data_source, (list, np.ndarray)):
        results = []
        num_batches = (len(data_source) + batch_size - 1) // batch_size  # Calculate total batches

        with tqdm(total=num_batches) as pbar:
            for start_idx in range(0, len(data_source), batch_size):
                batch_arrays = data_source[start_idx:start_idx + batch_size]
                det_results = self.predictor.stream_inference(batch_arrays)

                for idx, preds in enumerate(det_results):
                    res = self.results_generation(preds, f"{start_idx + idx}", id_strip)
                    # Get size directly from numpy array
                    img_height, img_width = batch_arrays[idx].shape[:2]
                    normalized_coords = [[x1/img_width, y1/img_height, x2/img_width, y2/img_height] 
                                    for x1, y1, x2, y2 in res["detections"].xyxy]
                    res["normalized_coords"] = normalized_coords
                    results.append(res)
                pbar.update(1)
        return results

    # Handle image directory input
    dataset = pw_data.DetectionImageFolder(
        data_source,
        transform=self.transform,
    )

    # Creating a DataLoader for batching and parallel processing of the images
    loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, 
                        pin_memory=True, num_workers=0, drop_last=False
                        )

    results = []
    with tqdm(total=len(loader)) as pbar:
        for batch_index, (imgs, paths, sizes) in enumerate(loader):
            det_results = self.predictor.stream_inference(paths)
            batch_results = []
            for idx, preds in enumerate(det_results):
                res = self.results_generation(preds, paths[idx], id_strip)
                size = preds.orig_shape
                # Normalize the coordinates for timelapse compatibility
                normalized_coords = [[x1 / size[1], y1 / size[0], x2 / size[1], y2 / size[0]] for x1, y1, x2, y2 in res["detections"].xyxy]
                res["normalized_coords"] = normalized_coords
                results.append(res)
            pbar.update(1)
            results.extend(batch_results)
    return results

results_generation(preds, img_id, id_strip=None)

Generate results for detection based on model predictions.

Parameters:

Name Type Description Default
preds Results

Model predictions.

 required
img_id str

Image identifier.

 required
id_strip str

Strip specific characters from img_id. Defaults to None.

 None

Returns:

Name Type Description
dict dict

Dictionary containing image ID, detections, and labels.
Source code in PytorchWildlife/models/detection/ultralytics_based/yolov8_base.py 
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def results_generation(self, preds, img_id, id_strip=None) -> dict:
    """
    Generate results for detection based on model predictions.

    Args:
        preds (ultralytics.engine.results.Results): 
            Model predictions.
        img_id (str): 
            Image identifier.
        id_strip (str, optional): 
            Strip specific characters from img_id. Defaults to None.

    Returns:
        dict: Dictionary containing image ID, detections, and labels.
    """
    xyxy = preds.boxes.xyxy.cpu().numpy()
    confidence = preds.boxes.conf.cpu().numpy()
    class_id = preds.boxes.cls.cpu().numpy().astype(int)

    results = {"img_id": str(img_id).strip(id_strip)}
    results["detections"] = sv.Detections(
        xyxy=xyxy,
        confidence=confidence,
        class_id=class_id
    )

    results["labels"] = [
        f"{self.CLASS_NAMES[class_id]} {confidence:0.2f}"  
        for _, _, confidence, class_id, _, _ in results["detections"] 
    ]

    return results

single_image_detection(img, img_path=None, det_conf_thres=0.2, id_strip=None)

Perform detection on a single image.

Parameters:

Name Type Description Default
img str or ndarray

Image path or ndarray of images.

 required
img_path str

Image path or identifier.

 None
det_conf_thres float

Confidence threshold for predictions. Defaults to 0.2.

 0.2
id_strip str

Characters to strip from img_id. Defaults to None.

 None

Returns:

Name Type Description
dict dict

Detection results.
Source code in PytorchWildlife/models/detection/ultralytics_based/yolov8_base.py 
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def single_image_detection(self, img, img_path=None, det_conf_thres=0.2, id_strip=None) -> dict:
    """
    Perform detection on a single image.

    Args:
        img (str or ndarray): 
            Image path or ndarray of images.
        img_path (str, optional): 
            Image path or identifier.
        det_conf_thres (float, optional): 
            Confidence threshold for predictions. Defaults to 0.2.
        id_strip (str, optional): 
            Characters to strip from img_id. Defaults to None.

    Returns:
        dict: Detection results.
    """

    if type(img) == str:
        if img_path is None:
            img_path = img
        img = np.array(Image.open(img_path).convert("RGB"))
    img_size = img.shape

    self.predictor.args.batch = 1
    self.predictor.args.conf = det_conf_thres

    det_results = list(self.predictor.stream_inference([img]))

    res = self.results_generation(det_results[0], img_path, id_strip)

    normalized_coords = [[x1 / img_size[1], y1 / img_size[0], x2 / img_size[1], y2 / img_size[0]] 
                         for x1, y1, x2, y2 in res["detections"].xyxy]
    res["normalized_coords"] = normalized_coords

    return res