Overlaying data on images in Matplotlib

Introduction

Overlaying data on images helps you see extra information on top of pictures. It makes it easier to understand patterns or highlight important parts.

You want to show points or lines on a photo, like marking locations on a map.

You need to compare data with a background image, like showing weather data on a satellite image.

You want to highlight areas of interest on a medical scan.

You want to add labels or markers on a chart that uses an image as background.

Syntax

Matplotlib

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

img = mpimg.imread('image.png')
plt.imshow(img)
plt.plot(x_values, y_values, 'ro')  # example: red dots
plt.show()

plt.imshow() shows the image.

plt.plot() adds data points or lines on top.

Examples

This example shows blue dots at two points on the image.

Matplotlib

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

img = mpimg.imread('image.png')
plt.imshow(img)
plt.plot([50, 100], [30, 80], 'bo')  # blue dots
plt.show()

This example draws a green line between two points on the image.

Matplotlib

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

img = mpimg.imread('image.png')
plt.imshow(img)
plt.plot([20, 80], [40, 90], 'g-')  # green line
plt.show()

This example uses scatter to add yellow points on the image.

Matplotlib

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

img = mpimg.imread('image.png')
plt.imshow(img)
plt.scatter([60, 120], [50, 100], color='yellow')  # yellow scatter points
plt.show()

Sample Program

This program creates a gray image and overlays red points and a blue line on it. It shows how to combine image display and data plotting.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

# Create a simple image (a gray square)
img = np.full((100, 100), 0.7)  # gray background

plt.imshow(img, cmap='gray')

# Overlay red circle points
x_points = [20, 50, 80]
y_points = [30, 60, 90]
plt.plot(x_points, y_points, 'ro', label='Red points')

# Overlay a blue line
plt.plot([10, 90], [10, 90], 'b-', label='Blue line')

plt.legend()
plt.title('Overlaying data on a gray image')
plt.show()

Important Notes

Make sure the data coordinates match the image size to place points correctly.

You can use different colors and markers to distinguish data on the image.

Use plt.imshow() first, then add data with plt.plot() or plt.scatter().

Summary

Overlaying data on images helps combine pictures with extra information.

Use plt.imshow() to show the image and plt.plot() or plt.scatter() to add data.

Check coordinates so data appears in the right place on the image.

Practice

1. What is the main purpose of using plt.imshow() in matplotlib when overlaying data on images?

easy

A. To save the plot as an image file

B. To display an image as the background for plotting data on top

C. To create a scatter plot of data points

D. To clear the current figure before plotting

Solution

1. Step 1: Understand the role of plt.imshow()

   This function is used to display images in matplotlib, which can serve as a background for other plots.

2. Step 2: Identify its use in overlaying data

   By showing an image first, you can then plot data points or lines on top to combine visual and numeric information.

3. Final Answer:

   To display an image as the background for plotting data on top -> Option B

4. Quick Check:

   plt.imshow() shows images [OK]

Hint: Remember: imshow shows images, not plots [OK]

Common Mistakes:

• Confusing imshow with scatter plot functions
• Thinking imshow saves images
• Using imshow to clear figures

2. Which of the following is the correct way to overlay a red scatter plot on an image using matplotlib?

easy

A. plt.scatter(x, y)
plt.show()
plt.imshow(image)

B. plt.scatter(x, y, color='red')
plt.imshow(image)
plt.show()

C. plt.imshow(image)
plt.scatter(x, y, color='red')
plt.show()

D. plt.imshow(image, color='red')
plt.scatter(x, y)
plt.show()

Solution

1. Step 1: Order of plotting matters

   The image must be shown first with plt.imshow() so that scatter points appear on top.

2. Step 2: Correct syntax for scatter color

   Use color='red' inside plt.scatter() to make points red.

3. Final Answer:

   plt.imshow(image) then plt.scatter(x, y, color='red') -> Option C

4. Quick Check:

   Image first, then scatter with color [OK]

Hint: Show image before scatter to overlay correctly [OK]

Common Mistakes:

• Plotting scatter before image hides points
• Passing color to imshow instead of scatter
• Calling plt.show() too early

3. What will be the output of the following code?

import matplotlib.pyplot as plt
import numpy as np

image = np.zeros((5,5))
x = [1, 3]
y = [2, 4]

plt.imshow(image, cmap='gray')
plt.scatter(x, y, color='blue')
plt.show()

medium

A. A white 5x5 image with two blue points at coordinates (1,2) and (3,4)

B. An error because x and y coordinates are swapped

C. A black 5x5 image with two red points at coordinates (2,1) and (4,3)

D. A black 5x5 image with two blue points at coordinates (1,2) and (3,4)

Solution

1. Step 1: Understand the image array

   The image is a 5x5 array of zeros, so it appears black with cmap='gray'.

2. Step 2: Plot scatter points

   Points at (x=1, y=2) and (x=3, y=4) are plotted in blue on top of the image.

3. Final Answer:

   A black 5x5 image with two blue points at coordinates (1,2) and (3,4) -> Option D

4. Quick Check:

   Zeros = black image, scatter color blue [OK]

Hint: Remember: imshow shows array as image, scatter uses x,y coords [OK]

Common Mistakes:

• Confusing x and y coordinates
• Assuming zeros array is white
• Mixing up scatter point colors

4. The following code is intended to overlay a green line on an image, but the line does not appear. What is the error?

import matplotlib.pyplot as plt
import numpy as np

image = np.ones((10,10))
plt.imshow(image)
plt.plot([1, 8], [1, 8], color='green')
plt.show()

medium

A. The image is white and the green line is not visible due to default alpha

B. The plot command should be called before imshow

C. The color argument should be 'c' instead of 'color'

D. The coordinates for the line are outside the image bounds

Solution

1. Step 1: Analyze the image color

   The image is an array of ones, which appears white by default.

2. Step 2: Check line visibility

   A green line on a white background may be hard to see if the line is thin and no linewidth is set.

3. Final Answer:

   The image is white and the green line is not visible due to default alpha -> Option A

4. Quick Check:

   White background hides thin green line [OK]

Hint: Check background and line colors for visibility [OK]

Common Mistakes:

• Plotting line before image hides image
• Using wrong color argument name
• Assuming coordinates are out of bounds

5. You want to overlay a heatmap of data values on top of a grayscale image using matplotlib. Which approach correctly combines the image and heatmap with transparency so both are visible?

hard

A. plt.imshow(image, cmap='gray')
plt.imshow(data, cmap='hot', alpha=0.5)
plt.show()

B. plt.imshow(data, cmap='hot')
plt.imshow(image, cmap='gray', alpha=0.5)
plt.show()

C. plt.imshow(image, cmap='gray', alpha=0.5)
plt.imshow(data, cmap='hot')
plt.show()

D. plt.imshow(image, cmap='hot')
plt.imshow(data, cmap='gray', alpha=0.5)
plt.show()

Solution

1. Step 1: Display base grayscale image first

   Use plt.imshow(image, cmap='gray') to show the background image.

2. Step 2: Overlay heatmap with transparency

   Plot data with cmap='hot' and alpha=0.5 to make it semi-transparent over the image.

3. Final Answer:

   Show grayscale image first, then heatmap with alpha=0.5 -> Option A

4. Quick Check:

   Base image first, overlay with alpha [OK]

Hint: Show base image first, overlay heatmap with alpha transparency [OK]

Common Mistakes:

• Plotting heatmap before image hides heatmap
• Not using alpha causes full coverage
• Swapping colormaps between image and data