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- </head>
- <body>
- <main>
- <article id="content">
- <header>
- <h1 class="title">Module <code>src.load_tracking</code></h1>
- </header>
- <section id="section-intro">
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">import os
- import sys
- from copy import deepcopy
- from os.path import join as oj
- sys.path.append('..')
- import mat4py
- import numpy as np
- import pandas as pd
- from matplotlib import pyplot as plt
- try:
- from skimage.external.tifffile import imread
- except:
- from skimage.io import imread
- pd.options.mode.chained_assignment = None # default='warn' - caution: this turns off setting with copy warning
- import pickle as pkl
- from viz import *
- import math
- import config
- from tqdm import tqdm
- def get_tracks(data_dir, split=None, pixel_data=False, video_data=False,
- processed_tracks_file=oj(config.DIR_TRACKS, 'tracks.pkl'),
- dset='orig'):
- '''Read and save tracks tracks from folders within data_dir into a dataframe
- Assumes (matlab) tracking has been run
- '''
- processed_tracks_file = processed_tracks_file[:-4] + '_' + dset + '.pkl'
- print('\t', processed_tracks_file, data_dir)
- if os.path.exists(processed_tracks_file):
- print('\tusing cached tracks!')
- return pd.read_pickle(processed_tracks_file)
- dfs = []
- if split['feature_selection'] is None:
- split = None
- if split is not None:
- flatten = lambda l: [item for sublist in l for item in sublist]
- split = flatten(split.values())
- # 2 directories of naming
- for upper_dir in sorted(os.listdir(data_dir)):
- print('dirs', upper_dir)
- if upper_dir.startswith('.') or 'Icon' in upper_dir:
- continue
- for cell_dir in sorted(os.listdir(oj(data_dir, upper_dir))):
-
- print('\t', cell_dir)
- if not 'Cell' in cell_dir:
- continue
- cell_num = oj(upper_dir, cell_dir.replace('Cell', '').replace('_1s', ''))
- if split is not None:
- if not cell_num in split:
- continue
- full_dir = f'{data_dir}/{upper_dir}/{cell_dir}'
- fname = full_dir + '/TagRFP/Tracking/ProcessedTracks.mat'
- print('\t', cell_num)
-
- # fname_image = oj(data_dir, upper_dir, cell_dir)
- mat = mat4py.loadmat(fname)
- tracks = mat['tracks']
- n = len(tracks['t'])
-
-
- # basic features
- t = np.array([tracks['t'][i] for i in range(n)])
- data = {
- 'lifetime': tracks['lifetime_s'],
- 'cell_num': [cell_num] * n,
- 'catIdx': tracks['catIdx'],
- 't': [t[i][0] for i in range(n)],
- }
-
-
- # displacement features
- totalDisplacement = []
- msd = [] # mean squared displacement
- for i in range(n):
- try:
- totalDisplacement.append(tracks['MotionAnalysis'][i]['totalDisplacement'])
- except:
- totalDisplacement.append(0)
- try:
- msd.append(np.nanmax(tracks['MotionAnalysis'][i]['MSD']))
- except:
- msd.append(0)
- data['mean_total_displacement'] = [totalDisplacement[i] / tracks['lifetime_s'][i] for i in range(n)]
- data['mean_square_displacement'] = msd
-
- # position features
- x_pos_seq = np.array(
- [tracks['x'][i][0] for i in range(n)]) # x-position for clathrin (auxilin is very similar)
- y_pos_seq = np.array(
- [tracks['y'][i][0] for i in range(n)]) # y-position for clathrin (auxilin is very similar)
- data['x_pos_seq'] = x_pos_seq
- data['y_pos_seq'] = y_pos_seq
- data['x_pos'] = [sum(x) / len(x) for x in x_pos_seq] # mean position in the image
- data['y_pos'] = [sum(y) / len(y) for y in y_pos_seq]
-
- # track features
- num_channels = len(tracks['A'][0])
- for idx_channel, prefix in zip(range(num_channels),
- ['X', 'Y', 'Z'][:num_channels]):
- # print(tracks.keys())
- track = np.array([tracks['A'][i][idx_channel] for i in range(n)])
- cs = np.array([tracks['c'][i][idx_channel] for i in range(n)])
- # print('track keys', tracks.keys())
- pvals = np.array([tracks['pval_Ar'][i][idx_channel] for i in range(n)])
- stds = np.array([tracks['A_pstd'][i][idx_channel] for i in range(n)])
- sigmas = np.array([tracks['sigma_r'][i][idx_channel] for i in range(n)])
- data[prefix + '_pvals'] = pvals
- starts = []
- starts_p = []
- starts_c = []
- starts_s = []
- starts_sig = []
- for d in tracks['startBuffer']:
- if len(d) == 0:
- starts.append([])
- starts_p.append([])
- starts_c.append([])
- starts_s.append([])
- starts_sig.append([])
- else:
- # print('buffkeys', d.keys())
- starts.append(d['A'][idx_channel])
- starts_p.append(d['pval_Ar'][idx_channel])
- starts_c.append(d['c'][idx_channel])
- starts_s.append(d['A_pstd'][idx_channel])
- starts_sig.append(d['sigma_r'][idx_channel])
- ends = []
- ends_p = []
- ends_c = []
- ends_s = []
- ends_sig = []
- for d in tracks['endBuffer']:
- if len(d) == 0:
- ends.append([])
- ends_p.append([])
- ends_c.append([])
- ends_s.append([])
- ends_sig.append([])
- else:
- ends.append(d['A'][idx_channel])
- ends_p.append(d['pval_Ar'][idx_channel])
- ends_c.append(d['c'][idx_channel])
- ends_s.append(d['A_pstd'][idx_channel])
- ends_sig.append(d['sigma_r'][idx_channel])
- # if prefix == 'X':
- data[prefix + '_extended'] = [starts[i] + track[i] + ends[i] for i in range(n)]
- data[prefix + '_pvals_extended'] = [starts_p[i] + pvals[i] + ends_p[i] for i in range(n)]
- data[prefix] = track
- data[prefix + '_c_extended'] = [starts_c[i] + cs[i] + ends_c[i] for i in range(n)]
- data[prefix + '_std_extended'] = [starts_s[i] + stds[i] + ends_s[i] for i in range(n)]
- data[prefix + '_sigma_extended'] = [starts_sig[i] + sigmas[i] + ends_sig[i] for i in range(n)]
- data[prefix + '_starts'] = starts
- data[prefix + '_ends'] = ends
- data['lifetime_extended'] = [len(x) for x in data['X_extended']]
- # pixel features
- if pixel_data:
- cla, aux = get_images(full_dir)
- pixel = np.array([[cla[int(t[i][j]), int(y_pos_seq[i][j]), int(x_pos_seq[i][j])]
- if not math.isnan(t[i][j]) else 0 for j in range(len(tracks['t'][i]))]
- for i in range(n)])
- pixel_up = np.array(
- [[cla[int(t[i][j]), min(int(y_pos_seq[i][j] + 1), cla.shape[1] - 1), int(x_pos_seq[i][j])]
- if not math.isnan(t[i][j]) else 0 for j in range(len(tracks['t'][i]))]
- for i in range(n)])
- pixel_down = np.array([[cla[int(t[i][j]), max(int(y_pos_seq[i][j] - 1), 0), int(x_pos_seq[i][j])]
- if not math.isnan(t[i][j]) else 0 for j in range(len(tracks['t'][i]))]
- for i in range(n)])
- pixel_left = np.array([[cla[int(t[i][j]), int(y_pos_seq[i][j]), max(int(x_pos_seq[i][j] - 1), 0)]
- if not math.isnan(t[i][j]) else 0 for j in range(len(tracks['t'][i]))]
- for i in range(n)])
- pixel_right = np.array(
- [[cla[int(t[i][j]), int(y_pos_seq[i][j]), min(int(x_pos_seq[i][j] + 1), cla.shape[2] - 1)]
- if not math.isnan(t[i][j]) else 0 for j in range(len(tracks['t'][i]))]
- for i in range(n)])
- data['pixel'] = pixel
- data['pixel_left'] = pixel_left
- data['pixel_right'] = pixel_right
- data['pixel_up'] = pixel_up
- data['pixel_down'] = pixel_down
- data['center_max'] = [max(pixel[i]) for i in range(n)]
- data['left_max'] = [max(pixel_left[i]) for i in range(n)]
- data['right_max'] = [max(pixel_right[i]) for i in range(n)]
- data['up_max'] = [max(pixel_up[i]) for i in range(n)]
- data['down_max'] = [max(pixel_down[i]) for i in range(n)]
-
- if video_data:
-
- # load video data
-
- X_video = []
- square_size = 10
- cla, aux = get_images(full_dir)
- for i in (range(n)):
- # only extract videos if lifetime > 15
- if data['lifetime'][i] >= 15:
-
- # range of positions of track
- x_pos_max, x_pos_min = int(max(data['x_pos_seq'][i])), int(min(data['x_pos_seq'][i]))
- y_pos_max, y_pos_min = int(max(data['y_pos_seq'][i])), int(min(data['y_pos_seq'][i]))
-
- # crop videos to 10X10 square
- # e.g. if x_pos_max = 52, x_pos_min = 48, then take x_left = 45, x_right = 54, etc.
- if x_pos_max - x_pos_min < square_size:
-
- x_left, x_right = int((x_pos_max + x_pos_min - square_size + 1) / 2), \
- int((x_pos_max + x_pos_min + square_size - 1) / 2)
- if x_left < 0:
- x_left, x_right = 0, square_size - 1
- if x_right > cla.shape[2] - 1:
- x_left, x_right = cla.shape[2] - square_size, cla.shape[2] - 1
-
- else:
- x_left, x_right = int((x_pos_max + x_pos_min - square_size + 1) / 2), \
- int((x_pos_max + x_pos_min + square_size - 1) / 2)
- if y_pos_max - y_pos_min < square_size:
-
- y_left, y_right = int((y_pos_max + y_pos_min - square_size + 1) / 2), \
- int((y_pos_max + y_pos_min + square_size - 1) / 2)
- if y_left < 0:
- y_left, y_right = 0, square_size - 1
- if y_right > cla.shape[1] - 1:
- y_left, y_right = cla.shape[1] - square_size, cla.shape[1] - 1
-
- else:
- y_left, y_right = int((y_pos_max + y_pos_min - square_size + 1) / 2), \
- int((y_pos_max + y_pos_min + square_size - 1) / 2)
-
- video = cla[int(np.nanmin(t[i])):int(np.nanmax(t[i]) + 1), :, :][:, y_left:(y_right + 1), :][:, :, x_left:(x_right + 1)]
- X_video.append(video)
- else:
- X_video.append(np.zeros(0))
- data['X_video'] = X_video
-
-
-
- df = pd.DataFrame.from_dict(data)
- dfs.append(deepcopy(df))
- df = pd.concat(dfs)
- os.makedirs(os.path.dirname(processed_tracks_file), exist_ok=True)
- df.to_pickle(processed_tracks_file)
- return df
- def get_images(cell_dir: str):
- '''Loads in X and Y for one cell
-
- Params
- ------
- cell_dir
- Path to directory for one cell
-
- Returns
- -------
- X : np.ndarray
- has shape (W, H, num_images)
- Y : np.ndarray
- has shape (W, H, num_images)
- '''
- for name in os.listdir(oj(cell_dir, 'TagRFP')):
- if 'tif' in name:
- fname1 = name
- for name in os.listdir(oj(cell_dir, 'EGFP')):
- if 'tif' in name:
- fname2 = name
- print(cell_dir)
- X = imread(oj(cell_dir, 'TagRFP', fname1)) # .astype(np.float32) # X = RFP(clathrin) (num_images x H x W)
- Y = imread(oj(cell_dir, 'EGFP', fname2)) # .astype(np.float32) # Y = EGFP (auxilin) (num_image x H x W)
- return X, Y</code></pre>
- </details>
- </section>
- <section>
- </section>
- <section>
- </section>
- <section>
- <h2 class="section-title" id="header-functions">Functions</h2>
- <dl>
- <dt id="src.load_tracking.get_images"><code class="name flex">
- <span>def <span class="ident">get_images</span></span>(<span>cell_dir)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Loads in X and Y for one cell</p>
- <h2 id="params">Params</h2>
- <dl>
- <dt><strong><code>cell_dir</code></strong></dt>
- <dd>Path to directory for one cell</dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><strong><code>X</code></strong> : <code>np.ndarray</code></dt>
- <dd>has shape (W, H, num_images)</dd>
- <dt><strong><code>Y</code></strong> : <code>np.ndarray</code></dt>
- <dd>has shape (W, H, num_images)</dd>
- </dl></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def get_images(cell_dir: str):
- '''Loads in X and Y for one cell
-
- Params
- ------
- cell_dir
- Path to directory for one cell
-
- Returns
- -------
- X : np.ndarray
- has shape (W, H, num_images)
- Y : np.ndarray
- has shape (W, H, num_images)
- '''
- for name in os.listdir(oj(cell_dir, 'TagRFP')):
- if 'tif' in name:
- fname1 = name
- for name in os.listdir(oj(cell_dir, 'EGFP')):
- if 'tif' in name:
- fname2 = name
- print(cell_dir)
- X = imread(oj(cell_dir, 'TagRFP', fname1)) # .astype(np.float32) # X = RFP(clathrin) (num_images x H x W)
- Y = imread(oj(cell_dir, 'EGFP', fname2)) # .astype(np.float32) # Y = EGFP (auxilin) (num_image x H x W)
- return X, Y</code></pre>
- </details>
- </dd>
- <dt id="src.load_tracking.get_tracks"><code class="name flex">
- <span>def <span class="ident">get_tracks</span></span>(<span>data_dir, split=None, pixel_data=False, video_data=False, processed_tracks_file='/accounts/projects/vision/chandan/auxilin-prediction/src/../data/tracks/tracks.pkl', dset='orig')</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Read and save tracks tracks from folders within data_dir into a dataframe
- Assumes (matlab) tracking has been run</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def get_tracks(data_dir, split=None, pixel_data=False, video_data=False,
- processed_tracks_file=oj(config.DIR_TRACKS, 'tracks.pkl'),
- dset='orig'):
- '''Read and save tracks tracks from folders within data_dir into a dataframe
- Assumes (matlab) tracking has been run
- '''
- processed_tracks_file = processed_tracks_file[:-4] + '_' + dset + '.pkl'
- print('\t', processed_tracks_file, data_dir)
- if os.path.exists(processed_tracks_file):
- print('\tusing cached tracks!')
- return pd.read_pickle(processed_tracks_file)
- dfs = []
- if split['feature_selection'] is None:
- split = None
- if split is not None:
- flatten = lambda l: [item for sublist in l for item in sublist]
- split = flatten(split.values())
- # 2 directories of naming
- for upper_dir in sorted(os.listdir(data_dir)):
- print('dirs', upper_dir)
- if upper_dir.startswith('.') or 'Icon' in upper_dir:
- continue
- for cell_dir in sorted(os.listdir(oj(data_dir, upper_dir))):
-
- print('\t', cell_dir)
- if not 'Cell' in cell_dir:
- continue
- cell_num = oj(upper_dir, cell_dir.replace('Cell', '').replace('_1s', ''))
- if split is not None:
- if not cell_num in split:
- continue
- full_dir = f'{data_dir}/{upper_dir}/{cell_dir}'
- fname = full_dir + '/TagRFP/Tracking/ProcessedTracks.mat'
- print('\t', cell_num)
-
- # fname_image = oj(data_dir, upper_dir, cell_dir)
- mat = mat4py.loadmat(fname)
- tracks = mat['tracks']
- n = len(tracks['t'])
-
-
- # basic features
- t = np.array([tracks['t'][i] for i in range(n)])
- data = {
- 'lifetime': tracks['lifetime_s'],
- 'cell_num': [cell_num] * n,
- 'catIdx': tracks['catIdx'],
- 't': [t[i][0] for i in range(n)],
- }
-
-
- # displacement features
- totalDisplacement = []
- msd = [] # mean squared displacement
- for i in range(n):
- try:
- totalDisplacement.append(tracks['MotionAnalysis'][i]['totalDisplacement'])
- except:
- totalDisplacement.append(0)
- try:
- msd.append(np.nanmax(tracks['MotionAnalysis'][i]['MSD']))
- except:
- msd.append(0)
- data['mean_total_displacement'] = [totalDisplacement[i] / tracks['lifetime_s'][i] for i in range(n)]
- data['mean_square_displacement'] = msd
-
- # position features
- x_pos_seq = np.array(
- [tracks['x'][i][0] for i in range(n)]) # x-position for clathrin (auxilin is very similar)
- y_pos_seq = np.array(
- [tracks['y'][i][0] for i in range(n)]) # y-position for clathrin (auxilin is very similar)
- data['x_pos_seq'] = x_pos_seq
- data['y_pos_seq'] = y_pos_seq
- data['x_pos'] = [sum(x) / len(x) for x in x_pos_seq] # mean position in the image
- data['y_pos'] = [sum(y) / len(y) for y in y_pos_seq]
-
- # track features
- num_channels = len(tracks['A'][0])
- for idx_channel, prefix in zip(range(num_channels),
- ['X', 'Y', 'Z'][:num_channels]):
- # print(tracks.keys())
- track = np.array([tracks['A'][i][idx_channel] for i in range(n)])
- cs = np.array([tracks['c'][i][idx_channel] for i in range(n)])
- # print('track keys', tracks.keys())
- pvals = np.array([tracks['pval_Ar'][i][idx_channel] for i in range(n)])
- stds = np.array([tracks['A_pstd'][i][idx_channel] for i in range(n)])
- sigmas = np.array([tracks['sigma_r'][i][idx_channel] for i in range(n)])
- data[prefix + '_pvals'] = pvals
- starts = []
- starts_p = []
- starts_c = []
- starts_s = []
- starts_sig = []
- for d in tracks['startBuffer']:
- if len(d) == 0:
- starts.append([])
- starts_p.append([])
- starts_c.append([])
- starts_s.append([])
- starts_sig.append([])
- else:
- # print('buffkeys', d.keys())
- starts.append(d['A'][idx_channel])
- starts_p.append(d['pval_Ar'][idx_channel])
- starts_c.append(d['c'][idx_channel])
- starts_s.append(d['A_pstd'][idx_channel])
- starts_sig.append(d['sigma_r'][idx_channel])
- ends = []
- ends_p = []
- ends_c = []
- ends_s = []
- ends_sig = []
- for d in tracks['endBuffer']:
- if len(d) == 0:
- ends.append([])
- ends_p.append([])
- ends_c.append([])
- ends_s.append([])
- ends_sig.append([])
- else:
- ends.append(d['A'][idx_channel])
- ends_p.append(d['pval_Ar'][idx_channel])
- ends_c.append(d['c'][idx_channel])
- ends_s.append(d['A_pstd'][idx_channel])
- ends_sig.append(d['sigma_r'][idx_channel])
- # if prefix == 'X':
- data[prefix + '_extended'] = [starts[i] + track[i] + ends[i] for i in range(n)]
- data[prefix + '_pvals_extended'] = [starts_p[i] + pvals[i] + ends_p[i] for i in range(n)]
- data[prefix] = track
- data[prefix + '_c_extended'] = [starts_c[i] + cs[i] + ends_c[i] for i in range(n)]
- data[prefix + '_std_extended'] = [starts_s[i] + stds[i] + ends_s[i] for i in range(n)]
- data[prefix + '_sigma_extended'] = [starts_sig[i] + sigmas[i] + ends_sig[i] for i in range(n)]
- data[prefix + '_starts'] = starts
- data[prefix + '_ends'] = ends
- data['lifetime_extended'] = [len(x) for x in data['X_extended']]
- # pixel features
- if pixel_data:
- cla, aux = get_images(full_dir)
- pixel = np.array([[cla[int(t[i][j]), int(y_pos_seq[i][j]), int(x_pos_seq[i][j])]
- if not math.isnan(t[i][j]) else 0 for j in range(len(tracks['t'][i]))]
- for i in range(n)])
- pixel_up = np.array(
- [[cla[int(t[i][j]), min(int(y_pos_seq[i][j] + 1), cla.shape[1] - 1), int(x_pos_seq[i][j])]
- if not math.isnan(t[i][j]) else 0 for j in range(len(tracks['t'][i]))]
- for i in range(n)])
- pixel_down = np.array([[cla[int(t[i][j]), max(int(y_pos_seq[i][j] - 1), 0), int(x_pos_seq[i][j])]
- if not math.isnan(t[i][j]) else 0 for j in range(len(tracks['t'][i]))]
- for i in range(n)])
- pixel_left = np.array([[cla[int(t[i][j]), int(y_pos_seq[i][j]), max(int(x_pos_seq[i][j] - 1), 0)]
- if not math.isnan(t[i][j]) else 0 for j in range(len(tracks['t'][i]))]
- for i in range(n)])
- pixel_right = np.array(
- [[cla[int(t[i][j]), int(y_pos_seq[i][j]), min(int(x_pos_seq[i][j] + 1), cla.shape[2] - 1)]
- if not math.isnan(t[i][j]) else 0 for j in range(len(tracks['t'][i]))]
- for i in range(n)])
- data['pixel'] = pixel
- data['pixel_left'] = pixel_left
- data['pixel_right'] = pixel_right
- data['pixel_up'] = pixel_up
- data['pixel_down'] = pixel_down
- data['center_max'] = [max(pixel[i]) for i in range(n)]
- data['left_max'] = [max(pixel_left[i]) for i in range(n)]
- data['right_max'] = [max(pixel_right[i]) for i in range(n)]
- data['up_max'] = [max(pixel_up[i]) for i in range(n)]
- data['down_max'] = [max(pixel_down[i]) for i in range(n)]
-
- if video_data:
-
- # load video data
-
- X_video = []
- square_size = 10
- cla, aux = get_images(full_dir)
- for i in (range(n)):
- # only extract videos if lifetime > 15
- if data['lifetime'][i] >= 15:
-
- # range of positions of track
- x_pos_max, x_pos_min = int(max(data['x_pos_seq'][i])), int(min(data['x_pos_seq'][i]))
- y_pos_max, y_pos_min = int(max(data['y_pos_seq'][i])), int(min(data['y_pos_seq'][i]))
-
- # crop videos to 10X10 square
- # e.g. if x_pos_max = 52, x_pos_min = 48, then take x_left = 45, x_right = 54, etc.
- if x_pos_max - x_pos_min < square_size:
-
- x_left, x_right = int((x_pos_max + x_pos_min - square_size + 1) / 2), \
- int((x_pos_max + x_pos_min + square_size - 1) / 2)
- if x_left < 0:
- x_left, x_right = 0, square_size - 1
- if x_right > cla.shape[2] - 1:
- x_left, x_right = cla.shape[2] - square_size, cla.shape[2] - 1
-
- else:
- x_left, x_right = int((x_pos_max + x_pos_min - square_size + 1) / 2), \
- int((x_pos_max + x_pos_min + square_size - 1) / 2)
- if y_pos_max - y_pos_min < square_size:
-
- y_left, y_right = int((y_pos_max + y_pos_min - square_size + 1) / 2), \
- int((y_pos_max + y_pos_min + square_size - 1) / 2)
- if y_left < 0:
- y_left, y_right = 0, square_size - 1
- if y_right > cla.shape[1] - 1:
- y_left, y_right = cla.shape[1] - square_size, cla.shape[1] - 1
-
- else:
- y_left, y_right = int((y_pos_max + y_pos_min - square_size + 1) / 2), \
- int((y_pos_max + y_pos_min + square_size - 1) / 2)
-
- video = cla[int(np.nanmin(t[i])):int(np.nanmax(t[i]) + 1), :, :][:, y_left:(y_right + 1), :][:, :, x_left:(x_right + 1)]
- X_video.append(video)
- else:
- X_video.append(np.zeros(0))
- data['X_video'] = X_video
-
-
-
- df = pd.DataFrame.from_dict(data)
- dfs.append(deepcopy(df))
- df = pd.concat(dfs)
- os.makedirs(os.path.dirname(processed_tracks_file), exist_ok=True)
- df.to_pickle(processed_tracks_file)
- return df</code></pre>
- </details>
- </dd>
- </dl>
- </section>
- <section>
- </section>
- </article>
- <nav id="sidebar">
- <h1>Index</h1>
- <div class="toc">
- <ul></ul>
- </div>
- <ul id="index">
- <li><h3>Super-module</h3>
- <ul>
- <li><code><a title="src" href="index.html">src</a></code></li>
- </ul>
- </li>
- <li><h3><a href="#header-functions">Functions</a></h3>
- <ul class="">
- <li><code><a title="src.load_tracking.get_images" href="#src.load_tracking.get_images">get_images</a></code></li>
- <li><code><a title="src.load_tracking.get_tracks" href="#src.load_tracking.get_tracks">get_tracks</a></code></li>
- </ul>
- </li>
- </ul>
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