def show_image(img, cv_kpts=None, fignum=0, title=None):
print('Drawing Image')
figure(fignum=fignum, doclf=True, title=title)
if not cv_kpts is None:
img = cv2.drawKeypoints(img, cv_kpts, flags=cv_flags)
imshow(img)
set_cmap('gray')
def show_image(img, cv_kpts=None, fignum=0, title=None):
print('Drawing Image')
figure(fignum=fignum, doclf=True, title=title)
if not cv_kpts is None:
img = cv2.drawKeypoints(img, cv_kpts, flags=cv_flags)
imshow(img)
set_cmap('gray')
l1_energy = np.sum(mysift)
l1_energy2 = np.sum(mysift2)
l2_energy = np.sqrt(np.sum(mysift**2))
l2_energy2 = np.sqrt(np.sum(mysift2**2))
print('Occlusion Difference: ')
print(' FREAK All : '+str(num_diff))+'/512'
print(' FREAK Fine : '+str(num_fine_diff))+'/32'
print(' FREAK Coarse: '+str(num_coarse_diff))+'/16'
print(' ------------')
print(' SIFT L2dist : '+str(l2_sift_dist)+\
' ( / '+str(l1_energy)+' / '+str(l1_energy2)+' / )')
print(' SIFT L1dist : '+str(l1_sift_dist)+\
' ( / '+str(l2_energy)+' / '+str(l2_energy2)+' / )')
# FIGURE 5
figure(5, title='Different Freak Dimensions', doclf=True)
hist(diff_dims, bins=512, color=[1,0,0])
ax = gca()
ax.set_xlim(0,512)
# FIGURE 6
figure(6, title='Freak Descriptor Histograms', doclf=True)
hist(np.nonzero(myfreak), bins=512, alpha=.3, color=[0,1,0])
hist(np.nonzero(myfreak2), bins=512, alpha=.3, color=[0,0,1])
ax = gca()
ax.set_xlim(0,512)
show_all_figures()
move_all_figures()
try:
__IPYTHON__
l1_energy = np.sum(mysift)
l1_energy2 = np.sum(mysift2)
l2_energy = np.sqrt(np.sum(mysift**2))
l2_energy2 = np.sqrt(np.sum(mysift2**2))
print('Occlusion Difference: ')
print(' FREAK All : ' + str(num_diff)) + '/512'
print(' FREAK Fine : ' + str(num_fine_diff)) + '/32'
print(' FREAK Coarse: ' + str(num_coarse_diff)) + '/16'
print(' ------------')
print(' SIFT L2dist : '+str(l2_sift_dist)+\
' ( / '+str(l1_energy)+' / '+str(l1_energy2)+' / )')
print(' SIFT L1dist : '+str(l1_sift_dist)+\
' ( / '+str(l2_energy)+' / '+str(l2_energy2)+' / )')
# FIGURE 5
figure(5, title='Different Freak Dimensions', doclf=True)
hist(diff_dims, bins=512, color=[1, 0, 0])
ax = gca()
ax.set_xlim(0, 512)
# FIGURE 6
figure(6, title='Freak Descriptor Histograms', doclf=True)
hist(np.nonzero(myfreak), bins=512, alpha=.3, color=[0, 1, 0])
hist(np.nonzero(myfreak2), bins=512, alpha=.3, color=[0, 0, 1])
ax = gca()
ax.set_xlim(0, 512)
show_all_figures()
move_all_figures()
try:
__IPYTHON__
import cv2
from pylab import *
from hotspotter.helpers import Timer, figure
import os
figure(1, doclf=True, title='SIFT Descriptors')
figure(2, doclf=True, title='FREAK Descriptors')
# Define Image
chip_fpath = 'D:/data/work/PZ_flankhack/images/img-0000001.jpg'
# Read Image
img_ = cv2.imread(chip_fpath)
img = cv2.cvtColor(img_, cv2.COLOR_BGR2GRAY)
imshow(img)
set_cmap('gray')
params = { 'kpts_type' :'SIFT'}
# Detect and Extract
print('Detect and Extract')
kpts_detector3 = cv2.FeatureDetector_create(params['kpts_type'])
kpts_detector = cv2.PyramidAdaptedFeatureDetector(kpts_detector3, maxLevel=5)
#kpts_detector = cv2.GridAdaptedFeatureDetector(kpts_detector3) # max number of features
with Timer(name=params['kpts_type']+' Keypoint Detector'):
cv_kpts2 = kpts_detector.detect(img)
sift_desc_extractor = cv2.DescriptorExtractor_create('SIFT')
freak_desc_extractor = cv2.DescriptorExtractor_create('FREAK')
with Timer(name='SIFT Descriptors'):
(sift_cv_kpts, sift_cv_desc) = sift_desc_extractor.compute(img, cv_kpts2)
with Timer(name='FREAK Descriptors'):
(freak_cv_kpts, freak_cv_desc) = freak_desc_extractor.compute(img, cv_kpts2)
# Draw
print('Draw')
figure(1)
