swirlspy.qpf.dl.helpers package¶
Submodules¶
swirlspy.qpf.dl.helpers.gifmaker module¶
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swirlspy.qpf.dl.helpers.gifmaker.
save_gif
(single_seq, fname)¶ Save a single gif consisting of image sequence in single_seq to fname.
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swirlspy.qpf.dl.helpers.gifmaker.
save_gifs
(seq, prefix)¶ Save several gifs.
Parameters: - seq (Shape (num_gifs, IMG_SIZE, IMG_SIZE)) –
- prefix (prefix-idx.gif will be the final filename.) –
swirlspy.qpf.dl.helpers.log_analysis module¶
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swirlspy.qpf.dl.helpers.log_analysis.
parse_log
(file_path, regex)¶ Parameters: - file_path –
- regex –
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swirlspy.qpf.dl.helpers.log_analysis.
remove_duplicates_and_convert_npy
(val_list)¶
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swirlspy.qpf.dl.helpers.log_analysis.
temporal_smoothing
(training_statistics, stride=10, window_size=100)¶ We always assume the first axis in statistics is the iteration
Parameters: - training_statistics –
- stride –
- window_size –
Returns: - smoothed_mean
- smoothed_std
swirlspy.qpf.dl.helpers.msssim module¶
Python implementation of MS-SSIM. Usage: python msssim.py –original_image=original.png –compared_image=distorted.png
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swirlspy.qpf.dl.helpers.msssim.
MultiScaleSSIM
(img1, img2, max_val=255, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03, weights=None)¶ Return the MS-SSIM score between img1 and img2. This function implements Multi-Scale Structural Similarity (MS-SSIM) Image Quality Assessment according to Zhou Wang’s paper, “Multi-scale structural similarity for image quality assessment” (2003). Link: https://ece.uwaterloo.ca/~z70wang/publications/msssim.pdf Author’s MATLAB implementation: http://www.cns.nyu.edu/~lcv/ssim/msssim.zip
Parameters: - img1 (Numpy array holding the first RGB image batch.) –
- img2 (Numpy array holding the second RGB image batch.) –
- max_val (the dynamic range of the images (i.e., the difference between the) – maximum the and minimum allowed values).
- filter_size (Size of blur kernel to use (will be reduced for small images)) –
- filter_sigma (Standard deviation for Gaussian blur kernel (will be reduced) – for small images).
- k1 (Constant used to maintain stability in the SSIM calculation (0.01 in) – the original paper).
- k2 (Constant used to maintain stability in the SSIM calculation (0.03 in) – the original paper).
- weights (List of weights for each level; if none, use five levels and the) – weights from the original paper.
Returns: Return type: MS-SSIM score between img1 and img2.
Raises: RuntimeError: If input images don’t have the same shape or don’t have four – dimensions: [batch_size, height, width, depth].
swirlspy.qpf.dl.helpers.ordered_easydict module¶
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class
swirlspy.qpf.dl.helpers.ordered_easydict.
OrderedEasyDict
(d=None, **kwargs)¶ Bases:
collections.OrderedDict
Using OrderedDict for the easydict package See Also https://pypi.python.org/pypi/easydict/
swirlspy.qpf.dl.helpers.visualization module¶
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swirlspy.qpf.dl.helpers.visualization.
flow_to_img
(flow_dat, max_displacement=None)¶ Convert optical flow data to HSV images
Parameters: - flow_dat (np.ndarray) – Shape: (seq_len, 2, H, W)
- max_displacement (float or None) –
Returns: rgb_dat – Shape: (seq_len, 3, H, W)
Return type: np.ndarray
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swirlspy.qpf.dl.helpers.visualization.
get_color_flow_legend_image
(size=50)¶
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swirlspy.qpf.dl.helpers.visualization.
merge_rgba_cv2
(front_img, back_img)¶ Merge the front image with the background image using the Painter’s algorithm
Parameters: - front_img (np.ndarray) –
- back_img (np.ndarray) –
Returns: result_img
Return type: np.ndarray
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swirlspy.qpf.dl.helpers.visualization.
save_hko_gif
(im_dat, save_path)¶ Save the HKO images to gif
Parameters: - im_dat (np.ndarray) – Shape: (seqlen, H, W)
- save_path (str) –
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swirlspy.qpf.dl.helpers.visualization.
save_hko_movie
(im_dat, datetime_list, mask_dat=None, save_path='hko.mp4', masked=False, fps=5, prediction_start=None)¶ Save the HKO images to a video file
Parameters: - im_dat (np.ndarray) – Shape : (seq_len, H, W)
- datetime_list (list) – list of datetimes
- mask_dat (np.ndarray or None) – Shape : (seq_len, H, W)
- save_path (str) –
- masked (bool) – whether the mask the inputs when saving the image
- fps (float) – the fps of the saved movie
- prediction_start (int or None) – The starting point of the prediction