多线程 yolov5

多线程在yolov5中的应用可以通过使用线程池来实现。线程池可以帮助管理线程，避免为每个任务创建一个线程，提高系统的效率。

以下是一个使用线程池实现多线程yolov5目标检测的示例代码：

import cv2
import torch
from torchvision import transforms
from yolov5.models.experimental import attempt_load
from yolov5.utils.general import non_max_suppression, scale_coords
from yolov5.utils.torch_utils import select_device

# 加载模型
weights = 'yolov5s.pt'
device = select_device('')
model = attempt_load(weights, map_location=device)
stride = int(model.stride.max())

# 定义目标类别
class_names = model.module.names if hasattr(model, 'module') else model.names

# 定义图像预处理函数
def preprocess_image(image):
    img = transforms.ToTensor()(image).unsqueeze(0)
    img = img.float() / 255.0
    return img

# 定义目标检测函数
def detect_objects(image):
    img = preprocess_image(image)
    img = img.to(device)

    # 进行目标检测
    with torch.no_grad():
        pred = model(img)[0]
        pred = non_max_suppression(pred, conf_thres=0.5, iou_thres=0.5)

    # 处理检测结果
    detections = []
    for det in pred:
        if det is not None and len(det):
            det[:, :4] = scale_coords(img.shape[2:], det[:, :4], image.shape).round()
            for *xyxy, conf, cls in reversed(det):
                label = f'{class_names[int(cls)]} {conf:.2f}'
                detections.append((label, xyxy))

    return detections

# 定义多线程目标检测函数
def multi_thread_detection(image_list):
    import concurrent.futures

    # 创建线程池
    num_threads = 4  # 设置线程数
    executor = concurrent.futures.ThreadPoolExecutor(max_workers=num_threads)

    # 提交任务到线程池
    futures = []
    for image in image_list:
        future = executor.submit(detect_objects, image)
        futures.append(future)

    # 获取任务结果
    results = []
    for future in concurrent.futures.as_completed(futures):
        result = future.result()
        results.append(result)

    return results

# 加载图像
image1 = cv2.imread('image1.jpg')
image2 = cv2.imread('image2.jpg')
image3 = cv2.imread('image3.jpg')

# 多线程目标检测
images = [image1, image2, image3]
detections = multi_thread_detection(images)

# 打印检测结果
for i, detection in enumerate(detections):
    print(f'Detections for image {i+1}:')
    for label, xyxy in detection:
        print(label, xyxy)

这段代码首先加载了yolov5模型和相关的类别信息，然后定义了图像预处理函数和目标检测函数。接着，通过创建线程池并提交任务到线程池中，实现了多线程的目标检测。最后，打印了检测结果。