Easy

Depth Error from Disparity Noise

~10 mincode completion

Implement depth_error(f, B, disparity, delta_d) returning .

Examples

Nearby: d=4, dd=0.1 gives 0.125

Input
depth_error(2, 10, 4, 0.1)
Output
0.125

Same noise four times farther (d=1) gives 2.0, sixteen times larger

Input
depth_error(2, 10, 1, 0.1)
Output
2

A 0.01-pixel error at d=0.05 is already 48 in depth

Input
depth_error(100, 0.12, 0.05, 0.01)
Output
48

Hints

Hint 1

Convert the input with before doing elementwise work.

Hint 2

Do not forget to square the disparity. That step is easy to skip.

Requirements

  • disparity: scalar or array

  • delta_d: disparity error, same shape as disparity or scalar

  • Return approximate |delta Z|

Constraints

  • Allowed library: NumPy only

  • Time limit: 200 ms, Memory: 64 MB

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Where this shows up

~10 min

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Python
import numpy as np


def depth_error(f, B, disparity, delta_d):
    """
    First-order depth uncertainty from a disparity error.

    Args:
        f, B:       focal length and baseline
        disparity:  scalar or array
        delta_d:    disparity error, same shape as disparity or scalar

    Returns:
        approximate |delta Z|
    """
    # YOUR CODE HERE
    pass
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