python detect.py --weights yolov5s.pt --source data/images/bus.jpg

python detect.py --weights yolov5s.pt --source screen

import torch

# Model  
model = torch.hub.load("./", "yolov5s", source="local")

#Images
img = "./data/images/zidane.jpg"

# Inference
results = model(img)

# Results
results.show()

import json
import os
import glob
import os.path as osp


def labelme2yolov2Seg(jsonfilePath="", resultDirPath="", classList=["YiBiao", "ZhiZhen"]):
    """
    此函数用来将labelme软件标注好的数据集转换为yolov5_7.0sege中使用的数据集
    :param jsonfilePath: labelme标注好的*.json文件所在文件夹
    :param resultDirPath: 转换好后的*.txt保存文件夹
    :param classList: 数据集中的类别标签
    :return:
    """
    # 0.创建保存转换结果的文件夹
    if(not os.path.exists(resultDirPath)):
        os.mkdir(resultDirPath)

    # 1.获取目录下所有的labelme标注好的Json文件，存入列表中
    jsonfileList = glob.glob(osp.join(jsonfilePath, "*.json"))
    print(jsonfileList)  # 打印文件夹下的文件名称

    # 2.遍历json文件，进行转换
    for jsonfile in jsonfileList:
        # 3. 打开json文件
        with open(jsonfile, "r") as f:
            file_in = json.load(f)

            # 4. 读取文件中记录的所有标注目标
            shapes = file_in["shapes"]

            # 5. 使用图像名称创建一个txt文件，用来保存数据
            with open(resultDirPath + "\\" + jsonfile.split("\\")[-1].replace(".json", ".txt"), "w") as file_handle:
                # 6. 遍历shapes中的每个目标的轮廓
                for shape in shapes:
                    # 7.根据json中目标的类别标签，从classList中寻找类别的ID，然后写入txt文件中
                    file_handle.writelines(str(classList.index(shape["label"])) + " ")

                    # 8. 遍历shape轮廓中的每个点，每个点要进行图像尺寸的缩放，即x/width, y/height
                    for point in shape["points"]:
                        x = point[0]/file_in["imageWidth"]  # mask轮廓中一点的X坐标
                        y = point[1]/file_in["imageHeight"]  # mask轮廓中一点的Y坐标
                        file_handle.writelines(str(x) + " " + str(y) + " ")  # 写入mask轮廓点

                    # 9.每个物体一行数据，一个物体遍历完成后需要换行
                    file_handle.writelines("\n")
            # 10.所有物体都遍历完，需要关闭文件
            file_handle.close()
        # 10.所有物体都遍历完，需要关闭文件
        f.close()

if __name__ == "__main__":
    jsonfilePath = "E:\\yolo\\yolov5-master\\datasets\\labelme\\json"  # 要转换的json文件所在目录
    resultDirPath = "E:\\yolo\\yolov5-master\\datasets\\labelme\\txt"  # 要生成的txt文件夹
    labelme2yolov2Seg(jsonfilePath=jsonfilePath, resultDirPath=resultDirPath, classList=["YiBiao", "ZhiZhen"])

path: ./datasets  # dataset root dir
train: images/train  # train images (relative to 'path') 128 images
val: images/val  # val images (relative to 'path') 128 images
test:  # test images (optional)

# Classes
names:
  0: daitu
  1: mingren
  

parser.add_argument('--data', type=str, default=ROOT / 'data/bvn.yaml', help='dataset.yaml path')

import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
#####上面是临时

tensorboard --logdir runs

cmd中  python detect.py --weights runs/train/exp3/weights/best.pt --source datasets/BVN.MP4 --view-img

pip install pyside6
然后
where python

import cv2
import sys
import torch
from PySide6.QtWidgets import QMainWindow, QApplication, QFileDialog
from PySide6.QtGui import QPixmap, QImage
from PySide6.QtCore import QTimer

from ui_main_window import Ui_MainWindow


def convert2QImage(img):
    height, width, channel = img.shape
    return QImage(img, width, height, width * channel, QImage.Format_RGB888)


class MainWindow(QMainWindow, Ui_MainWindow):
    def __init__(self):
        super(MainWindow, self).__init__()
        self.setupUi(self)
        self.model = torch.hub.load("./", "custom", path="runs/train/exp3/weights/best.pt", source="local")
        self.timer = QTimer()
        self.timer.setInterval(1)
        self.video = None
        self.bind_slots()

    def image_pred(self, file_path):
        results = self.model(file_path)
        image = results.render()[0]
        return convert2QImage(image)

    def open_image(self):
        print("点击了检测图片！")
        self.timer.stop()
        file_path = QFileDialog.getOpenFileName(self, dir="./datasets/images/train", filter="*.jpg;*.png;*.jpeg")
        if file_path[0]:
            file_path = file_path[0]
            qimage = self.image_pred(file_path)
            self.input.setPixmap(QPixmap(file_path))
            self.output.setPixmap(QPixmap.fromImage(qimage))

    def video_pred(self):
        ret, frame = self.video.read()
        if not ret:
            self.timer.stop()
        else:
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            self.input.setPixmap(QPixmap.fromImage(convert2QImage(frame)))
            results = self.model(frame)
            image = results.render()[0]
            self.output.setPixmap(QPixmap.fromImage(convert2QImage(image)))

    def open_video(self):
        print("点击了检测视频！")
        file_path = QFileDialog.getOpenFileName(self, dir="./datasets", filter="*.mp4")
        if file_path[0]:
            file_path = file_path[0]
            self.video = cv2.VideoCapture(file_path)
            self.timer.start()

    def bind_slots(self):
        self.det_image.clicked.connect(self.open_image)
        self.det_video.clicked.connect(self.open_video)
        self.timer.timeout.connect(self.video_pred)


if __name__ == "__main__":
    app = QApplication(sys.argv)

    window = MainWindow()
    window.show()

    app.exec()

pip install gradio

import torch
import gradio as gr

model = torch.hub.load("./", "custom", path="runs/train/exp3/weights/best.pt", source="local")

title = "基于Gradio的YOLOv5演示项目"

desc = "这是一个基于Gradio的YOLOv5演示项目，非常简洁，非常方便！"

base_conf, base_iou = 0.25, 0.45

def det_image(img, conf_thres, iou_thres):
    model.conf = conf_thres
    model.iou = iou_thres
    return model(img).render()[0]

gr.Interface(inputs=["image", gr.Slider(minimum=0, maximum=1, value=base_conf), gr.Slider(minimum=0, maximum=1, value=base_iou)],
             outputs=["image"],
             fn=det_image,
             title=title,
             description=desc,
             live=True,
             examples=[["./datasets/images/train/30.jpg", base_conf, base_iou], ["./datasets/images/train/60.jpg", 0.3, base_iou]]).launch(share=True)