We also define a buffer size to receive the read data. Note that the used FFmpeg command is slightly changed, to define the channel of choice and the encoding to use.įor more on that you can refer either to 1 or 2. uint16, offset = 8 * 44 ) 21 sig, fs = audio_np, target_fs stderr ) 18 19 # read signal as numpy array and assign sampling rate 20 audio_np = np. run ( ffmpeg_command, 12 stdout = subprocess. 1 import subprocess 2 import numpy as np 3 4 # init command 5 ffmpeg_command = 9 10 # excute ffmpeg command 11 pipe = subprocess.
For mode, you need scipy.stats module (it works on NumPy arrays). It has mean and median functions built-in. If you want to calculate the mask differently, I suggest using NumPy arrays. For simplicity, I saved the shape values to 3 lists and manually calculated the mean ( sum()/len()). For multiple files, you can add a for loop using the iglob iterator to get the HDF5 file names. Here is a basic outline of the process to open 1 HDF5 file and loop thru the datasets (by key name) to get the dataset shape (image size). For reference, the key, value terminology is how h5py refers to HDF5 dataset names and datasets. Do you want to find the average image size (either mean, median, or mode)? If so, it is "relatively straight-forward" (if you know Python) to open each file and get the dimension of the image data (the shape of each dataset - what you call the values). Please clarify what you mean by " find out the final mask that represents the majority". It sounds like you need to do more that that. Links to all HDF5 utilities are at the top of the page: HDF5 Tools You can get more info about h5diff here: h5diff utility.
If you just want to compare the files to see if they are the same, you can use the h5diff utility from The HDF5 Group.
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