From Image Detection to Safe Control Commands

Use simulated inputs to examine how sign errors and oscillation arise when horizontal and vertical image errors are converted into movement commands.

The basic activities use NumPy and Matplotlib. Use OpenCV when connecting to the previous unit.

Starting conditions

The center of a 160×120 image is (80,60), and the observed center is (110,50). x points right and y points down.

Procedure

1. Calculate $e_x=x-80$ and $e_y=y-60$ using the same sign convention as command(center) in the example. Plot the image center and observed point, and label both errors on an arrow from the center to the observed point.
2. For each axis, set the command to 0 if $|e|\le10$ pixels; otherwise calculate it using $0.02e$, then limit it to $[-0.5,0.5]$. Calculate both axis commands for $(110,50)$ and record them beside the function outputs.

Results to retain

• A center/error arrow diagram and example commands.

Starting conditions

x observations are [80,82,79,110,None,108], with $y=60$. None denotes a frame where the object was not found.

Procedure

1. For each valid observation, calculate the command by applying command((x,60)) directly, and separately by applying it to the mean x of up to the 3 most recent valid observations. At startup, if fewer than 3 samples are available, average only the collected samples.
2. At None, set the command to 0 and clear the averaging history. Treat the later 108 as the first sample in a new history.
3. Record frame order, raw observations, mean x, and both commands in a table and plot them against the same frame axis. Explain the differences by identifying both commands at the frame where x changes to 110 and the command immediately after a missed detection.

Results to retain

• Observation/command time series and missed detection rules.

Starting conditions

Connect the previous unit's mask center calculation to command. The basic activities use only saved or generated images.

Procedure

1. Separate the function returning center, area, and detection time from the command function.
2. Test an empty mask, a valid region, and a frame more than 0.2 seconds past detection, in that order. Identify stale frames by the difference between the current time and frame detection time, and set commands to 0 in that case.
3. Describe additional coordinate transformations, velocity limits, and stop conditions needed for a real device.

Results to retain

• An input→detection→validation→command flowchart and an error case table.

• OpenCV · Color spaces and region extraction
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