Feature extraction approach in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to extract features from an image using a simple method.

Computer Vision

import cv2
image = cv2.imread('image.jpg', 0)
features = cv2.Canny(image, [1], 150)
print(features.shape)

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

A50

B100

C200

D300

Attempts:

3 left

💡 Hint

Common Mistakes

Using a threshold too high or too low can miss edges or detect too many.

The Canny edge detector requires two threshold values. Here, 100 is a common lower threshold to detect edges.

2fill in blank

medium

Complete the code to compute Histogram of Oriented Gradients (HOG) features from an image.

Computer Vision

from skimage.feature import hog
from skimage import data, exposure
image = data.astronaut()
features, hog_image = hog(image, pixels_per_cell=(8, 8), cells_per_block=(2, 2), visualize=True, [1]=True)
print(features.shape)

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

Amultichannel

Bnormalize

Cblock_norm

Dtransform_sqrt

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to set multichannel=True causes errors or wrong features.

The 'multichannel=True' parameter tells the function to treat the input as a color image.

3fill in blank

hard

Fix the error in the code to extract SIFT features from an image.

Computer Vision

import cv2
image = cv2.imread('image.jpg', 0)
sift = cv2.SIFT_create()
keypoints, descriptors = sift.[1](image)
print(len(keypoints))

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

AdetectAndCompute

Bdetect

Ccompute

DdetectAndDescribe

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'detect' or 'compute' alone causes errors or incomplete results.

The correct method to detect keypoints and compute descriptors in one step is 'detectAndCompute'.

4fill in blank

hard

Fill both blanks to create a dictionary of feature descriptors for each keypoint.

Computer Vision

features_dict = [1](kp.pt: desc for kp, desc in zip(keypoints, [2]))
print(len(features_dict))

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

Adict

Bdescriptors

Clist

Dkeypoints

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'list' instead of 'dict' or wrong variable names.

We use 'dict' to create a dictionary, and 'descriptors' holds the feature vectors for each keypoint.

5fill in blank

hard

Fill all three blanks to filter features with descriptor length greater than 100.

Computer Vision

filtered_features = {kp: desc for kp, desc in features_dict.items() if len(desc) [1] [2]
print(len(filtered_features))

threshold = [3]

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

A>

B100

C<

D50

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' or wrong threshold values.

The code filters descriptors with length greater than 100, so the operator is '>' and threshold is 100.

Practice

1. What is the main purpose of feature extraction in computer vision?

easy

A. To increase the size of image files

B. To change image colors randomly

C. To convert images into numbers that describe important parts

D. To delete parts of the image

Solution

1. Step 1: Understand feature extraction goal

   Feature extraction transforms images into numerical data representing key details.

2. Step 2: Compare options to this goal

   Only To convert images into numbers that describe important parts describes this process correctly; others describe unrelated actions.

3. Final Answer:

   To convert images into numbers that describe important parts -> Option C

4. Quick Check:

   Feature extraction = convert images to numbers [OK]

Hint: Feature extraction means turning images into numbers [OK]

Common Mistakes:

• Thinking feature extraction changes image colors
• Confusing feature extraction with image resizing
• Believing it deletes image parts

2. Which of the following is a correct way to describe SIFT in feature extraction?

easy

A. A way to convert images to grayscale

B. A method that detects and describes local features in images

C. A technique to increase image resolution

D. A method to compress image files

Solution

1. Step 1: Recall what SIFT does

   SIFT finds and describes important local features in images for matching and recognition.

2. Step 2: Match options to SIFT's function

   Only A method that detects and describes local features in images correctly describes SIFT; others describe unrelated image processes.

3. Final Answer:

   A method that detects and describes local features in images -> Option B

4. Quick Check:

   SIFT = local feature detection [OK]

Hint: SIFT finds key points and describes them [OK]

Common Mistakes:

• Confusing SIFT with image resizing
• Thinking SIFT changes image colors
• Believing SIFT compresses images

3. Given the following Python code using OpenCV, what will be the shape of the feature vector extracted by SIFT for an image with 500 keypoints?

import cv2
img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)
sift = cv2.SIFT_create()
keypoints, descriptors = sift.detectAndCompute(img, None)
print(descriptors.shape)

medium

A. (null, 128)

B. (128, 500)

C. (500, 64)

D. (500, 128)

Solution

1. Step 1: Understand SIFT descriptor shape

   SIFT descriptors have 128 features per keypoint, so shape is (number_of_keypoints, 128).

2. Step 2: Apply to given keypoints

   With 500 keypoints, descriptors shape is (500, 128).

3. Final Answer:

   (500, 128) -> Option D

4. Quick Check:

   SIFT descriptors shape = (keypoints, 128) [OK]

Hint: SIFT descriptors = keypoints x 128 features [OK]

Common Mistakes:

• Swapping dimensions of descriptors
• Assuming 64 features per keypoint
• Thinking descriptors shape depends on image size

4. You wrote this code to extract features using SIFT but get an error:

import cv2
img = cv2.imread('image.jpg')
sift = cv2.SIFT_create()
keypoints, descriptors = sift.detectAndCompute(img, None)
print(len(keypoints))

What is the likely cause of the error?

medium

A. The image is not loaded in grayscale, causing SIFT to fail

B. SIFT_create() is not a valid OpenCV function

C. detectAndCompute requires a mask argument

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

Solution

1. Step 1: Check image loading method

   The image is loaded in color by default; SIFT expects grayscale images.

2. Step 2: Identify error cause

   Not converting to grayscale can cause detectAndCompute to fail or return null.

3. Final Answer:

   The image is not loaded in grayscale, causing SIFT to fail -> Option A

4. Quick Check:

   Load image grayscale for SIFT [OK]

Hint: Always load images in grayscale for SIFT [OK]

Common Mistakes:

• Thinking SIFT_create() is invalid
• Believing mask argument is mandatory
• Assuming print syntax is wrong

5. You want to extract features from images for a complex object recognition task. Which approach is best to capture detailed and high-level features?

hard

A. Use a deep learning model like a convolutional neural network (CNN)

B. Use simple edge detection filters only

C. Use random pixel values as features

D. Use image resizing without feature extraction

Solution

1. Step 1: Understand feature needs for complex tasks

   Complex object recognition requires capturing detailed and abstract features.

2. Step 2: Compare methods for feature extraction

   Deep learning models like CNNs learn rich features automatically, outperforming simple filters or random values.

3. Final Answer:

   Use a deep learning model like a convolutional neural network (CNN) -> Option A

4. Quick Check:

   Complex features need CNNs [OK]

Hint: Deep models capture complex features best [OK]

Common Mistakes:

• Relying only on simple filters
• Using random pixels as features
• Skipping feature extraction by resizing only