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| import cv2 | |
| import mediapipe as mp | |
| import time | |
| import matplotlib.pyplot as plt | |
| import math | |
| import numpy as np | |
| import pandas as pd | |
| from moviepy.video.io.bindings import mplfig_to_npimage | |
| import os | |
| plt.rcParams['font.sans-serif'] = ['SimHei'] | |
| # 导入姿态跟踪方法 | |
| mpPose = mp.solutions.pose # 姿态识别方法 | |
| pose = mpPose.Pose(static_image_mode=False, # 静态图模式，False代表置信度高时继续跟踪，True代表实时跟踪检测新的结果 | |
| #upper_body_only=False, # 是否只检测上半身 | |
| smooth_landmarks=True, # 平滑，一般为True | |
| min_detection_confidence=0.5, # 检测置信度 | |
| min_tracking_confidence=0.5) # 跟踪置信度 | |
| # 检测置信度大于0.5代表检测到了，若此时跟踪置信度大于0.5就继续跟踪，小于就沿用上一次，避免一次又一次重复使用模型 | |
| # 导入绘图方法 | |
| mpDraw = mp.solutions.drawing_utils | |
| # 定义一个处理视频的函数 | |
| def video_processor(video): | |
| # 创建一个VideoCapture对象 | |
| cap = cv2.VideoCapture(video) | |
| number_of_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) | |
| width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
| height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
| fps = cap.get(cv2.CAP_PROP_FPS) | |
| d = os.path.dirname(video) | |
| # 创建一个VideoWriter对象，输出为mp4格式 | |
| for fourcc in cv2.VideoWriter_fourcc(*'ALL'): | |
| print(f'{fourcc:04X}: {cv2.VideoWriter_fourccText(fourcc)}') | |
| fourcc = cv2.VideoWriter_fourcc(*'avc1') | |
| out = cv2.VideoWriter(os.path.join(d,'output.mp4'), fourcc, fps, (width, height)) | |
| print(os.path.exists(os.path.join(d,'output.mp4'))) | |
| # 创建一个图形对象 | |
| fig, ax = plt.subplots(figsize=(3, 3)) | |
| # ax.set_position((0.1, 0.1, 0.8, 0.4)) | |
| ax.set_xlim(0, number_of_frames) | |
| ax.set_ylim(-10, 200) | |
| ax.set_title('左腿夹角') | |
| ax.set_xlabel('Time') | |
| ax.set_ylabel('Angle') | |
| line, = ax.plot([], [], 'r-') | |
| # 定义更新曲线图的函数 | |
| def update_graph(frame, angle): | |
| # 获取当前时间 | |
| time = frame | |
| # 添加新的数据点 | |
| line.set_xdata(np.append(line.get_xdata(), time)) | |
| line.set_ydata(np.append(line.get_ydata(), angle)) | |
| # 返回更新后的曲线 | |
| return line | |
| # 处理每一帧图像 | |
| # lmlist = [] # 存放人体关键点信息 | |
| left_23 = [] # 存 左髋关节的点 | |
| left_25 = [] # 存 左髋关节的点 | |
| a = [] # 存着所有角度 | |
| def angle(p1, p2): | |
| x1, y1 = p1 | |
| x2, y2 = p2 | |
| dx = x2 - x1 | |
| dy = y2 - y1 | |
| return math.degrees(math.atan2(dy, dx)) | |
| frame_count = 0 # 记录帧数 | |
| while True: | |
| # 读取一帧图像 | |
| success, img = cap.read() | |
| if not success: | |
| print('视频结束') | |
| break | |
| frame_count += 1 | |
| # 将图像转为RGB格式 | |
| imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| # 将图像传给姿态识别模型 | |
| results = pose.process(imgRGB) | |
| # 如果检测到体态就执行下面内容，没检测到就不执行 | |
| if results.pose_landmarks: | |
| # 绘制姿态坐标点 | |
| mpDraw.draw_landmarks(img, results.pose_landmarks, mpPose.POSE_CONNECTIONS) | |
| # 获取32个人体关键点坐标, index记录是第几个关键点 | |
| for index, lm in enumerate(results.pose_landmarks.landmark): | |
| # 保存每帧图像的宽、高、通道数 | |
| h, w, c = img.shape | |
| # 转换为像素坐标 | |
| cx, cy = int(lm.x * w), int(lm.y * h) | |
| # 保存坐标信息 | |
| # lmlist.append((cx, cy)) | |
| if index == 23: | |
| left_23.append([cx, cy]) | |
| if index == 25: | |
| left_25.append([cx, cy]) | |
| # 在关键点上画圆圈 | |
| cv2.circle(img, (cx,cy), 3, (0,255,0), cv2.FILLED) | |
| # 计算左腿夹角 | |
| if len(left_23) != 0 and len(left_25) != 0: | |
| a.append(angle(left_23[-1],left_25[-1])) | |
| # 更新曲线图 | |
| update_graph(frame_count, a[-1]) | |
| # 将图像转换为numpy数组 | |
| graph = mplfig_to_npimage(fig) | |
| # 将曲线图叠加到视频上 | |
| img[-graph.shape[0]:,-graph.shape[1]:,:] = graph | |
| # 写入新的视频文件 | |
| out.write(img) | |
| # 释放资源 | |
| out.release() | |
| cap.release() | |
| # 返回输出视频的路径 | |
| print(video) | |
| print(os.path.join(d,'output.mp4')) | |
| print(os.path.exists(os.path.join(d,'output.mp4'))) | |
| return os.path.join(d,'output.mp4') | |
| import gradio as gr | |
| demo = gr.Interface(fn=video_processor, title='滑板招式姿态识别', | |
| inputs=gr.Video(source="upload"), outputs=gr.Video() | |
| # examples=["Ollie120.mp4"] | |
| ) | |
| demo.launch() | |