YOLOv8 onnx 文件推理多线程加速视频流_onnx加速

运行环境：

• MacOS：14.0
• Python 3.9
• Pytorch2.1
• onnx 运行时

模型文件：

https://wwxd.lanzouu.com/iBqiA1g49pbc
密码:f40v

• 下载 best.apk后将后缀名修改为 onnx 即可
• 模型在英伟达 T4GPU 使用 coco128 训练了 200 轮
• 如遇下载不了可私信获取

代码：

import copy
import time
 
import onnxruntime as rt
import numpy as np
import cv2
import concurrent.futures
 
 
# 前处理
def resize_image(image, size, letterbox_image):
    """
        对输入图像进行resize
    Args:
        size:目标尺寸
        letterbox_image: bool 是否进行letterbox变换
    Returns:指定尺寸的图像
    """
    ih, iw, _ = image.shape
    h, w = size
    # letterbox_image = False
    if letterbox_image:
        scale = min(w / iw, h / ih)
        nw = int(iw * scale)
        nh = int(ih * scale)
        image = cv2.resize(image, (nw, nh), interpolation=cv2.INTER_LINEAR)
        image_back = np.ones((h, w, 3), dtype=np.uint8) * 128
        image_back[(h - nh) // 2: (h - nh) // 2 + nh, (w - nw) // 2:(w - nw) // 2 + nw, :] = image
    else:
        image_back = image
    return image_back
 
 
def img2input(img):
    img = np.transpose(img, (2, 0, 1))
    img = img / 255
    return np.expand_dims(img, axis=0).astype(np.float32)
 
 
def std_output(pred):
    """
    将（1，84，8400）处理成（8400， 85）  85= box:4  conf:1 cls:80
    """
    pred = np.squeeze(pred)
    pred = np.transpose(pred, (1, 0))
    pred_class = pred[..., 4:]
    pred_conf = np.max(pred_class, axis=-1)
    pred = np.insert(pred, 4, pred_conf, axis=-1)
    return pred
 
 
def xywh2xyxy(*box):
    """
    将xywh转换为左上角点和左下角点
    Args:
        box:
    Returns: x1y1x2y2
    """
    ret = [box[0] - box[2] // 2, box[1] - box[3] // 2, \
           box[0] + box[2] // 2, box[1] + box[3] // 2]
    return ret
 
 
def get_inter(box1, box2):
    """
    计算相交部分面积
    Args:
        box1: 第一个框
        box2: 第二个狂
    Returns: 相交部分的面积
    """
    x1, y1, x2, y2 = xywh2xyxy(*box1)
    x3, y3, x4, y4 = xywh2xyxy(*box2)
    # 验证是否存在交集
    if x1 >= x4 or x2 <= x3:
        return 0
    if y1 >= y4 or y2 <= y3:
        return 0
    # 将x1,x2,x3,x4排序，因为已经验证了两个框相交，所以x3-x2就是交集的宽
    x_list = sorted([x1, x2, x3, x4])
    x_inter = x_list[2] - x_list[1]
    # 将y1,y2,y3,y4排序，因为已经验证了两个框相交，所以y3-y2就是交集的宽
    y_list = sorted([y1, y2, y3, y4])
    y_inter = y_list[2] - y_list[1]
    # 计算交集的面积
    inter = x_inter * y_inter
    return inter
 
 
def get_iou(box1, box2):
    """
    计算交并比： (A n B)/(A + B - A n B)
    Args:
        box1: 第一个框
        box2: 第二个框
    Returns:  # 返回交并比的值
    """
    box1_area = box1[2] * box1[3]  # 计算第一个框的面积
    box2_area = box2[2] * box2[3]  # 计算第二个框的面积
    inter_area = get_inter(box1, box2)
    union = box1_area + box2_area - inter_area  # (A n B)/(A + B - A n B)
    iou = inter_area / union
    return iou
 
 
def nms(pred, conf_thres, iou_thres):
    """
    非极大值抑制nms
    Args:
        pred: 模型输出特征图
        conf_thres: 置信度阈值
        iou_thres: iou阈值
    Returns: 输出后的结果
    """
    box = pred[pred[..., 4] > conf_thres]  # 置信度筛选
    cls_conf = box[..., 5:]
    cls = []
    for i in range(len(cls_conf)):
        cls.append(int(np.argmax(cls_conf[i])))
    total_cls = list(set(cls))  # 记录图像内共出现几种物体
    output_box = []
    # 每个预测类别分开考虑
    for i in range(len(total_cls)):
        clss = total_cls[i]
        cls_box = []
        temp = box[:, :6]
        for j in range(len(cls)):
            # 记录[x,y,w,h,conf(最大类别概率),class]值
            if cls[j] == clss:
                temp[j][5] = clss
                cls_box.append(temp[j][:6])
        #  cls_box 里面是[x,y,w,h,conf(最大类别概率),class]
        cls_box = np.array(cls_box)
        sort_cls_box = sorted(cls_box, key=lambda x: -x[4])  # 将cls_box按置信度从大到小排序
        # box_conf_sort = np.argsort(-box_conf)
        # 得到置信度最大的预测框
        max_conf_box = sort_cls_box[0]
        output_box.append(max_conf_box)
        sort_cls_box = np.delete(sort_cls_box, 0, 0)
        # 对除max_conf_box外其他的框进行非极大值抑制
        while len(sort_cls_box) > 0:
            # 得到当前最大的框
            max_conf_box = output_box[-1]
            del_index = []
            for j in range(len(sort_cls_box)):
                current_box = sort_cls_box[j]
                iou = get_iou(max_conf_box, current_box)
                if iou > iou_thres:
                    # 筛选出与当前最大框Iou大于阈值的框的索引
                    del_index.append(j)
            # 删除这些索引
            sort_cls_box = np.delete(sort_cls_box, del_index, 0)
            if len(sort_cls_box) > 0:
                # 我认为这里需要将clas_box先按置信度排序， 才能每次取第一个
                output_box.append(sort_cls_box[0])
                sort_cls_box = np.delete(sort_cls_box, 0, 0)
    return output_box
 
 
def cod_trf(result, pre, after):
    """
    因为预测框是在经过letterbox后的图像上做预测所以需要将预测框的坐标映射回原图像上
    Args:
        result:  [x,y,w,h,conf(最大类别概率),class]
        pre:    原尺寸图像
        after:  经过letterbox处理后的图像
    Returns: 坐标变换后的结果,
    """
    res = np.array(result)
    x, y, w, h, conf, cls = res.transpose((1, 0))
    x1, y1, x2, y2 = xywh2xyxy(x, y, w, h)  # 左上角点和右下角的点
    h_pre, w_pre, _ = pre.shape
    h_after, w_after, _ = after.shape
    scale = max(w_pre / w_after, h_pre / h_after)  # 缩放比例
    h_pre, w_pre = h_pre / scale, w_pre / scale  # 计算原图在等比例缩放后的尺寸
    x_move, y_move = abs(w_pre - w_after) // 2, abs(h_pre - h_after) // 2  # 计算平移的量
    ret_x1, ret_x2 = (x1 - x_move) * scale, (x2 - x_move) * scale
    ret_y1, ret_y2 = (y1 - y_move) * scale, (y2 - y_move) * scale
    ret = np.array([ret_x1, ret_y1, ret_x2, ret_y2, conf, cls]).transpose((1, 0))
    return ret
 
 
def draw(res, image, cls):
    """
    将预测框绘制在image上
    Args:
        res: 预测框数据
        image: 原图
        cls: 类别列表，类似["apple", "banana", "people"]  可以自己设计或者通过数据集的yaml文件获取
    Returns:
    """
    for r in res:
        # 画框
        image = cv2.rectangle(image, (int(r[0]), int(r[1])), (int(r[2]), int(r[3])), (255, 0, 0), 1)
        # 表明类别
        text = "{}:{}".format(cls[int(r[5])], round(float(r[4]), 2))
        h, w = int(r[3]) - int(r[1]), int(r[2]) - int(r[0])  # 计算预测框的长宽
        font_size = min(h / 640, w / 640) * 3  # 计算字体大小（随框大小调整）
        image = cv2.putText(image, text, (max(10, int(r[0])), max(20, int(r[1]))), cv2.FONT_HERSHEY_COMPLEX,
                            max(font_size, 0.3), (0, 0, 255), 1)  # max()为了确保字体不过界
    return image
 
def display_fps(frame, start_time):
    global global_fps
    end_time = time.time()
    elapsed_time = end_time - start_time
    global_fps = 1 / elapsed_time
 
    # 在图像上显示帧率
    cv2.putText(frame, f"FPS: {global_fps:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
 
global_fps = 0.0
 
if __name__ == '__main__':
    cap = cv2.VideoCapture(0)
    sess = rt.InferenceSession('./best.onnx')
    cv2.namedWindow('Video Stream', cv2.WINDOW_NORMAL)
    names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
             'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
             'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
             'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
             'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
             'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
             'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard',
             'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
             'teddy bear', 'hair drier', 'toothbrush']
    def inference_task(frame):
        class_list = list(names)
        std_h, std_w = 640, 640
        img_after = resize_image(frame, (std_w, std_h), True)
        data = img2input(img_after)
        input_name = sess.get_inputs()[0].name
        label_name = sess.get_outputs()[0].name
        pred = sess.run([label_name], {input_name: data})[0]
        pred = std_output(pred)
        result = nms(pred, 0.6, 0.4)
        result = cod_trf(result, frame, img_after)
        image = draw(result, frame, class_list)
        return image
 
 
    with concurrent.futures.ThreadPoolExecutor(max_workers=20) as executor:
 
        while True:
            start_time = time.time()
            # 读取一帧
            ret, frame = cap.read()
 
            if not ret:
                print("无法读取帧")
                break
 
            # 提交任务并获取 Future 对象
            future = executor.submit(inference_task, frame)
            display_fps(frame, start_time)
            # 获取结果
            try:
                image = future.result()
                # 显示窗口
                cv2.imshow('Video Stream', image)
                cv2.waitKey(1)
            except Exception as e:
                cv2.imshow('Video Stream', frame)
                cv2.waitKey(1)
 
    # 释放资源
    cap.release()
    cv2.destroyAllWindows()