- #Video codec opencv for mac osx how to
- #Video codec opencv for mac osx software
- #Video codec opencv for mac osx code
write ( frame ) def _clear_old_splits ( self ): for f in glob. last_frame_delay = int (( now - meta ) * 1000 ) if now > self. _clear_old_splits () def write ( self ): if self. _gen_split_name (), "Last_Frame_Delay", self. split_size )) def _start_new_split ( self, frame ): self.
frameno = 0 def _gen_split_name ( self ): return os. Import zmq import cv2 from time import time import os import glob import lz4.frame import pickle class SplitWriter : def _init_ ( self, split_size = 30, pub_address = "tcp://127.0.0.1:5557", directory = '/tmp', split_history = 2, split_prefix = 'split', compressed = True, fps = 30 ): self. The Main PipelineĪ basic implementation of the main pipeline is as follows: Let’s look at the implementation in Python.
#Video codec opencv for mac osx code
Since the transmitted frame takes up a significant amount of memory, the code implements LZ4 frame compression which reduces the network link requirement and enables the transfer of 2K video through the 1Gbit/s channel.
#Video codec opencv for mac osx software
ZeroMQ can be deployed over several different transport backends – unicast or multicast network sockets, Unix sockets, which allows implementing of the distributed data processing easily, without the need for additional software and sophisticated network programming.the first just saves video as is, the second converts FPS and the resolution several additional pipelines can be used mutually in parallel – e.g.the main pipeline will not block even if the additional (ring buffer) pipeline is not launched or broken (the property of Pub/Sub).This mechanism is ideal for solving this problem because: The frame is transferred from the main pipeline to the additional pipeline by the ZeroMQ Pub/Sub. To save video fragments, an additional processing pipeline is used, which is represented by the Sub → Encode → Files chain. Within the shown architecture, there is the main processing pipeline, which is represented by a chain Decode → Pub → Process → DB which performs analytical operations and can itself be represented by a distributed processing graph. The architecture of the solution is presented below: In real implementations, removal of redundant frames from a stream, a change in resolution, etc., may take place. For simplicity, we assume that the FPS for video files is the same as FPS of a stream, that is, all video frames from the stream are written to files. To solve the problem OpenCV, Python, LZ4, and ZeroMQ will be used.
#Video codec opencv for mac osx how to
You will learn how to develop such a ring buffer, which connects to the main video processing pipeline and manages the creation and deletion of video files that form the buffer. The buffer is a ring because old files are deleted from the disk (for example, after 10 minutes have passed), so the buffer always takes a fixed amount of space on the storage. So, when the application detects that some of them contain important signals, it copies the files that include the signal from the ring buffer into the permanent storage. It is convenient to solve this task with a ring buffer of video fragments, presented by small files, for example, for 30 seconds.
A fragment is a set of one or more small video files that contain both the event itself and the history and development of the situation associated with it. In general, a user wants to get an access to a fragment that contains some identified events. The problem described in this article is often met in intelligent video analytics solutions.
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