First image processing program in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this image processing code?

Consider this Python code that loads an image, converts it to grayscale, and prints the shape of the resulting image array. What will be printed?

Computer Vision

import cv2
image = cv2.imread('sample.jpg')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
print(gray_image.shape)

A (width, height)

B (height, width, 3)

C Error: cv2.cvtColor requires 3-channel input

D (height, width)

💡 Hint

Grayscale images have only one channel, so the shape has two dimensions.

The cv2.cvtColor function converts the color image to grayscale, resulting in a 2D array with shape (height, width).

❓ Model Choice

intermediate

Which model is best for simple image classification?

You want to classify images of cats and dogs with a small dataset. Which model is the best choice?

A A decision tree using image file names as features

B A simple logistic regression model using raw pixel values

C A pretrained convolutional neural network with transfer learning

D A deep convolutional neural network with many layers trained from scratch

💡 Hint

Using pretrained models helps when data is limited.

Pretrained CNNs with transfer learning leverage learned features and perform well on small datasets.

❓ Hyperparameter

advanced

Which hyperparameter change will most reduce overfitting in image classification?

You trained a CNN that performs well on training images but poorly on new images. Which hyperparameter adjustment will most likely reduce overfitting?

A Add dropout layers with a dropout rate of 0.5

B Increase the number of convolutional layers

C Decrease the batch size to 1

D Remove data augmentation

💡 Hint

Dropout randomly disables neurons during training to prevent memorization.

Adding dropout helps the model generalize better by reducing reliance on specific neurons.

❓ Metrics

advanced

Which metric is best to evaluate image segmentation quality?

You built a model to segment objects in images. Which metric best measures how well the predicted segments match the true segments?

A Accuracy

B Intersection over Union (IoU)

C Mean Squared Error

D Precision

💡 Hint

This metric compares the overlap between predicted and true segments.

IoU measures the overlap area divided by the union area of predicted and true segments, making it ideal for segmentation.

🔧 Debug

expert

What error does this image processing code raise?

This code tries to resize an image but raises an error. What is the error?

Computer Vision

import cv2
image = cv2.imread('nonexistent.jpg')
resized = cv2.resize(image, (100, 100))

A cv2.error: OpenCV(…) error: (-215:Assertion failed) !ssize.empty() in function 'resize'

B FileNotFoundError: No such file or directory: 'nonexistent.jpg'

C TypeError: 'NoneType' object is not subscriptable

D ValueError: invalid literal for int() with base 10

💡 Hint

cv2.imread returns None if the file is missing, causing resize to fail.

cv2.resize fails because the input image is None, triggering an assertion error inside OpenCV.

Practice

1. What does the OpenCV function imread do in an image processing program?

easy

A. It displays an image on the screen.

B. It reads an image file and loads it into the program.

C. It converts an image from color to grayscale.

D. It saves an image to a file.

Solution

1. Step 1: Understand the purpose of imread

   The function imread is used to load an image from a file into the program's memory.

2. Step 2: Differentiate from other functions

   Functions like imshow display images, and cvtColor changes image colors, so they do not read files.

3. Final Answer:

   It reads an image file and loads it into the program. -> Option B

4. Quick Check:

   imread = load image [OK]

Hint: imread always loads images from files [OK]

Common Mistakes:

• Confusing imread with imshow
• Thinking imread changes image colors
• Assuming imread saves images

2. Which of the following is the correct syntax to display an image stored in variable img using OpenCV?

easy

A. cv2.display(img)

B. cv2.showimage(img)

C. cv2.show('Window', img)

D. cv2.imshow('Window', img)

Solution

1. Step 1: Recall the OpenCV display function

   The correct function to show an image is cv2.imshow, which takes a window name and the image variable.

2. Step 2: Check the syntax of options

   Only cv2.imshow('Window', img) uses cv2.imshow with correct parameters: a string window name and the image.

3. Final Answer:

   cv2.imshow('Window', img) -> Option D

4. Quick Check:

   imshow = show image [OK]

Hint: imshow needs a window name and image [OK]

Common Mistakes:

• Using non-existent functions like display or showimage
• Forgetting the window name argument
• Swapping argument order

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

import cv2
img = cv2.imread('photo.jpg')
print(img.shape)

medium

A. It prints the image pixel values.

B. It raises an error because shape is not valid.

C. It prints the dimensions of the image as (height, width, channels).

D. It prints the file size of 'photo.jpg'.

Solution

1. Step 1: Understand what img.shape returns

   In OpenCV, img.shape gives the dimensions of the image as a tuple: (height, width, number of color channels).

2. Step 2: Differentiate from other outputs

   It does not print pixel values or file size, and shape is a valid attribute for images loaded by imread.

3. Final Answer:

   It prints the dimensions of the image as (height, width, channels). -> Option C

4. Quick Check:

   img.shape = image size [OK]

Hint: shape shows image size and channels [OK]

Common Mistakes:

• Expecting pixel data instead of shape
• Thinking shape is a method, not attribute
• Confusing file size with image dimensions

4. Identify the error in this code snippet:

import cv2
img = cv2.imread('image.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('Gray Image')
cv2.waitKey(0)
cv2.destroyAllWindows()

medium

A. Missing the image argument in cv2.imshow function.

B. cv2.cvtColor cannot convert color images.

C. cv2.waitKey requires an argument of 1, not 0.

D. cv2.destroyAllWindows should be called before imshow.

Solution

1. Step 1: Check the usage of cv2.imshow

   The function cv2.imshow requires two arguments: a window name and the image to display. Here, the image argument is missing.

2. Step 2: Verify other function calls

   cv2.cvtColor correctly converts color images, waitKey(0) waits indefinitely, and destroyAllWindows is correctly placed after showing images.

3. Final Answer:

   Missing the image argument in cv2.imshow function. -> Option A

4. Quick Check:

   imshow needs image argument [OK]

Hint: imshow always needs image to show [OK]

Common Mistakes:

• Forgetting the image argument in imshow
• Misunderstanding waitKey argument
• Calling destroyAllWindows too early

5. You want to write a program that reads an image, converts it to grayscale, and then saves the grayscale image. Which sequence of OpenCV functions is correct?

hard

A. cv2.imread() -> cv2.cvtColor() -> cv2.imwrite()

B. cv2.imshow() -> cv2.cvtColor() -> cv2.imwrite()

C. cv2.imread() -> cv2.imshow() -> cv2.cvtColor()

D. cv2.imwrite() -> cv2.imread() -> cv2.cvtColor()

Solution

1. Step 1: Understand the task steps

   The program must first read the image, then convert it to grayscale, and finally save the new image.

2. Step 2: Match functions to steps

   cv2.imread() reads the image, cv2.cvtColor() converts color spaces, and cv2.imwrite() saves the image to a file.

3. Final Answer:

   cv2.imread() -> cv2.cvtColor() -> cv2.imwrite() -> Option A

4. Quick Check:

   Read -> Convert -> Save = imread, cvtColor, imwrite [OK]

Hint: Read first, convert second, save last [OK]

Common Mistakes:

• Trying to save before reading
• Showing image before converting
• Mixing order of functions