三天从YOLOV8关键点检测入门到实战（第二天）——用python调用YOLOV8预测图片并解析结果_yolov8打印关键点信息解析出来

1引用

[1] 同济子豪兄的github项目
[2] 小破站关键点检测视频

本节所用是调用yolov8的函数完成预测，并使用python解析预测结果，并绘制预测结果。

2 YOLOV8预训练模型预测-Python API-图像

2.1 导入相关包，检验GPU是否可用

from ultralytics import YOLO

import cv2
import matplotlib.pyplot as plt
%matplotlib inline

import torch
# 有 GPU 就用 GPU，没有就用 CPU
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('device:', device)
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2.2 载入模型

model = YOLO('yolov8n-pose.pt')
# 切换计算设备
model.to(device)
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2.3 预测

img_path = 'images/two_runners.jpg'
results = model(img_path)

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2.4 解析目标检测预测结果

2.5解析关键点检测预测结果

2.6 OpenCV可视化关键点

# 框（rectangle）可视化配置
bbox_color = (150, 0, 0)             # 框的 BGR 颜色
bbox_thickness = 6                   # 框的线宽

# 框类别文字
bbox_labelstr = {
    'font_size':6,         # 字体大小
    'font_thickness':14,   # 字体粗细
    'offset_x':0,          # X 方向，文字偏移距离，向右为正
    'offset_y':-80,        # Y 方向，文字偏移距离，向下为正
}
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'
运行
运行

# 关键点 BGR 配色
kpt_color_map = {
    0:{'name':'Nose', 'color':[0, 0, 255], 'radius':25},                # 鼻尖
    1:{'name':'Right Eye', 'color':[255, 0, 0], 'radius':25},           # 右边眼睛
    2:{'name':'Left Eye', 'color':[255, 0, 0], 'radius':25},            # 左边眼睛
    3:{'name':'Right Ear', 'color':[0, 255, 0], 'radius':25},           # 右边耳朵
    4:{'name':'Left Ear', 'color':[0, 255, 0], 'radius':25},            # 左边耳朵
    5:{'name':'Right Shoulder', 'color':[193, 182, 255], 'radius':25},  # 右边肩膀
    6:{'name':'Left Shoulder', 'color':[193, 182, 255], 'radius':25},   # 左边肩膀
    7:{'name':'Right Elbow', 'color':[16, 144, 247], 'radius':25},      # 右侧胳膊肘
    8:{'name':'Left Elbow', 'color':[16, 144, 247], 'radius':25},       # 左侧胳膊肘
    9:{'name':'Right Wrist', 'color':[1, 240, 255], 'radius':25},       # 右侧手腕
    10:{'name':'Left Wrist', 'color':[1, 240, 255], 'radius':25},       # 左侧手腕
    11:{'name':'Right Hip', 'color':[140, 47, 240], 'radius':25},       # 右侧胯
    12:{'name':'Left Hip', 'color':[140, 47, 240], 'radius':25},        # 左侧胯
    13:{'name':'Right Knee', 'color':[223, 155, 60], 'radius':25},      # 右侧膝盖
    14:{'name':'Left Knee', 'color':[223, 155, 60], 'radius':25},       # 左侧膝盖
    15:{'name':'Right Ankle', 'color':[139, 0, 0], 'radius':25},        # 右侧脚踝
    16:{'name':'Left Ankle', 'color':[139, 0, 0], 'radius':25},         # 左侧脚踝
}

# 点类别文字
kpt_labelstr = {
    'font_size':4,             # 字体大小
    'font_thickness':10,       # 字体粗细
    'offset_x':0,             # X 方向，文字偏移距离，向右为正
    'offset_y':150,            # Y 方向，文字偏移距离，向下为正
}

# 骨架连接 BGR 配色
skeleton_map = [
    {'srt_kpt_id':15, 'dst_kpt_id':13, 'color':[0, 100, 255], 'thickness':5},       # 右侧脚踝-右侧膝盖
    {'srt_kpt_id':13, 'dst_kpt_id':11, 'color':[0, 255, 0], 'thickness':5},         # 右侧膝盖-右侧胯
    {'srt_kpt_id':16, 'dst_kpt_id':14, 'color':[255, 0, 0], 'thickness':5},         # 左侧脚踝-左侧膝盖
    {'srt_kpt_id':14, 'dst_kpt_id':12, 'color':[0, 0, 255], 'thickness':5},         # 左侧膝盖-左侧胯
    {'srt_kpt_id':11, 'dst_kpt_id':12, 'color':[122, 160, 255], 'thickness':5},     # 右侧胯-左侧胯
    {'srt_kpt_id':5, 'dst_kpt_id':11, 'color':[139, 0, 139], 'thickness':5},        # 右边肩膀-右侧胯
    {'srt_kpt_id':6, 'dst_kpt_id':12, 'color':[237, 149, 100], 'thickness':5},      # 左边肩膀-左侧胯
    {'srt_kpt_id':5, 'dst_kpt_id':6, 'color':[152, 251, 152], 'thickness':5},       # 右边肩膀-左边肩膀
    {'srt_kpt_id':5, 'dst_kpt_id':7, 'color':[148, 0, 69], 'thickness':5},          # 右边肩膀-右侧胳膊肘
    {'srt_kpt_id':6, 'dst_kpt_id':8, 'color':[0, 75, 255], 'thickness':5},          # 左边肩膀-左侧胳膊肘
    {'srt_kpt_id':7, 'dst_kpt_id':9, 'color':[56, 230, 25], 'thickness':5},         # 右侧胳膊肘-右侧手腕
    {'srt_kpt_id':8, 'dst_kpt_id':10, 'color':[0,240, 240], 'thickness':5},         # 左侧胳膊肘-左侧手腕
    {'srt_kpt_id':1, 'dst_kpt_id':2, 'color':[224,255, 255], 'thickness':5},        # 右边眼睛-左边眼睛
    {'srt_kpt_id':0, 'dst_kpt_id':1, 'color':[47,255, 173], 'thickness':5},         # 鼻尖-左边眼睛
    {'srt_kpt_id':0, 'dst_kpt_id':2, 'color':[203,192,255], 'thickness':5},         # 鼻尖-左边眼睛
    {'srt_kpt_id':1, 'dst_kpt_id':3, 'color':[196, 75, 255], 'thickness':5},        # 右边眼睛-右边耳朵
    {'srt_kpt_id':2, 'dst_kpt_id':4, 'color':[86, 0, 25], 'thickness':5},           # 左边眼睛-左边耳朵
    {'srt_kpt_id':3, 'dst_kpt_id':5, 'color':[255,255, 0], 'thickness':5},          # 右边耳朵-右边肩膀
    {'srt_kpt_id':4, 'dst_kpt_id':6, 'color':[255, 18, 200], 'thickness':5}         # 左边耳朵-左边肩膀
]
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for idx in range(num_bbox): # 遍历每个框
    
    # 获取该框坐标
    bbox_xyxy = bboxes_xyxy[idx] 
    
    # 获取框的预测类别（对于关键点检测，只有一个类别）
    bbox_label = results[0].names[0]
    
    # 画框
    img_bgr = cv2.rectangle(img_bgr, (bbox_xyxy[0], bbox_xyxy[1]), (bbox_xyxy[2], bbox_xyxy[3]), bbox_color, bbox_thickness)
    
    # 写框类别文字：图片，文字字符串，文字左上角坐标，字体，字体大小，颜色，字体粗细
    img_bgr = cv2.putText(img_bgr, bbox_label, (bbox_xyxy[0]+bbox_labelstr['offset_x'], bbox_xyxy[1]+bbox_labelstr['offset_y']), cv2.FONT_HERSHEY_SIMPLEX, bbox_labelstr['font_size'], bbox_color, bbox_labelstr['font_thickness'])
    
    bbox_keypoints = bboxes_keypoints[idx] # 该框所有关键点坐标和置信度
    
    # 画该框的骨架连接
    for skeleton in skeleton_map:
        
        # 获取起始点坐标
        srt_kpt_id = skeleton['srt_kpt_id']
        srt_kpt_x = bbox_keypoints[srt_kpt_id][0]
        srt_kpt_y = bbox_keypoints[srt_kpt_id][1]
        
        # 获取终止点坐标
        dst_kpt_id = skeleton['dst_kpt_id']
        dst_kpt_x = bbox_keypoints[dst_kpt_id][0]
        dst_kpt_y = bbox_keypoints[dst_kpt_id][1]
        
        # 获取骨架连接颜色
        skeleton_color = skeleton['color']
        
        # 获取骨架连接线宽
        skeleton_thickness = skeleton['thickness']
        
        # 画骨架连接
        img_bgr = cv2.line(img_bgr, (srt_kpt_x, srt_kpt_y),(dst_kpt_x, dst_kpt_y),color=skeleton_color,thickness=skeleton_thickness)
        
    # 画该框的关键点
    for kpt_id in kpt_color_map:
        
        # 获取该关键点的颜色、半径、XY坐标
        kpt_color = kpt_color_map[kpt_id]['color']
        kpt_radius = kpt_color_map[kpt_id]['radius']
        kpt_x = bbox_keypoints[kpt_id][0]
        kpt_y = bbox_keypoints[kpt_id][1]
        
        # 画圆：图片、XY坐标、半径、颜色、线宽（-1为填充）
        img_bgr = cv2.circle(img_bgr, (kpt_x, kpt_y), kpt_radius, kpt_color, -1)
        
        # 写关键点类别文字：图片，文字字符串，文字左上角坐标，字体，字体大小，颜色，字体粗细
        kpt_label = str(kpt_id) # 写关键点类别 ID（二选一）
        # kpt_label = str(kpt_color_map[kpt_id]['name']) # 写关键点类别名称（二选一）
        img_bgr = cv2.putText(img_bgr, kpt_label, (kpt_x+kpt_labelstr['offset_x'], kpt_y+kpt_labelstr['offset_y']), cv2.FONT_HERSHEY_SIMPLEX, kpt_labelstr['font_size'], kpt_color, kpt_labelstr['font_thickness'])
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3相关文章

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[2] 三天从YOLOV8关键点检测入门到实战（第二天）——用python调用YOLOV8预测图片并解析结果
[3]三天从YOLOV8关键点检测入门到实战（第二天）——用python调用YOLOV8预测视频并解析结果
[4]三天从YOLOV8关键点检测入门到实战（第三天）——用onnx部署yolov8