Color channel handling in Matplotlib - Step-by-Step Execution

Concept Flow - Color channel handling

Load Image as Array

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Separate Color Channels

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Red

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Modify or Analyze Channels

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Combine Channels Back

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Display or Save Image

The image is loaded as an array, color channels are separated, optionally modified or analyzed, then combined back and displayed.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
img = plt.imread('image.png')
red = img[:,:,0]
plt.imshow(red, cmap='Reds')
plt.show()

This code loads an image, extracts the red channel, and displays it using a red color map.

Execution Table

Step	Action	Variable	Value/Result
1	Load image	img	3D array shape (height, width, 3) with RGB values
2	Extract red channel	red	2D array shape (height, width) with red values
3	Display red channel	plt.imshow(red, cmap='Reds')	Image shown with red intensities
4	End	-	Image display complete

💡 Image displayed and process ends

Variable Tracker

Variable	Start	After Step 1	After Step 2	Final
img	undefined	3D array with RGB	3D array with RGB	3D array with RGB
red	undefined	undefined	2D array red channel	2D array red channel

Key Moments - 2 Insights

Why do we use img[:,:,0] to get the red channel?

Because the image array stores colors in the last dimension as [Red, Green, Blue], so index 0 selects the red values (see execution_table step 2).

Why does plt.imshow(red, cmap='Reds') show a red image?

Because we tell matplotlib to use the 'Reds' color map, which colors the grayscale red channel data in shades of red (see execution_table step 3).

Visual Quiz - 3 Questions

Look at the execution table, what is the shape of 'red' after step 2?

A 2D array with red channel values

B 3D array with 3 color channels

C 1D array with pixel values

D Scalar value

💡 Hint

Check the 'Variable' and 'Value/Result' columns in step 2 of execution_table

At which step is the image actually shown on screen?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look for plt.imshow and plt.show actions in execution_table

If we want to extract the blue channel, which index should we use?

A 1

B 2

C 0

D 3

💡 Hint

Recall RGB channels are indexed as 0=Red, 1=Green, 2=Blue (see key_moments question 1)

Concept Snapshot

Color channel handling:
- Images are arrays with shape (height, width, 3)
- Channels: 0=Red, 1=Green, 2=Blue
- Extract channel: img[:,:,channel_index]
- Display single channel with plt.imshow(channel, cmap='ColorMap')
- Combine channels back with np.stack or similar

Full Transcript

We start by loading an image as a 3D array with RGB channels. Then we extract one color channel by selecting the appropriate slice in the last dimension. For example, img[:,:,0] gives the red channel. We can display this channel using matplotlib's imshow with a color map like 'Reds' to see the intensity of red in the image. This process helps us analyze or modify individual color channels before combining them back to form a full color image.