class SIFT(object):
"""class for SIFT"""
def __init__(self, num_points=NUM_POINTS):
"""contructor"""
super(SIFT, self).__init__()
self.algo = cv2.xfeatures2d.SIFT_create(num_points)
def get_key_points_n_discriptors(self, gray):
"""method for extracting SIFT features and their descriptors"""
return self.algo.detectAndCompute(gray, None)
def visualise(self, gray):
"""to visualise sift algorithm ouput"""
kp, des = self.algo.detectAndCompute(gray, None)
imgf = cv2.drawKeypoints(gray, kp, np.array([[]]))
plt.figure(figsize=(12, 8))
plt.imshow(imgf)
plt.show()
if __name__ == "__main__":
sift = SIFT(num_points=500)
data_folder_path = "./../data/viewpoints/chatnoir/test"
file_path = f"{data_folder_path}/image_color/img1.png"
img = DataLoader.load_img(file_path)
gray = ProcImg.get_gray_img(img)
sift.visualise(gray)
import os
from utils.data_loader import DataLoader
from utils.proc_img import ProcImg
from config import *
if __name__ == "__main__":
data_folder_path = DataLoader.get_data_path(DATA_FOLDER, data_collection,
data_object)
img_dir = f'{data_folder_path}/image_{"color" if clr_o_grey else "gray"}'
homg_dir = f'{data_folder_path}/homography'
print(f"available images : {os.listdir(img_dir)}\n")
img = DataLoader.load_img(f'{img_dir}/img1.png')
gray = ProcImg.get_gray_img(img)
kp, des = feature_point_detector.get_key_points_n_discriptors(gray)
feature_point_detector.visualise(gray)
imgf = cv2.drawKeypoints(gray, kp, img)
for file in sorted(os.listdir(img_dir)):
if file != "img1.png":
img2_num = file[3]
print(f"loading img : {file}")
img2 = DataLoader.load_img(f'{img_dir}/{file}')
gray2 = ProcImg.get_gray_img(img2)
kp2, des2 = feature_point_detector.get_key_points_n_discriptors(
gray2)
img2f = cv2.drawKeypoints(gray2, kp2, img2)
