jnhg_ai_code/liq_demo/person_speed.py

import cv2
import numpy as np
from ultralytics import YOLO
from collections import defaultdict, deque
import datetime
import time
import math
# Load the YOLO11 model
model = YOLO("yolo11m.pt")

# Open the video file
video_path = r"E:\desktop_file\速度标定\run.mp4"
# video_path = r"E:\wx_file\WeChat Files\wxid_1lcmt2w2jdwl22\FileStorage\File\2024-11\3.4-13时胶乳包装.mp4"
cap = cv2.VideoCapture(video_path)

# 存储最近的200帧用于回溯
frame_buffer = deque(maxlen=100)  # 新增帧缓冲区
# 后续帧收集任务列表，元素格式：(pre_buffer, post_buffer, 剩余帧数, 触发时间)
active_tasks = []
# Store the track history
track_history = defaultdict(lambda: [])
# 用于存储每个 track_id 最近的时间戳
time_stamps = defaultdict(lambda: deque(maxlen=200))  # 固定长度为 50
# 用于存储瞬时速度
instantaneous_velocities = defaultdict(lambda: deque(maxlen=100))


def apply_bias(position):
    """
    偏置函数：使用 x/ln(1+x) 计算偏置
    已弃用
    """
    x, y = position
    bias_x = np.log1p(x) if x > 0 else 0
    bias_y = np.log1p(y) if y > 0 else 0
    return np.array([bias_x, bias_y])


def save_high_speed_video(buffer, trigger_time):
    """将缓冲区中的帧保存为MP4文件"""
    if len(buffer) < 1:
        return

    # 生成唯一文件名
    timestamp = trigger_time.strftime("%Y%m%d%H%M%S%f")
    output_path = f"high_speed_{timestamp}.mp4"

    # 使用MP4编码（需确保OpenCV支持）
    fourcc_mp4 = cv2.VideoWriter_fourcc(*'x264')
    writer = cv2.VideoWriter(output_path, fourcc_mp4, fps, (frame_width, frame_height))

    for frame in buffer:
        writer.write(frame)
    writer.release()


def map_to_ellipse(position):
    x, y = position
    center_x = 640
    center_y = 360
    a = 580
    b = 280

    x_norm = x / 1280
    y_norm = y / 720

    d_norm = math.sqrt((x_norm - 0.5) ** 2 + (y_norm - 0.5) ** 2)
    theta_norm = math.atan2(y_norm - 0.5, x_norm - 0.5)
    f = d_norm
    a_new = a * f
    b_new = b * f

    bias_x = center_x + a_new * math.cos(theta_norm)
    bias_y = center_y + b_new * math.sin(theta_norm)

    return np.array([bias_x, bias_y])

# 创建 VideoWriter 对象以保存输出视频
fourcc = cv2.VideoWriter_fourcc(*'XVID')  # 视频编码格式
output_file = "output_video.avi"  # 输出文件名
fps = 25  # 帧率
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
out = cv2.VideoWriter(output_file, fourcc, fps, (frame_width, frame_height))

speed_threshold = 20  # 速度阈值
high_velocity_count_threshold = 20  # 高速度计数阈值

# Loop through the video frames
while cap.isOpened():
    # 记录当前时间
    current_time = time.time()

    # Read a frame from the video
    success, frame = cap.read()

    if success:
        # 将当前帧加入缓冲区（深拷贝避免覆盖）
        frame_buffer.append(frame.copy())  # 新增

        # 处理后续帧收集任务
        for i in reversed(range(len(active_tasks))):
            pre_buffer, post_buffer, frames_left, trigger_time = active_tasks[i]
            post_buffer.append(frame.copy())
            frames_left -= 1
            if frames_left <= 0:
                combined = pre_buffer + list(post_buffer)
                save_high_speed_video(combined, trigger_time)
                del active_tasks[i]
            else:
                active_tasks[i] = (pre_buffer, post_buffer, frames_left, trigger_time)

        # Run YOLO11 tracking on the frame, persisting tracks between frames
        results = model.track(frame, persist=True, classes=0, conf=0.6)

        if results[0].boxes and results[0].boxes.id is not None:
            # Get the boxes and track IDs
            boxes = results[0].boxes.xywh.cpu()
            track_ids = results[0].boxes.id.int().cpu().tolist()

            for box, track_id in zip(boxes, track_ids):
                x, y, w, h = box

                # 绘制边界框
                cv2.rectangle(frame, (int(x - w / 2), int(y - h / 2)), (int(x + w / 2), int(y + h / 2)), (0, 255, 0), 2)
                # 计算左下角坐标
                bottom_left_x = int(x - w / 2)
                bottom_left_y = int(y + h / 2)

                # 计算中心点
                center_x = int(x)
                center_y = int(y)

                # 绘制中心点
                cv2.circle(frame, (center_x, center_y), 5, (255, 0, 0), -1)  # 红色中心点，半径为 5

                # 记录位置
                track_history[track_id].append((bottom_left_x, bottom_left_y))
                if len(track_history[track_id]) > 100:
                    del track_history[track_id][:-50]  # 维持历史长度

                # 记录每一帧的时间
                time_stamps[track_id].append(current_time)

                # 计算时间间隔
                if len(time_stamps[track_id]) > 1:
                    delta_time = time_stamps[track_id][-1] - time_stamps[track_id][-2]  # 最近两帧的时间差
                else:
                    delta_time = 0

                instantaneous_velocity = 0
                # 计算二维瞬时速度
                if len(track_history[track_id]) >= 2:
                    pos1 = np.array(track_history[track_id][-1])  # 最新位置
                    pos2 = np.array(track_history[track_id][-2])  # 前一个位置

                    pos1 = map_to_ellipse(pos1)
                    pos2 = map_to_ellipse(pos2)
                    distance = np.linalg.norm(pos1 - pos2)

                    # 使用时间间隔进行速度计算
                    instantaneous_velocity = distance / delta_time if delta_time > 0 else np.zeros(2)

                    instantaneous_velocity_magnitude = round(np.linalg.norm(instantaneous_velocity), 1)
                    instantaneous_velocities[track_id].append(instantaneous_velocity_magnitude)
                else:
                    instantaneous_velocity_magnitude = 0

                # 判断是否有足够数量的高速度
                high_velocity_count = sum(1 for velocity in instantaneous_velocities[track_id] if velocity > speed_threshold)

                if high_velocity_count >= high_velocity_count_threshold:

                    # 原逻辑：截图，标红
                    # cv2.putText(frame, str(instantaneous_velocity_magnitude), (int(x), int(y)),
                    #             cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
                    # data_time = str(datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))
                    # file_name = "high_speed_" + data_time + ".jpg"
                    # cv2.imwrite(file_name, frame)

                    # 触发保存逻辑
                    pre_buffer = list(frame_buffer)  # 深拷贝前200帧
                    post_buffer = deque(maxlen=100)
                    trigger_time = datetime.datetime.now()
                    active_tasks.append((pre_buffer, post_buffer, 100, trigger_time))

                    # 新增逻辑：删除超过 speed_threshold 的瞬时速度
                    instantaneous_velocities[track_id] = deque(
                        [velocity for velocity in instantaneous_velocities[track_id] if velocity <= speed_threshold],
                        maxlen=100
                    )
                    # 新增保存视频逻辑
                    data_time = datetime.datetime.now()
                    save_high_speed_video(frame_buffer, data_time)
                else:
                    cv2.putText(frame, str(instantaneous_velocity_magnitude), (int(x), int(y)),
                                cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 121, 23), 2)

            # Save the annotated frame to the output video
            out.write(frame)  # 将处理后的视频帧写入文件

            # Display the annotated frame
            cv2.imshow("YOLO11 Tracking", 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()
out.release()  # 释放 VideoWriter 对象
cv2.destroyAllWindows()