Movatterモバイル変換

[0]ホーム
URL:

Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

 Sign up
Appearance settings

Commit487b308

Browse files
committed
add sift tutorial
1 parent4dc67b1 commit487b308

File tree

9 files changed

+56
-0
lines changed

9 files changed

+56
-0
lines changed

‎README.md

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -55,6 +55,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
5555
-[How to Make a Barcode Reader in Python](https://www.thepythoncode.com/article/making-a-barcode-scanner-in-python). ([code](general/barcode-reader))
5656
-[Image Transformations using OpenCV in Python](https://www.thepythoncode.com/article/image-transformations-using-opencv-in-python). ([code](machine-learning/image-transformation))
5757
-[How to Apply HOG Feature Extraction in Python](https://www.thepythoncode.com/article/hog-feature-extraction-in-python). ([code](machine-learning/hog-feature-extraction))
58+
-[SIFT Feature Extraction using OpenCV in Python](https://www.thepythoncode.com/article/sift-feature-extraction-using-opencv-in-python). ([code](machine-learning/sift))
5859
-[Building a Speech Emotion Recognizer using Scikit-learn](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn). ([code](machine-learning/speech-emotion-recognition))
5960
-[How to Convert Speech to Text in Python](https://www.thepythoncode.com/article/using-speech-recognition-to-convert-speech-to-text-python). ([code](machine-learning/speech-recognition))
6061
-[Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).

‎machine-learning/sift/README.md

Lines changed: 5 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,5 @@
1+
#[SIFT Feature Extraction using OpenCV in Python](https://www.thepythoncode.com/article/sift-feature-extraction-using-opencv-in-python)
2+
To run this:
3+
-`pip3 install -r requirements.txt`
4+
- For feature keypoints extraction, use`sift.py`
5+
- For feature matching, use`feature_match.py`

‎machine-learning/sift/book.jpg

39.9 KB
Loading
Lines changed: 29 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,29 @@
1+
importcv2
2+
3+
# read the images
4+
img1=cv2.imread('book.jpg')
5+
img2=cv2.imread('table.jpg')
6+
7+
# convert images to grayscale
8+
img1=cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
9+
img2=cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)
10+
11+
# create SIFT object
12+
sift=cv2.xfeatures2d.SIFT_create()
13+
# detect SIFT features in both images
14+
keypoints_1,descriptors_1=sift.detectAndCompute(img1,None)
15+
keypoints_2,descriptors_2=sift.detectAndCompute(img2,None)
16+
# create feature matcher
17+
bf=cv2.BFMatcher(cv2.NORM_L1,crossCheck=True)
18+
# match descriptors of both images
19+
matches=bf.match(descriptors_1,descriptors_2)
20+
# sort matches by distance
21+
matches=sorted(matches,key=lambdax:x.distance)
22+
# draw first 50 matches
23+
matched_img=cv2.drawMatches(img1,keypoints_1,img2,keypoints_2,matches[:50],img2,flags=2)
24+
# show the image
25+
cv2.imshow('image',matched_img)
26+
# save the image
27+
cv2.imwrite("matched_images.jpg",matched_img)
28+
cv2.waitKey(0)
29+
cv2.destroyAllWindows()
105 KB
Loading
Lines changed: 3 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,3 @@
1+
opencv-contrib-python==3.4.2.16
2+
opencv-python==3.4.2.16
3+
numpy

‎machine-learning/sift/sift.py

Lines changed: 18 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,18 @@
1+
importcv2
2+
3+
# reading the image
4+
img=cv2.imread('table.jpg')
5+
# convert to greyscale
6+
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
7+
# create SIFT feature extractor
8+
sift=cv2.xfeatures2d.SIFT_create()
9+
# detect features from the image
10+
keypoints,descriptors=sift.detectAndCompute(img,None)
11+
# draw the detected key points
12+
sift_image=cv2.drawKeypoints(gray,keypoints,img)
13+
# show the image
14+
cv2.imshow('image',sift_image)
15+
# save the image
16+
cv2.imwrite("table-sift.jpg",sift_image)
17+
cv2.waitKey(0)
18+
cv2.destroyAllWindows()

‎machine-learning/sift/table-sift.jpg

64.9 KB
Loading

‎machine-learning/sift/table.jpg

89.2 KB
Loading

0 commit comments

Comments
 (0)
[8]ページ先頭
©2009-2025 Movatter.jp