OpenCV-Python 视频基本操作

WSKH0929
165 阅读2分钟 
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
# 设置兼容中文
plt.rcParams['font.family'] = ['sans-serif']
plt.rcParams['font.sans-serif'] = ['SimHei']

D:\Anaconda\AZWZ\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
D:\Anaconda\AZWZ\lib\site-packages\numpy\.libs\libopenblas.NOIJJG62EMASZI6NYURL6JBKM4EVBGM7.gfortran-win_amd64.dll
D:\Anaconda\AZWZ\lib\site-packages\numpy\.libs\libopenblas.WCDJNK7YVMPZQ2ME2ZZHJJRJ3JIKNDB7.gfortran-win_amd64.dll
  warnings.warn("loaded more than 1 DLL from .libs:\n%s" %

1.视频文件的读取

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-I4ItiooG-1637755673389)(attachment:image.png)]

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-5s26ix61-1637755673391)(attachment:image.png)]

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-9lwfjjn0-1637755673394)(attachment:image.png)]

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-UTKrGdcP-1637755673395)(attachment:image.png)]

# 获取视频对象
cap = cv.VideoCapture('viedo/DOG.wmv')

# 判断是否获取成功
print(cap.isOpened())

True

while(cap.isOpened()):
    # 获取每一帧的图像
    ret,frame = cap.read()
    # 获取成功显示图像
    if ret == True:
        cv.imshow('frame',frame)
    # 每一帧间隔25ms 当视频播放完毕，且按下q键时，会弹出循环
    if cv.waitKey(25) & 0xFF == ord('q'):
        break
# 释放视频对象
cap.release()
cv.destroyAllWindows()

2.视频文件的保存

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-cfAq4FYP-1637755673397)(attachment:image.png)]

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-cDP566f3-1637755673397)(attachment:image.png)]

# 读取视频
cap = cv.VideoCapture('viedo/DOG.wmv')

# 获取视频的宽和高
width = int(cap.get(3))
height = int(cap.get(4))
print("宽：",width,"，高：",height)

宽： 444 ，高： 480

# 创建保存视频的对象，设置编码格式，帧率，图像的宽高等
out = cv.VideoWriter('viedo/outpy.avi',cv.VideoWriter_fourcc('M','J','P','G'),10,(width,height))

# 
while(True):
    ret,frame = cap.read()
    if ret==True:
        # 将每一帧图像写入一个文件中
        out.write(frame)
    else:
        # 读取完毕
        break
# 释放资源
cap.release()
out.release()
cv.destroyAllWindows()
print("保存成功!")

保存成功!

总结

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-Y2og246l-1637755673399)(attachment:image.png)]

3.MeanShift实现视频追踪

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-Ojrfzjny-1637755673399)(attachment:image.png)]

# 获取视频
cap = cv.VideoCapture('viedo/DOG.wmv')
# 获取第一帧图像
ret,frame = cap.read()
# 指定第一帧图像上的目标位置 (行，高，列，宽)
r,h,c,w = 197,141,0,208
track_window = (c,r,w,h)
# 指定目标的感兴趣区域
roi = frame[r:r+h,c:c+w]

# 计算直方图
# 转换色彩空间(HSV)
hsv_roi = cv.cvtColor(roi,cv.COLOR_BGR2HSV)
# 计算直方图
roi_hist = cv.calcHist([hsv_roi],[0],None,[180],[0,180])
# 归一化 min_max归一化
cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)

array([[2.3043747e+00],
       [3.1423289e-01],
       [5.2372152e-01],
       [9.4269872e-01],
       [3.1423289e-01],
       [9.6888477e-01],
       [4.6611214e+00],
       [1.3878620e+00],
       [4.5563769e+00],
       [4.3730745e+00],
       [1.4507086e+01],
       [5.4205173e+01],
       [8.2669441e+01],
       [6.8764633e+01],
       [6.6434074e+01],
       [7.7222733e+01],
       [2.5500000e+02],
       [4.2971348e+01],
       [1.5790203e+01],
       [2.3043747e+00],
       [1.5711645e+00],
       [2.6186076e-01],
       [1.0474430e-01],
       [1.3093038e-01],
       [7.8558221e-02],
       [0.0000000e+00],
       [1.3093038e-01],
       [5.2372150e-02],
       [1.0474430e-01],
       [2.6186075e-02],
       [2.6186076e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [2.0948860e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [2.0948860e-01],
       [0.0000000e+00],
       [3.1423289e-01],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [2.0948860e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [2.0948860e-01],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [1.7806531e+00],
       [8.3795440e-01],
       [6.2846577e-01],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [4.1897721e+00],
       [0.0000000e+00],
       [1.7806531e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.1521873e+00],
       [3.4827480e+00],
       [0.0000000e+00],
       [7.8034506e+00],
       [1.1521873e+00],
       [7.5939620e-01],
       [1.6759088e+00],
       [0.0000000e+00],
       [7.9605670e+00],
       [1.1521873e+00],
       [8.4842882e+00],
       [2.6186075e-02],
       [1.3093038e-01],
       [9.4269872e-01],
       [1.0474430e-01],
       [0.0000000e+00],
       [1.4926063e+00],
       [0.0000000e+00],
       [6.2846577e-01],
       [2.6186075e-02],
       [8.3795440e-01],
       [6.5465188e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [6.0227972e-01],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [2.4614911e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [8.3795440e-01],
       [0.0000000e+00],
       [2.0948860e-01],
       [5.2372152e-01],
       [5.2372152e-01],
       [1.0474430e-01],
       [4.1897720e-01],
       [5.2372152e-01],
       [1.0474430e-01],
       [2.6709797e+00],
       [1.0736290e+00],
       [8.3795440e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.3093038e-01],
       [1.3093038e-01],
       [1.3878620e+00],
       [4.1897720e-01],
       [1.2569315e+00],
       [3.1423289e-01],
       [2.0948860e-01]], dtype=float32)

# 目标追踪
# 设置窗口搜索终止条件，最大迭代次数，窗口中心飘移最小值
term_crit = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT,10,1)

while(True):
    ret,frame = cap.read()
    if ret == True:
        # 计算直方图的反向投影
        hsv = cv.cvtColor(frame,cv.COLOR_BGR2HSV)
        dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)
        # 进行meanshift目标追踪
        ret,track_window = cv.meanShift(dst,track_window,term_crit)
        # 将追踪的位置绘制在视频中
        x,y,w,h = track_window
        res = cv.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2)
        cv.imshow('frame',res)
        # 终止
        if cv.waitKey(60) & 0xFF == ord('q'):
            break
    else:
        break
# 释放资源
cap.release()
cv.destroyAllWindows()

4.CamShift算法实现视频追踪

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-Rdo1qxJ8-1637755673400)(attachment:image.png)]

# 获取视频
cap = cv.VideoCapture('viedo/DOG.wmv')
# 获取第一帧图像
ret,frame = cap.read()
# 指定第一帧图像上的目标位置 (行，高，列，宽)
r,h,c,w = 197,141,0,208
track_window = (c,r,w,h)
# 指定目标的感兴趣区域
roi = frame[r:r+h,c:c+w]

# 计算直方图
# 转换色彩空间(HSV)
hsv_roi = cv.cvtColor(roi,cv.COLOR_BGR2HSV)
# 计算直方图
roi_hist = cv.calcHist([hsv_roi],[0],None,[180],[0,180])
# 归一化 min_max归一化
cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)

array([[2.3043747e+00],
       [3.1423289e-01],
       [5.2372152e-01],
       [9.4269872e-01],
       [3.1423289e-01],
       [9.6888477e-01],
       [4.6611214e+00],
       [1.3878620e+00],
       [4.5563769e+00],
       [4.3730745e+00],
       [1.4507086e+01],
       [5.4205173e+01],
       [8.2669441e+01],
       [6.8764633e+01],
       [6.6434074e+01],
       [7.7222733e+01],
       [2.5500000e+02],
       [4.2971348e+01],
       [1.5790203e+01],
       [2.3043747e+00],
       [1.5711645e+00],
       [2.6186076e-01],
       [1.0474430e-01],
       [1.3093038e-01],
       [7.8558221e-02],
       [0.0000000e+00],
       [1.3093038e-01],
       [5.2372150e-02],
       [1.0474430e-01],
       [2.6186075e-02],
       [2.6186076e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [2.0948860e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [2.0948860e-01],
       [0.0000000e+00],
       [3.1423289e-01],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [2.0948860e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [2.0948860e-01],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [1.7806531e+00],
       [8.3795440e-01],
       [6.2846577e-01],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [4.1897721e+00],
       [0.0000000e+00],
       [1.7806531e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.1521873e+00],
       [3.4827480e+00],
       [0.0000000e+00],
       [7.8034506e+00],
       [1.1521873e+00],
       [7.5939620e-01],
       [1.6759088e+00],
       [0.0000000e+00],
       [7.9605670e+00],
       [1.1521873e+00],
       [8.4842882e+00],
       [2.6186075e-02],
       [1.3093038e-01],
       [9.4269872e-01],
       [1.0474430e-01],
       [0.0000000e+00],
       [1.4926063e+00],
       [0.0000000e+00],
       [6.2846577e-01],
       [2.6186075e-02],
       [8.3795440e-01],
       [6.5465188e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [6.0227972e-01],
       [0.0000000e+00],
       [1.0474430e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [2.4614911e+00],
       [0.0000000e+00],
       [1.0474430e-01],
       [8.3795440e-01],
       [0.0000000e+00],
       [2.0948860e-01],
       [5.2372152e-01],
       [5.2372152e-01],
       [1.0474430e-01],
       [4.1897720e-01],
       [5.2372152e-01],
       [1.0474430e-01],
       [2.6709797e+00],
       [1.0736290e+00],
       [8.3795440e-01],
       [0.0000000e+00],
       [0.0000000e+00],
       [0.0000000e+00],
       [1.3093038e-01],
       [1.3093038e-01],
       [1.3878620e+00],
       [4.1897720e-01],
       [1.2569315e+00],
       [3.1423289e-01],
       [2.0948860e-01]], dtype=float32)

# 目标追踪
# 设置窗口搜索终止条件，最大迭代次数，窗口中心飘移最小值
term_crit = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT,10,1)

while(True):
    ret,frame = cap.read()
    if ret == True:
        # 计算直方图的反向投影
        hsv = cv.cvtColor(frame,cv.COLOR_BGR2HSV)
        dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)
        # 进行meanshift目标追踪
        ret,track_window = cv.CamShift(dst,track_window,term_crit)
        # 将追踪的位置绘制在视频中
        pts = cv.boxPoints(ret)
        pts = np.int0(pts)
        res = cv.polylines(frame,[pts],True,255,2)
        cv.imshow('frame',res)
        # 终止
        if cv.waitKey(60) & 0xFF == ord('q'):
            break
    else:
        break
# 释放资源
cap.release()
cv.destroyAllWindows()

总结

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-pasHYwxz-1637755673400)(attachment:image.png)]

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-AZPV71Mc-1637755673402)(attachment:image.png)]

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