Yolov8官方给出的是单类别的人体姿态关键点检测，本文将记录如果实现训练自己的多类别的关键点检测。

一、YOLOV8环境准备

1.1 下载安装最新的YOLOv8代码

 仓库地址： https://github.com/ultralytics/ultralytics

1.2 配置环境

  pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

二、数据准备

2.1 安装labelme标注软件

pip install labelme

2.1.2 打开roLabelImg软件

使用Anaconda Prompt启动labeme标注工具

2.2 标注自己的数据

不同的目标的关键点可以自己定义，关键点数量少的目标，再下一步转换标签格式的时候，需要将关键点的数量补齐。例如，下图所示的摩托车有9个关键点，汽车有2个关键点，那边汽车的标签还需要补上7个 0 0 0 (坐标为0，且不可见)。这样才能进行多目标的关键点检测的训练。
我这里的关键点命名按 11 , 22, 33, … ，99来命名的，读者可以根据自己的实际情况来命名。

2.3 数据转换

2.3.1 运行下面代码，将xml标签格式转为txt标签格式

关键点数量对齐

# 将labelme标注的json文件转为yolo格式
import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
import glob
import json
import tqdm
# 物体类别

class_list = ["motorbike","car","cone"]
# 关键点的顺序
keypoint_list = ["11", "22", "33", "44","55", "66", "77", "88", "99"]
def json_to_yolo(img_data ,json_data):
    h ,w = img_data.shape[:2]
    # 步骤：
    # 1. 找出所有的矩形，记录下矩形的坐标，以及对应group_id
    # 2. 遍历所有的head和tail，记下点的坐标，以及对应group_id，加入到对应的矩形中
    # 3. 转为yolo格式
    rectangles = {}
    # 遍历初始化
    for shape in json_data["shapes"]:
        label = shape["label"] # pen, head, tail
        group_id = shape["group_id"] # 0, 1, 2, ...
        points = shape["points"] # x,y coordinates
        shape_type = shape["shape_type"]

        # 只处理矩形,读矩形
        if shape_type == "rectangle":
            if group_id not in rectangles:
                rectangles[group_id] = {
                "label": label,
                "rect": points[0] + points[1],  # Rectangle [x1, y1, x2, y2]
                "keypoints_list": []
        }
    # 遍历更新，将点加入对应group_id的矩形中，读关键点，根据group_id匹配
    for keypoint in keypoint_list:
        for shape in json_data["shapes"]:
            label = shape["label"]
            group_id = shape["group_id"]
            points = shape["points"]
            # 如果匹配到了对应的keypoint
            if label == keypoint:
                rectangles[group_id]["keypoints_list"].append(points[0])
            #else:
             #   rectangles[group_id]["keypoints_list"].append([0,0])

    # 转为yolo格式
    yolo_list = []
    for id, rectangle in rectangles.items():
        result_list  = []
        if rectangle['label'] not in class_list:
            continue
        label_id = class_list.index(rectangle["label"])
        # x1,y1,x2,y2
        x1 ,y1 ,x2 ,y2 = rectangle["rect"]
        # center_x, center_y, width, height
        center_x = (x1 +x2 ) /2
        center_y = (y1 +y2 ) /2
        width = abs(x1 -x2)
        height = abs(y1 -y2)
        # normalize
        center_x /= w
        center_y /= h
        width /= w
        height /= h

        # 保留6位小数
        center_x = round(center_x, 6)
        center_y = round(center_y, 6)
        width = round(width, 6)
        height = round(height, 6)

        # 添加 label_id, center_x, center_y, width, height
        result_list = [label_id, center_x, center_y, width, height]

        # 添加 p1_x, p1_y, p1_v, p2_x, p2_y, p2_v
        for point in rectangle["keypoints_list"]:
            x ,y = point
            x ,y = int(x), int(y)
            x /= w
            y /= h
            # 保留6位小数
            x = round(x, 6)
            y = round(y, 6)
            result_list.extend([x ,y ,2])
        if len(rectangle["keypoints_list"]) == 4:
            result_list.extend([0, 0, 0])
            result_list.extend([0, 0, 0])
            result_list.extend([0, 0, 0])
            result_list.extend([0, 0, 0])
            result_list.extend([0, 0, 0])

        if len(rectangle["keypoints_list"]) == 2:
            result_list.extend([0, 0, 0])
            result_list.extend([0, 0, 0])
            result_list.extend([0, 0, 0])
            result_list.extend([0, 0, 0])
            result_list.extend([0, 0, 0])
            result_list.extend([0, 0, 0])
            result_list.extend([0, 0, 0])
        
        yolo_list.append(result_list)
    return yolo_list
# 获取所有的图片
img_list = glob.glob("D:/study/cnn/yolo/yolov8-mokpt/ultralytics/data_mokpt/*.png")
for img_path in tqdm.tqdm( img_list ):

    img = cv2.imread(img_path)
    print(img_path)
    json_file = img_path.replace('png', 'json')
    with open(json_file) as json_file:
        json_data = json.load(json_file)

    yolo_list = json_to_yolo(img, json_data)
    yolo_txt_path = img_path.replace('png', 'txt')

    with open(yolo_txt_path, "w") as f:
        for yolo in yolo_list:
            for i in range(len(yolo)):
                if i == 0:
                    f.write(str(yolo[i]))
                else:
                    f.write(" " + str(yolo[i]))
            f.write("\n")

运行上面代码，就可以获得TXT格式标签文件

2.3.2 运行下面代码，检查txt标签转换是否正确

import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
import glob

img_path = "D:/study/cnn/yolo/yolov8-mokpt/ultralytics/data_mokpt/1.png"

plt.figure(figsize=(15, 10))
img = cv2.imread(img_path)
plt.imshow(img[:, :, ::-1])
plt.axis('off')

yolo_txt_path = img_path.replace('png', 'txt')
print(yolo_txt_path)

with open(yolo_txt_path, 'r') as f:
    lines = f.readlines()

lines = [x.strip() for x in lines]

label = np.array([x.split() for x in lines], dtype=np.float32)

# 物体类别
class_list = ["motorbike","car","cone"]

# 类别的颜色
class_color = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),(255, 0, 0), (0, 255, 0)]
# 关键点的顺序
keypoint_list = ["11", "22", "33", "44","55", "66", "77", "88", "99"]
# 关键点的颜色
keypoint_color = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),(255, 0, 0), (0, 255, 0)]

# 绘制检测框
img_copy = img.copy()
h, w = img_copy.shape[:2]
for id, l in enumerate(label):
    # label_id ,center x,y and width, height
    label_id, cx, cy, bw, bh = l[0:5]
    label_text = class_list[int(label_id)]
    # rescale to image size
    cx *= w
    cy *= h
    bw *= w
    bh *= h

    # draw the bounding box
    xmin = int(cx - bw / 2)
    ymin = int(cy - bh / 2)
    xmax = int(cx + bw / 2)
    ymax = int(cy + bh / 2)
    cv2.rectangle(img_copy, (xmin, ymin), (xmax, ymax), class_color[int(label_id)], 2)
    cv2.putText(img_copy, label_text, (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, class_color[int(label_id)], 2)

# display the image
plt.figure(figsize=(15, 10))
plt.imshow(img_copy[:, :, ::-1])
plt.axis('off')
# save the image
cv2.imwrite("./tmp.png", img_copy)

img_copy = img.copy()
h, w = img_copy.shape[:2]
for id, l in enumerate(label):
    # label_id ,center x,y and width, height
    label_id, cx, cy, bw, bh = l[0:5]
    label_text = class_list[int(label_id)]
    # rescale to image size
    cx *= w
    cy *= h
    bw *= w
    bh *= h

    # draw the bounding box
    xmin = int(cx - bw / 2)
    ymin = int(cy - bh / 2)
    xmax = int(cx + bw / 2)
    ymax = int(cy + bh / 2)
    cv2.rectangle(img_copy, (xmin, ymin), (xmax, ymax), class_color[int(label_id)], 2)
    cv2.putText(img_copy, label_text, (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 2, class_color[int(label_id)], 2)

    # draw 17 keypoints, px,py,pv,px,py,pv...
    for i in range(5, len(l), 3):
        px, py = l[i:i + 2]
        # rescale to image size
        px *= w
        py *= h
        # puttext the index
        index = int((i - 5) / 2)
        # draw the keypoints
        if(int(px)>0):
         cv2.circle(img_copy, (int(px), int(py)), 10, (0,255,255), -1)

plt.figure(figsize=(15, 10))
plt.imshow(img_copy[:, :, ::-1])
plt.axis('off')
# save
cv2.imwrite('./tmp.png', img_copy)
cv2.imshow('tmp', img_copy)
cv2.waitKey(0)

可视化结果如下

ultralytics\ultralytics\路径下,创建data文件夹，将图片和标签按下面的结构摆放：

三、配置文件设置

3.1 修改coco-pose.yaml

修改ultralytics\ultralytics\cfg\datasets\coco-pose.yaml配置文件内容：

path: ultralytics/data/images   
train: train 
val: val

# Keypoints
# 9：多目标中关键点最多的那个关键点数量
# 3： x, y和关键点可见性
kpt_shape: [9, 3]
flip_idx: [0, 1, 2, 3,4,5,6,7,8,9]

# Classes
names:
  0: motorbike
  1: car
  2: cone

四、训练

4.1 下载预训练权重

在YOLOv8 github上下载预训练权重：yolov8n-pose.pt，ultralytics\ultralytics\路径下,新建weight文件夹，预训练权重放入其中。

4.2 训练

步骤一：修改ultralytics\ultralytics\cfg\default.yaml文件中的训练参数（根据自己的实际情况决定）
步骤二：执行下面代码：

from ultralytics import YOLO

# Load a model
model = YOLO('ultralytics/weights/yolov8n-pose.pt')

# Train the model
results = model.train(data='D:/study/cnn/yolo/yolov8-mokpt/ultralytics/ultralytics/cfg/datasets/coco-pose.yaml', epochs=300, imgsz=640)

五、验证

from ultralytics import YOLO
 
def main():
    model = YOLO(r'runs/pose/train/weights/best.pt')
    model.val(data='data/multi-pose.yaml', imgsz=1024, batch=4, workers=4)
if __name__ == '__main__':
    main()

六、推理

根据自己实际的情况，修改

# 测试图片
from ultralytics import YOLO
import cv2
import numpy as np
import sys

# 读取命令行参数
weight_path = 'E:/YOLO/yolov8-mokpt/ultralytics/runs/pose/best.pt'
media_path = "demo/bev_2_1034.png"

# 加载模型
model = YOLO(weight_path)

# 获取类别
objs_labels = model.names  # get class labels
print(objs_labels)

# 类别的颜色
class_color = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),(255, 0, 0), (0, 255, 0)]
# 关键点的顺序
class_list = ["motorbike","car","cone"]

# 关键点的颜色
keypoint_color = [(255, 0, 0), (0, 255, 0),(255, 0, 0), (0, 255, 0),(255, 0, 0), (0, 255, 0),(255, 0, 0), (0, 255, 0),(255, 0, 0), (0, 255, 0)]

# 读取图片
frame = cv2.imread(media_path)
frame = cv2.resize(frame, (frame.shape[1] // 2, frame.shape[0] // 2))
# rotate
# 检测
result = list(model(frame, conf=0.3, stream=True))[0]  # inference，如果stream=False，返回的是一个列表，如果stream=True，返回的是一个生成器
boxes = result.boxes  # Boxes object for bbox outputs
boxes = boxes.cpu().numpy()  # convert to numpy array

# 遍历每个框
for box in boxes.data:
    l, t, r, b = box[:4].astype(np.int32)  # left, top, right, bottom
    conf, id = box[4:]  # confidence, class
    id = int(id)
    # 绘制框
    cv2.rectangle(frame, (l, t), (r, b), (0, 0, 255), 2)
    # 绘制类别+置信度（格式：98.1%）
    cv2.putText(frame, f"{objs_labels[id]} {conf * 100:.1f}", (l, t - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
                (0, 0, 255), 1)

# 遍历keypoints
keypoints = result.keypoints  # Keypoints object for pose outputs
keypoints = keypoints.cpu().numpy()  # convert to numpy array

# draw keypoints, set first keypoint is red, second is blue
for keypoint in keypoints.data:
    for i in range(len(keypoint)):
        x, y ,_ = keypoint[i]
        x, y = int(x), int(y)
        cv2.circle(frame, (x, y), 3, (0, 255, 0), -1)
        #cv2.putText(frame, f"{keypoint_list[i]}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, keypoint_color[i], 2)

    if len(keypoint) >= 2:
        # draw arrow line from tail to half between head and tail
        x0, y0 ,_= keypoint[0]
        x1, y1 ,_= keypoint[1]
        x2, y2 ,_= keypoint[2]
        x3, y3 ,_= keypoint[3]
        x4, y4 ,_= keypoint[4]
        x5, y5 ,_= keypoint[5]
        x6, y6 ,_= keypoint[6]
        x7, y7 ,_= keypoint[7]
        x8, y8 ,_= keypoint[8]


        cv2.line(frame, (int(x0), int(y0)), (int(x1), int(y1)), (255, 0, 255), 1)
        cv2.line(frame, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 255), 1)
        cv2.line(frame, (int(x2), int(y2)), (int(x3), int(y3)), (255, 0, 255), 1)
        cv2.line(frame, (int(x3), int(y3)), (int(x4), int(y4)), (255, 0, 255), 1)
        cv2.line(frame, (int(x4), int(y4)), (int(x5), int(y5)), (255, 0, 255), 1)
        cv2.line(frame, (int(x5), int(y5)), (int(x6), int(y6)), (255, 0, 255), 1)
        cv2.line(frame, (int(x6), int(y6)), (int(x7), int(y7)), (255, 0, 255), 1)
        cv2.line(frame, (int(x7), int(y7)), (int(x8), int(y8)), (255, 0, 255), 1)
        cv2.line(frame, (int(x8), int(y8)), (int(x0), int(y0)), (255, 0, 255), 1)


        #center_x, center_y = (x1 + x2) / 2, (y1 + y2) / 2
       # cv2.arrowedLine(frame, (int(x2), int(y2)), (int(center_x), int(center_y)), (255, 0, 255), 4,
        #                line_type=cv2.LINE_AA, tipLength=0.1)

# save image
cv2.imwrite("result.jpg", frame)
print("save result.jpg")