Question

我正在研究我的MSc项目，该项目正在研究在数字文件存储中自动删除低价值内容。我特别关注在自然历史拍摄中经常出现的那种长镜头，其中静态相机被滚动以捕捉稀有雪豹或其他任何东西。这些镜头可能只有大约60个有用的内容，可能有几个小时无用的内容。

作为第一步，我有一个简单的动作检测程序来自Adrian Rosebrock的教程[http://www.pyimagesearch.com/2015/05/25/basic-motion-detection-and-tracking-with-python-and-opencv/#comment-393376]。接下来我打算使用FFMPEG来分割视频。

我想要帮助的是如何根据视频中检测到运动的第一个和最后一个点进出点。

如果您希望看到以下代码......

# import the necessary packages
import argparse
import datetime
import imutils
import time
import cv2

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the video file")
ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
args = vars(ap.parse_args())

# if the video argument is None, then we are reading from webcam
if args.get("video", None) is None:
camera = cv2.VideoCapture(0)
time.sleep(0.25)

# otherwise, we are reading from a video file
else:
    camera = cv2.VideoCapture(args["video"])

# initialize the first frame in the video stream
firstFrame = None

# loop over the frames of the video
while True:
    # grab the current frame and initialize the occupied/unoccupied
    # text
    (grabbed, frame) = camera.read()
    text = "Unoccupied"

    # if the frame could not be grabbed, then we have reached the end
    # of the video
    if not grabbed:
        break

    # resize the frame, convert it to grayscale, and blur it
    frame = imutils.resize(frame, width=500)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (21, 21), 0)

    # if the first frame is None, initialize it
    if firstFrame is None:
        firstFrame = gray
        continue

    # compute the absolute difference between the current frame and
    # first frame
    frameDelta = cv2.absdiff(firstFrame, gray)
    thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]

    # dilate the thresholded image to fill in holes, then find contours
    # on thresholded image
    thresh = cv2.dilate(thresh, None, iterations=2)
    (_, cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # loop over the contours
    for c in cnts:
        # if the contour is too small, ignore it
        if cv2.contourArea(c) < args["min_area"]:
            continue

        # compute the bounding box for the contour, draw it on the frame,
        # and update the text
        (x, y, w, h) = cv2.boundingRect(c)
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
        text = "Occupied"

    # draw the text and timestamp on the frame
    cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
    cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
        (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)

    # show the frame and record if the user presses a key
    cv2.imshow("Security Feed", frame)
    cv2.imshow("Thresh", thresh)
    cv2.imshow("Frame Delta", frameDelta)
    key = cv2.waitKey(1) & 0xFF

    # if the `q` key is pressed, break from the lop
    if key == ord("q"):
        break

# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()

如何使用python / OpenCV获取视频分割的时间标记

0 个答案: