（openCV 十四）运动对象跟踪

最新推荐文章于 2024-08-07 19:59:13 发布

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#目标跟踪

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本文介绍了一种基于OpenCV的运动目标检测与跟踪算法。通过使用背景减除、高斯模糊、形态学操作等技术，从实时视频流中检测并跟踪运动目标。详细展示了算法的实现过程，包括获取视频流、背景初始化、图像预处理、差分图像计算、阈值化处理、轮廓检测及矩形框绘制等关键步骤。

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import cv2
import numpy as np

camera = cv2.VideoCapture(0)

es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10))
kernel = np.ones((5, 5), np.uint8)
background = None

while (True):
    ret, frame = camera.read()
    if background is None:
        background = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        background = cv2.GaussianBlur(background, (21, 21), 0)
        continue

    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray_frame = cv2.GaussianBlur(gray_frame, (21, 21), 0)
    diff = cv2.absdiff(background, gray_frame)
    diff = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1]
    diff = cv2.dilate(diff, es, iterations=2)
    image, cnts, hierarchy = cv2.findContours(diff.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    for c in cnts:
        if cv2.contourArea(c) < 1500:
            continue
        (x, y, w, h) = cv2.boundingRect(c)
        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 0), 2)

    cv2.imshow("contours", frame)
    cv2.imshow("dif", diff)
    if  0xff == ord("q"):
        break

cv2.destroyAllWindows()
camera.release()