def extract_or_load_features(im, kp_fname, feat_fname):
if isfile(kp_fname) and isfile(feat_fname):
#return pickle.load(open(kp_fname, 'rb'))[0], pickle.load(open(feat_fname, 'rb'))[0]
kps, feats = pickle.load(open(kp_fname, 'rb')), pickle.load(open(feat_fname, 'rb'))
kps = np.concatenate(kps, axis=0)
feats = np.concatenate(feats, axis=0)
return kps, feats
detector = SIFT(im)
_ = detector.get_features()
pickle.dump(detector.kp_pyr, open(kp_fname, 'wb'))
pickle.dump(detector.feats, open(feat_fname, 'wb'))
return np.concatenate(detector.kp_pyr, axis=0), np.concatenate(detector.feats, axis=0)
num_img = 3
kp_pyrs = []
ims = []
im = imread('C:/Users/Shantanu/img2.jpg')
im = cv2.resize(im, (1268, 720))
ims.append(im)
if isfile('C:/Users/Shantanu/kp_pyr.pkl'):
kp_pyrs.append(pickle.load(open('C:/Users/Shantanu/kp_pyr.pkl', 'rb')))
print('Performing SIFT on image')
sift_detector = SIFT(im)
_ = sift_detector.get_features()
kp_pyrs.append(sift_detector.kp_pyr)
pickle.dump(sift_detector.kp_pyr, open('C:/Users/Shantanu/kp_pyr.pkl',
'wb'))
pickle.dump(sift_detector.feats,
open('C:/Users/Shantanu/feat_pyr.pkl', 'wb'))
import matplotlib.pyplot as plt
_, ax = plt.subplots(1, 1)
ax.imshow(ims[0])
kps = kp_pyrs[0][0] * (2**0)
ax.scatter(kps[:, 0], kps[:, 1], c='b', s=2)
#plt.show()