add sift tutorial

Author: x4nth055

README.md

@@ -55,6 +55,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
         - [How to Make a Barcode Reader in Python](https://www.thepythoncode.com/article/making-a-barcode-scanner-in-python). ([code](general/barcode-reader))
         - [Image Transformations using OpenCV in Python](https://www.thepythoncode.com/article/image-transformations-using-opencv-in-python). ([code](machine-learning/image-transformation))
         - [How to Apply HOG Feature Extraction in Python](https://www.thepythoncode.com/article/hog-feature-extraction-in-python). ([code](machine-learning/hog-feature-extraction))
+        - [SIFT Feature Extraction using OpenCV in Python](https://www.thepythoncode.com/article/sift-feature-extraction-using-opencv-in-python). ([code](machine-learning/sift))
     - [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))
     - [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))
     - [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

@@ -0,0 +1,5 @@
+# [SIFT Feature Extraction using OpenCV in Python](https://www.thepythoncode.com/article/sift-feature-extraction-using-opencv-in-python)
+To run this:
+- `pip3 install -r requirements.txt`
+- For feature keypoints extraction, use `sift.py`
+- For feature matching, use `feature_match.py`

machine-learning/sift/book.jpg

@@ -0,0 +1,29 @@
+import cv2
+
+# read the images
+img1 = cv2.imread('book.jpg')  
+img2 = cv2.imread('table.jpg')
+
+# convert images to grayscale
+img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+
+# create SIFT object
+sift = cv2.xfeatures2d.SIFT_create()
+# detect SIFT features in both images
+keypoints_1, descriptors_1 = sift.detectAndCompute(img1,None)
+keypoints_2, descriptors_2 = sift.detectAndCompute(img2,None)
+# create feature matcher
+bf = cv2.BFMatcher(cv2.NORM_L1, crossCheck=True)
+# match descriptors of both images
+matches = bf.match(descriptors_1,descriptors_2)
+# sort matches by distance
+matches = sorted(matches, key = lambda x:x.distance)
+# draw first 50 matches
+matched_img = cv2.drawMatches(img1, keypoints_1, img2, keypoints_2, matches[:50], img2, flags=2)
+# show the image
+cv2.imshow('image', matched_img)
+# save the image
+cv2.imwrite("matched_images.jpg", matched_img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()

machine-learning/sift/feature_match.py

@@ -0,0 +1,3 @@
+opencv-contrib-python==3.4.2.16
+opencv-python==3.4.2.16
+numpy

machine-learning/sift/matched_images.jpg

@@ -0,0 +1,18 @@
+import cv2
+
+# reading the image
+img = cv2.imread('table.jpg')
+# convert to greyscale
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+# create SIFT feature extractor
+sift = cv2.xfeatures2d.SIFT_create()
+# detect features from the image
+keypoints, descriptors = sift.detectAndCompute(img, None)
+# draw the detected key points
+sift_image = cv2.drawKeypoints(gray, keypoints, img)
+# show the image
+cv2.imshow('image', sift_image)
+# save the image
+cv2.imwrite("table-sift.jpg", sift_image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()