Liezl Maree roomrys

## sleap_instance_groups_from_track_names.py
import sleap
from tqdm import tqdm


def main(ds: str, ds_out: str = None):
    """Removes all InstanceGroups and creates new ones based on track names.

    Args:
        ds: Path to the project file.
        ds_out: Path to the output project file.

## triangulation_timing_vecotrized_vs_looped.py
from __future__ import annotations

import time
from collections.abc import Callable

import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm


## triangulation_dlt_vectorized_implementation.py
import numpy as np


extrinsics = np.array(
    [
        [
            [-3.11213376e-01, -6.97047702e-01, 6.45964965e-01, -5.55457784e02],
            [9.26706235e-01, -7.19443053e-02, 3.68835426e-01, -2.94434950e02],
            [-2.10622385e-01, 7.13406278e-01, 6.68348481e-01, -1.90821965e02],
            [0.00000000e00, 0.00000000e00, 0.00000000e00, 1.00000000e00],

## triangulation_dlt_vectorized_formulation.py
import numpy as np

n_cameras = 6
n_coords = 3

# Fill our points and projection matrices with strings so we can verify reshaping
points = np.zeros((n_cameras, n_coords, 2), dtype="U3")
for cam_idx in range(n_cameras):
    for coord_idx in range(n_coords):
        for point_idx in range(2):

## trace_methods_and_attrs_of_class.py
"""Call triangulate function and analyze the traceback of certain functions.

The class containing the methods of interest should be wrapped with the
`trace_method_calls` decorator defined below.

This file serves as an example (excluding the modifications made to the
aniposelib/cameras.py file), follow the TODO instructions below to modify to your needs.
"""

import numpy as np

## sleap_split_labels_into_subsets.py
from __future__ import annotations

import os
import random
from pathlib import Path

import sleap
from sleap import Labels


## sleap_multiview_association.py
"""This module implements cycle consistent matching using pairs of views."""

from __future__ import annotations
from typing import Generator

import cv2
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

## sleap_slp_summary_csv.py
"""Outline number of labeled frames, recording sessions, and video paths for all SLPs in directory.

This script recursively finds SLP files in a directory and outputs a CSV outlining how many labeled frames, recording
sessions, and videos there are in each slp project within a directory.
"""


import pathlib

import pandas as pd

## sio_find_timestamps_in_roi.py
"""Script to find when an annotation is in a ROI of a given image.

# Installation
```bash
conda create -c conda-forge sleap-io shapely -n sio
```

# Usage
Make changes to the __main__ block at the bottom of the file. Then activate the
environment and run the script.

## sio_rfid_proofreading_v0.py
import numpy as np
import pandas as pd
import shapely
import sleap_io as sio

from shapely import Polygon
from sleap_io import Instance, Labels, LabeledFrame, PredictedInstance, Track

def load_rfid_rois(ds_rfid: str):
    """Load a dictionary of RFID ROI `Polygons`.
	import sleap
	from tqdm import tqdm


	def main(ds: str, ds_out: str = None):
	"""Removes all InstanceGroups and creates new ones based on track names.

	Args:
	ds: Path to the project file.
	ds_out: Path to the output project file.
	from __future__ import annotations

	import time
	from collections.abc import Callable

	import numpy as np
	import matplotlib.pyplot as plt
	from tqdm import tqdm
	import numpy as np


	extrinsics = np.array(
	[
	[
	[-3.11213376e-01, -6.97047702e-01, 6.45964965e-01, -5.55457784e02],
	[9.26706235e-01, -7.19443053e-02, 3.68835426e-01, -2.94434950e02],
	[-2.10622385e-01, 7.13406278e-01, 6.68348481e-01, -1.90821965e02],
	[0.00000000e00, 0.00000000e00, 0.00000000e00, 1.00000000e00],
	import numpy as np

	n_cameras = 6
	n_coords = 3

	# Fill our points and projection matrices with strings so we can verify reshaping
	points = np.zeros((n_cameras, n_coords, 2), dtype="U3")
	for cam_idx in range(n_cameras):
	for coord_idx in range(n_coords):
	for point_idx in range(2):
	"""Call triangulate function and analyze the traceback of certain functions.

	The class containing the methods of interest should be wrapped with the
	`trace_method_calls` decorator defined below.

	This file serves as an example (excluding the modifications made to the
	aniposelib/cameras.py file), follow the TODO instructions below to modify to your needs.
	"""

	import numpy as np
	from __future__ import annotations

	import os
	import random
	from pathlib import Path

	import sleap
	from sleap import Labels
	"""This module implements cycle consistent matching using pairs of views."""

	from __future__ import annotations
	from typing import Generator

	import cv2
	import matplotlib.patches as patches
	import matplotlib.pyplot as plt
	import numpy as np
	import seaborn as sns
	"""Outline number of labeled frames, recording sessions, and video paths for all SLPs in directory.

	This script recursively finds SLP files in a directory and outputs a CSV outlining how many labeled frames, recording
	sessions, and videos there are in each slp project within a directory.
	"""


	import pathlib

	import pandas as pd
	"""Script to find when an annotation is in a ROI of a given image.

	# Installation
	```bash
	conda create -c conda-forge sleap-io shapely -n sio
	```

	# Usage
	Make changes to the __main__ block at the bottom of the file. Then activate the
	environment and run the script.
	import numpy as np
	import pandas as pd
	import shapely
	import sleap_io as sio

	from shapely import Polygon
	from sleap_io import Instance, Labels, LabeledFrame, PredictedInstance, Track

	def load_rfid_rois(ds_rfid: str):
	"""Load a dictionary of RFID ROI `Polygons`.