Why edge deployment enables real-time CV in Computer Vision - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an image for real-time processing on an edge device.

Computer Vision

import cv2
image = cv2.imread([1])

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

A'image.jpg'

Bimage.jpg

Cload_image

Dcv2.image

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the filename

Using a variable name without defining it

The cv2.imread function requires the image file path as a string, so it must be enclosed in quotes.

2fill in blank

medium

Complete the code to convert the image to grayscale for faster edge processing.

Computer Vision

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

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

Acv2.COLOR_BGR2RGB

Bcv2.COLOR_BGR2GRAY

Ccv2.COLOR_RGB2GRAY

Dcv2.COLOR_GRAY2BGR

Attempts:

3 left

💡 Hint

Common Mistakes

Using COLOR_BGR2RGB instead of grayscale

Using COLOR_GRAY2BGR which is reverse

To convert a BGR image to grayscale, use cv2.COLOR_BGR2GRAY.

3fill in blank

hard

Fix the error in the code to perform edge detection using Canny on the grayscale image.

Computer Vision

edges = cv2.Canny([1], 100, 200)

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

Acv2

Bimage

Cedges

Dgray_image

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the original color image

Passing the output variable itself

Canny edge detection requires a single-channel (grayscale) image, so we must use gray_image.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each pixel coordinate to its edge value if the edge value is greater than zero.

Computer Vision

edge_points = {(x, y): [1][y, x] for y in range(edges.shape[0]) for x in range(edges.shape[1]) if [2][y, x] > 0}

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

Aedges

Bgray_image

Cimage

Dedge_points

Attempts:

3 left

💡 Hint

Common Mistakes

Using grayscale image instead of edges for values

Using wrong variable names

We want to map coordinates to their edge values, so both blanks should use the edges array.

5fill in blank

hard

Fill all three blanks to create a function that runs edge detection on an input image and returns the edge points dictionary.

Computer Vision

def detect_edges([1]):
    gray = cv2.cvtColor([2], cv2.COLOR_BGR2GRAY)
    edges = cv2.Canny(gray, 50, 150)
    return {(x, y): edges[y, x] for y in range(edges.shape[0]) for x in range(edges.shape[1]) if edges[y, x] > [3]

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

Ainput_image

Bimage

C0

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names for input and processing

Checking for edges > 1 instead of 0

The function takes input_image as input, converts it to grayscale, detects edges, and returns points where edge value is greater than 0.

Practice

1. Why does deploying computer vision models at the edge help achieve real-time processing?

easy

A. Because it processes data near the source, reducing delay

B. Because it sends all data to the cloud for faster computation

C. Because it uses larger models that take more time

D. Because it requires a constant internet connection

Solution

1. Step 1: Understand edge deployment location

   Edge deployment means running models close to where data is collected, like cameras or sensors.

2. Step 2: Connect location to speed

   Processing near the source reduces the time data travels, so results come faster.

3. Final Answer:

   Because it processes data near the source, reducing delay -> Option A

4. Quick Check:

   Edge location = faster results [OK]

Hint: Edge means close to data source for speed [OK]

Common Mistakes:

• Thinking cloud processing is faster for real-time
• Assuming edge needs constant internet
• Confusing model size with deployment location

2. Which of the following is the correct way to describe edge deployment in computer vision?

easy

A. Running CV models on devices near the data source

B. Running CV models on a remote cloud server

C. Sending all images to a central server for processing

D. Using only offline datasets without live data

Solution

1. Step 1: Define edge deployment

   Edge deployment means running models on devices close to where data is created, like cameras or phones.

2. Step 2: Match definition to options

   Running CV models on devices near the data source matches this definition exactly, others describe cloud or offline processing.

3. Final Answer:

   Running CV models on devices near the data source -> Option A

4. Quick Check:

   Edge = near data source [OK]

Hint: Edge means near data source, not cloud [OK]

Common Mistakes:

• Confusing edge with cloud computing
• Thinking edge means offline only
• Mixing up data sending and processing location

3. Consider this Python code simulating edge deployment latency:

def process_at_edge(data):
    # Simulate fast processing
    return f"Processed {data} quickly"

def process_in_cloud(data):
    # Simulate delay
    import time
    time.sleep(2)  # 2 seconds delay
    return f"Processed {data} slowly"

result = process_at_edge('image1')
print(result)

What will be printed?

medium

A. Processed image1 slowly

B. Processed image1 quickly

C. SyntaxError

D. No output

Solution

1. Step 1: Analyze function calls

   The code calls process_at_edge('image1'), which returns immediately with a quick message.

2. Step 2: Understand output

   It prints the returned string: 'Processed image1 quickly'. The cloud function is not called here.

3. Final Answer:

   Processed image1 quickly -> Option B

4. Quick Check:

   Edge function returns fast output [OK]

Hint: Look at which function is called before print [OK]

Common Mistakes:

• Assuming cloud function runs instead
• Confusing sleep delay with output
• Expecting syntax errors from imports

4. This code tries to simulate edge deployment but has a bug:

def edge_process(data):
    return f"Processed {data}"

result = edge_process
print(result('frame1'))

What is the error and how to fix it?

medium

A. TypeError because result is a function, fix by assigning result = edge_process('frame1')

B. TypeError because result is a string, fix by calling edge_process()

C. No error, code runs fine

D. SyntaxError due to missing colon

Solution

1. Step 1: Identify variable assignment

   result = edge_process assigns the function object itself to result (function reference).

2. Step 2: Analyze print statement

   print(result('frame1')) calls the function via result and prints 'Processed frame1'. No error occurs; code runs fine.

3. Final Answer:

   No error, code runs fine -> Option C

4. Quick Check:

   Function reference is callable [OK]

Hint: Assigning function to variable allows direct calling [OK]

Common Mistakes:

• Thinking calling result('frame1') causes TypeError
• Confusing function reference with function call
• Misreading print statement or expecting syntax error

5. A security camera system needs to detect intruders instantly. Which edge deployment setup best supports this real-time need?

hard

A. Send video to cloud for processing, then wait for results

B. Store video locally and analyze once a day

C. Use a slow but highly accurate model on a remote server

D. Process video on local device with lightweight CV model

Solution

1. Step 1: Identify real-time requirement

   Instant detection means minimal delay between capturing and alerting.

2. Step 2: Match deployment to speed

   Processing on local device with a lightweight model reduces delay and avoids internet dependency.

3. Step 3: Evaluate other options

   Cloud or remote processing adds delay; storing and analyzing later is not real-time.

4. Final Answer:

   Process video on local device with lightweight CV model -> Option D

5. Quick Check:

   Local lightweight model = real-time [OK]

Hint: Local lightweight models reduce delay for instant detection [OK]

Common Mistakes:

• Choosing cloud processing for real-time
• Ignoring model speed vs accuracy tradeoff
• Thinking storing data delays detection