YOLO V8-Detection 【批量图片推理】 推理详解及部署实现_yolov8批量测试图片

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。在下面的文章中，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【单张图片推理】检测代码给修改成 【批量图片推理】代码，进行批量推理。

一、批量推理的前处理

原始代码

@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

处理方式

我们要先知道在原始处理方式中是如何操作的：

它包含以下步骤：

• self.pre_transform：即 letterbox 添加灰条

• img.transpose((2, 0, 1))[::-1]：HWC to CHW, BGR to RGB

• torch.from_numpy：to Tensor

• img.float() ：uint8 to fp32

• im /= 255：除以 255，归一化

• img[None]：增加维度

在上述处理过程中我们最主要进行修改的就是 self.pre_transform 里面的操作，其余部分都是可以直接进行批量操作的。

在 letterbox 中最主要的操作就是下面两个函数，使用 opencv 进行实现的。我们要进行批量操作，那么 opencv 库是不能实现的，进行批量操作一般会用 广播机制 或者 tensor操作 来实现。

im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) 

由于最终输入到模型里面的是一个tensor，所以在这里我们使用 tensor的操作方式进行实现。

尺寸修改

原始方法：
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
 
现在方法：
resized_tensor = F.interpolate(image_tensor, size=new_unpad, mode='bilinear', align_corners=False)

两者的实现效果：

原始方法：(1176, 1956, 3) --》(385, 640, 3)

现在方法：(1176, 1956, 3) --》(385, 640, 3)

添加边框

原始方式：
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) 
 
现在方法：
padded_tensor = F.pad(resized_tensor, (top, bottom, left, right), mode='constant', value=padding_value)

两者的实现效果：

原始方法：(385, 640, 3) --》(416, 640, 3)

现在方法：(385, 640, 3) --》(416, 640, 3)

修改后的代码

def tensor_process(self, image_cv):
    img_shape = image_cv.shape[1:]
    new_shape = [640, 640]
    r = min(new_shape[0] / img_shape[0], new_shape[1] / img_shape[1])
    # Compute padding
    new_unpad = int(round(img_shape[0] * r)), int(round(img_shape[1] * r))
 
    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
    dw, dh = np.mod(dw, 32), np.mod(dh, 32)  # wh padding
    dw /= 2  # divide padding into 2 sides
    dh /= 2
 
    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
 
    padding_value = 114
 
    image_tensor = torch.from_numpy(image_cv).permute(0, 3, 1, 2).float()
    image_tensor = image_tensor.to(self.device)
 
    resized_tensor = F.interpolate(image_tensor, size=new_unpad, mode='bilinear', align_corners=False)
 
    padded_tensor = F.pad(resized_tensor, (top, bottom, left, right), mode='constant', value=padding_value)
    infer_tensor = padded_tensor / 255.0
 
    return infer_tensor

二、批量推理的后处理

原始代码

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

处理方式

我们要先知道在原始处理方式中是如何操作的。

在这个里面，最主要的操作就是 nms 操作，这里的 nms 操作就是一张图一张图的结果进行处理，不是多张图的结果一起处理，我们最主要的就是要修改这里的代码。

但是在这里，我们要先理解在原始的处理方式是怎样的逻辑。

原始 nms 逻辑

在这里就只给出关键步骤：

计算第4列到第mi列中的最大值，然后将这个最大值与conf_thres进行比较，得到一个布尔值结果。最终的输出是一个布尔张量，表示每一行是否存在大于conf_thres的最大值。

xc = prediction[:, 4:mi].amax(1) > conf_thres  # candidates

将原始预测框中的 xywh 转为 xyxy

prediction[..., :4] = xywh2xyxy(prediction[..., :4])

从原始结果中选择出为True的结果，得到初步的筛选结果

x = x[xc[xi]]  # confidence

分离出 标注框，类别，掩码

box, cls, mask = x.split((4, nc, nm), 1)

再次根据 cls 进行筛选，并拼接成新的推理结果

 conf, j = cls.max(1, keepdim=True)
 x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]

计算nms

boxes = x[:, :4] + c  # boxes (offset by class)
i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS

只选出前 max_det的输出结果，避免有多余的输出

i = i[:max_det]  # limit detections

现在 nms 逻辑

将 prediction 中所有批次的结果进行统一，处理成一个批次，在将这个批次送入到 batched_nms 中，最后在进行处理，得到标注框，类别，置信度。

筛选出为true的索引（批次数）和行数

true_indices = torch.nonzero(xc)

根据索引和行数筛选出 prediction 中真实的结果，注意：这个结果是所有的批次的结果

selected_rows = prediction[true_indices[:, 0], true_indices[:, 1]]

将批次数添加在筛选出的结果中，用于区分出那个结果是那个批次的。注意：这个的批次也可以看成是对应的图片

new_prediction = torch.cat((selected_rows, true_indices[:, 0].unsqueeze(1).float()), dim=1)

分割出标注框、类别、掩码、索引（批次）

box, cls, mask, idxs = new_prediction.split((4, nc, nm, 1), 1)

筛选出最大的类别的置信和类别索引

conf, j = cls.max(1, keepdim=True)

根据类别置信度再次进行筛选，选出符合的结果，并进行拼接，得到一个新的结果

x = torch.cat((box, conf, j.float()), 1)[conf.squeeze(-1) > conf_thres]

将标注框，置信度，索引，iou值送入到 batched_nms 中，选出最终的预测结果的索引标签。

cls = x[:, 5]  # classes
c = x[:, 5:6] * (0 if agnostic else max_wh)
boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
idxs = idxs.t().squeeze(0)
 
keep = torchvision.ops.batched_nms(boxes, scores, idxs, iou_thres)

batched_nms：以批处理方式执行非最大值抑制。每个索引值对应一个类别，NMS不会应用于不同类别的元素之间。

参数：

boxes （Tensor[N, 4]）：标注框，为 (x1, y1, x2, y2) 格式，其中 0 <= x1 < x2 和 0 <= y1 < y2。

scores （Tensor[N]）：每个标注框的得分

idxs （Tensor[N]）：每个标注框的类别索引。

iou_threshold（float）：剔除 IoU > iou_threshold 的所有重叠方框

返回值：

Tensor：int64，为 NMS 保留的元素索引，按分数递减排序

def batched_nms(boxes: Tensor, scores: Tensor, idxs: Tensor, iou_threshold: float,) -> Tensor:

根据 nms 的筛选结果，选择出最终的预测结果

boxes[keep] = self.scale_boxes(inferShape, boxes[keep], orgShape)
 
boxes = boxes[keep].cpu().numpy().tolist()
scores = scores[keep].cpu().numpy().tolist()
cls = cls[keep].cpu().numpy().tolist()
idxs = idxs[keep].cpu().numpy().tolist()

修改后的代码

def non_max_suppression(self, prediction, inferShape, orgShape, conf_thres=0.25, iou_thres=0.45, agnostic=True, multi_label=False,
                            max_wh=7680, nc=0):
    prediction = prediction[0]  # select only inference output
 
    nc = nc  # 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
    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)
    prediction[..., :4] = self.xywh2xyxy(prediction[..., :4])  # xywh to xyxy
 
    true_indices = torch.nonzero(xc)
    selected_rows = prediction[true_indices[:, 0], true_indices[:, 1]]
    new_prediction = torch.cat((selected_rows, true_indices[:, 0].unsqueeze(1).float()), dim=1)
 
    if new_prediction.shape[0] == 0:
        return
 
    box, cls, mask, idxs = new_prediction.split((4, nc, nm, 1), 1)
    conf, j = cls.max(1, keepdim=True)
    x = torch.cat((box, conf, j.float()), 1)[conf.squeeze(-1) > conf_thres]
    if not x.shape[0]:  # no boxes
        return
 
    cls = x[:, 5]  # classes
    c = x[:, 5:6] * (0 if agnostic else max_wh)
    boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
    idxs = idxs.t().squeeze(0)
 
    keep = torchvision.ops.batched_nms(boxes, scores, idxs, iou_thres)
 
    boxes[keep] = self.scale_boxes(inferShape, boxes[keep], orgShape)
 
    boxes = boxes[keep].cpu().numpy()
    scores = scores[keep].cpu().numpy()
    cls = cls[keep].cpu().numpy()
    idxs = idxs[keep].cpu().numpy()
 
    results = np.hstack((boxes, np.expand_dims(scores, axis=1)))
    results = np.hstack((results, np.expand_dims(cls, axis=1)))
    results = np.hstack((results, np.expand_dims(idxs, axis=1)))
    return results

三、完整代码

通过上面的解析，我们了解了 YOLO V8-Detection 如何进行批量的推理图片的方法，并对每一步进行了实现。

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: m21z50c71@163.com
# @VX：fylaicai
 
import os.path
import random
import cv2
import numpy as np
import torch
import torchvision
 
from ultralytics.nn.autobackend import AutoBackend
import torch.nn.functional as F
 
 
class YOLOV8DetectionInfer:
    def __init__(self, weights, cuda, conf_thres, iou_thres) -> None:
        self.imgsz = 640
        self.device = cuda
        self.model = AutoBackend(weights, device=torch.device(cuda))
        self.model.eval()
        self.names = self.model.names
        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_array = np.array([img_src])
 
        img = self.tensor_process(img_array)
        preds = self.model(img)
        results = self.non_max_suppression(preds, img.shape[2:], img_src.shape, self.conf, self.iou, nc=len(self.names))
 
        for result in results:
            self.draw_box(img_array[int(result[6])], result[:4], result[4], self.names[result[5]])
 
        for i in range(img_array.shape[0]):
            cv2.imwrite(os.path.join(save_path, f"{i}.jpg"), img_array[i])
 
    def draw_box(self, img_src, box, conf, cls_name):
        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
 
        color = self.color[cls_name]
        label = f'{cls_name} {conf:.4f}'
        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)
 
    def tensor_process(self, image_cv):
        img_shape = image_cv.shape[1:]
        new_shape = [640, 640]
        r = min(new_shape[0] / img_shape[0], new_shape[1] / img_shape[1])
        # Compute padding
        new_unpad = int(round(img_shape[0] * r)), int(round(img_shape[1] * r))
 
        dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
        dw, dh = np.mod(dw, 32), np.mod(dh, 32)  # wh padding
        dw /= 2  # divide padding into 2 sides
        dh /= 2
 
        top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
        left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
 
        padding_value = 114
 
        # Convert
        image_cv = image_cv[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW, (n, 3, h, w)
        image_cv = np.ascontiguousarray(image_cv)  # contiguous
        image_tensor = torch.from_numpy(image_cv).float()
        image_tensor = image_tensor.to(self.device)
 
        resized_tensor = F.interpolate(image_tensor, size=new_unpad, mode='bilinear', align_corners=False)
        padded_tensor = F.pad(resized_tensor, (top, bottom, left, right), mode='constant', value=padding_value)
        infer_tensor = padded_tensor / 255.0
 
        return infer_tensor
 
    def non_max_suppression(self, prediction, inferShape, orgShape, conf_thres=0.25, iou_thres=0.45, agnostic=True, multi_label=False,
                            max_wh=7680, nc=0):
        prediction = prediction[0]  # select only inference output
 
        nc = nc  # 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
        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)
        prediction[..., :4] = self.xywh2xyxy(prediction[..., :4])  # xywh to xyxy
 
        true_indices = torch.nonzero(xc)
        selected_rows = prediction[true_indices[:, 0], true_indices[:, 1]]
        new_prediction = torch.cat((selected_rows, true_indices[:, 0].unsqueeze(1).float()), dim=1)
 
        if new_prediction.shape[0] == 0:
            return
 
        box, cls, mask, idxs = new_prediction.split((4, nc, nm, 1), 1)
        conf, j = cls.max(1, keepdim=True)
        x = torch.cat((box, conf, j.float()), 1)[conf.squeeze(-1) > conf_thres]
        if not x.shape[0]:  # no boxes
            return
 
        cls = x[:, 5]  # classes
        c = x[:, 5:6] * (0 if agnostic else max_wh)
        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
        idxs = idxs.t().squeeze(0)
 
        keep = torchvision.ops.batched_nms(boxes, scores, idxs, iou_thres)
 
        boxes[keep] = self.scale_boxes(inferShape, boxes[keep], orgShape)
 
        boxes = boxes[keep].cpu().numpy()
        scores = scores[keep].cpu().numpy()
        cls = cls[keep].cpu().numpy()
        idxs = idxs[keep].cpu().numpy()
 
        results = np.hstack((boxes, np.expand_dims(scores, axis=1)))
        results = np.hstack((results, np.expand_dims(cls, axis=1)))
        results = np.hstack((results, np.expand_dims(idxs, axis=1)))
        return results
 
    def xywh2xyxy(self, x):
        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 clip_boxes(self, boxes, shape):
        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 scale_boxes(self, img1_shape, boxes, img0_shape, ratio_pad=None, padding=True, xywh=False):
        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 self.clip_boxes(boxes, img0_shape)
 
 
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, 0.25, 0.45)
 
    img_path = r'./ultralytics/assets/bus.jpg'
    model.infer(img_path, save_path)

四、书籍推荐

推荐一本书籍：《深度学习图解》

关于本书：《深度学习图解》旨在帮助你在深度学习领域打下基础 ，以便能 够从更高层面掌握深度学习的主要框架。它从关注神经网络的基础概念开始 ， 然后深入讲解那些更高级的网络设计和架构。

目标读者：阅读本书，不需要提前掌握线性代数、微积分、凸优化甚至机器学习等任何知识。理解深度学习所需的一切知识都会在本书的阅读过程中得到解释。如果你学过高中数学，并且能够使用 Python 编程，那么己经为阅读本书做好了准备。

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五、链接作者

欢迎关注我的公众号：@AI算法与电子竞赛

硬性的标准其实限制不了无限能的我们，所以啊！少年们加油吧！