![]() You can check whether it is initialized or not by the method cap.isOpened(). Sometimes, cap may not have initialized the capture. So you can check for the end of the video by checking this returned value. If the frame is read correctly, it will be True. We can simplify things even further by creating a derived class that automatically adds an audio and video capture () returns a bool ( True/ False). Data is returned as a dictionary, indexed by a string name for the data source. ![]() The delete and exit logic is added to clean up the camera object – without these the camera is kept open and locked, which can cause problems. ![]() If source._class_._name_ = "VideoSource": Source is a derived class from SensorSourceįor name, source in (): """Object to combine multiple modalities.""" To do this, we create a wrapper object that allows us to iterate through each added sensor data source. Now we have defined sensor data sources, we can combine them so that we only need to perform one read() call to obtain data from all sources. In both cases, the read() method returns a tuple: (data_check_value, data) where the data_check_value is a value returned from the underlying capture objects. When we read the data, we convert the queue to a 16-bit integer numpy array. The struct library is used to decode the binary data from the alsaaudio object and convert it into integer values that we can add to the queue. The update method is run continuously in the background within the thread and adds new samples to the queue over time, with old samples falling off the back of the queue. The deque object acts as a FIFO queue and provides this buffer. In the audio case, we are recording a (time) series of audio samples, so we do this in a buffer of length nb_samples. We set up an audio input source and a threading lock in the _init_ method. The approach for audio is similar to video. Return self.l, np.asarray(self._s_fifo, dtype=np.int16) Raw_smp_l = struct.unpack('h' * self.l, data)į'Sampler error occur (l=)' Self._s_fifo = deque( * nb_samples, maxlen=nb_samples) # set attributes: Mono, frequency, 16 bit little endian samples I had more success with alsaaudio: conda install alsaaudio or pip install alsaaudio.ĭef _init_(self, sample_freq=44100, nb_samples=65536): I couldn’t get this to work in a conda environment due to an issue with the underlying PortAudio library. You’ll see many posts online that use pyaudio for audio capture. ![]() You might also want to set to YUV mode by adding the following to the _init_ method: (16, 0) Audio Also remember OpenCV provides the data in BGR format – so channels 0, 1, 2 correspond to Blue, Green and Red rather than RGB. The exit line means that the camera resource is released when the object is deleted or the Python kernel is stopped so you can then use the camera in other applications.īeware: I had issues setting the width and height so I have commented out those lines. The data may then be (asynchronously) accessed using the read() method on the object. The update method is run as part of the thread to continuously update the ame data. The initialisation sets up the camera and the threading lock. abbed, ame = ()ĭef _exit_(self, exec_type, exc_value, traceback):
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |