Displaying images (cv2.imshow, matplotlib) in Computer Vision - Model Metrics & Evaluation

Metrics & Evaluation - Displaying images (cv2.imshow, matplotlib)

Which metric matters for displaying images and WHY

When showing images using cv2.imshow or matplotlib, the key metric is image clarity and correctness. This means the image should appear as expected without distortion, correct colors, and proper size. Since this is about visualization, metrics like accuracy or precision do not apply. Instead, the focus is on visual correctness to ensure the image data is displayed properly for human interpretation.

Confusion matrix or equivalent visualization

For image display, there is no confusion matrix. Instead, we can think of a simple checklist to verify the display:

    +-------------------------+
    | Image Display Checklist  |
    +-------------------------+
    | 1. Image loaded correctly |
    | 2. Color channels right  |
    | 3. Image size correct    |
    | 4. Window opens properly |
    | 5. Image updates if needed|
    +-------------------------+
    

If any of these fail, the image may not show correctly.

Tradeoff: cv2.imshow vs matplotlib.pyplot.imshow

cv2.imshow is fast and simple for quick image display in OpenCV workflows. It opens a separate window and is good for real-time updates.

matplotlib.pyplot.imshow is more flexible for plotting images inside notebooks or scripts with titles, axes, and color maps. It is slower but better for detailed visualization.

Tradeoff example:

• If you want to quickly check frames from a video, use cv2.imshow.
• If you want to show images with labels or multiple images in one figure, use matplotlib.pyplot.imshow.

What "good" vs "bad" image display looks like

Good display:

• Image window opens without error.
• Colors look natural (e.g., RGB images show correct colors).
• Image size matches expected dimensions.
• Image updates correctly if changed.

Bad display:

• Window does not open or crashes.
• Colors look strange (e.g., blue and red swapped).
• Image is stretched or squished.
• Image does not update or shows old data.

Common pitfalls when displaying images

• Color channel order: OpenCV uses BGR order, but matplotlib uses RGB. Forgetting to convert colors causes wrong colors.
• Not calling waitKey with cv2.imshow: Without cv2.waitKey(), the window may not display or close immediately.
• Not calling plt.show() with matplotlib: The image may not appear if plt.show() is missing.
• Image data type: Using wrong data types (e.g., float instead of uint8) can cause display issues.
• Window blocking: cv2.imshow blocks code execution until a key press, which can confuse beginners.

Self-check question

Your code uses cv2.imshow to show an image, but the colors look strange (reds appear blue). What is likely the problem and how do you fix it?

Answer: The image is likely in RGB format (e.g., loaded with PIL or matplotlib), but cv2.imshow expects BGR. Fix by converting with cv2.cvtColor(image, cv2.COLOR_RGB2BGR).

Key Result

For image display, visual correctness (color, size, window behavior) is the key metric, not numeric scores.

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