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Compute Depth Map from Disparity

Given a disparity map, focal length $f$ , and baseline $b$ , compute the depth map using the stereo depth equation.

In a rectified stereo system, depth $Z$ is inversely proportional to disparity $d$ :

$Z = \frac{f \cdot b}{d}$

where:

$f$ is the focal length in pixels
$b$ is the baseline distance between cameras
$d$ is the disparity (difference in x-coordinates between left and right images)

For zero disparity (no correspondence found), set the depth to infinity (float('inf')).

Return the depth map as a 2D list with values rounded to 4 decimal places.

Input:

disparity_map = [[10, 20, 0],
                 [5, 15, 25]]
focal_length = 500.0
baseline = 0.1

Output:

[[5.0, 2.5, inf], [10.0, 3.3333, 2.0]]

Reasoning:

The depth map is computed by applying the stereo depth equation to each element in the disparity map: $Z = \frac{f \cdot b}{d}$ , where $f = 500.0$ , $b = 0.1$ , and $d$ is the disparity value.
For each element in the disparity map, we plug in the values into the equation: for the first element, $Z = \frac{500.0 \cdot 0.1}{10} = 5.0$ ; for the second element, $Z = \frac{500.0 \cdot 0.1}{20} = 2.5$ ; and so on.
When the disparity value is 0 (indicating no correspondence found), the depth is set to infinity: $Z = \frac{500.0 \cdot 0.1}{0} = \infty$ or float('inf').
The computed depth values are then rounded to 4 decimal places and returned as a 2D list, resulting in the output: [[5.0, 2.5, inf], [10.0, 3.3333, 2.0]].

Editor

Python 3.13.1

0/0

Run code to see test results.

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