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| params: | ||
| params('alpha', 'max_Niter', 'convergence_limit', 'gaussian_sigma', | ||
| 'derivative_filter', 'use_phases', config=config) | ||
| 'derivative_filter', 'use_phases', 'n_jobs', 'max_padding_iterations', config=config) |
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These parameters would also need to be added to the config_template.yaml file.
I think the number of jobs isn't really a parameter that should be set by the user, but rather set automatically by inferring the available resources, for example, by using multiprocessing.cpu_count()
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Ok we will modify the config_template.yaml file coherently.
It may depend on the specific user and platform.
In addition, multiprocessing.cpu_count() does not differentiate between physical and logical cores. Thus allowing the optical_flow.py script to use all the output of multiprocessing.cpu_count() may not result into computational advantage and may interfere with other processes running.
For this reason we suggest the usage of psutil library and a ratio of psutil.cpu_count(logical = False) output to set the number of parallel processes based on the number of physical cores, in case the user does not set explicitly the n_jobs parameter (which can be set to None as default).
| for i in tqdm(range(len(frames[:-1])), ascii=True)) | ||
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| vector_frames = np.asarray(vector_frames, dtype=complex) | ||
| vector_frames[:,nan_channels[0],nan_channels[1]] = np.nan + np.nan*1j |
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This formulation does not generalize to possible 1D frames
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If the 1D case is represented as a vector-like matrix of shape (dim_x, 1), this notation would still be consistent.
Otherwise if the shape would just be (dim_x), then the script would break here and elsewhere in the code, e.g.
- in many parts of the code we explicitly extract the shape:
dim_y, dim_x = frames[0].shape
- in plot_opticalflow function we assume 2D arrays and explicitly extract the shape:
dim_y, dim_x = vec_frame.shape
And could be not properly defined the algorithm when we compute the derivatives on both the x and y axes:
fx = phase_conv2d(frame, kernelX) \
+ phase_conv2d(next_frame, kernelX)
fy = phase_conv2d(frame, kernelY) \
+ phase_conv2d(next_frame, kernelY)
| return frames | ||
| else: | ||
| grid_y, grid_x = np.meshgrid([-1, 0, 1], [-1, 0, 1], indexing='ij') | ||
| print('Frames interpolation') |
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remove print statement. instead you could set this as a title for tqdm
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Ok, we will add a title to the tqdm progress bar.
| grid_x=grid_x, | ||
| are_phases=are_phases, | ||
| max_iters=max_iters) | ||
| for i in tqdm(range(len(frames)), ascii=True)) |
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tqdm would be a new dependency of cobrawap and would need to be added to the environment and pyproject definition
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Yes, we will update the environment file and the pyproject accordingly.
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Introduction of joblib library to parallelize the execution of interpolate_empty_sites and horn_schunck functions over the different time frames.
Speed up time depends on machine possibility to parallelize multiple processes over the cores.
WARNING: This implementation also fixes the vector_frames size issue taken into account by PR #71.
Please consider to merge PR #71 before this one for result consistency.