yolov8使用：数据格式转换（目标检测、图像分类）多目标跟踪

默凉2024-06-05 12:50

安装

yolov8地址：https://github.com/ultralytics/ultralytics

bash 复制代码 
git clone https://github.com/ultralytics/ultralytics.git

安装环境：

bash 复制代码 
pip install ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple

目标检测

标注格式转换

若使用 labelimg 做为标注工具时：

VOC标注输出的是xml格式的标注文件

需要将xml文件转txt文件，yolo才能训练
标签格式转换xml转txt ：https://blog.csdn.net/qq_42102546/article/details/125303080

YOLO标注输出的是txt格式的标注文件，可直接用于训练。

输出的是json标注文件（注意是这样的json;[{"image": "zha1_1478.jpg", "annotations": [{"label": "w", "coordinates": {"x": 290.0, "y": 337.0, "width": 184.0, "height": 122.0}}]}]）

不要使用这个

若使用 labelme 作为标注工具，输出文件为：json格式

转换代码如下：json转txt

python 复制代码 
import json
import os


def convert(img_size, box):
    dw = 1. / (img_size[0])
    dh = 1. / (img_size[1])
    x = (box[0] + box[2]) / 2.0 - 1
    y = (box[1] + box[3]) / 2.0 - 1
    w = box[2] - box[0]
    h = box[3] - box[1]
    x = x * dw
    w = w * dw
    y = y * dh
    h = h * dh
    return (x, y, w, h)


def decode_json(json_floder_path, json_name):
    global path
    # 转换好txt的标签路径
    txt_name = path + json_name[0:-5] + '.txt'
    txt_file = open(txt_name, 'w')

    json_path = os.path.join(json_floder_path, json_name)
    data = json.load(open(json_path, 'r', encoding='gb2312'))
    print(data)

    img_w = data['imageWidth']
    img_h = data['imageHeight']

    for i in data['shapes']:

        label_name = i['label']
        if (i['shape_type'] == 'rectangle'):
            x1 = int(i['points'][0][0])
            y1 = int(i['points'][0][1])
            x2 = int(i['points'][1][0])
            y2 = int(i['points'][1][1])

            bb = (x1, y1, x2, y2)
            bbox = convert((img_w, img_h), bb)
            txt_file.write(str(name2id[label_name]) + " " + " ".join([str(a) for a in bbox]) + '\n')


if __name__ == "__main__":
    # 使用labelme标注后生成的 json 转 txt
    # 原始json标签路径
    json_floder_path = 'D:\\yolo_\\mu_biao_gen_zong\\data2\\'
    # 目标txt 保存路径
    path = 'D:\\yolo_\\mu_biao_gen_zong\\d\\'
    # 类别
    name2id = {'w': 0, 'f': 1}  # 具体自己数据集类别

    json_names = os.listdir(json_floder_path)
    print(json_names)
    for json_name in json_names:
        decode_json(json_floder_path, json_name)

数据集划分（目标检测）

暂无

训练代码

python 复制代码 
from ultralytics import YOLO

# Load a model
# model = YOLO("yolov8n.yaml")  # build a new model from YAML
# 目标检测 n s m l x 
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
# 图像分类
# model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
# model = YOLO("dataset.yaml").load("yolov8n.pt")  # build from YAML and transfer weights

# Train the model
results = model.train(data="dataset.yaml", epochs=40, imgsz=640)  # 40次 输入图像缩放大小640
# results = model.train(data="D:/yolo_/mu_biao_gen_zong/data", epochs=40, imgsz=640)

dataset.yaml 文件内容

数据集根目录

训练集目录

测试集目录

bash 复制代码 
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: D:/yolo_/mu_biao_gen_zong/data # dataset root dir，数据集根目录，使用绝对路径
train: D:/yolo_/mu_biao_gen_zong/data/train  # train images (relative to 'path') ，训练集图片目录（相对于path）
val: D:/yolo_/mu_biao_gen_zong/data/val  # val images (relative to 'path') ，测试集图片目录（相对于path）
test:  # test images (optional

# Classes，类别
names:
    0: roses
    1: sunflowers

推理代码

读取目录下的所有图像进行推理绘制矩形并保存在另一个目录中

python 复制代码 
from ultralytics import YOLO
import numpy as np
import cv2
import os
import time


def cv_show(neme, img):
    cv2.imshow(neme, img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


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

path_r = "./save_jpg/"
path_s = "./save_jpg_1/"
jpg_list = os.listdir(path_r)
for jpg_name in jpg_list:
    path_jpg_name = path_r + jpg_name

    # results = model('./save_jpg/1712411336861392961.jpg')  # return a list of Results objects
    results = model(path_jpg_name)

    print(type(results))
    print(len(results))


    for result in results:

        boxes = result.boxes  # Boxes object for bounding box outputs
        # print(type(result.orig_img))
        for i in range(len(boxes)):
            boxes = boxes.cpu()
            x1 = int(np.array(boxes[i].xyxy)[0][0])
            y1 = int(np.array(boxes[i].xyxy)[0][1])

            x2 = int(np.array(boxes[i].xyxy)[0][2])
            y2 = int(np.array(boxes[i].xyxy)[0][3])
            print(x1, y1, x2, y2)
            # 绘制矩形
            cv2.rectangle(result.orig_img, (x1, y1), (x2, y2), (0, 255, 0), 3)
        #cv_show("neme", result.orig_img)
        path_name_save = path_s + str(time.time()) + ".jpg"

        cv2.imwrite(path_name_save,result.orig_img)


        # masks = result.masks  # Masks object for segmentation masks outputs
        # keypoints = result.keypoints  # Keypoints object for pose outputs
        # probs = result.probs  # Probs object for classification outputs

        # print(probs)
        #result.show()  # display to screen
        #result.save(filename='result.jpg')  # save to disk

图像分类

数据集划分（图像分类）

分类图像数据集划分

默认总文件夹下 data_name 里面是具体分类的类别。

例如：

data_name

└──Cat 该文件夹里面是对应类型的图像

└──Dog 该文件夹里面是对应类型的图像

python 复制代码 
import argparse
import os
from shutil import copy
import random


def mkfile(file):
    if not os.path.exists(file):
        os.makedirs(file)


# './data_name'
def data_list(path, percentage, name):
    # 获取data文件夹下所有文件夹名（即需要分类的类名）
    file_path = path
    flower_class = [cla for cla in os.listdir(file_path)]

    # # 创建 训练集train 文件夹，并由类名在其目录下创建5个子目录
    pwd2 = name + "/train"
    mkfile(name)
    for cla in flower_class:
        mkfile(pwd2 + "/" + cla)

    # 创建 验证集val 文件夹，并由类名在其目录下创建子目录
    pwd3 = name + "/val"
    mkfile(name)
    for cla in flower_class:
        mkfile(pwd3 + "/" + cla)

    # 划分比例，训练集 : 验证集 = 9 : 1
    split_rate = percentage

    # 遍历所有类别的全部图像并按比例分成训练集和验证集
    for cla in flower_class:
        cla_path = file_path + '/' + cla + '/'  # 某一类别的子目录
        images = os.listdir(cla_path)  # iamges 列表存储了该目录下所有图像的名称
        num = len(images)
        eval_index = random.sample(images, k=int(num * split_rate))  # 从images列表中随机抽取 k 个图像名称
        for index, image in enumerate(images):
            # eval_index 中保存验证集val的图像名称
            if image in eval_index:
                image_path = cla_path + image
                new_path = pwd3 + "/" + cla
                copy(image_path, new_path)  # 将选中的图像复制到新路径

            # 其余的图像保存在训练集train中
            else:
                image_path = cla_path + image
                new_path = pwd2 + "/" + cla
                copy(image_path, new_path)
            print("\r[{}] processing [{}/{}]".format(cla, index + 1, num), end="")  # processing bar
        print()

    print("processing done!")


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="开始分离\"训练集\"与\"测试集\"百分比,"
                                                 "默认读取同级目录文件名：data_name,"
                                                 "默认训练集80%，测试集20%"
                                                 "默认保存文件名：data"
                                                 "train-->训练集"
                                                 "val  -->测试集")
    parser.add_argument('--path', type=str, default="./data_name", help='输入目标文件的路径')
    parser.add_argument('--percentage', type=float, default=0.2, help='指定测试集比例，例如:"0.2",训练集80%，测试集20%')
    parser.add_argument('--name', type=str, default="./data", help='另存为命名')
    args = parser.parse_args()
    path, percentage, name = args.path, args.percentage, args.name
    data_list(path, percentage, name)

训练代码

使用不同的预训练权重，直接运行默认下载。

python 复制代码 
from ultralytics import YOLO

# Load a model
# model = YOLO("yolov8n.yaml")  # build a new model from YAML
# 目标检测
# model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
# 图像分类
model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
# model = YOLO("dataset.yaml").load("yolov8n.pt")  # build from YAML and transfer weights

# Train the model
# results = model.train(data="dataset.yaml", epochs=40, imgsz=640)  # 40次 输入图像缩放大小640
results = model.train(data="D:/yolo_/mu_biao_gen_zong/data", epochs=40, imgsz=640)

推理代码

python 复制代码 
from ultralytics import YOLO

# Load a model
model = YOLO("best.pt")  # pretrained YOLOv8n model

# Run batched inference on a list of images
results = model(["im1.jpg", "im2.jpg"])  # return a list of Results objects

# Process results list
for result in results:
    # boxes = result.boxes  # 目标检测
    masks = result.masks  # 分割
    keypoints = result.keypoints  # 姿态检测
    probs = result.probs  # 分类
    obb = result.obb  # Oriented boxes object for OBB outputs
    print("分类")
    print(dir(probs))
    print(probs.top1)
    # result.show()  # 显示
    # result.save(filename="result.jpg")  # save to disk

多目标跟踪

yolov8自带调用

多目标跟踪官方文档：https://docs.ultralytics.com/zh/modes/track/

python 复制代码 
from collections import defaultdict
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
import cv2
import numpy as np

from ultralytics import YOLO

# Load the YOLOv8 model
model = YOLO('weights/yolov8n.pt')

# Open the video file
video_path = "./data0/testvideo1.mp4"
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
size = (width, height)

# Store the track history
track_history = defaultdict(lambda: [])

# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()

    if success:
        # Run YOLOv8 tracking on the frame, persisting tracks between frames
        results = model.track(frame, persist=True)

        # Get the boxes and track IDs
        if results[0].boxes.id != None:
            boxes = results[0].boxes.xywh.cpu()
            track_ids = results[0].boxes.id.int().cpu().tolist()

            # Visualize the results on the frame
            annotated_frame = results[0].plot()

            # Plot the tracks
            for box, track_id in zip(boxes, track_ids):
                x, y, w, h = box
                track = track_history[track_id]
                track.append((float(x), float(y)))  # x, y center point
                if len(track) > 30:  # retain 90 tracks for 90 frames
                    track.pop(0)

                # Draw the tracking lines
                points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
                cv2.polylines(annotated_frame, [points], isClosed=False, color=(0, 0, 255), thickness=2)

            # Display the annotated frame
            cv2.imshow("YOLOv8 Tracking", annotated_frame)

            # videoWriter.write(annotated_frame)

            # Break the loop if 'q' is pressed
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
    else:
        # Break the loop if the end of the video is reached
        break

# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()
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