Why computer vision enables intelligent flight in Drone Programming - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an image using OpenCV.

Drone Programming

image = cv2.[1]('drone_view.jpg')

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

Aresize

Bimread

Cimwrite

Dimshow

Attempts:

3 left

💡 Hint

Common Mistakes

Using imshow instead of imread, which only displays images.

The imread function loads an image from a file, which is essential for computer vision tasks in drones.

2fill in blank

medium

Complete the code to convert the image to grayscale for easier processing.

Drone Programming

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

Choosing color space conversions that do not produce grayscale images.

Converting to grayscale simplifies the image by removing color, which helps the drone focus on shapes and edges.

3fill in blank

hard

Fix the error in the code to detect edges using the Canny method.

Drone Programming

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

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

Aedges

Bimage

Cgray_image

Dcv2

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the original color image instead of the grayscale image.

The Canny edge detector requires a grayscale image as input, so we use gray_image.

4fill in blank

hard

Fill both blanks to create a dictionary of detected objects with their confidence scores.

Drone Programming

detected_objects = [1](object_name: confidence for object_name, confidence in [2].items() if confidence > 0.5)

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

Adict

Blist

Cdetections

Dset

Attempts:

3 left

💡 Hint

Common Mistakes

Using list or set instead of dict, or wrong variable name for detections.

We use dict to create a dictionary and detections as the source data holding object names and confidence scores.

5fill in blank

hard

Fill all three blanks to train a simple neural network model for flight path prediction.

Drone Programming

model = Sequential()
model.add(Dense([1], activation='relu', input_shape=([2],)))
model.add(Dense([3], activation='softmax'))

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

A64

B10

C100

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up input shape and number of neurons, or wrong output layer size.

The first Dense layer has 64 neurons, input shape is 5 features, and output layer has 10 classes for prediction.

Practice

1. Why is computer vision important for drones to fly intelligently?

easy

A. It helps drones see and understand their surroundings.

B. It makes drones fly faster without any sensors.

C. It allows drones to recharge their batteries mid-flight.

D. It controls the drone's motor speed directly.

Solution

1. Step 1: Understand the role of computer vision in drones

   Computer vision lets drones capture images and interpret what they see around them.

2. Step 2: Connect vision to intelligent flight

   By understanding surroundings, drones can avoid obstacles and navigate safely.

3. Final Answer:

   It helps drones see and understand their surroundings. -> Option A

4. Quick Check:

   Computer vision = understanding environment [OK]

Hint: Think: vision means 'seeing' and understanding [OK]

Common Mistakes:

• Confusing vision with speed control
• Thinking vision recharges batteries
• Assuming vision replaces all sensors

2. Which Python code snippet correctly imports a library used for computer vision in drones?

easy

A. import computer_vision

B. import dronevision

C. import cv2

D. import vision.cv

Solution

1. Step 1: Identify common computer vision libraries

   OpenCV is a popular library for computer vision, imported as cv2 in Python.

2. Step 2: Check the import syntax

   Correct import is 'import cv2' without errors or typos.

3. Final Answer:

   import cv2 -> Option C

4. Quick Check:

   OpenCV import = cv2 [OK]

Hint: Remember OpenCV is imported as cv2 in Python [OK]

Common Mistakes:

• Using non-existent library names
• Wrong import syntax
• Confusing module names

3. What will the following Python code print when a drone detects an obstacle using computer vision?

import cv2
obstacle_detected = True
if obstacle_detected:
    print('Stop and avoid obstacle')
else:
    print('Continue flying')

medium

A. Continue flying

B. Stop and avoid obstacle

C. Error: obstacle_detected undefined

D. No output

Solution

1. Step 1: Check the value of obstacle_detected

   The variable obstacle_detected is set to True, meaning an obstacle is detected.

2. Step 2: Follow the if-else logic

   Since obstacle_detected is True, the code prints 'Stop and avoid obstacle'.

3. Final Answer:

   Stop and avoid obstacle -> Option B

4. Quick Check:

   True condition prints stop message [OK]

Hint: True triggers first print statement [OK]

Common Mistakes:

• Ignoring the True value and picking else output
• Thinking variable is undefined
• Assuming no output without print

4. The following code is meant to detect obstacles and print a warning, but it does not work as expected. What is the error?

obstacle_detected = False
if obstacle_detected = True:
    print('Obstacle ahead!')
else:
    print('Path is clear')

medium

A. Missing import statement for cv2

B. Variable obstacle_detected is not defined

C. Print statement syntax is wrong

D. Using '=' instead of '==' in the if condition

Solution

1. Step 1: Identify the if condition syntax

   The code uses '=' which is assignment, not comparison. It should use '==' to compare values.

2. Step 2: Understand why this causes error

   Using '=' in if condition causes a syntax error, stopping the code from running.

3. Final Answer:

   Using '=' instead of '==' in the if condition -> Option D

4. Quick Check:

   Comparison needs '==' not '=' [OK]

Hint: Use '==' for comparison in if statements [OK]

Common Mistakes:

• Confusing assignment '=' with comparison '=='
• Thinking import is needed for this error
• Assuming print syntax is wrong

5. A drone uses computer vision to detect obstacles and decide its flight path. Which approach best enables the drone to avoid collisions while following a moving target?

hard

A. Use a camera to detect obstacles and a tracking algorithm to follow the target.

B. Fly randomly and hope to avoid obstacles by chance.

C. Only use GPS data without any vision sensors.

D. Disable obstacle detection to increase speed.

Solution

1. Step 1: Understand the need for obstacle detection

   To avoid collisions, the drone must see obstacles using a camera or sensors.

2. Step 2: Combine obstacle detection with target tracking

   Following a moving target requires tracking algorithms that work with vision data.

3. Step 3: Evaluate other options

   Random flying or ignoring obstacles risks crashes; GPS alone lacks obstacle info.

4. Final Answer:

   Use a camera to detect obstacles and a tracking algorithm to follow the target. -> Option A

5. Quick Check:

   Vision + tracking = safe, smart flight [OK]

Hint: Combine vision for obstacles and tracking for targets [OK]

Common Mistakes:

• Ignoring obstacle detection
• Relying only on GPS
• Flying randomly without control