Week 03 · lesson

Lab: Inspect the Pixel Pipeline

Mission

Run the local Pixel Inspector Lab. Trace one supplied scene from RGB values to grayscale and edge evidence, compare controlled conditions, and export a Pixel Evidence Record.

Starting evidence

The baseline is normal, RGB, threshold 90. At coordinate [2,3], the supplied pixel is RGB(230,92,28) and grayscale 126. At threshold 90, normal produces 12 marked pixels, dim produces 8, and cropped produces 7.

Guided start

Select normal and RGB; run the pipeline.
Find coordinate [2,3] and preserve all three channel values.
Switch only the representation to grayscale; record 126.
Switch to edge mask at threshold 90; record 12/36.
State what stayed fixed: scene, grid, coordinate, and supplied data.

The scene did not change during those runs. Only the representation changed.

Procedure

Predict which channel gives the strongest supplied bottle-to-background contrast.
Run RGB, one individual channel, and grayscale. Preserve [2,3].
Keep edge mode and threshold 90 fixed. Compare normal with dim or cropped.
Return to normal. Change only the threshold and record the new edge count.
Preserve your first claim before correcting it.
Complete Observation, Measurement, Decision, and Limitation.
Export the evidence JSON and confirm it names ai-for-beginners-pixel-inspector-lab.

Checkpoints and success criteria

one coordinate includes exact RGB and grayscale values;
two scenes are compared at the same threshold;
two thresholds are compared on the same scene;
the original claim remains visible beside its correction;
the conclusion says edge pixels, not bottle detected; and
the limitation rejects identity, understanding, general accuracy, and deployment claims.

Troubleshooting

If the count does not change, select edge mode and rerun after changing a control. If two variables changed, reset and reproduce the baseline. Backend View is an explicitly fictional local record, not a captured production log. Every numeric result also appears in accessible text and tables.

Safety and privacy boundary

Use only the supplied grids. Do not upload an image or activate a camera. The Lab contacts no service, identifies no person, and supports no surveillance or safety decision.

Robotnix Lab · contract v1

AI for Beginners Pixel Inspector Lab

Trace one supplied scene through pixels, channels, grayscale, and a fixed edge mask, then compare controlled capture conditions.

Help and boundaries

A deterministic browser simulator renders supplied 6-by-6 grids and an output-only trace. It runs no vision model and contacts no service.

No personal data, private prompts, or API keys. This Lab makes no hidden remote writes and does not persist your work after the page session.

No result activates a camera, identifies a person, controls a device, or supports a surveillance or safety decision.

Lab draft is active for this page session only.

Objectives

Trace one coordinate through RGB channels and grayscale.
Change one capture or threshold condition while holding the other settings fixed.
Repair a claim that mistakes edge evidence for object identity.

Procedure

Inspect pixelsRun the normal RGB grid and preserve the exact values at coordinate [2,3].
Compare channelsView at least one individual channel and grayscale without changing the scene.
Change one conditionCompare two scenes at the same edge threshold, then two thresholds on the same scene.
Repair the claimPreserve the first edge-count claim, name its defect, and write a bounded revision.

Workbench

Inspect the numbers before naming the scene.

The grids are supplied teaching records. The generated photograph is visual context, not the source of these measurements.

Local experiment · deterministic simulation

Pixel Pipeline Simulator

Trace a supplied scene through capture, pixels, channels, a selected transform, measured evidence, and a bounded claim.

Representation

A readable explanation of the same local run.

01
Physical SceneWhich supplied capture condition represents the bottle scene?
02
Capture ConditionWhat lighting or crop condition shaped the recorded values?
03
Pixel GridHow many coordinate-based measurements are stored?
04
RGB ChannelsWhich red, green, and blue intensities are stored at the selected coordinate?
05
TransformationWhich representation or edge threshold is being applied?
06
Feature EvidenceWhat exact numeric evidence did the transform produce?
07
Condition TestWhat changes when one condition changes and the rest stay fixed?
08
Claim BoundaryWhat does this pixel evidence fail to identify or prove?

Read the student trace as text

robotnix-ai-lab :: local deterministic pixel pipeline
Select a scene and representation, then run the pipeline.
No camera, upload, trained model, or network service is active.

Scene ConditionThreshold

Scene Condition · normal | Representation · rgb | Threshold · 90Selected Pixel [2,3] · RGB(230,92,28) · gray 126

Edge Count · 12 of 36 marked pixels

Fixed edge counts for every supplied condition
Scene	Threshold 60	Threshold 90	Threshold 120
normal	16	12	8
dim	12	8	4
cropped	10	7	5

Lab boundary: supplied grids demonstrate representation sensitivity. They do not identify the bottle, run a trained model, measure a real camera, or establish performance on other images.

Text and static fallback

Every setting, selected pixel, RGB channel, grayscale value, edge count, and comparison appears as selectable text and in an accessible table.

At threshold 90 the normal, dim, and cropped grids produce 12, 8, and 7 edge pixels. The normal grid produces 16, 12, and 8 at thresholds 60, 90, and 120.

Evidence record

Record only the evidence requested. Do not enter names, personal information, private prompts, or credentials. This draft stays in memory only unless you explicitly export it.

Pixel trace *Record the scene, coordinate, RGB values, grayscale value, and what stayed fixed.Controlled measurements *Record two scene edge counts at one threshold and two threshold counts for one scene.Claim repair *Preserve the original edge-count claim, name its defect, and write a bounded revision.Limitation *What do these supplied pixels and transforms fail to prove about object identity or other images?

Success criteria

One coordinate is preserved through RGB and grayscale.
Scene and threshold comparisons each change only one declared condition.
The original claim remains visible beside a measurement-bound revision.
The conclusion rejects identity, understanding, general accuracy, and deployment claims.

Complete every procedure step and required evidence field before export.

Import evidence JSON

Vocabulary lab

Flip the idea, not just the card

Explain the term before you reveal the back. Then compare your explanation with the definition, example, and warning.

Read all terms without animation

pixel pipeline: The ordered path from captured values through representations and transforms to measured evidence. Example: RGB grid to grayscale to edge mask to edge count. Do not confuse it with: It is not automatically a trained AI model.
RGB channel: One of the red, green, or blue numeric components stored for a color pixel. Example: The selected pixel stores 230, 92, and 28. Do not confuse it with: A channel is not a human color name or object label.
edge mask: A binary grid marking locations that satisfy the supplied edge rule. Example: Twelve of 36 locations are marked in the normal case at threshold 90. Do not confuse it with: It does not name the object.
selected pixel: One coordinate whose exact values are preserved across representations. Example: Coordinate 2,3 changes from RGB values to gray 126. Do not confuse it with: One pixel is not proof about the entire image.
evidence record: An export preserving settings, measurements, conclusion, and limitation. Example: The Pixel Evidence Record JSON from this Lab. Do not confuse it with: Export does not establish mastery or deployment readiness.