CV applications (autonomous driving, medical, retail) in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an image using OpenCV.

Computer Vision

import cv2
image = cv2.[1]('road.jpg')

Drag options to blanks, or click blank then click option'

Aresize

Bimshow

Cimwrite

Dimread

Attempts:

3 left

💡 Hint

Common Mistakes

Using imshow instead of imread will cause an error because imshow displays images, not loads them.

Using imwrite tries to save an image, not load it.

The imread function loads an image from a file into memory.

2fill in blank

medium

Complete the code to convert a color image to grayscale.

Computer Vision

gray_image = cv2.cvtColor(image, [1])

Drag options to blanks, or click blank then click option'

Acv2.COLOR_BGR2GRAY

Bcv2.COLOR_RGB2BGR

Ccv2.COLOR_GRAY2BGR

Dcv2.COLOR_BGR2RGB

Attempts:

3 left

💡 Hint

Common Mistakes

Using COLOR_BGR2RGB changes color format but does not convert to grayscale.

Using COLOR_GRAY2BGR converts grayscale to color, which is the opposite.

The cv2.COLOR_BGR2GRAY flag converts a color image to grayscale.

3fill in blank

hard

Fix the error in the code to detect edges using Canny edge detector.

Computer Vision

edges = cv2.Canny(image, [1], 150)

Drag options to blanks, or click blank then click option'

A100

B'100'

CNone

Dimage

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a string instead of a number causes a type error.

Passing None will cause the function to fail.

The Canny function requires numeric thresholds. Using a string or None causes errors.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps image names to their sizes if width {{BLANK_1}} 500 and height {{BLANK_2}} 500.

Computer Vision

sizes = {name: (img.shape[1], img.shape[0]) for name, img in images.items() if img.shape[1] [1] 500 and img.shape[0] [2] 500}

Drag options to blanks, or click blank then click option'

A>

B<

C>=

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong comparison operators reverses the filter logic.

Using >= or <= changes the condition boundaries.

The code filters images with width greater than 500 and height less than 500.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps patient IDs to their diagnosis if confidence {{BLANK_1}} 0.8 and diagnosis {{BLANK_2}} 'positive' and patient ID {{BLANK_3}} starts with 'P'.

Computer Vision

results = {pid: diag for pid, (diag, conf) in data.items() if conf [1] 0.8 and diag [2] 'positive' and pid.[3]('P')}

Drag options to blanks, or click blank then click option'

A>=

B==

Cstartswith

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' instead of '==' for diagnosis causes wrong filtering.

Using '>' instead of '>=' excludes confidence exactly 0.8.

Using a wrong string method for patient ID causes errors.

The code filters patients with confidence at least 0.8, diagnosis equal to 'positive', and IDs starting with 'P'.

Practice

1. Which of the following is a common use of computer vision in autonomous driving?

easy

A. Detecting pedestrians and other vehicles on the road

B. Managing inventory in a warehouse

C. Analyzing blood samples in a lab

D. Recommending products to online shoppers

Solution

1. Step 1: Understand autonomous driving needs

   Autonomous cars need to see and understand their surroundings to drive safely.

2. Step 2: Match computer vision tasks to driving

   Detecting pedestrians and vehicles helps the car avoid accidents and navigate roads.

3. Final Answer:

   Detecting pedestrians and other vehicles on the road -> Option A

4. Quick Check:

   Autonomous driving = detecting road objects [OK]

Hint: Autonomous driving means seeing road and traffic [OK]

Common Mistakes:

• Confusing retail or medical uses with driving
• Thinking CV only works for product tracking
• Mixing up lab analysis with driving tasks

2. Which Python library is commonly used for image processing in computer vision tasks?

easy

A. NumPy

B. Pandas

C. OpenCV

D. Matplotlib

Solution

1. Step 1: Identify libraries for image processing

   OpenCV is designed specifically for computer vision and image tasks.

2. Step 2: Compare other libraries

   NumPy handles arrays, Pandas handles tables, Matplotlib is for plotting, but OpenCV processes images.

3. Final Answer:

   OpenCV -> Option C

4. Quick Check:

   Image processing library = OpenCV [OK]

Hint: OpenCV is the go-to for CV image tasks [OK]

Common Mistakes:

• Choosing NumPy for image processing only
• Confusing Pandas with image libraries
• Picking Matplotlib which is for plotting

3. What will the following Python code output when using a pre-trained model to classify an image in a retail store?

import cv2
model = cv2.dnn.readNetFromONNX('product_classifier.onnx')
image = cv2.imread('shelf.jpg')
blob = cv2.dnn.blobFromImage(image, 1/255.0, (224,224), swapRB=True)
model.setInput(blob)
predictions = model.forward()
print(predictions.argmax())

medium

A. The raw image pixels as a list

B. The size of the input image

C. An error because the model file is missing

D. The index of the most likely product class detected

Solution

1. Step 1: Understand the code flow

   The code loads a model, prepares the image, runs prediction, and prints the class with highest score.

2. Step 2: Interpret the output

   predictions.argmax() returns the index of the class with the highest confidence, meaning the predicted product.

3. Final Answer:

   The index of the most likely product class detected -> Option D

4. Quick Check:

   Model prediction = class index [OK]

Hint: argmax gives highest scoring class index [OK]

Common Mistakes:

• Thinking it prints raw pixels
• Assuming it prints image size
• Expecting an error without checking file presence

4. A medical imaging model is not detecting tumors correctly. The code snippet is:

image = cv2.imread('scan.png')
blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(224,224))
model.setInput(blob)
pred = model.forward()

What is the likely issue causing poor detection?

medium

A. The image is not resized before blob creation

B. The scalefactor should normalize pixel values (e.g., 1/255.0)

C. The model input is missing color channel swap

D. The model file is not loaded

Solution

1. Step 1: Check image preprocessing

   Pixel values usually need normalization (scaling to 0-1) for good model input.

2. Step 2: Identify scalefactor problem

   Using scalefactor=1.0 keeps pixel values 0-255, which can confuse the model expecting 0-1.

3. Final Answer:

   The scalefactor should normalize pixel values (e.g., 1/255.0) -> Option B

4. Quick Check:

   Normalize pixels for model input [OK]

Hint: Normalize pixels with scalefactor 1/255.0 [OK]

Common Mistakes:

• Ignoring pixel normalization
• Assuming resizing alone fixes issues
• Forgetting color channel order matters

5. In an autonomous driving system, how can computer vision help improve safety during night driving?

hard

A. By using infrared cameras to detect pedestrians in low light

B. By increasing the car's speed automatically

C. By disabling sensors to save power

D. By only relying on GPS data

Solution

1. Step 1: Understand night driving challenges

   Low light makes it hard for normal cameras to see pedestrians and obstacles.

2. Step 2: Identify CV solution for low light

   Infrared cameras capture heat signatures, helping detect people even in darkness.

3. Final Answer:

   By using infrared cameras to detect pedestrians in low light -> Option A

4. Quick Check:

   Infrared helps see in dark [OK]

Hint: Infrared cameras detect heat at night [OK]

Common Mistakes:

• Thinking speed increase improves safety
• Disabling sensors reduces safety
• Relying only on GPS ignores vision needs