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atharvas

import torch
import time
import tracemalloc
import matplotlib.pyplot as plt

def masks_to_boxes(masks: torch.Tensor) -> torch.Tensor:
    """
    Compute the bounding boxes around the provided masks.

    Returns a [N, 4] tensor containing bounding boxes. The boxes are in ``(x1, y1, x2, y2)`` format with

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atharvas

Has the following (formatted) output:

cy | me  | blu : res = < 0.06,  2.04,  0.71> | gt = < 3.00, -3.00,  0.70>
cy | me  | red : res = < 1.32,  2.75,  0.92> | gt = < 0.00, -3.00,  0.70>
cy | me  | yel : res = < 1.09,  1.51,  0.97> | gt = <-3.00, -3.00,  0.70>
cy | ru  | blu : res = < 1.34,  1.01,  0.71> | gt = < 3.00,  0.00,  0.70>
cy | ru  | bro : res = < 1.13, -1.36,  0.90> | gt = <-3.00,  3.00,  0.70>
cy | ru | cya : res = &lt; 2.08, 0.21, 0.68&gt; | gt = &lt; 3.00, 3.00, 0.70&gt;

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import numpy as np
import scipy

l                                = 0.09218/2                                # dist b.w. 2 sides of hexagon
f_s_orig                         = 16000                                    # sampling frequency
c                                = scipy.constants.speed_of_sound           # speed of sound              
user                             = "2"
setting                          = "2"
file_path                        = f"./data/user{user}_setting{setting}/*.csv"

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atharvas

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atharvas

atharvas