- DESKTOP FRAME WITH 6 X 9 OPENING HOW TO
- DESKTOP FRAME WITH 6 X 9 OPENING INSTALL
- DESKTOP FRAME WITH 6 X 9 OPENING UPDATE
- DESKTOP FRAME WITH 6 X 9 OPENING CODE
- DESKTOP FRAME WITH 6 X 9 OPENING SERIES
read method on Line 21 returns a 2-tuple containing: On Line 19 we start looping over the frames of our video file.Ī call to the.
DESKTOP FRAME WITH 6 X 9 OPENING UPDATE
# show the frame and update the FPS counter # display a piece of text to the frame (so we can benchmarkĬv2.putText(frame, "Slow Method", (10, 30),Ĭv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) # resize the frame and convert it to grayscale (while stillįrame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # if the frame was not grabbed, then we have reached the end # grab the frame from the threaded video file stream With cv2.VideoCapture instantiated, we can start reading frames from the video file and processing them one-by-one: # loop over frames from the video file stream Line 15 opens a pointer to the -video file using the cv2.VideoCapture class while Line 16 starts a timer that we can use to measure FPS, or more specifically, the throughput rate of our video processing pipeline. We only need a single switch for this script, -video, which is the path to our input video file. Lines 9-12 then parse our command line arguments.
DESKTOP FRAME WITH 6 X 9 OPENING INSTALL
If you don’t already have imutils installed or if you are using a previous version, you can install/upgrade imutils by using the following command: $ pip install -upgrade imutils
DESKTOP FRAME WITH 6 X 9 OPENING SERIES
We’ll be using my imutils library, a series of convenience functions to make image and video processing operations easier with OpenCV and Python. Lines 2-6 import our required Python packages. # open a pointer to the video stream and start the FPS timer # construct the argument parse and parse the argumentsĪp.add_argument("-v", "-video", required=True,
To start, open up a new file, name it read_frames_slow.py, and insert the following code: # import the necessary packages The goal of this section is to obtain a baseline on our video frame processing throughput rate using OpenCV and Python. The slow, naive method to reading video frames with OpenCV To accomplish this latency decrease our goal will be to move the reading and decoding of video file frames to an entirely separate thread of the program, freeing up our main thread to handle the actual image processing.īut before we can appreciate the faster, threaded method to video frame processing, we first need to set a benchmark/baseline with the slower, non-threaded version. read method to finish reading and decoding a frame instead, there is always a pre-decoded frame ready for us to process. This increase in frame processing rate (and therefore our overall video processing pipeline) comes from dramatically reducing latency - we don’t have to wait for the. read method is a blocking operation- the main thread of your Python + OpenCV application is entirely blocked (i.e., stalled) until the frame is read from the video file, decoded, and returned to the calling function.īy moving these blocking I/O operations to a separate thread and maintaining a queue of decoded frames we can actually improve our FPS processing rate by over 52%! The problem (and the reason why this method can feel slow and sluggish) is that you’re both reading and decoding the frame in your main processing thread!Īs I’ve mentioned in previous posts, the. read method of cv2.VideoCapture to poll the next frame from the video file so you can process it in your pipeline. When working with video files and OpenCV you are likely using the cv2.VideoCapture function.įirst, you instantiate your cv2.VideoCapture object by passing in the path to your input video file.
DESKTOP FRAME WITH 6 X 9 OPENING CODE
Looking for the source code to this post? Jump Right To The Downloads Section Faster video file FPS with cv2.VideoCapture and OpenCV
DESKTOP FRAME WITH 6 X 9 OPENING HOW TO
In the remainder of today’s blog post, I’ll demonstrate how to use threading and a queue data structure to improve your video file FPS rate by over 52%!
That’s just computationally wasteful - and there is a better way. The answer is almost always video compression and frame decoding.ĭepending on your video file type, the codecs you have installed, and not to mention, the physical hardware of your machine, much of your video processing pipeline can actually be consumed by reading and decoding the next frame in the video file. read method to poll another frame from your video file? Why, at times, does it seem like an eternity for cv2.VideoCapture and the associated. Your entire video processing pipeline crawls along, unable to process more than one or two frames per second - even though you aren’t doing any type of computationally expensive image processing operations. I’ve been there - and I know exactly how it feels. Have you ever worked with a video file via OpenCV’s cv2.VideoCapture function and found that reading frames just felt slow and sluggish? Click here to download the source code to this post
0 kommentar(er)
