def matchPics(I1, I2, opts):
#I1, I2 : Images to match
#opts: input opts
ratio = opts.ratio #'ratio for BRIEF feature descriptor'
sigma = opts.sigma #'threshold for corner detection using FAST feature detector'
#Convert Images to GrayScale
img_1 = rgb2gray(I1)
img_2 = rgb2gray(I2)
#Detect Features in Both Images
locs1 = corner_detection(img_1, sigma)
locs2 = corner_detection(img_2, sigma)
#Obtain descriptors for the computed feature locations
desc1, locs1 = computeBrief(img_1, locs1)
desc2, locs2 = computeBrief(img_2, locs2)
#print(desc1.shape)
#print(locs1.shape)
#Match features using the descriptors
matches = briefMatch(desc1, desc2, ratio)
#print(matches.shape)
return matches, locs1, locs2
def matchPics(I1, I2, opts):
#I1, I2 : Images to match
#opts: input opts
ratio = opts.ratio #'ratio for BRIEF feature descriptor'
sigma = opts.sigma #'threshold for corner detection using FAST feature detector'
#%%
#Convert Images to GrayScale
# I1 = cv2.imread(I1, 0)
I1 = skimage.color.rgb2gray(I1)
I2 = skimage.color.rgb2gray(I2)
# I2 = cv2.imread(I2, 0)
#%%
#Detect Features in Both Images
# print('Here is good')
locs1 = corner_detection(I1, sigma)
locs2 = corner_detection(I2, sigma)
#%%
#Obtain descriptors for the computed feature locations
desc1, locs1 = computeBrief(I1, locs1)
desc2, locs2 = computeBrief(I2, locs2)
#Match features using the descriptors
matches = briefMatch(desc1, desc2, ratio)
# Change the locs to the corresponding1
# locs1 = locs1[matches[:,0],:]
# locs2 = locs2[matches[:,1],:]
#%%
return matches, locs1, locs2
def matchPics(I1, I2, opts):
#I1, I2 : Images to match
#opts: input opts
ratio = opts.ratio #'ratio for BRIEF feature descriptor'
sigma = opts.sigma #'threshold for corner detection using FAST feature detector'
locs1 = [] # N x 2 matrix containing x,y coords or matched point pairs
locs2 = [] # N x 2 matrix containing x,y coords or matched point pairs
matches = [
] # p x 2 matrix - first col are indices for descriptor of I1 features; 2nd col are indices related to I2
#Convert Images to GrayScale
img1_grey = cv2.cvtColor(I1, cv2.COLOR_BGR2GRAY)
img2_grey = cv2.cvtColor(I2, cv2.COLOR_BGR2GRAY)
#Detect Features in Both Images with corner_detection() fn.
locs1 = corner_detection(img1_grey, sigma)
locs2 = corner_detection(img2_grey, sigma)
#Obtain descriptors for the computed feature locations with computeBrief() fn.
img1_descrp, locs1 = computeBrief(img1_grey, locs1)
img2_descrp, locs2 = computeBrief(img2_grey, locs2)
#Match features using the descriptors
matches = briefMatch(img1_descrp, img2_descrp, ratio)
return matches, locs1, locs2
def matchPics(I1, I2):
# I1, I2 : Images to match
# Convert Images to GrayScale
I1 = cv2.cvtColor(I1, cv2.COLOR_RGB2GRAY)
I2 = cv2.cvtColor(I2, cv2.COLOR_RGB2GRAY)
# Detect Features in Both Images
locs1 = corner_detection(I1)
locs2 = corner_detection(I2)
# Obtain descriptors for the computed feature locations
desc1, locs1 = computeBrief(I1, locs1)
desc2, locs2 = computeBrief(I2, locs2)
# Match features using the descriptors
locs1 = np.flip(locs1, axis=1)
locs2 = np.flip(locs2, axis=1)
matches = briefMatch(desc1, desc2, 0.8)
print(matches.shape)
return matches, locs1, locs2
def matchPics(I1, I2, opts):
ratio = opts.ratio #'ratio for BRIEF feature descriptor']
sigma = opts.sigma #'threshold for corner detection using FAST feature detector'
#Convert Images to GrayScale
I1 = cv2.cvtColor(I1, cv2.COLOR_BGR2GRAY)
I2 = cv2.cvtColor(I2, cv2.COLOR_BGR2GRAY)
locs1 = corner_detection(I1, sigma)
#Detect Features in Both Images
locs2 = corner_detection(I2, sigma)
#Obtain descriptors for the computed feature locations
desc1, locs1 = computeBrief(I1, locs1)
#Match features using the descritors
desc2, locs2 = computeBrief(I2, locs2)
#Match features using the descritors
matches = briefMatch(desc1, desc2, ratio)
return matches, locs1, locs2
def matchPics(I1, I2):
#I1, I2 : Images to match
#Convert Images to GrayScale
img1 = cv2.cvtColor(I1, cv2.COLOR_BGR2GRAY)
img2 = cv2.cvtColor(I2, cv2.COLOR_BGR2GRAY)
#Detect Features in Both Images
# fast = cv2.FastFeatureDetector_create(threshold = 100, nonmaxSuppression=True)
locs1 = corner_detection(img1)
locs2 = corner_detection(img2)
#Obtain descriptors for the computed feature locations
#
img1_decs, img1_locs = computeBrief(img1, locs1)
img2_decs, img2_locs = computeBrief(img2, locs2)
#Match features using the descriptors
matches = briefMatch(img1_decs, img2_decs)
locs1[:, :] = locs1[:, [1, 0]]
locs2[:, :] = locs2[:, [1, 0]]
return matches, locs1, locs2