Détection de fonctionnalités AKAZE

import cv2 as cv
import matplotlib.pyplot as plt
 
# Open and convert the input and training-set image from BGR to GRAYSCALE
image1 = cv.imread(filename = 'image1.jpg',
                   flags = cv.IMREAD_GRAYSCALE)
 
image2 = cv.imread(filename = 'image2.jpg',
                   flags = cv.IMREAD_GRAYSCALE)
 
# Initiate AKAZE descriptor
AKAZE = cv.AKAZE_create()
 
# Find the keypoints and compute the descriptors for input and training-set image
keypoints1, descriptors1 = AKAZE.detectAndCompute(image1, None)
keypoints2, descriptors2 = AKAZE.detectAndCompute(image2, None)
 
# create BFMatcher object
BFMatcher = cv.BFMatcher(normType = cv.NORM_HAMMING,
                         crossCheck = True)
 
# Matching descriptor vectors using Brute Force Matcher
matches = BFMatcher.match(queryDescriptors = descriptors1,
                          trainDescriptors = descriptors2)
 
# Sort them in the order of their distance
matches = sorted(matches, key = lambda x: x.distance)
 
# Draw first 15 matches
output = cv.drawMatches(img1 = image1,
                        keypoints1 = keypoints1,
                        img2 = image2,
                        keypoints2 = keypoints2,
                        matches1to2 = matches[:15],
                        outImg = None,
                        flags = cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
 
plt.imshow(output)
plt.show()
 

# Imports
from matplotlib import pyplot as plt
import cv2 as cv
import os
import pickle
 
# Open and convert a input
# image from BGR to GRAYSCALE
image = cv.imread(filename = 'Figures/pexels-bestbe-models-2412691.jpg',
                   flags = cv.IMREAD_GRAYSCALE)
 
# AKAZE is a feature detector and descriptor

# Initiate AKAZE detector
AKAZE = cv.AKAZE_create()
 
# Find the keypoints with AKAZE
keypoints = AKAZE.detect(image, None)
 
# Print number of keypoints detected
print("Number of keypoints Detected:", len(keypoints), "\n")
 
# Save Keypoints to a file

index = []
 
for point in keypoints:
    temp = (point.pt,
            point.size,
            point.angle,
            point.response,
            point.octave, 
            point.class_id)
 
    index.append(temp)
 
# File name
filename = "Outputs/AKAZE-keypoints.txt"
 
# Delete a file if it exists
if os.path.exists(filename):
    os.remove(filename)
 
# Open a file
file = open(filename, "wb")
 
# Write 
file.write(pickle.dumps(index))
 
# Close a file
file.close()
 
# Compute the descriptors with AKAZE
keypoints, descriptors = AKAZE.compute(image, keypoints)
 
# Print the descriptor size in bytes
print("Size of Descriptor:", AKAZE.descriptorSize(), "\n")
 
# Print the descriptor type
print("Type of Descriptor:", AKAZE.descriptorType(), "\n")
 
# Print the default norm type
print("Default Norm Type:", AKAZE.defaultNorm(), "\n")
 
# Print shape of descriptor
print("Shape of Descriptor:", descriptors.shape, "\n")
 
# Draw only 50 keypoints on input image
image = cv.drawKeypoints(image = image,
                         keypoints = keypoints[:50],
                         outImage = None,
                         flags = cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
 
# Plot input image

# Turn interactive plotting off
plt.ioff()
 
# Create a new figure
plt.figure()
plt.axis('off')
plt.imshow(image)
plt.show()
 
plt.imsave(fname = 'Figures/feature-detection-AKAZE.jpg',
           arr = image,
           dpi = 300)
 
# Close it
plt.close()