ORB features in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an ORB detector using OpenCV.

Computer Vision

import cv2
orb = cv2.[1]()

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

AFAST_create

BSIFT_create

CBRISK_create

DORB_create

Attempts:

3 left

💡 Hint

Common Mistakes

Using SIFT_create instead of ORB_create

Using FAST_create which is a different detector

Trying to instantiate ORB directly without the create method

The correct function to create an ORB detector in OpenCV is ORB_create().

2fill in blank

medium

Complete the code to detect keypoints and compute descriptors from an image using ORB.

Computer Vision

keypoints, descriptors = orb.[1](image)

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

Adetect

Bcompute

CdetectAndCompute

DfindKeypoints

Attempts:

3 left

💡 Hint

Common Mistakes

Using detect only, which returns keypoints but no descriptors

Using compute only, which requires keypoints as input

Using a non-existent method like findKeypoints

The detectAndCompute method detects keypoints and computes descriptors in one step.

3fill in blank

hard

Fix the error in the code to correctly draw keypoints on the image.

Computer Vision

output_image = cv2.drawKeypoints(image, keypoints, [1], flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

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

Akeypoints

BNone

Coutput_image

Dimage

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the input image as the output image argument

Passing keypoints instead of an image

Passing an uninitialized variable

The third argument to drawKeypoints should be None to create a new output image with keypoints drawn.

4fill in blank

hard

Fill both blanks to create a dictionary of ORB keypoints and their sizes for keypoints larger than 20.

Computer Vision

keypoint_sizes = {kp.pt: kp.[1] for kp in keypoints if kp.[1] [2] 20}

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

Asize

Bangle

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'angle' instead of 'size' for the attribute

Using '<' instead of '>' for filtering

Trying to access non-existent attributes

We access the size attribute of each keypoint and filter those with size greater than 20.

5fill in blank

hard

Fill all three blanks to create a dictionary of keypoint coordinates and their response values for keypoints with response above 0.01.

Computer Vision

keypoint_responses = {(int(kp.pt[0]), int(kp.pt[1])): kp.[1] for kp in keypoints if kp.[2] [3] 0.01}

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

Aresponse

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for filtering

Using wrong attribute names

Not converting coordinates to integers

The response attribute measures the strength of a keypoint. We filter keypoints with response greater than 0.01.

Practice

1. What is the main purpose of ORB features in computer vision?

easy

A. To find important points and describe them in images

B. To increase the resolution of images

C. To convert images to grayscale

D. To compress images for storage

Solution

1. Step 1: Understand ORB's role

   ORB is designed to detect key points (important points) in images and create descriptors that describe these points.

2. Step 2: Compare options

   The other options describe unrelated image processing tasks, not feature detection and description.

3. Final Answer:

   To find important points and describe them in images -> Option A

4. Quick Check:

   ORB = key points + descriptors [OK]

Hint: Remember ORB finds and describes key points fast [OK]

Common Mistakes:

• Confusing ORB with image enhancement
• Thinking ORB compresses images
• Assuming ORB changes image colors

2. Which of the following is the correct way to create an ORB detector in OpenCV with 500 features?

easy

A. orb = cv2.ORB_create(nfeatures=500)

B. orb = cv2.ORB(500)

C. orb = cv2.createORB(500)

D. orb = cv2.ORB_create(features=500)

Solution

1. Step 1: Recall ORB creation syntax

   The correct OpenCV function to create an ORB detector is cv2.ORB_create(), and the parameter to set number of features is nfeatures.

2. Step 2: Check options

   orb = cv2.ORB_create(nfeatures=500) uses correct function and parameter name. The other options use incorrect function names or parameter names.

3. Final Answer:

   orb = cv2.ORB_create(nfeatures=500) -> Option A

4. Quick Check:

   Use ORB_create with nfeatures [OK]

Hint: Use cv2.ORB_create(nfeatures=...) to set features [OK]

Common Mistakes:

• Using wrong function name like ORB()
• Using incorrect parameter name like features
• Missing parentheses in function call

3. Given the code below, what is the type of the variable kp after running kp, des = orb.detectAndCompute(img, None)?

import cv2
img = cv2.imread('image.jpg', 0)
orb = cv2.ORB_create(nfeatures=1000)
kp, des = orb.detectAndCompute(img, None)

medium

A. A numpy array of descriptors

B. A list of keypoint objects

C. A single keypoint object

D. An integer count of keypoints

Solution

1. Step 1: Understand detectAndCompute output

   The detectAndCompute method returns two values: keypoints and descriptors. Keypoints are returned as a list of keypoint objects.

2. Step 2: Match variable types

   Here, kp receives the keypoints list, des receives the descriptors numpy array.

3. Final Answer:

   A list of keypoint objects -> Option B

4. Quick Check:

   kp = list of keypoints [OK]

Hint: detectAndCompute returns (list, array) [OK]

Common Mistakes:

• Thinking kp is a numpy array
• Assuming kp is a single keypoint
• Confusing descriptors with keypoints

4. What is wrong with this code snippet for detecting ORB features?

import cv2
img = cv2.imread('image.jpg')
orb = cv2.ORB_create(nfeatures=300)
kp, des = orb.detectAndCompute(img, None)
print(len(kp))

medium

A. detectAndCompute requires a mask argument

B. nfeatures parameter is invalid

C. Image is not read in grayscale, causing detectAndCompute to fail

D. print(len(kp)) is incorrect syntax

Solution

1. Step 1: Check image reading mode

   ORB works best with grayscale images. The code reads the image without specifying grayscale, so img is color (3 channels).

2. Step 2: Understand impact on detectAndCompute

   detectAndCompute expects a single channel image; passing a color image can cause incorrect or no keypoints detected.

3. Final Answer:

   Image is not read in grayscale, causing detectAndCompute to fail -> Option C

4. Quick Check:

   Read image with cv2.imread('image.jpg', 0) [OK]

Hint: Always read images in grayscale for ORB [OK]

Common Mistakes:

• Ignoring image color channels
• Thinking nfeatures is invalid
• Assuming mask is mandatory

5. You want to match ORB features between two images but notice very few matches. Which change is most likely to improve the number of good matches?

hard

A. Use a different color space like HSV for detection

B. Decrease the image resolution before detecting features

C. Set the mask parameter to None explicitly

D. Increase the nfeatures parameter when creating the ORB detector

Solution

1. Step 1: Understand nfeatures impact

   nfeatures controls how many keypoints ORB tries to find. Increasing it allows more keypoints to be detected, increasing chances of matches.

2. Step 2: Evaluate other options

   Decreasing resolution reduces detail, hurting matches. Changing color space doesn't affect ORB which works on grayscale. Mask None is default and doesn't affect matches.

3. Final Answer:

   Increase the nfeatures parameter when creating the ORB detector -> Option D

4. Quick Check:

   More features = more matches [OK]

Hint: More features means more chances to match [OK]

Common Mistakes:

• Reducing image size to get more features
• Changing color space for ORB detection
• Misunderstanding mask parameter role