Why Displaying images (cv2.imshow, matplotlib) in Computer Vision? - Purpose & Use Cases

The Big Idea

What if you could instantly see and compare hundreds of images without leaving your code?

The Scenario

Imagine you have hundreds of photos from a trip and you want to check each one to find the best shots. Opening each image manually one by one on your computer is tiring and slow.

The Problem

Manually opening images takes a lot of time and effort. You might miss details or make mistakes because it's hard to compare images quickly. Also, you can't easily add notes or see changes side by side.

The Solution

Using tools like cv2.imshow or matplotlib lets you quickly display images right inside your program. You can automate showing many images, compare them easily, and even add titles or annotations to understand them better.

Before vs After

✗ Before

Open each image file manually in a photo viewer.

✓ After

cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.show()

What It Enables

This lets you instantly see and compare images during your work, making image analysis faster and more interactive.

Real Life Example

A photographer can quickly preview and compare hundreds of photos from a shoot inside a program, choosing the best ones without leaving the coding environment.

Key Takeaways

Manually viewing images is slow and error-prone.

Displaying images in code speeds up review and comparison.

Tools like cv2.imshow and matplotlib make image display easy and interactive.

Practice

1. What is the main purpose of using cv2.imshow in computer vision?

easy

A. To resize an image

B. To save an image to disk

C. To convert an image from BGR to RGB

D. To open a window that displays an image

Solution

1. Step 1: Understand the function of cv2.imshow

   cv2.imshow is used to open a new window that shows the image you provide.

2. Step 2: Differentiate from other functions

   Saving images uses cv2.imwrite, color conversion uses cv2.cvtColor, and resizing uses cv2.resize.

3. Final Answer:

   To open a window that displays an image -> Option D

4. Quick Check:

   cv2.imshow shows images in a window [OK]

Hint: cv2.imshow always opens a window to show images [OK]

Common Mistakes:

• Confusing cv2.imshow with saving or converting images
• Forgetting that cv2.imshow opens a separate window
• Thinking cv2.imshow changes image data

2. Which of the following is the correct sequence to display an image using OpenCV in Python?

easy

A. cv2.imshow(), cv2.waitKey(), cv2.destroyAllWindows()

B. cv2.waitKey(), cv2.imshow(), cv2.destroyAllWindows()

C. cv2.destroyAllWindows(), cv2.imshow(), cv2.waitKey()

D. cv2.imshow(), cv2.destroyAllWindows(), cv2.waitKey()

Solution

1. Step 1: Recall the correct order of OpenCV display functions

   First, cv2.imshow() opens the image window, then cv2.waitKey() waits for a key press, and finally cv2.destroyAllWindows() closes the window.

2. Step 2: Check the options order

   Only cv2.imshow(), cv2.waitKey(), cv2.destroyAllWindows() follows this correct sequence.

3. Final Answer:

   cv2.imshow(), cv2.waitKey(), cv2.destroyAllWindows() -> Option A

4. Quick Check:

   Display, wait, then close windows [OK]

Hint: Always call waitKey after imshow before destroying windows [OK]

Common Mistakes:

• Calling destroyAllWindows before waitKey
• Not calling waitKey causing window to close immediately
• Mixing order of functions

3. What will be the output of this code snippet?

import cv2
import matplotlib.pyplot as plt
img = cv2.imread('image.jpg')
plt.imshow(img)
plt.show()

medium

A. The image displays with correct colors

B. The image displays but colors look incorrect (blue and red swapped)

C. The code throws an error because plt.imshow cannot display images

D. The image window opens but closes immediately

Solution

1. Step 1: Understand color format difference

   OpenCV loads images in BGR format, but matplotlib expects RGB format.

2. Step 2: Effect on plt.imshow

   Displaying BGR image directly with plt.imshow causes colors to appear swapped, especially red and blue.

3. Final Answer:

   The image displays but colors look incorrect (blue and red swapped) -> Option B

4. Quick Check:

   OpenCV BGR images show wrong colors in matplotlib [OK]

Hint: Convert BGR to RGB before plt.imshow to fix colors [OK]

Common Mistakes:

• Assuming plt.imshow shows correct colors without conversion
• Confusing BGR and RGB formats
• Expecting plt.imshow to throw error on BGR images

4. You wrote this code to display an image but the window closes immediately. What is the error?

import cv2
img = cv2.imread('photo.png')
cv2.imshow('Photo', img)
cv2.destroyAllWindows()

medium

A. Missing cv2.waitKey() call after cv2.imshow()

B. cv2.destroyAllWindows() should be before cv2.imshow()

C. cv2.imread() cannot read PNG files

D. Window name 'Photo' is invalid

Solution

1. Step 1: Identify missing waitKey()

   Without cv2.waitKey(), the window opens and closes immediately because the program does not wait for a key press.

2. Step 2: Confirm other options are incorrect

   Destroying windows before showing is wrong, cv2.imread supports PNG, and window names can be any string.

3. Final Answer:

   Missing cv2.waitKey() call after cv2.imshow() -> Option A

4. Quick Check:

   Always call waitKey to pause window [OK]

Hint: Always add cv2.waitKey() after imshow to keep window open [OK]

Common Mistakes:

• Forgetting waitKey causes window to close instantly
• Thinking destroyAllWindows controls window display timing
• Assuming cv2.imread can't read PNG images

5. You want to display an image inside a Jupyter notebook using matplotlib with correct colors. Which code snippet is correct?

hard

A. import cv2
import matplotlib.pyplot as plt
img = cv2.imread('img.jpg')
plt.imshow(img)
plt.show()

B. import matplotlib.pyplot as plt
img = cv2.imread('img.jpg')
plt.imshow(img)
plt.show()

C. import cv2
import matplotlib.pyplot as plt
img = cv2.imread('img.jpg')
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img_rgb)
plt.show()

D. import cv2
img = cv2.imread('img.jpg')
cv2.imshow('Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Solution

1. Step 1: Understand color format for matplotlib display

   OpenCV loads images in BGR format, but matplotlib expects RGB, so conversion is needed.

2. Step 2: Check each option

   import cv2 import matplotlib.pyplot as plt img = cv2.imread('img.jpg') plt.imshow(img) plt.show() misses color conversion, so colors will be wrong. import cv2 import matplotlib.pyplot as plt img = cv2.imread('img.jpg') img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) plt.imshow(img_rgb) plt.show() converts BGR to RGB correctly before display. import matplotlib.pyplot as plt img = cv2.imread('img.jpg') plt.imshow(img) plt.show() misses cv2 import (NameError) and color conversion. import cv2 img = cv2.imread('img.jpg') cv2.imshow('Image', img) cv2.waitKey(0) cv2.destroyAllWindows() uses cv2.imshow which opens a separate window, not inside notebook.

3. Final Answer:

   correctly converts BGR to RGB and displays image inside notebook -> Option C

4. Quick Check:

   Convert BGR to RGB before matplotlib display [OK]

Hint: Convert BGR to RGB before plt.imshow for correct colors [OK]

Common Mistakes:

• Skipping color conversion causing wrong colors
• Using cv2.imshow inside notebooks expecting inline display
• Assuming plt.imread always works for all image types