前言

在实际处理过程中，我们使用YOLO V8进行推理时，通常会针对一张图片进行推理。如果需要对多张图片进行推理，则可以通过一个循环来实现对图片逐张进行推理。

单张图片推理时，需要注意图片的尺寸必须是32的倍数，否则可能导致推理失败。在下面的示例中，我们展示了如何使用PyTorch和Ultralytics库进行单张图片的推理：

import torch
from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')

# Create a random torch tensor of BCHW shape (1, 3, 640, 640) with values in range [0, 1] and type float32
source = torch.rand(1, 3, 640, 640, dtype=torch.float32)

# Run inference on the source
results = model(source)  # list of Results objects

批量图片推理时，也需要注意图片的尺寸必须是32的倍数。在下面的示例中，我们展示了如何使用PyTorch和Ultralytics库进行多张图片的批量推理：

import torch
from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')

# Create a random torch tensor of BCHW shape (1, 3, 640, 640) with values in range [0, 1] and type float32
source = torch.rand(4, 3, 640, 640, dtype=torch.float32)

# Run inference on the source
results = model(source)  # list of Results objects

需要注意的是，在批量推理时，虽然一次推理了多张图片，但实际处理方式仍然是通过循环进行的。在后续的文章中，我们将介绍如何使用更高效的方式进行批量推理，以获得更快的推理速度和更好的性能。

下面我们介绍如何将检测推理代码给单独提取出来，进行推理。

一、YOLO V8-Detection 预测

在官方中，进行推理时，直接使用两行代码就能实现目标检测的功能。

from ultralytics import YOLO

# Load a model
model = YOLO('yolov8n.pt')
# Run batched inference on a list of images
model.predict("./ultralytics/assets/bus.jpg", imgsz=640, save=True, device=0)

模型推理保存的结果图像如下所示：

模型预测成功，我们就需要自己动手来写下 YOLO V8-Detection 的模型加载、预处理和后处理，以便我们进行相关的操作，我们先来看看预处理的实现

二、YOLO V8-Detection 模型加载

原始加载方式

模型文件：ultralytics\engine\model.py

def _load(self, weights: str, task=None):
    """
    Initializes a new model and infers the task type from the model head.

    Args:
        weights (str): model checkpoint to be loaded
        task (str | None): model task
    """
    suffix = Path(weights).suffix
    if suffix == ".pt":
        self.model, self.ckpt = attempt_load_one_weight(weights)
        self.task = self.model.args["task"]
        self.overrides = self.model.args = self._reset_ckpt_args(self.model.args)
        self.ckpt_path = self.model.pt_path
    else:
        weights = checks.check_file(weights)
        self.model, self.ckpt = weights, None
        self.task = task or guess_model_task(weights)
        self.ckpt_path = weights
    self.overrides["model"] = weights
    self.overrides["task"] = self.task

模型文件：ultralytics/nn/tasks.py

def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False):
    """Loads a single model weights."""
    ckpt, weight = torch_safe_load(weight)  # load ckpt
    args = {**DEFAULT_CFG_DICT, **(ckpt.get("train_args", {}))}  # combine model and default args, preferring model args
    model = (ckpt.get("ema") or ckpt["model"]).to(device).float()  # FP32 model

    # Model compatibility updates
    model.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS}  # attach args to model
    model.pt_path = weight  # attach *.pt file path to model
    model.task = guess_model_task(model)
    if not hasattr(model, "stride"):
        model.stride = torch.tensor([32.0])

    model = model.fuse().eval() if fuse and hasattr(model, "fuse") else model.eval()  # model in eval mode

    # Module updates
    for m in model.modules():
        t = type(m)
        if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Segment, Pose, OBB):
            m.inplace = inplace
        elif t is nn.Upsample and not hasattr(m, "recompute_scale_factor"):
            m.recompute_scale_factor = None  # torch 1.11.0 compatibility

    # Return model and ckpt
    return model, ckpt

上述两个代码是加载模型的原始方法，这种方法不仅会加载模型的权重，还会加载一系列相关的配置文件，这个并不是我们想要的。我们只加载模型权重，其余相关的都不需要加载，因此要使用下面这种方式进行加载。

修改后的加载方式

模型文件：ultralytics/nn/autobackend.py

@torch.no_grad()
def __init__(
    self,
    weights="yolov8n.pt",
    device=torch.device("cpu"),
    dnn=False,
    data=None,
    fp16=False,
    fuse=True,
    verbose=True,
):

参数介绍

weights：模型权重文件的路径。默认为 "yolov8n.pt"。 
device (torch.device)：运行模型的设备。默认为 CPU。 
dnn：使用 OpenCV DNN 模块进行 ONNX 推断。默认为假。 
data：包含类名的附加 data.yaml 文件的路径。可选。 
fp16：启用半精度推理。仅在特定后端支持。默认为 False。 
fuse：融合 Conv2D + BatchNorm 层进行优化。默认为 True。 
verbose：启用详细日志记录。默认为 True。

.pt 加载方式

elif pt:  # PyTorch
    from ultralytics.nn.tasks import attempt_load_weights

    model = attempt_load_weights(
        weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse
    )
    if hasattr(model, "kpt_shape"):
        kpt_shape = model.kpt_shape  # pose-only
    stride = max(int(model.stride.max()), 32)  # model stride
    names = model.module.names if hasattr(model, "module") else model.names  # get class names
    model.half() if fp16 else model.float()
    self.model = model  # explicitly assign for to(), cpu(), cuda(), half()

最终代码

from ultralytics.nn.autobackend import AutoBackend

weights = 'yolov8n.pt'
model = AutoBackend(weights, device=torch.device("cuda:0"))

三、YOLO V8-Detection 预处理

原始加载方式

模型文件：ultralytics/engine/predictor.py

from ultralytics.data.augment import LetterBox

@smart_inference_mode()
def stream_inference(self, source=None, model=None, *args, **kwargs):
    """Streams real-time inference on camera feed and saves results to file."""
    .
    .
    .
    # Preprocess
    with profilers[0]:
        im = self.preprocess(im0s)
     .
     .
     .
    
    
def pre_transform(self, im):
    """
    Pre-transform input image before inference.

    Args:
        im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.

    Returns:
        (list): A list of transformed images.
    """
    same_shapes = all(x.shape == im[0].shape for x in im)
    letterbox = LetterBox(self.imgsz, auto=same_shapes and self.model.pt, stride=self.model.stride)
    return [letterbox(image=x) for x in im]
    
def preprocess(self, im):
    """
    Prepares input image before inference.

    Args:
        im (torch.Tensor | List(np.ndarray)): BCHW for tensor, [(HWC) x B] for list.
    """
    not_tensor = not isinstance(im, torch.Tensor)
    if not_tensor:
        im = np.stack(self.pre_transform(im))
        im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW, (n, 3, h, w)
        im = np.ascontiguousarray(im)  # contiguous
        im = torch.from_numpy(im)

    im = im.to(self.device)
    im = im.half() if self.model.fp16 else im.float()  # uint8 to fp16/32
    if not_tensor:
        im /= 255  # 0 - 255 to 0.0 - 1.0
    return im

它包含以下步骤：

• self.pre_transform：即 letterbox 添加灰条

• im[…,::-1]：BGR → RGB

• transpose((0, 3, 1, 2))：添加 batch 维度，HWC → CHW

• torch.from_numpy：to Tensor

• im /= 255：除以 255，归一化

大家如果对 YOLO V5 的预处理熟悉的话，会发现 YOLO V8 的预处理和 YOLO V5 的预处理一模一样，因此我们不难写出对应的预处理代码，如下所示：

修改后的加载方式

def letterbox(self, im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True,
              stride=32):
    # Resize and pad image while meeting stride-multiple constraints
    shape = im.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        new_shape = (new_shape, new_shape)

    # Scale ratio (new / old)
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
    if not scaleup:  # only scale down, do not scale up (for better val mAP)
        r = min(r, 1.0)

    # Compute padding
    ratio = r, r  # width, height ratios
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
    if auto:  # minimum rectangle
        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
    elif scaleFill:  # stretch
        dw, dh = 0.0, 0.0
        new_unpad = (new_shape[1], new_shape[0])
        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios

    dw /= 2  # divide padding into 2 sides
    dh /= 2

    if shape[::-1] != new_unpad:  # resize
        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    return im, ratio, (dw, dh)

def precess_image(self, img_src, img_size, stride, half, device):
    # Padded resize
    img = self.letterbox(img_src, img_size, stride=stride)[0]
    
    # Convert
    img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
    img = np.ascontiguousarray(img)
    img = torch.from_numpy(img).to(device)

    img = img.half() if half else img.float()  # uint8 to fp16/32
    img = img / 255  # 0 - 255 to 0.0 - 1.0
    
    if len(img.shape) == 3:
        img = img[None]  # expand for batch dim
    return img

四、YOLO V8-Detection 后处理

原始加载方式

模型文件：ultralytics/engine/predictor.py

@smart_inference_mode()
def stream_inference(self, source=None, model=None, *args, **kwargs):
    """Streams real-time inference on camera feed and saves results to file."""
    .
    .
    .
    # Postprocess
    with profilers[2]:
        self.results = self.postprocess(preds, im, im0s)
     .
     .
     .

模型文件：ultralytics/models/yolo/detect/predict.py

def postprocess(self, preds, img, orig_imgs):
    """Post-processes predictions and returns a list of Results objects."""
    preds = ops.non_max_suppression(
        preds,
        self.args.conf,
        self.args.iou,
        agnostic=self.args.agnostic_nms,
        max_det=self.args.max_det,
        classes=self.args.classes,
    )

    if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
        orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

    results = []
    for i, pred in enumerate(preds):
        orig_img = orig_imgs[i]
        pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
        img_path = self.batch[0][i]
        results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
    return results

可以看到原始的后处理部分一共有两个部分，分别为 nms 处理 和 坐标绘图 处理这两步。

nms 处理

模型文件：ultralytics/utils/ops.py

def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None, padding=True, xywh=False):
    """
    Rescales bounding boxes (in the format of xyxy by default) from the shape of the image they were originally
    specified in (img1_shape) to the shape of a different image (img0_shape).

    Args:
        img1_shape (tuple): The shape of the image that the bounding boxes are for, in the format of (height, width).
        boxes (torch.Tensor): the bounding boxes of the objects in the image, in the format of (x1, y1, x2, y2)
        img0_shape (tuple): the shape of the target image, in the format of (height, width).
        ratio_pad (tuple): a tuple of (ratio, pad) for scaling the boxes. If not provided, the ratio and pad will be
            calculated based on the size difference between the two images.
        padding (bool): If True, assuming the boxes is based on image augmented by yolo style. If False then do regular
            rescaling.
        xywh (bool): The box format is xywh or not, default=False.

    Returns:
        boxes (torch.Tensor): The scaled bounding boxes, in the format of (x1, y1, x2, y2)
    """
    if ratio_pad is None:  # calculate from img0_shape
        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
        pad = (
            round((img1_shape[1] - img0_shape[1] * gain) / 2 - 0.1),
            round((img1_shape[0] - img0_shape[0] * gain) / 2 - 0.1),
        )  # wh padding
    else:
        gain = ratio_pad[0][0]
        pad = ratio_pad[1]

    if padding:
        boxes[..., 0] -= pad[0]  # x padding
        boxes[..., 1] -= pad[1]  # y padding
        if not xywh:
            boxes[..., 2] -= pad[0]  # x padding
            boxes[..., 3] -= pad[1]  # y padding
    boxes[..., :4] /= gain
    return clip_boxes(boxes, img0_shape)
    
def clip_boxes(boxes, shape):
    """
    Takes a list of bounding boxes and a shape (height, width) and clips the bounding boxes to the shape.

    Args:
        boxes (torch.Tensor): the bounding boxes to clip
        shape (tuple): the shape of the image

    Returns:
        (torch.Tensor | numpy.ndarray): Clipped boxes
    """
    if isinstance(boxes, torch.Tensor):  # faster individually (WARNING: inplace .clamp_() Apple MPS bug)
        boxes[..., 0] = boxes[..., 0].clamp(0, shape[1])  # x1
        boxes[..., 1] = boxes[..., 1].clamp(0, shape[0])  # y1
        boxes[..., 2] = boxes[..., 2].clamp(0, shape[1])  # x2
        boxes[..., 3] = boxes[..., 3].clamp(0, shape[0])  # y2
    else:  # np.array (faster grouped)
        boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2
        boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2
    return boxes

def xywh2xyxy(x):
    """
    Convert bounding box coordinates from (x, y, width, height) format to (x1, y1, x2, y2) format where (x1, y1) is the
    top-left corner and (x2, y2) is the bottom-right corner.

    Args:
        x (np.ndarray | torch.Tensor): The input bounding box coordinates in (x, y, width, height) format.

    Returns:
        y (np.ndarray | torch.Tensor): The bounding box coordinates in (x1, y1, x2, y2) format.
    """
    assert x.shape[-1] == 4, f"input shape last dimension expected 4 but input shape is {x.shape}"
    y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x)  # faster than clone/copy
    dw = x[..., 2] / 2  # half-width
    dh = x[..., 3] / 2  # half-height
    y[..., 0] = x[..., 0] - dw  # top left x
    y[..., 1] = x[..., 1] - dh  # top left y
    y[..., 2] = x[..., 0] + dw  # bottom right x
    y[..., 3] = x[..., 1] + dh  # bottom right y
    return y

def non_max_suppression(
    prediction,
    conf_thres=0.25,
    iou_thres=0.45,
    classes=None,
    agnostic=False,
    multi_label=False,
    labels=(),
    max_det=300,
    nc=0,  # number of classes (optional)
    max_time_img=0.05,
    max_nms=30000,
    max_wh=7680,
    rotated=False,
):
    """
    Perform non-maximum suppression (NMS) on a set of boxes, with support for masks and multiple labels per box.

    Args:
        prediction (torch.Tensor): A tensor of shape (batch_size, num_classes + 4 + num_masks, num_boxes)
            containing the predicted boxes, classes, and masks. The tensor should be in the format
            output by a model, such as YOLO.
        conf_thres (float): The confidence threshold below which boxes will be filtered out.
            Valid values are between 0.0 and 1.0.
        iou_thres (float): The IoU threshold below which boxes will be filtered out during NMS.
            Valid values are between 0.0 and 1.0.
        classes (List[int]): A list of class indices to consider. If None, all classes will be considered.
        agnostic (bool): If True, the model is agnostic to the number of classes, and all
            classes will be considered as one.
        multi_label (bool): If True, each box may have multiple labels.
        labels (List[List[Union[int, float, torch.Tensor]]]): A list of lists, where each inner
            list contains the apriori labels for a given image. The list should be in the format
            output by a dataloader, with each label being a tuple of (class_index, x1, y1, x2, y2).
        max_det (int): The maximum number of boxes to keep after NMS.
        nc (int, optional): The number of classes output by the model. Any indices after this will be considered masks.
        max_time_img (float): The maximum time (seconds) for processing one image.
        max_nms (int): The maximum number of boxes into torchvision.ops.nms().
        max_wh (int): The maximum box width and height in pixels

    Returns:
        (List[torch.Tensor]): A list of length batch_size, where each element is a tensor of
            shape (num_boxes, 6 + num_masks) containing the kept boxes, with columns
            (x1, y1, x2, y2, confidence, class, mask1, mask2, ...).
    """

    # Checks
    assert 0 <= conf_thres <= 1, f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0"
    assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0"
    if isinstance(prediction, (list, tuple)):  # YOLOv8 model in validation model, output = (inference_out, loss_out)
        prediction = prediction[0]  # select only inference output

    bs = prediction.shape[0]  # batch size
    nc = nc or (prediction.shape[1] - 4)  # number of classes
    nm = prediction.shape[1] - nc - 4
    mi = 4 + nc  # mask start index
    xc = prediction[:, 4:mi].amax(1) > conf_thres  # candidates

    # Settings
    # min_wh = 2  # (pixels) minimum box width and height
    time_limit = 0.5 + max_time_img * bs  # seconds to quit after
    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)

    prediction = prediction.transpose(-1, -2)  # shape(1,84,6300) to shape(1,6300,84)
    if not rotated:
        prediction[..., :4] = xywh2xyxy(prediction[..., :4])  # xywh to xyxy

    t = time.time()
    output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs
    for xi, x in enumerate(prediction):  # image index, image inference
        # Apply constraints
        # x[((x[:, 2:4] < min_wh) | (x[:, 2:4] > max_wh)).any(1), 4] = 0  # width-height
        x = x[xc[xi]]  # confidence

        # Cat apriori labels if autolabelling
        if labels and len(labels[xi]) and not rotated:
            lb = labels[xi]
            v = torch.zeros((len(lb), nc + nm + 4), device=x.device)
            v[:, :4] = xywh2xyxy(lb[:, 1:5])  # box
            v[range(len(lb)), lb[:, 0].long() + 4] = 1.0  # cls
            x = torch.cat((x, v), 0)

        # If none remain process next image
        if not x.shape[0]:
            continue

        # Detections matrix nx6 (xyxy, conf, cls)
        box, cls, mask = x.split((4, nc, nm), 1)

        if multi_label:
            i, j = torch.where(cls > conf_thres)
            x = torch.cat((box[i], x[i, 4 + j, None], j[:, None].float(), mask[i]), 1)
        else:  # best class only
            conf, j = cls.max(1, keepdim=True)
            x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]

        # Filter by class
        if classes is not None:
            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]

        # Check shape
        n = x.shape[0]  # number of boxes
        if not n:  # no boxes
            continue
        if n > max_nms:  # excess boxes
            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence and remove excess boxes

        # Batched NMS
        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
        scores = x[:, 4]  # scores
        if rotated:
            boxes = torch.cat((x[:, :2] + c, x[:, 2:4], x[:, -1:]), dim=-1)  # xywhr
            i = nms_rotated(boxes, scores, iou_thres)
        else:
            boxes = x[:, :4] + c  # boxes (offset by class)
            i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
        i = i[:max_det]  # limit detections

        # # Experimental
        # merge = False  # use merge-NMS
        # if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
        #     # Update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
        #     from .metrics import box_iou
        #     iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
        #     weights = iou * scores[None]  # box weights
        #     x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
        #     redundant = True  # require redundant detections
        #     if redundant:
        #         i = i[iou.sum(1) > 1]  # require redundancy

        output[xi] = x[i]
        if (time.time() - t) > time_limit:
            LOGGER.warning(f"WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded")
            break  # time limit exceeded

    return output

图片绘制

模型文件：ultralytics/engine/results.py

class Boxes(BaseTensor):
    """
    A class for storing and manipulating detection boxes.

    Args:
        boxes (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the detection boxes,
            with shape (num_boxes, 6) or (num_boxes, 7). The last two columns contain confidence and class values.
            If present, the third last column contains track IDs.
        orig_shape (tuple): Original image size, in the format (height, width).

    Attributes:
        xyxy (torch.Tensor | numpy.ndarray): The boxes in xyxy format.
        conf (torch.Tensor | numpy.ndarray): The confidence values of the boxes.
        cls (torch.Tensor | numpy.ndarray): The class values of the boxes.
        id (torch.Tensor | numpy.ndarray): The track IDs of the boxes (if available).
        xywh (torch.Tensor | numpy.ndarray): The boxes in xywh format.
        xyxyn (torch.Tensor | numpy.ndarray): The boxes in xyxy format normalized by original image size.
        xywhn (torch.Tensor | numpy.ndarray): The boxes in xywh format normalized by original image size.
        data (torch.Tensor): The raw bboxes tensor (alias for `boxes`).

    Methods:
        cpu(): Move the object to CPU memory.
        numpy(): Convert the object to a numpy array.
        cuda(): Move the object to CUDA memory.
        to(*args, **kwargs): Move the object to the specified device.
    """

    def __init__(self, boxes, orig_shape) -> None:
        """Initialize the Boxes class."""
        if boxes.ndim == 1:
            boxes = boxes[None, :]
        n = boxes.shape[-1]
        assert n in (6, 7), f"expected 6 or 7 values but got {n}"  # xyxy, track_id, conf, cls
        super().__init__(boxes, orig_shape)
        self.is_track = n == 7
        self.orig_shape = orig_shape

    @property
    def xyxy(self):
        """Return the boxes in xyxy format."""
        return self.data[:, :4]

    @property
    def conf(self):
        """Return the confidence values of the boxes."""
        return self.data[:, -2]

    @property
    def cls(self):
        """Return the class values of the boxes."""
        return self.data[:, -1]

    @property
    def id(self):
        """Return the track IDs of the boxes (if available)."""
        return self.data[:, -3] if self.is_track else None

    @property
    @lru_cache(maxsize=2)  # maxsize 1 should suffice
    def xywh(self):
        """Return the boxes in xywh format."""
        return ops.xyxy2xywh(self.xyxy)

    @property
    @lru_cache(maxsize=2)
    def xyxyn(self):
        """Return the boxes in xyxy format normalized by original image size."""
        xyxy = self.xyxy.clone() if isinstance(self.xyxy, torch.Tensor) else np.copy(self.xyxy)
        xyxy[..., [0, 2]] /= self.orig_shape[1]
        xyxy[..., [1, 3]] /= self.orig_shape[0]
        return xyxy

    @property
    @lru_cache(maxsize=2)
    def xywhn(self):
        """Return the boxes in xywh format normalized by original image size."""
        xywh = ops.xyxy2xywh(self.xyxy)
        xywh[..., [0, 2]] /= self.orig_shape[1]
        xywh[..., [1, 3]] /= self.orig_shape[0]
        return xywh
        
class Results(SimpleClass):
    .
    .
    .
    def plot(
        self,
        conf=True,
        line_width=None,
        font_size=None,
        font="Arial.ttf",
        pil=False,
        img=None,
        im_gpu=None,
        kpt_radius=5,
        kpt_line=True,
        labels=True,
        boxes=True,
        masks=True,
        probs=True,
    ):
        # Plot Detect results
        if pred_boxes is not None and show_boxes:
            for d in reversed(pred_boxes):
                c, conf, id = int(d.cls), float(d.conf) if conf else None, None if d.id is None else int(d.id.item())
                name = ("" if id is None else f"id:{id} ") + names[c]
                label = (f"{name} {conf:.2f}" if conf else name) if labels else None
                box = d.xyxyxyxy.reshape(-1, 4, 2).squeeze() if is_obb else d.xyxy.squeeze()
                annotator.box_label(box, label, color=colors(c, True), rotated=is_obb)
                
    .
    .
    .

模型文件：ultralytics/utils/plotting.py

def box_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), rotated=False):
    """Add one xyxy box to image with label."""
    if isinstance(box, torch.Tensor):
        box = box.tolist()
    if self.pil or not is_ascii(label):
        if rotated:
            p1 = box[0]
            # NOTE: PIL-version polygon needs tuple type.
            self.draw.polygon([tuple(b) for b in box], width=self.lw, outline=color)
        else:
            p1 = (box[0], box[1])
            self.draw.rectangle(box, width=self.lw, outline=color)  # box
        if label:
            w, h = self.font.getsize(label)  # text width, height
            outside = p1[1] - h >= 0  # label fits outside box
            self.draw.rectangle(
                (p1[0], p1[1] - h if outside else p1[1], p1[0] + w + 1, p1[1] + 1 if outside else p1[1] + h + 1),
                fill=color,
            )
            # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0
            self.draw.text((p1[0], p1[1] - h if outside else p1[1]), label, fill=txt_color, font=self.font)
    else:  # cv2
        if rotated:
            p1 = [int(b) for b in box[0]]
            # NOTE: cv2-version polylines needs np.asarray type.
            cv2.polylines(self.im, [np.asarray(box, dtype=int)], True, color, self.lw)
        else:
            p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
            cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
        if label:
            w, h = cv2.getTextSize(label, 0, fontScale=self.sf, thickness=self.tf)[0]  # text width, height
            outside = p1[1] - h >= 3
            p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
            cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
            cv2.putText(
                self.im,
                label,
                (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
                0,
                self.sf,
                txt_color,
                thickness=self.tf,
                lineType=cv2.LINE_AA,
            )

它包含以下步骤：

• ops.non_max_suppression：非极大值抑制，即 NMS

• ops.scale_boxes：框的解码，即 decode boxes

• annotator.box_label：绘制矩形框到原图上

修改后的加载方式

def draw_box(self, img_src, box, conf, cls_name, lw, sf, tf):
    color = self.color[cls_name]
    label = f'{cls_name} {conf}'
    p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
    # 绘制矩形框
    cv2.rectangle(img_src, p1, p2, color, thickness=lw, lineType=cv2.LINE_AA)
    # text width, height
    w, h = cv2.getTextSize(label, 0, fontScale=sf, thickness=tf)[0]
    # label fits outside box
    outside = box[1] - h - 3 >= 0
    p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
    # 绘制矩形框填充
    cv2.rectangle(img_src, p1, p2, color, -1, cv2.LINE_AA)
    # 绘制标签
    cv2.putText(img_src, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
                0, sf, self.color["font"], thickness=2, lineType=cv2.LINE_AA)

for i, pred in enumerate(det):
    lw = max(round(sum(img_src.shape) / 2 * 0.003), 2)  # line width
    tf = max(lw - 1, 1)  # font thickness
    sf = lw / 3  # font scale
    pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], img_src.shape)
    results = pred.cpu().detach().numpy()
    for result in results:
        self.draw_box(img_src, result[:4], result[4], self.names[result[5]], lw, sf, tf)

五、YOLO V8-Detection 推理

通过上面的解析，我们了解了 YOLO V8-Detection 的整个处理过程，以及使用到的模型文件和模函数。

完整的推理代码如下：

# -*- coding:utf-8 -*-
# @author: 牧锦程
# @微信公众号: AI算法与电子竞赛
# @Email: [email protected]
# @VX：fylaicai

import os.path
import random
import cv2
import numpy as np
import torch

from ultralytics.nn.autobackend import AutoBackend
from ultralytics.utils import ops


class YOLOV8DetectionInfer:
    def __init__(self, weights, cuda, conf_thres, iou_thres) -> None:
        self.imgsz = 640
        self.cuda = cuda
        self.model = AutoBackend(weights, device=torch.device(cuda))
        self.model.eval()
        self.names = self.model.names
        self.half = False
        self.conf = conf_thres
        self.iou = iou_thres
        self.color = {"font": (255, 255, 255)}
        self.color.update(
            {self.names[i]: (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
             for i in range(len(self.names))})

    def infer(self, img_path, save_path):
        img_src = cv2.imread(img_path)
        img = self.precess_image(img_src, self.imgsz, self.half, self.cuda)
        preds = self.model(img)
        det = ops.non_max_suppression(preds, self.conf, self.iou, classes=None, agnostic=False, max_det=300, nc=len(self.names))

        for i, pred in enumerate(det):
            lw = max(round(sum(img_src.shape) / 2 * 0.003), 2)  # line width
            tf = max(lw - 1, 1)  # font thickness
            sf = lw / 3  # font scale
            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], img_src.shape)
            results = pred.cpu().detach().numpy()
            for result in results:
                self.draw_box(img_src, result[:4], result[4], self.names[result[5]], lw, sf, tf)

        cv2.imwrite(os.path.join(save_path, os.path.split(img_path)[-1]), img_src)

    def draw_box(self, img_src, box, conf, cls_name, lw, sf, tf):
        color = self.color[cls_name]
        label = f'{cls_name} {conf}'
        p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
        # 绘制矩形框
        cv2.rectangle(img_src, p1, p2, color, thickness=lw, lineType=cv2.LINE_AA)
        # text width, height
        w, h = cv2.getTextSize(label, 0, fontScale=sf, thickness=tf)[0]
        # label fits outside box
        outside = box[1] - h - 3 >= 0
        p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
        # 绘制矩形框填充
        cv2.rectangle(img_src, p1, p2, color, -1, cv2.LINE_AA)
        # 绘制标签
        cv2.putText(img_src, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
                    0, sf, self.color["font"], thickness=2, lineType=cv2.LINE_AA)

    @staticmethod
    def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), scaleup=True, stride=32):
        # Resize and pad image while meeting stride-multiple constraints
        shape = im.shape[:2]  # current shape [height, width]
        if isinstance(new_shape, int):
            new_shape = (new_shape, new_shape)

        # Scale ratio (new / old)
        r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
        if not scaleup:  # only scale down, do not scale up (for better val mAP)
            r = min(r, 1.0)

        # Compute padding
        ratio = r, r  # width, height ratios
        new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
        dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
        # minimum rectangle
        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
        dw /= 2  # divide padding into 2 sides
        dh /= 2

        if shape[::-1] != new_unpad:  # resize
            im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)

        top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
        left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
        im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
        return im, ratio, (dw, dh)

    def precess_image(self, img_src, img_size, half, device):
        # Padded resize
        img = self.letterbox(img_src, img_size)[0]
        # Convert
        img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
        img = np.ascontiguousarray(img)
        img = torch.from_numpy(img).to(device)

        img = img.half() if half else img.float()  # uint8 to fp16/32
        img = img / 255  # 0 - 255 to 0.0 - 1.0
        if len(img.shape) == 3:
            img = img[None]  # expand for batch dim
        return img


if __name__ == '__main__':
    weights = r'yolov8n.pt'
    cuda = 'cuda:0'
    save_path = "./runs"

    if not os.path.exists(save_path):
        os.mkdir(save_path)

    model = YOLOV8DetectionInfer(weights, cuda, 0.45, 0.45)

    img_path = r'"./ultralytics/assets/bus.jpg"'
    model.infer(img_path, save_path)

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