Opencv play multiple videos


  • Python | Play a video in reverse mode using OpenCV
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  • Creating Video from Images using OpenCV-Python
  • #001 How to read a video and access a webcam with OpenCV in Python?
  • Python | Play a video using OpenCV
  • Python | Play a video in reverse mode using OpenCV

    Click here to download the source code to this post Last updated on July 9, Ever have your car stolen? Mine was stolen over the weekend. Parking is hard to find in our neighborhood, so I was in need of a parking garage. I heard about a garage, signed up, and started parking my car there. Fast forward to this past Sunday. My wife and I arrive at the parking garage to grab my car.

    We were about to head down to Maryland to visit my parents and have some blue crab Maryland is famous for its crabs. I walked to my car and took off the cover. After a few short minutes I realized the reality — my car was stolen.

    While I continue to do paperwork with the police, insurance, etc, you can begin to arm yourself with Raspberry Pi cameras to catch bad guys wherever you live and work.

    Update July Added two new sections. The first section provides suggestions for using Django as an alternative to the Flask web framework. The second section discusses using ImageZMQ to stream live video over a network from multiple camera sources to a single central server. Looking for the source code to this post? Putting all these pieces together results in a home surveillance system capable of performing motion detection and then streaming the video result to your web browser.

    The Flask web framework Figure 1: Flask is a micro web framework for Python image source. Flask is a popular micro web framework written in the Python programming language.

    However, unlike Django, Flask is very lightweight, making it super easy to build basic web applications. The webstreaming. In order for our web browser to have something to display, we need to populate the contents of index.

    By use of background subtraction for motion detection, we have detected motion where I am moving in my chair. Our motion detector algorithm will detect motion by form of background subtraction. We can easily extend this method to handle multiple regions of motion as well.

    Open up the singlemotiondetector. All of these are fairly standard, including NumPy for numerical processing, imutils for our convenience functions, and cv2 for our OpenCV bindings. We then define our SingleMotionDetector class on Line 6. The class accepts an optional argument, accumWeight, which is the factor used to our accumulated weighted average. The larger accumWeight is, the less the background bg will be factored in when accumulating the weighted average. Conversely, the smaller accumWeight is, the more the background bg will be considered when computing the average.

    Otherwise, we compute the weighted average between the input frame, the existing background bg, and our corresponding accumWeight factor. Given our input image we compute the absolute difference between the image and the bg Line A series of erosions and dilations are performed to remove noise and small, localized areas of motion that would otherwise be considered false-positives likely due to reflections or rapid changes in light.

    We then initialize two sets of bookkeeping variables to keep track of the location where any motion is contained Lines 40 and Otherwise, motion does exist in the frame so we need to start looping over the contours Line For each contour we compute the bounding box and then update our bookkeeping variables Lines , finding the minimum and maximum x, y -coordinates that all motion has taken place it.

    Finally, we return the bounding box location to the calling function. Open up the webstreaming. Line 7 imports the threading library to ensure we can support concurrency i. We then create a lock on Line 18 which will be used to ensure thread-safe behavior when updating the ouputFrame i. Line 21 initialize our Flask app itself while Lines access our video stream: If you are using a USB webcam, you can leave the code as is.

    However, if you are using a RPi camera module you should uncomment Line 25 and comment out Line The next function, index, will render our index.

    Our next function is responsible for: Looping over frames from our video stream Applying motion detection Drawing any results on the outputFrame And furthermore, this function must perform all of these operations in a thread safe manner to ensure concurrency is supported. Converting to grayscale. Gaussian blurring to reduce noise. We then grab the current timestamp and draw it on the frame Lines If so, we apply the. If motion is None, then we know no motion has taken place in the current frame.

    Otherwise, if motion is not None Line 67 , then we need to draw the bounding box coordinates of the motion region on the frame. Line 76 updates our motion detection background model while Line 77 increments the total number of frames read from the camera thus far. Finally, Line 81 acquires the lock required to support thread concurrency while Line 82 sets the outputFrame. We need to acquire the lock to ensure the outputFrame variable is not accidentally being read by a client while we are trying to update it.

    Then generate starts an infinite loop on Line 89 that will continue until we kill the script. Inside the loop, we: First acquire the lock Line Ensure the outputFrame is not empty Line 94 , which may happen if a frame is dropped from the camera sensor. Check to see if the success flag has failed Lines and , implying that the JPEG compression failed and we should ignore the frame.

    Finally, serve the encoded JPEG frame as a byte array that can be consumed by a web browser. That was quite a lot of work in a short amount of code, so definitely make sure you review this function a few times to ensure you understand how it works. The app. Your web browser is smart enough to take this byte array and display it in your browser as a live feed.

    ArgumentParser ap. We need three arguments here, including: --ip: The IP address of the system you are launching the webstream. By default, we use 32 frames to build the background model. Lines launch a thread that will be used to perform motion detection. Finally, Lines and launches the Flask app itself.

    The template itself is populated by the Flask web framework and then served to the web browser. Your web browser then takes the generated HTML and renders it to your screen. Our web browser is then smart enough to properly render the webpage and serve up the live video stream. Do not use it in a production deployment. Use a production WSGI server instead. I even pulled out my iPhone and opened a few connections from there.

    Flask is arguably one of the most easy-to-use, lightweight Python web frameworks, and while there are many, many alternatives to build websites with Python, the other super framework you may want to use is Django.

    It definitely takes a bit more code to build websites in Django, but it also includes features that Flask does not, making it a potentially better choice for larger production websites. Alternative methods for video streaming Figure 4: The ImageZMQ library is designed for streaming video efficiently over a network. It is a Python package and integrates with OpenCV.

    The library is designed to pass video frames, from multiple cameras, across a network in real-time. What's next? I recommend PyImageSearch University. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or has to involve complex mathematics and equations? Or requires a degree in computer science? All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms.

    My mission is to change education and how complex Artificial Intelligence topics are taught. If you're serious about learning computer vision, your next stop should be PyImageSearch University, the most comprehensive computer vision, deep learning, and OpenCV course online today.

    Join me in computer vision mastery. Click here to join PyImageSearch University Summary In this tutorial you learned how to stream frames from a server machine to a client web browser.

    Using this web streaming we were able to build a basic security application to monitor a room of our house for motion. Background subtraction is an extremely common method utilized in computer vision. Typically, these algorithms are computationally efficient, making them suitable for resource-constrained devices, such as the Raspberry Pi.

    Furthermore, our implementation supports multiple clients, browsers, or tabs — something that you will not find in most other implementations. To download the source code to this post, and be notified when future posts are published here on PyImageSearch, just enter your email address in the form below!

    Inside you'll find my hand-picked tutorials, books, courses, and libraries to help you master CV and DL! Download the code! All too often I see developers, students, and researchers wasting their time, studying the wrong things, and generally struggling to get started with Computer Vision, Deep Learning, and OpenCV.

    I created this website to show you what I believe is the best possible way to get your start. Reader Interactions.

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    VideoCapture has the device index or the name of a video file. The device index is just an integer to define a which Camera. If we pass 0, then it is for the first or primary camera, 1 for the second camera so on. We capture the video frame by frame.

    OpenCV Open Source Computer Vision is the computer vision library that contains various methods to perform operations on Images or videos. OpenCV library can be used to perform multiple operations on videos. We can do the following task: Read video, display video, and save the Video. Capture from the Camera and display it. We need to create an object of VideoCapture class to capture a video. It accepts either a device index or the name of a video file. A number which is specifying to the Camera is called device index.

    We can select a Camera bypassing the O or 1 as an argument. After that, we can capture the video frame-by-frame. See the following code. In this example, first, we have imported the cv2 module. Then we have created an object of VideoCapture class. To obtain Video from the camera, we have to create an object of class VideoCapture.

    As an input, the constructor of the VideoCapture class receives an index of the device we want to use. If we just have a single camera connected to the computer, we can simply pass a value 0. You can pass one of the following values based on your requirements. VideoCapture 0 : Means first camera or webcam. VideoCapture 1 : Means second camera or webcam. VideoCapture "file name. We do this by calling the read method on the VideoCapture object. This method takes no arguments and returns a tuple.

    The first returned value is a Boolean indicating if a frame was read correctly True or not False. We can use this value for error checking if we want to confirm no problem occurred while reading the frame.

    But for this tutorial demo, we will assume everything went well, so we are not going to look into this value. In the next step, we have used a while loop. Inside the while loop, we are reading the video frame by frame and pass to the cv2. The cv2. The window automatically fits the Video or image size. Until we press the q key or 1 key, it will keep opening a web camera and capture the Video. Load video in Grayscale mode To load webcam video in Grayscale mode, we can use cv2.

    We just have to pass the cv2. It will return True, if the frame is read correctly. How to Play Video from a file in Python To play the video from a file, you have to provide the video path in the VideoCapture method.

    It is similar to capturing from the Camera by changing the camera index with the file name. The time must be appropriate for cv2. If the time is too less, then the Video will be very fast. VideoCapture 'friends. Then we read frame by frame show it to the Python window. To show grayscale video, use cv2.

    Creating Video from Images using OpenCV-Python

    Otherwise, if motion is not None Line 67then we need to draw the bounding box coordinates of the motion region on the frame. Line 76 updates our motion detection background model while Line 77 increments the total number of frames read from the camera thus far. Finally, Line 81 acquires the lock required to support thread concurrency while Line 82 sets the outputFrame. We need to acquire the lock to ensure the outputFrame variable is not accidentally being read by a client while we are trying to update it.

    Then generate starts an infinite loop on Line 89 that will continue until we kill the script. Inside the loop, we: First acquire the lock Line Ensure the outputFrame is not empty Line 94which may happen if a frame is dropped from the camera sensor.

    Check to see if the success flag has failed Lines andimplying that the JPEG compression failed and we should ignore the frame. Finally, serve the encoded JPEG frame as a byte array that can be consumed by a web browser. That was quite a lot of work in a short amount of code, so definitely make sure you review this function a few times to ensure you understand how it works.

    The app. Your web browser is smart enough to take this byte array and display it in your browser as a live feed.

    ArgumentParser ap. We need three arguments here, including: --ip: The IP address of the system you are launching the webstream. By default, we use 32 frames to build the background model. Lines launch a thread that will be used to perform motion detection.

    Finally, Lines and launches the Flask app itself. The template itself is populated by the Flask web framework and then served to the web browser. Your web browser then takes the generated HTML and renders it to your screen.

    Our web browser is then smart enough to properly render the webpage and serve up the live video stream. Do not use it in a production deployment. Use a production WSGI server instead. I even pulled out my iPhone and opened a few connections from there. Flask is arguably one of the most easy-to-use, lightweight Python web frameworks, and while there are many, many alternatives to build websites with Python, the other super framework you may want to use is Django.

    It definitely takes a bit more code to build websites in Django, but it also includes features that Flask does not, making it a potentially better choice for larger production websites.

    Alternative methods for video streaming Figure 4: The ImageZMQ library is designed for streaming video efficiently over a network.

    #001 How to read a video and access a webcam with OpenCV in Python?

    It is a Python package and integrates with OpenCV. The library is designed to pass video frames, from multiple cameras, across a network in real-time. What's next? I recommend PyImageSearch University.

    Python | Play a video using OpenCV

    Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or has to involve complex mathematics and equations? We can do this with a command cap. Our frame is stored in a frame variable and ret is boolean data type that returns True if Python is able to read the VideoCapture object.

    After we finish this process we can release our output with command cap. So, for this example, we recommend to use standalone Python installation e. Anaconda cv2. The code will be similar to the previous example. In particular, you need to define a variable for example gray and use it to store a newly generated frame. Next, we will call a method called cv2. This will convert a color image BGR frame to a grayscale image frame.

    It tells us how many milliseconds to wait for smoother video display. Commonly we chose from 1 to 50 milliseconds. Once, we are finished, it is a good practice to close all windows, by calling a cv2. The windows will be automatically closed. Finally, this code snippet will give us two outputs. The first one will be a color BGR video, whereas the second one will be a grayscale video.

    Output: 3. How to save a video using OpenCV in Python? Video Processing Pipeline using Single Process We will first define a method to process video using a single process. This is the way how we would normally read a video file, process each frame and write the output frames back to the disk. The was the above function works is that the video processing job, that is normally done using one process, is now divided equally amongst the total number of processors available on the executing device.

    If there are 4 processes, and total number of frames in the video to be processed isthen each process gets frames to process, which are executed in parallel. This means that each process will create a separate output file, and so when the video processing is completed, there are 4 different output videos.

    To combine these output videos, we will use FFmpeg. Create a pipeline to run the multiprocessing of video and calculate time to execute and frames processed per second.


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