pose_utils.py 36.3 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
# @author lucasmiranda42
# encoding: utf-8
# module deepof

"""

Functions and general utilities for rule-based pose estimation. See documentation for details

"""

lucas_miranda's avatar
lucas_miranda committed
11
12
import os
import warnings
13
14
from itertools import combinations
from typing import Any, List, NewType
lucas_miranda's avatar
lucas_miranda committed
15

16
17
18
19
20
21
import cv2
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import regex as re
import seaborn as sns
lucas_miranda's avatar
lucas_miranda committed
22
23
24
from scipy import stats

import deepof.utils
25

lucas_miranda's avatar
lucas_miranda committed
26
27
28
29
# Ignore warning with no downstream effect
warnings.filterwarnings("ignore", message="All-NaN slice encountered")

# Create custom string type
30
Coordinates = NewType("Coordinates", Any)
31
32
33


def close_single_contact(
34
35
36
37
38
39
    pos_dframe: pd.DataFrame,
    left: str,
    right: str,
    tol: float,
    arena_abs: int,
    arena_rel: int,
40
41
42
) -> np.array:
    """Returns a boolean array that's True if the specified body parts are closer than tol.

43
44
45
46
47
48
49
50
    Parameters:
        - pos_dframe (pandas.DataFrame): DLC output as pandas.DataFrame; only applicable
        to two-animal experiments.
        - left (string): First member of the potential contact
        - right (string): Second member of the potential contact
        - tol (float): maximum distance for which a contact is reported
        - arena_abs (int): length in mm of the diameter of the real arena
        - arena_rel (int): length in pixels of the diameter of the arena in the video
51

52
53
54
    Returns:
        - contact_array (np.array): True if the distance between the two specified points
        is less than tol, False otherwise"""
55

56
57
    close_contact = None

58
    if isinstance(right, str):
59
        close_contact = (
60
61
            np.linalg.norm(pos_dframe[left] - pos_dframe[right], axis=1) * arena_abs
        ) / arena_rel < tol
62

63
    elif isinstance(right, list):
64
65
66
67
68
69
70
71
72
        close_contact = np.any(
            [
                (np.linalg.norm(pos_dframe[left] - pos_dframe[r], axis=1) * arena_abs)
                / arena_rel
                < tol
                for r in right
            ],
            axis=0,
        )
73
74
75
76
77

    return close_contact


def close_double_contact(
78
79
80
81
82
83
84
85
86
    pos_dframe: pd.DataFrame,
    left1: str,
    left2: str,
    right1: str,
    right2: str,
    tol: float,
    arena_abs: int,
    arena_rel: int,
    rev: bool = False,
87
88
89
) -> np.array:
    """Returns a boolean array that's True if the specified body parts are closer than tol.

90
91
92
93
94
95
96
97
98
99
100
    Parameters:
        - pos_dframe (pandas.DataFrame): DLC output as pandas.DataFrame; only applicable
        to two-animal experiments.
        - left1 (string): First contact point of animal 1
        - left2 (string): Second contact point of animal 1
        - right1 (string): First contact point of animal 2
        - right2 (string): Second contact point of animal 2
        - tol (float): maximum distance for which a contact is reported
        - arena_abs (int): length in mm of the diameter of the real arena
        - arena_rel (int): length in pixels of the diameter of the arena in the video
        - rev (bool): reverses the default behaviour (nose2tail contact for both mice)
101

102
103
104
    Returns:
        - double_contact (np.array): True if the distance between the two specified points
        is less than tol, False otherwise"""
105
106
107

    if rev:
        double_contact = (
108
109
110
111
112
113
114
115
            (np.linalg.norm(pos_dframe[right1] - pos_dframe[left2], axis=1) * arena_abs)
            / arena_rel
            < tol
        ) & (
            (np.linalg.norm(pos_dframe[right2] - pos_dframe[left1], axis=1) * arena_abs)
            / arena_rel
            < tol
        )
116
117
118

    else:
        double_contact = (
119
120
121
122
123
124
125
126
            (np.linalg.norm(pos_dframe[right1] - pos_dframe[left1], axis=1) * arena_abs)
            / arena_rel
            < tol
        ) & (
            (np.linalg.norm(pos_dframe[right2] - pos_dframe[left2], axis=1) * arena_abs)
            / arena_rel
            < tol
        )
127
128
129
130

    return double_contact


131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
def rotate(origin, point, ang):
    """Auxiliar function to climb_wall and sniff_object. Rotates x,y coordinates over a pivot"""

    ox, oy = origin
    px, py = point

    qx = ox + np.cos(ang) * (px - ox) - np.sin(ang) * (py - oy)
    qy = oy + np.sin(ang) * (px - ox) + np.cos(ang) * (py - oy)
    return qx, qy


def outside_ellipse(x, y, e_center, e_axes, e_angle, threshold=0.0):
    """Auxiliar function to climb_wall and sniff_object. Returns True if the passed x, y coordinates
    are outside the ellipse denoted by e_center, e_axes and e_angle, with a certain threshold"""

    x, y = rotate(e_center, (x, y), np.radians(e_angle))

    term_x = (x - e_center[0]) ** 2 / (e_axes[0] + threshold) ** 2
    term_y = (y - e_center[1]) ** 2 / (e_axes[1] + threshold) ** 2
    return term_x + term_y > 1


153
def climb_wall(
154
155
156
157
158
159
    arena_type: str,
    arena: np.array,
    pos_dict: pd.DataFrame,
    tol: float,
    nose: str,
    centered_data: bool = False,
160
161
162
) -> np.array:
    """Returns True if the specified mouse is climbing the wall

163
164
165
166
167
168
169
170
171
    Parameters:
        - arena_type (str): arena type; must be one of ['circular']
        - arena (np.array): contains arena location and shape details
        - pos_dict (table_dict): position over time for all videos in a project
        - tol (float): minimum tolerance to report a hit
        - nose (str): indicates the name of the body part representing the nose of
        the selected animal
        - arena_dims (int): indicates radius of the real arena in mm
        - centered_data (bool): indicates whether the input data is centered
172

173
174
175
    Returns:
        - climbing (np.array): boolean array. True if selected animal
        is climbing the walls of the arena"""
176
177
178
179

    nose = pos_dict[nose]

    if arena_type == "circular":
180
181
182
183
184
185
186
187
188
189
190
        center = np.zeros(2) if centered_data else np.array(arena[0])
        axes = arena[1]
        angle = arena[2]
        climbing = outside_ellipse(
            x=nose["x"],
            y=nose["y"],
            e_center=center,
            e_axes=axes,
            e_angle=-angle,
            threshold=tol,
        )
191
192
193
194
195
196
197

    else:
        raise NotImplementedError("Supported values for arena_type are ['circular']")

    return climbing


198
def sniff_object(
199
200
201
202
203
204
205
206
    speed_dframe: pd.DataFrame,
    arena_type: str,
    arena: np.array,
    pos_dict: pd.DataFrame,
    tol: float,
    tol_speed: float,
    nose: str,
    centered_data: bool = False,
207
    s_object: str = "arena",
208
    animal_id: str = "",
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
):
    """Returns True if the specified mouse is sniffing an object

    Parameters:
        - speed_dframe (pandas.DataFrame): speed of body parts over time
        - arena_type (str): arena type; must be one of ['circular']
        - arena (np.array): contains arena location and shape details
        - pos_dict (table_dict): position over time for all videos in a project
        - tol (float): minimum tolerance to report a hit
        - nose (str): indicates the name of the body part representing the nose of
        the selected animal
        - arena_dims (int): indicates radius of the real arena in mm
        - centered_data (bool): indicates whether the input data is centered
        - object (str): indicates the object that the animal is sniffing.
        Can be one of ['arena', 'partner']

    Returns:
        - sniffing (np.array): boolean array. True if selected animal
        is sniffing the selected object"""

    nose, nosing = pos_dict[nose], True

231
232
233
    if animal_id != "":
        animal_id += "_"

234
    if s_object == "arena":
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
        if arena_type == "circular":
            center = np.zeros(2) if centered_data else np.array(arena[0])
            axes = arena[1]
            angle = arena[2]

            nosing_min = outside_ellipse(
                x=nose["x"],
                y=nose["y"],
                e_center=center,
                e_axes=axes,
                e_angle=-angle,
                threshold=-tol,
            )
            nosing_max = outside_ellipse(
                x=nose["x"],
                y=nose["y"],
                e_center=center,
                e_axes=axes,
                e_angle=-angle,
                threshold=tol,
            )
            nosing = nosing_min & (~nosing_max)

258
    elif s_object == "partner":
259
260
261
262
263
264
265
266
267
268
269
        raise NotImplementedError

    else:
        raise ValueError("object should be one of [arena, partner]")

    speed = speed_dframe[animal_id + "Center"] < tol_speed
    sniffing = nosing & speed

    return sniffing


270
def huddle(
271
272
273
274
275
    pos_dframe: pd.DataFrame,
    speed_dframe: pd.DataFrame,
    tol_forward: float,
    tol_speed: float,
    animal_id: str = "",
276
) -> np.array:
277
    """Returns true when the mouse is huddling using simple rules.
278

279
280
281
282
283
284
285
286
287
288
289
    Parameters:
        - pos_dframe (pandas.DataFrame): position of body parts over time
        - speed_dframe (pandas.DataFrame): speed of body parts over time
        - tol_forward (float): Maximum tolerated distance between ears and
        forward limbs
        - tol_rear (float): Maximum tolerated average distance between spine
        body parts
        - tol_speed (float): Maximum tolerated speed for the center of the mouse

    Returns:
        hudd (np.array): True if the animal is huddling, False otherwise
290
    """
291
292
293
294
295

    if animal_id != "":
        animal_id += "_"

    forward = (
296
297
298
299
300
301
302
303
304
305
306
307
        np.linalg.norm(
            pos_dframe[animal_id + "Left_bhip"] - pos_dframe[animal_id + "Left_fhip"],
            axis=1,
        )
        < tol_forward
    ) | (
        np.linalg.norm(
            pos_dframe[animal_id + "Right_bhip"] - pos_dframe[animal_id + "Right_fhip"],
            axis=1,
        )
        < tol_forward
    )
308
309

    speed = speed_dframe[animal_id + "Center"] < tol_speed
lucas_miranda's avatar
lucas_miranda committed
310
    hudd = forward & speed
311
312
313
314

    return hudd


315
def dig(
316
317
318
319
320
    speed_dframe: pd.DataFrame,
    likelihood_dframe: pd.DataFrame,
    tol_speed: float,
    tol_likelihood: float,
    animal_id: str = "",
321
):
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
    """Returns true when the mouse is digging using simple rules.

    Parameters:
        - speed_dframe (pandas.DataFrame): speed of body parts over time
        - likelihood_dframe (pandas.DataFrame): likelihood of body part tracker over time,
        as directly obtained from DeepLabCut
        - tol_speed (float): Maximum tolerated speed for the center of the mouse
        - tol_likelihood (float): Maximum tolerated likelihood for the nose (if the animal
        is digging, the nose is momentarily occluded).

    Returns:
        dig (np.array): True if the animal is digging, False otherwise
    """

    if animal_id != "":
        animal_id += "_"

    speed = speed_dframe[animal_id + "Center"] < tol_speed
340
341
    nose_likelihood = likelihood_dframe[animal_id + "Nose"] < tol_likelihood
    digging = speed & nose_likelihood
342

343
    return digging
344
345


346
def look_around(
347
348
349
350
351
    speed_dframe: pd.DataFrame,
    likelihood_dframe: pd.DataFrame,
    tol_speed: float,
    tol_likelihood: float,
    animal_id: str = "",
352
):
353
    """Returns true when the mouse is digging using simple rules.
354

355
356
357
358
359
360
361
    Parameters:
        - speed_dframe (pandas.DataFrame): speed of body parts over time
        - likelihood_dframe (pandas.DataFrame): likelihood of body part tracker over time,
        as directly obtained from DeepLabCut
        - tol_speed (float): Maximum tolerated speed for the center of the mouse
        - tol_likelihood (float): Maximum tolerated likelihood for the nose (if the animal
        is digging, the nose is momentarily occluded).
362

363
364
365
366
367
368
369
370
371
372
373
374
375
376
    Returns:
        lookaround (np.array): True if the animal is standing still and looking around, False otherwise
    """

    if animal_id != "":
        animal_id += "_"

    speed = speed_dframe[animal_id + "Center"] < tol_speed
    nose_speed = speed_dframe[animal_id + "Center"] < speed_dframe[animal_id + "Nose"]
    nose_likelihood = likelihood_dframe[animal_id + "Nose"] > tol_likelihood

    lookaround = speed & nose_likelihood & nose_speed

    return lookaround
377
378


379
def following_path(
380
381
382
383
384
385
    distance_dframe: pd.DataFrame,
    position_dframe: pd.DataFrame,
    follower: str,
    followed: str,
    frames: int = 20,
    tol: float = 0,
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
) -> np.array:
    """For multi animal videos only. Returns True if 'follower' is closer than tol to the path that
    followed has walked over the last specified number of frames

        Parameters:
            - distance_dframe (pandas.DataFrame): distances between bodyparts; generated by the preprocess module
            - position_dframe (pandas.DataFrame): position of bodyparts; generated by the preprocess module
            - follower (str) identifier for the animal who's following
            - followed (str) identifier for the animal who's followed
            - frames (int) frames in which to track whether the process consistently occurs,
            - tol (float) Maximum distance for which True is returned

        Returns:
            - follow (np.array): boolean sequence, True if conditions are fulfilled, False otherwise"""

    # Check that follower is close enough to the path that followed has passed though in the last frames
    shift_dict = {
        i: position_dframe[followed + "_Tail_base"].shift(i) for i in range(frames)
    }
    dist_df = pd.DataFrame(
        {
            i: np.linalg.norm(
                position_dframe[follower + "_Nose"] - shift_dict[i], axis=1
            )
            for i in range(frames)
        }
    )

    # Check that the animals are oriented follower's nose -> followed's tail
    right_orient1 = (
416
417
418
419
        distance_dframe[tuple(sorted([follower + "_Nose", followed + "_Tail_base"]))]
        < distance_dframe[
            tuple(sorted([follower + "_Tail_base", followed + "_Tail_base"]))
        ]
420
421
422
    )

    right_orient2 = (
423
424
        distance_dframe[tuple(sorted([follower + "_Nose", followed + "_Tail_base"]))]
        < distance_dframe[tuple(sorted([follower + "_Nose", followed + "_Nose"]))]
425
426
    )

427
    # noinspection PyArgumentList
428
    follow = np.all(
429
430
        np.array([(dist_df.min(axis=1) < tol), right_orient1, right_orient2]),
        axis=0,
431
432
433
434
435
436
    )

    return follow


def single_behaviour_analysis(
437
438
439
440
441
442
443
    behaviour_name: str,
    treatment_dict: dict,
    behavioural_dict: dict,
    plot: int = 0,
    stat_tests: bool = True,
    save: str = None,
    ylim: float = None,
444
445
) -> list:
    """Given the name of the behaviour, a dictionary with the names of the groups to compare, and a dictionary
446
    with the actual tags, outputs a box plot and a series of significance tests amongst the groups
447

448
449
450
451
452
453
454
455
     Parameters:
         - behaviour_name (str): name of the behavioural trait to analize
         - treatment_dict (dict): dictionary containing video names as keys and experimental conditions as values
         - behavioural_dict (dict): tagged dictionary containing video names as keys and annotations as values
         - plot (int): Silent if 0; otherwise, indicates the dpi of the figure to plot
         - stat_tests (bool): performs FDR corrected Mann-U non-parametric tests among all groups if True
         - save (str): Saves the produced figure to the specified file
         - ylim (float): y-limit for the boxplot. Ignored if plot == False
456

457
458
459
     Returns:
         - beh_dict (dict): dictionary containing experimental conditions as keys and video names as values
         - stat_dict (dict): dictionary containing condition pairs as keys and stat results as values"""
460
461
462
463
464

    beh_dict = {condition: [] for condition in treatment_dict.keys()}

    for condition in beh_dict.keys():
        for ind in treatment_dict[condition]:
465
466
            beh_dict[condition] += np.sum(behavioural_dict[ind][behaviour_name]) / len(
                behavioural_dict[ind][behaviour_name]
467
468
469
470
471
472
473
474
475
            )

    return_list = [beh_dict]

    if plot > 0:

        fig, ax = plt.subplots(dpi=plot)

        sns.boxplot(
476
477
478
479
            x=list(beh_dict.keys()),
            y=list(beh_dict.values()),
            orient="vertical",
            ax=ax,
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
        )

        ax.set_title("{} across groups".format(behaviour_name))
        ax.set_ylabel("Proportion of frames")

        if ylim is not None:
            ax.set_ylim(ylim)

        if save is not None:  # pragma: no cover
            plt.savefig(save)

        return_list.append(fig)

    if stat_tests:
        stat_dict = {}
        for i in combinations(treatment_dict.keys(), 2):
            # Solves issue with automatically generated examples
lucas_miranda's avatar
lucas_miranda committed
497
            if np.any(
498
499
500
501
502
503
504
                np.array(
                    [
                        beh_dict[i[0]] == beh_dict[i[1]],
                        np.var(beh_dict[i[0]]) == 0,
                        np.var(beh_dict[i[1]]) == 0,
                    ]
                )
505
506
507
508
509
510
511
512
513
514
515
516
            ):
                stat_dict[i] = "Identical sources. Couldn't run"
            else:
                stat_dict[i] = stats.mannwhitneyu(
                    beh_dict[i[0]], beh_dict[i[1]], alternative="two-sided"
                )
        return_list.append(stat_dict)

    return return_list


def max_behaviour(
517
    behaviour_dframe: pd.DataFrame, window_size: int = 10, stepped: bool = False
518
519
520
) -> np.array:
    """Returns the most frequent behaviour in a window of window_size frames

521
522
523
524
525
526
    Parameters:
            - behaviour_dframe (pd.DataFrame): boolean matrix containing occurrence
            of tagged behaviours per frame in the video
            - window_size (int): size of the window to use when computing
            the maximum behaviour per time slot
            - stepped (bool): sliding windows don't overlap if True. False by default
527

528
529
530
    Returns:
        - max_array (np.array): string array with the most common behaviour per instance
        of the sliding window"""
531
532
533

    speeds = [col for col in behaviour_dframe.columns if "speed" in col.lower()]

534
    behaviour_dframe = behaviour_dframe.drop(speeds, axis=1).astype("float")
535
536
537
538
539
540
541
542
    win_array = behaviour_dframe.rolling(window_size, center=True).sum()
    if stepped:
        win_array = win_array[::window_size]
    max_array = win_array[1:].idxmax(axis=1)

    return np.array(max_array)


543
# noinspection PyDefaultArgument
lucas_miranda's avatar
lucas_miranda committed
544
545
546
def get_hparameters(hparams: dict = {}) -> dict:
    """Returns the most frequent behaviour in a window of window_size frames

547
548
    Parameters:
        - hparams (dict): dictionary containing hyperparameters to overwrite
lucas_miranda's avatar
lucas_miranda committed
549

550
    Returns:
551
        - defaults (dict): dictionary with overwritten parameters. Those not
552
        specified in the input retain their default values"""
lucas_miranda's avatar
lucas_miranda committed
553
554

    defaults = {
lucas_miranda's avatar
lucas_miranda committed
555
        "speed_pause": 5,
556
        "climb_tol": 10,
557
558
559
560
        "close_contact_tol": 35,
        "side_contact_tol": 80,
        "follow_frames": 10,
        "follow_tol": 5,
lucas_miranda's avatar
lucas_miranda committed
561
        "huddle_forward": 15,
562
        "huddle_speed": 2,
563
        "nose_likelihood": 0.85,
lucas_miranda's avatar
lucas_miranda committed
564
        "fps": 24,
lucas_miranda's avatar
lucas_miranda committed
565
    }
566

lucas_miranda's avatar
lucas_miranda committed
567
568
    for k, v in hparams.items():
        defaults[k] = v
569

lucas_miranda's avatar
lucas_miranda committed
570
571
572
    return defaults


573
574
575
576
# noinspection PyDefaultArgument
def frame_corners(w, h, corners: dict = {}):
    """Returns a dictionary with the corner positions of the video frame

577
578
579
580
    Parameters:
        - w (int): width of the frame in pixels
        - h (int): height of the frame in pixels
        - corners (dict): dictionary containing corners to overwrite
581

582
583
584
    Returns:
        - defaults (dict): dictionary with overwriten parameters. Those not
        specified in the input retain their default values"""
585
586
587
588
589
590
591
592
593
594
595
596
597
598

    defaults = {
        "downleft": (int(w * 0.3 / 10), int(h / 1.05)),
        "downright": (int(w * 6.5 / 10), int(h / 1.05)),
        "upleft": (int(w * 0.3 / 10), int(h / 20)),
        "upright": (int(w * 6.3 / 10), int(h / 20)),
    }

    for k, v in corners.items():
        defaults[k] = v

    return defaults


599
# noinspection PyDefaultArgument,PyProtectedMember
600
def rule_based_tagging(
601
602
603
604
    coordinates: Coordinates,
    coords: Any,
    dists: Any,
    speeds: Any,
605
    video: str,
606
    params: dict = {},
607
608
609
610
611
) -> pd.DataFrame:
    """Outputs a dataframe with the registered motives per frame. If specified, produces a labeled
    video displaying the information in real time

    Parameters:
612
613
614
615
        - coordinates (deepof.data.coordinates): coordinates object containing the project information
        - coords (deepof.data.table_dict): table_dict with already processed coordinates
        - dists (deepof.data.table_dict): table_dict with already processed distances
        - speeds (deepof.data.table_dict): table_dict with already processed speeds
616
        - video (str): string name of the experiment to tag
617
618
        - params (dict): dictionary to overwrite the default values of the parameters of the functions
        that the rule-based pose estimation utilizes. See documentation for details.
619
620
621
622
623

    Returns:
        - tag_df (pandas.DataFrame): table with traits as columns and frames as rows. Each
        value is a boolean indicating trait detection at a given time"""

624
625
626
627
628
629
630
    # Extract useful information from coordinates object
    tracks = list(coordinates._tables.keys())
    vid_index = coordinates._videos.index(video)

    arena_params = coordinates._arena_params[vid_index]
    arena_type = coordinates._arena

lucas_miranda's avatar
lucas_miranda committed
631
    params = get_hparameters(params)
632
    animal_ids = coordinates._animal_ids
633
    undercond = "_" if len(animal_ids) > 1 else ""
lucas_miranda's avatar
lucas_miranda committed
634

635
    try:
636
        vid_name = re.findall("(.*)DLC", tracks[vid_index])[0]
637
638
    except IndexError:
        vid_name = tracks[vid_index]
639

640
    coords = coords[vid_name]
641
    dists = dists[vid_name]
642
    speeds = speeds[vid_name]
643
    likelihoods = coordinates.get_quality()[vid_name]
644
645
646
647
648
    arena_abs = coordinates.get_arenas[1][0]

    # Dictionary with motives per frame
    tag_dict = {}

649
650
651
652
653
654
655
656
657
658
659
660
661
    # Bulk body parts
    main_body = [
        "Left_ear",
        "Right_ear",
        "Spine_1",
        "Center",
        "Spine_2",
        "Left_fhip",
        "Right_fhip",
        "Left_bhip",
        "Right_bhip",
    ]

662
663
    def onebyone_contact(bparts: List):
        """Returns a smooth boolean array with 1to1 contacts between two mice"""
664
        nonlocal coords, animal_ids, params, arena_abs, arena_params
665

666
667
668
669
670
        try:
            left = animal_ids[0] + bparts[0]
        except TypeError:
            left = [animal_ids[0] + "_" + suffix for suffix in bparts[0]]

671
672
673
674
675
        try:
            right = animal_ids[1] + bparts[-1]
        except TypeError:
            right = [animal_ids[1] + "_" + suffix for suffix in bparts[-1]]

676
        return deepof.utils.smooth_boolean_array(
677
678
            close_single_contact(
                coords,
679
680
                (left if not isinstance(left, list) else right),
                (right if not isinstance(left, list) else left),
lucas_miranda's avatar
lucas_miranda committed
681
                params["close_contact_tol"],
682
                arena_abs,
683
                arena_params[1][1],
684
685
            )
        )
686
687
688
689

    def twobytwo_contact(rev):
        """Returns a smooth boolean array with side by side contacts between two mice"""

690
        nonlocal coords, animal_ids, params, arena_abs, arena_params
691
        return deepof.utils.smooth_boolean_array(
692
693
694
695
696
697
            close_double_contact(
                coords,
                animal_ids[0] + "_Nose",
                animal_ids[0] + "_Tail_base",
                animal_ids[1] + "_Nose",
                animal_ids[1] + "_Tail_base",
lucas_miranda's avatar
lucas_miranda committed
698
                params["side_contact_tol"],
699
                rev=rev,
700
                arena_abs=arena_abs,
701
                arena_rel=arena_params[1][1],
702
703
            )
        )
704

705
    def overall_speed(ovr_speeds, _id, ucond):
lucas_miranda's avatar
lucas_miranda committed
706
707
708
709
710
711
712
713
714
715
716
717
718
        bparts = [
            "Center",
            "Spine_1",
            "Spine_2",
            "Nose",
            "Left_ear",
            "Right_ear",
            "Left_fhip",
            "Right_fhip",
            "Left_bhip",
            "Right_bhip",
            "Tail_base",
        ]
719
        array = ovr_speeds[[_id + ucond + bpart for bpart in bparts]]
lucas_miranda's avatar
lucas_miranda committed
720
721
722
        avg_speed = np.nanmedian(array[1:], axis=1)
        return np.insert(avg_speed, 0, np.nan, axis=0)

723
    if len(animal_ids) == 2:
724
725
726
727
728
729
730
        # Define behaviours that can be computed on the fly from the distance matrix
        tag_dict["nose2nose"] = onebyone_contact(bparts=["_Nose"])

        tag_dict["sidebyside"] = twobytwo_contact(rev=False)

        tag_dict["sidereside"] = twobytwo_contact(rev=True)

731
732
733
734
735
736
        tag_dict[animal_ids[0] + "_nose2tail"] = onebyone_contact(
            bparts=["_Nose", "_Tail_base"]
        )
        tag_dict[animal_ids[1] + "_nose2tail"] = onebyone_contact(
            bparts=["_Tail_base", "_Nose"]
        )
737
738
739
740
741
742
743
744
745
        tag_dict[animal_ids[0] + "_nose2body"] = onebyone_contact(
            bparts=[
                "_Nose",
                main_body,
            ]
        )
        tag_dict[animal_ids[1] + "_nose2body"] = onebyone_contact(
            bparts=[
                main_body,
746
                "_Nose",
747
748
            ]
        )
749

750
751
752
        for _id in animal_ids:
            tag_dict[_id + "_following"] = deepof.utils.smooth_boolean_array(
                following_path(
753
                    dists,
754
755
756
                    coords,
                    follower=_id,
                    followed=[i for i in animal_ids if i != _id][0],
lucas_miranda's avatar
lucas_miranda committed
757
758
                    frames=params["follow_frames"],
                    tol=params["follow_tol"],
759
760
761
                )
            )

762
763
    for _id in animal_ids:
        tag_dict[_id + undercond + "climbing"] = deepof.utils.smooth_boolean_array(
764
765
            climb_wall(
                arena_type,
766
                arena_params,
767
                coords,
lucas_miranda's avatar
lucas_miranda committed
768
                params["climb_tol"],
769
770
                _id + undercond + "Nose",
            )
771
        )
772
773
774
775
        tag_dict[_id + undercond + "sniffing"] = deepof.utils.smooth_boolean_array(
            sniff_object(
                speeds,
                arena_type,
776
                arena_params,
777
778
779
780
                coords,
                params["climb_tol"],
                params["huddle_speed"],
                _id + undercond + "Nose",
781
                s_object="arena",
782
783
784
                animal_id=_id,
            )
        )
785
        tag_dict[_id + undercond + "huddle"] = deepof.utils.smooth_boolean_array(
lucas_miranda's avatar
lucas_miranda committed
786
787
788
            huddle(
                coords,
                speeds,
lucas_miranda's avatar
lucas_miranda committed
789
790
                params["huddle_forward"],
                params["huddle_speed"],
791
                animal_id=_id,
lucas_miranda's avatar
lucas_miranda committed
792
            )
793
        )
794
795
796
797
798
799
800
801
802
        tag_dict[_id + undercond + "dig"] = deepof.utils.smooth_boolean_array(
            dig(
                speeds,
                likelihoods,
                params["huddle_speed"],
                params["nose_likelihood"],
                animal_id=_id,
            )
        )
803
804
805
806
807
808
809
810
811
        tag_dict[_id + undercond + "lookaround"] = deepof.utils.smooth_boolean_array(
            look_around(
                speeds,
                likelihoods,
                params["huddle_speed"],
                params["nose_likelihood"],
                animal_id=_id,
            )
        )
812
813
814
        # NOTE: It's important that speeds remain the last columns.
        # Preprocessing for weakly supervised autoencoders relies on this
        tag_dict[_id + undercond + "speed"] = overall_speed(speeds, _id, undercond)
815

816
    tag_df = pd.DataFrame(tag_dict).fillna(0)
817
818
819
820

    return tag_df


lucas_miranda's avatar
lucas_miranda committed
821
def tag_rulebased_frames(
822
823
824
825
826
827
828
829
830
831
832
833
    frame,
    font,
    frame_speeds,
    animal_ids,
    corners,
    tag_dict,
    fnum,
    undercond,
    hparams,
    arena,
    debug,
    coords,
lucas_miranda's avatar
lucas_miranda committed
834
):
835
    """Helper function for rule_based_video. Annotates a given frame with on-screen information
lucas_miranda's avatar
lucas_miranda committed
836
837
    about the recognised patterns"""

838
    arena, w, h = arena
lucas_miranda's avatar
lucas_miranda committed
839

lucas_miranda's avatar
lucas_miranda committed
840
841
    def write_on_frame(text, pos, col=(255, 255, 255)):
        """Partial closure over cv2.putText to avoid code repetition"""
842
        return cv2.putText(frame, text, pos, font, 0.75, col, 2)
lucas_miranda's avatar
lucas_miranda committed
843

844
845
846
847
848
849
850
    def conditional_flag():
        """Returns a tag depending on a condition"""
        if frame_speeds[animal_ids[0]] > frame_speeds[animal_ids[1]]:
            return left_flag
        else:
            return right_flag

lucas_miranda's avatar
lucas_miranda committed
851
852
853
854
855
856
857
858
859
860
861
862
863
    def conditional_pos():
        """Returns a position depending on a condition"""
        if frame_speeds[animal_ids[0]] > frame_speeds[animal_ids[1]]:
            return corners["downleft"]
        else:
            return corners["downright"]

    def conditional_col(cond=None):
        """Returns a colour depending on a condition"""
        if cond is None:
            cond = frame_speeds[animal_ids[0]] > frame_speeds[animal_ids[1]]
        if cond:
            return 150, 255, 150
lucas_miranda's avatar
lucas_miranda committed
864
865
        else:
            return 150, 150, 255
lucas_miranda's avatar
lucas_miranda committed
866

867
868
869
870
871
    # Keep track of space usage in the output video
    # The flags are set to False as soon as the lower
    # corners are occupied with text
    left_flag, right_flag = True, True

872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
    if debug:
        # Print arena for debugging
        cv2.ellipse(frame, arena[0], arena[1], arena[2], 0, 360, (0, 255, 0), 3)
        # Print body parts for debuging
        for bpart in coords.columns.levels[0]:
            if not np.isnan(coords[bpart]["x"][fnum]):
                cv2.circle(
                    frame,
                    (int(coords[bpart]["x"][fnum]), int(coords[bpart]["y"][fnum])),
                    radius=3,
                    color=(
                        (255, 0, 0) if bpart.startswith(animal_ids[0]) else (0, 0, 255)
                    ),
                    thickness=-1,
                )
        # Print frame number
        write_on_frame("Frame " + str(fnum), (int(w * 0.3 / 10), int(h / 1.15)))
889

890
    if len(animal_ids) > 1:
891

892
        if tag_dict["nose2nose"][fnum]:
lucas_miranda's avatar
lucas_miranda committed
893
            write_on_frame("Nose-Nose", conditional_pos())
894
895
896
897
898
899
900
901
902
903
904
905
906
907
            if frame_speeds[animal_ids[0]] > frame_speeds[animal_ids[1]]:
                left_flag = False
            else:
                right_flag = False

        if tag_dict[animal_ids[0] + "_nose2body"][fnum] and left_flag:
            write_on_frame("nose2body", corners["downleft"])
            left_flag = False

        if tag_dict[animal_ids[1] + "_nose2body"][fnum] and right_flag:
            write_on_frame("nose2body", corners["downright"])
            right_flag = False

        if tag_dict[animal_ids[0] + "_nose2tail"][fnum] and left_flag:
lucas_miranda's avatar
lucas_miranda committed
908
            write_on_frame("Nose-Tail", corners["downleft"])
909
910
911
            left_flag = False

        if tag_dict[animal_ids[1] + "_nose2tail"][fnum] and right_flag:
lucas_miranda's avatar
lucas_miranda committed
912
            write_on_frame("Nose-Tail", corners["downright"])
913
914
915
            right_flag = False

        if tag_dict["sidebyside"][fnum] and left_flag and conditional_flag():
lucas_miranda's avatar
lucas_miranda committed
916
            write_on_frame(
917
918
                "Side-side",
                conditional_pos(),
lucas_miranda's avatar
lucas_miranda committed
919
            )
920
921
922
923
924
925
            if frame_speeds[animal_ids[0]] > frame_speeds[animal_ids[1]]:
                left_flag = False
            else:
                right_flag = False

        if tag_dict["sidereside"][fnum] and left_flag and conditional_flag():
lucas_miranda's avatar
lucas_miranda committed
926
            write_on_frame(
927
928
                "Side-Rside",
                conditional_pos(),
lucas_miranda's avatar
lucas_miranda committed
929
            )
930
931
932
933
934
            if frame_speeds[animal_ids[0]] > frame_speeds[animal_ids[1]]:
                left_flag = False
            else:
                right_flag = False

935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
    zipped_pos = list(
        zip(
            animal_ids,
            [corners["downleft"], corners["downright"]],
            [corners["upleft"], corners["upright"]],
            [left_flag, right_flag],
        )
    )

    for _id, down_pos, up_pos, flag in zipped_pos:

        if flag:

            if tag_dict[_id + undercond + "climbing"][fnum]:
                write_on_frame("climbing", down_pos)
            elif tag_dict[_id + undercond + "huddle"][fnum]:
                write_on_frame("huddling", down_pos)
            elif tag_dict[_id + undercond + "sniffing"][fnum]:
                write_on_frame("sniffing", down_pos)
            elif tag_dict[_id + undercond + "dig"][fnum]:
                write_on_frame("digging", down_pos)
            elif tag_dict[_id + undercond + "lookaround"][fnum]:
                write_on_frame("lookaround", down_pos)

959
960
961
962
963
964
965
966
967
        #     if (
        #         tag_dict[_id + "_following"][fnum]
        #         and not tag_dict[_id + "_climbing"][fnum]
        #     ):
        #         write_on_frame(
        #             "*f",
        #             (int(w * 0.3 / 10), int(h / 10)),
        #             conditional_col(),
        #         )
lucas_miranda's avatar
lucas_miranda committed
968
969
970
971
972

        # Define the condition controlling the colour of the speed display
        if len(animal_ids) > 1:
            colcond = frame_speeds[_id] == max(list(frame_speeds.values()))
        else:
973
            colcond = hparams["huddle_speed"] < frame_speeds
lucas_miranda's avatar
lucas_miranda committed
974
975

        write_on_frame(
976
            str(
977
978
979
                np.round(
                    (frame_speeds if len(animal_ids) == 1 else frame_speeds[_id]), 2
                )
980
981
            )
            + " mmpf",
lucas_miranda's avatar
lucas_miranda committed
982
983
984
985
986
            up_pos,
            conditional_col(cond=colcond),
        )


lucas_miranda's avatar
lucas_miranda committed
987
# noinspection PyProtectedMember,PyDefaultArgument
988
def rule_based_video(
989
990
    coordinates: Coordinates,
    tag_dict: pd.DataFrame,
991
    vid_index: int,
992
993
    frame_limit: int = np.inf,
    debug: bool = False,
994
    params: dict = {},
lucas_miranda's avatar
lucas_miranda committed
995
) -> True:
996
997
998
999
    """Renders a version of the input video with all rule-based taggings in place.

    Parameters:
        - coordinates (deepof.preprocessing.coordinates): coordinates object containing the project information
1000
1001
        - debug (bool): if True, several debugging attributes (such as used body parts and arena) are plotted in
        the output video
1002
        - vid_index: for internal usage only; index of the video to tag in coordinates._videos
1003
        - frame_limit (float): limit the number of frames to output. Generates all annotated frames by default
lucas_miranda's avatar
lucas_miranda committed
1004
        - params (dict): dictionary to overwrite the default values of the hyperparameters of the functions
1005
1006
1007
1008
1009
1010
1011
1012
1013
        that the rule-based pose estimation utilizes. Values can be:
            - speed_pause (int): size of the rolling window to use when computing speeds
            - close_contact_tol (int): maximum distance between single bodyparts that can be used to report the trait
            - side_contact_tol (int): maximum distance between single bodyparts that can be used to report the trait
            - follow_frames (int): number of frames during which the following trait is tracked
            - follow_tol (int): maximum distance between follower and followed's path during the last follow_frames,
            in order to report a detection
            - huddle_forward (int): maximum distance between ears and forward limbs to report a huddle detection
            - huddle_speed (int): maximum speed to report a huddle detection
1014
1015
            - fps (float): frames per second of the analysed video. Same as input by default

1016
1017
1018
1019
1020
1021

    Returns:
        True

    """

1022
1023
1024
1025
1026
    # Extract useful information from coordinates object
    tracks = list(coordinates._tables.keys())
    videos = coordinates._videos
    path = os.path.join(coordinates._path, "Videos")

lucas_miranda's avatar
lucas_miranda committed
1027
    params = get_hparameters(params)
1028
    animal_ids = coordinates._animal_ids
lucas_miranda's avatar
lucas_miranda committed
1029
    undercond = "_" if len(animal_ids) > 1 else ""
1030

1031
    try:
1032
        vid_name = re.findall("(.*)DLC", tracks[vid_index])[0]
1033
1034
    except IndexError:
        vid_name = tracks[vid_index]
1035

1036
1037
    arena_params = coordinates._arena_params[vid_index]
    h, w = coordinates._video_resolution[vid_index]
1038
    corners = frame_corners(h, w)
1039

lucas_miranda's avatar
lucas_miranda committed
1040
1041
1042
1043
    cap = cv2.VideoCapture(os.path.join(path, videos[vid_index]))
    # Keep track of the frame number, to align with the tracking data
    fnum = 0
    writer = None
lucas_miranda's avatar
lucas_miranda committed
1044
1045
1046
    frame_speeds = (
        {_id: -np.inf for _id in animal_ids} if len(animal_ids) > 1 else -np.inf
    )
1047

lucas_miranda's avatar
lucas_miranda committed
1048
1049
    # Loop over the frames in the video
    while cap.isOpened() and fnum < frame_limit:
1050

lucas_miranda's avatar
lucas_miranda committed
1051
1052
1053
1054
1055
        ret, frame = cap.read()
        # if frame is read correctly ret is True
        if not ret:  # pragma: no cover
            print("Can't receive frame (stream end?). Exiting ...")
            break
1056

1057
        font = cv2.FONT_HERSHEY_DUPLEX
1058

lucas_miranda's avatar
lucas_miranda committed
1059
1060
1061
        # Capture speeds
        try:
            if (
1062
1063
                list(frame_speeds.values())[0] == -np.inf
                or fnum % params["speed_pause"] == 0
lucas_miranda's avatar
lucas_miranda committed
1064
1065
            ):
                for _id in animal_ids:
1066
                    frame_speeds[_id] = tag_dict[_id + undercond + "speed"][fnum]
lucas_miranda's avatar
lucas_miranda committed
1067
        except AttributeError:
lucas_miranda's avatar
lucas_miranda committed
1068
            if frame_speeds == -np.inf or fnum % params["speed_pause"] == 0:
1069
                frame_speeds = tag_dict["speed"][fnum]
lucas_miranda's avatar
lucas_miranda committed
1070
1071

        # Display all annotations in the output video
lucas_miranda's avatar
lucas_miranda committed
1072
1073
1074
1075
        tag_rulebased_frames(
            frame,
            font,
            frame_speeds,
lucas_miranda's avatar
lucas_miranda committed
1076
            animal_ids,
lucas_miranda's avatar
lucas_miranda committed
1077
1078
1079
1080
            corners,
            tag_dict,
            fnum,
            undercond,
lucas_miranda's avatar
lucas_miranda committed
1081
            params,
1082
            (arena_params, h, w),
1083
1084
            debug,
            coordinates.get_coords(center=False)[vid_name],
lucas_miranda's avatar
lucas_miranda committed
1085
1086
        )

lucas_miranda's avatar
lucas_miranda committed
1087
1088
1089
1090
1091
        if writer is None:
            # Define the codec and create VideoWriter object.The output is stored in 'outpy.avi' file.
            # Define the FPS. Also frame size is passed.
            writer = cv2.VideoWriter()
            writer.open(
1092
                vid_name + "_tagged.avi",
lucas_miranda's avatar
lucas_miranda committed
1093
                cv2.VideoWriter_fourcc(*"MJPG"),
lucas_miranda's avatar
lucas_miranda committed
1094
                params["fps"],
lucas_miranda's avatar
lucas_miranda committed
1095
1096
1097
                (frame.shape[1], frame.shape[0]),
                True,
            )
1098

lucas_miranda's avatar
lucas_miranda committed
1099
        writer.write(frame)
lucas_miranda's avatar
lucas_miranda committed
1100
        fnum += 1
1101

lucas_miranda's avatar
lucas_miranda committed
1102
1103
    cap.release()
    cv2.destroyAllWindows()
lucas_miranda's avatar
lucas_miranda committed
1104
1105

    return True
1106

1107

1108
# TODO:
1109
#    - Is border sniffing anything you might consider interesting?