def rawDescriptorMatches(threshold):
# keys:
# ['__version__', '__header__', '__globals__', 'im1', 'descriptors2', 'im2', 'scales1', 'scales2', 'positions2',
# 'descriptors1', 'positions1', 'orients2', 'orients1']
mat = scipy.io.loadmat("twoFrameData.mat")
im1 = mat['im1']
im2 = mat['im2']
# user select descriptors
plt.imshow(im1)
MyROI = roipoly(roicolor='r')
Ind = MyROI.getIdx(im1, mat['positions1'])
selectedDescriptors = mat['descriptors1'][Ind]
# all image2 descriptors
im2Descriptors = mat['descriptors2']
# distance matrix
# entry = dist2(x,c)
# entry[i][j] corresponds to the distance between x[i] and c[j]
dist = dist2(selectedDescriptors, im2Descriptors)
# locate the distances that are less than threshold, and output the column as the index for descriptor2
# set used for removing duplicates
matchedIndices = list(set(np.where(dist < threshold)[1]))
# display boxes
fig = plt.figure()
bx = fig.add_subplot(111)
bx.imshow(im2)
coners = displaySIFTPatches(mat['positions2'][matchedIndices, :],
mat['scales2'][matchedIndices, :],
mat['orients2'][matchedIndices, :])
for j in range(len(coners)):
bx.plot([coners[j][0][1], coners[j][1][1]],
[coners[j][0][0], coners[j][1][0]],
color='g',
linestyle='-',
linewidth=1)
bx.plot([coners[j][1][1], coners[j][2][1]],
[coners[j][1][0], coners[j][2][0]],
color='g',
linestyle='-',
linewidth=1)
bx.plot([coners[j][2][1], coners[j][3][1]],
[coners[j][2][0], coners[j][3][0]],
color='g',
linestyle='-',
linewidth=1)
bx.plot([coners[j][3][1], coners[j][0][1]],
[coners[j][3][0], coners[j][0][0]],
color='g',
linestyle='-',
linewidth=1)
bx.set_xlim(0, im2.shape[1])
bx.set_ylim(0, im2.shape[0])
plt.gca().invert_yaxis()
plt.show()
import dist2
import numpy as np
mat = scipy.io.loadmat("twoFrameData.mat")
image1 = mat['im1']
image2 = mat['im2']
pl.imshow(image1)
myRoi = roipoly(roicolor='r')
Index1 = myRoi.getIdx(image1, mat['positions1'])
descriptors1 = mat['descriptors1'][Index1, :]
descriptors2 = mat['descriptors2']
dists = dist2.dist2(descriptors1, descriptors2)
Index2 = np.argmin(dists, axis=1)
fig=plt.figure()
bx=fig.add_subplot(111)
bx.imshow(image2)
corners = displaySIFTPatches(mat['positions2'][Index2,:], mat['scales2'][Index2,:], mat['orients2'][Index2,:])
for j in range(len(corners)):
bx.plot([corners[j][0][1], corners[j][1][1]], [corners[j][0][0], corners[j][1][0]], color='g', linestyle='-', linewidth=1)
bx.plot([corners[j][1][1], corners[j][2][1]], [corners[j][1][0], corners[j][2][0]], color='g', linestyle='-', linewidth=1)
bx.plot([corners[j][2][1], corners[j][3][1]], [corners[j][2][0], corners[j][3][0]], color='g', linestyle='-', linewidth=1)
bx.plot([corners[j][3][1], corners[j][0][1]], [corners[j][3][0], corners[j][0][0]], color='g', linestyle='-', linewidth=1)
bx.set_xlim(0, image2.shape[1])
bx.set_ylim(0, image2.shape[0])
plt.gca().invert_yaxis()
plt.show()
fname = siftdir + fnames[i]
mat = scipy.io.loadmat(fname)
numfeats = mat['descriptors'].shape[0]
#read the associated image
imname = framesdir + fnames[i][:-4]
im = misc.imread(imname)
print 'imname = %s contains %d total features, each of dimension %d' %(imname, numfeats, mat['descriptors'].shape[1]);
fig=plt.figure()
ax=fig.add_subplot(111)
ax.imshow(im)
Ind = [750]
coners = displaySIFTPatches(mat['positions'][Ind,:], mat['scales'][Ind,:], mat['orients'][Ind,:])
print coners
for j in range(len(coners)):
ax.plot([coners[j][0][1], coners[j][1][1]], [coners[j][0][0], coners[j][1][0]], color='g', linestyle='-', linewidth=1)
ax.plot([coners[j][1][1], coners[j][2][1]], [coners[j][1][0], coners[j][2][0]], color='g', linestyle='-', linewidth=1)
ax.plot([coners[j][2][1], coners[j][3][1]], [coners[j][2][0], coners[j][3][0]], color='g', linestyle='-', linewidth=1)
ax.plot([coners[j][3][1], coners[j][0][1]], [coners[j][3][0], coners[j][0][0]], color='g', linestyle='-', linewidth=1)
ax.set_xlim(0, im.shape[1])
ax.set_ylim(0, im.shape[0])
plt.gca().invert_yaxis()
plt.show()
#%%
patch_num = 750
# load that file
fname = siftdir + fnames[i]
mat = scipy.io.loadmat(fname)
numfeats = mat['descriptors'].shape[0]
#read the associated image
imname = framesdir + fnames[i][:-4]
im = misc.imread(imname)
# display the image and its SIFT features drawn as squares
print 'imname = %s contains %d total features, each of dimension %d' %(imname, numfeats, mat['descriptors'].shape[1]);
fig=plt.figure()
ax=fig.add_subplot(111)
ax.imshow(im)
coners = displaySIFTPatches(mat['positions'], mat['scales'], mat['orients'])
for j in range(len(coners)):
ax.plot([coners[j][0][1], coners[j][1][1]], [coners[j][0][0], coners[j][1][0]], color='g', linestyle='-', linewidth=1)
ax.plot([coners[j][1][1], coners[j][2][1]], [coners[j][1][0], coners[j][2][0]], color='g', linestyle='-', linewidth=1)
ax.plot([coners[j][2][1], coners[j][3][1]], [coners[j][2][0], coners[j][3][0]], color='g', linestyle='-', linewidth=1)
ax.plot([coners[j][3][1], coners[j][0][1]], [coners[j][3][0], coners[j][0][0]], color='g', linestyle='-', linewidth=1)
ax.set_xlim(0, im.shape[1])
ax.set_ylim(0, im.shape[0])
plt.gca().invert_yaxis()
plt.show()
# now display the same image but only show N of the features
print 'now showing a random subset of %d of those for visualization purposes only...' %(N)
randinds = np.random.permutation(numfeats)
fig=plt.figure()
matches = []
for descriptor in range(len(mat['descriptors2'])):
for i in ind:
vector1 = np.array(mat['descriptors1'][i]).reshape((-1, 1))
vector2 = np.array(mat['descriptors2'][descriptor]).reshape((-1, 1))
distance = dist2(np.array(vector1), np.array(vector2)).mean()
if distance < .0075:
matches.append(descriptor)
break
print len(matches)
fig = plt.figure()
bx = fig.add_subplot(111)
bx.imshow(im2)
coners = displaySIFTPatches(mat['positions2'][matches, :],
mat['scales2'][matches, :],
mat['orients2'][matches, :])
for j in range(len(coners)):
bx.plot([coners[j][0][1], coners[j][1][1]],
[coners[j][0][0], coners[j][1][0]],
color='g',
linestyle='-',
linewidth=1.5)
bx.plot([coners[j][1][1], coners[j][2][1]],
[coners[j][1][0], coners[j][2][0]],
color='g',
linestyle='-',
linewidth=1.5)
bx.plot([coners[j][2][1], coners[j][3][1]],
[coners[j][2][0], coners[j][3][0]],
#select a region within image 1
print ('use the mouse to draw a polygon, right click to end it')
pl.imshow(im1)
MyROI = roipoly(roicolor='r')
#run this later___the blocking feature is not working in my IDE
Ind = MyROI.getIdx(im1, im1_mat['positions'])
print("features in region", len(Ind))
#from load data script - run this to view the SIFT features in image1
fig=plt.figure()
bx=fig.add_subplot(111)
bx.imshow(im1)
coners = displaySIFTPatches(im1_mat['positions'][Ind,:], im1_mat['scales'][Ind,:], im1_mat['orients'][Ind,:])
for j in range(len(coners)):
bx.plot([coners[j][0][1], coners[j][1][1]], [coners[j][0][0], coners[j][1][0]], color='g', linestyle='-', linewidth=1)
bx.plot([coners[j][1][1], coners[j][2][1]], [coners[j][1][0], coners[j][2][0]], color='g', linestyle='-', linewidth=1)
bx.plot([coners[j][2][1], coners[j][3][1]], [coners[j][2][0], coners[j][3][0]], color='g', linestyle='-', linewidth=1)
bx.plot([coners[j][3][1], coners[j][0][1]], [coners[j][3][0], coners[j][0][0]], color='g', linestyle='-', linewidth=1)
bx.set_xlim(0, im1.shape[1])
bx.set_ylim(0, im1.shape[0])
plt.gca().invert_yaxis()
plt.show()
#
## extract the image patch
#
#patch_num = 1
#img_patch = getPatchFromSIFTParameters(mat['positions'][patch_num,:], mat['scales'][patch_num], mat['orients'][patch_num], rgb2gray(im1))
fname = siftdir + fnames[i]
mat = scipy.io.loadmat(fname)
numfeats = mat['descriptors'].shape[0]
#read the associated image
imname = framesdir + fnames[i][:-4]
im = misc.imread(imname)
# display the image and its SIFT features drawn as squares
print 'imname = %s contains %d total features, each of dimension %d' % (
imname, numfeats, mat['descriptors'].shape[1])
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(im)
coners = displaySIFTPatches(mat['positions'], mat['scales'],
mat['orients'])
for j in range(len(coners)):
ax.plot([coners[j][0][1], coners[j][1][1]],
[coners[j][0][0], coners[j][1][0]],
color='g',
linestyle='-',
linewidth=1)
ax.plot([coners[j][1][1], coners[j][2][1]],
[coners[j][1][0], coners[j][2][0]],
color='g',
linestyle='-',
linewidth=1)
ax.plot([coners[j][2][1], coners[j][3][1]],
[coners[j][2][0], coners[j][3][0]],
color='g',
pos1 = contents['positions1']
scales1 = contents['scales1']
orients1 = contents['orients1']
desc1 = contents['descriptors1']
im2 = contents['im2']
pos2 = contents['positions2']
scales2 = contents['scales2']
orients2 = contents['orients2']
fig, ax = plt.subplots()
ax.imshow(im1)
roiplotter = roipoly(roicolor='r')
fig, ax = plt.subplots()
ax.imshow(im1)
roiplotter.displayROI()
plt.savefig("roi.png")
plt.show()
indices = roiplotter.getIdx(im1, pos1)
# np.save('desc_chosen', indices)
desc_chosen = desc1[indices]
desc2 = contents['descriptors2']
distances = dist2(desc_chosen, desc2)
matches = np.argmin(distances, axis=1)
displaySIFTPatches(im2, pos2[matches], scales2[matches], orients2[matches])
plt.savefig("desc_match.png")
plt.show()
def rawDescriptorMatches(fileName):
mat = scipy.io.loadmat(fileName)
#numfeats=mat['im1'].shape[0]
#numdimention=mat[im1].shape[1]
#print(mat)
im1 = mat['im1']
fig = plt.figure()
#ax=fig.add_subplot(111)
#ax.imshow(im1)
# now show how to select a subset of the features using polygon drawing
print('nuse the mouse to draw a polygon, right click to end it')
plt.imshow(im1)
MyROI = roipoly(roicolor='r')
# Ind contains the indices of the SIFT features whose centers fall
Ind = MyROI.getIdx(im1, mat['positions1'])
descriptor_1 = mat['descriptors1'][Ind]
print(descriptor_1.shape)
descriptor_2 = mat['descriptors2']
distance_list = np.empty((0, 2))
for i in descriptor_1:
sift_descriptor_vector = np.array(i, ndmin=2)
distance = dist2(sift_descriptor_vector, descriptor_2)
distance_list = np.vstack(
(distance_list, [np.amin(distance),
np.argmin(distance)]))
print("shape of distance_list", distance_list.shape)
distance_mean = np.mean(distance_list[:, 0], axis=0)
distance_list_mask = distance_list[:, 0] < (distance_mean * ratio)
print("shape of distance_mask", distance_list_mask.shape)
distance_list = distance_list[distance_list_mask, :]
print("shape of after mask", distance_list.shape)
distance_array = np.array(distance_list, dtype=np.uint)
im2 = mat['im2']
fig = plt.figure()
bx = fig.add_subplot(111)
#coners = displaySIFTPatches(mat['positions2'][min_distance], mat['scales2'][min_distance], mat['orients2'][min_distance])
coners = displaySIFTPatches(mat['positions2'][distance_array[:, 1], :],
mat['scales2'][distance_array[:, 1], :],
mat['orients2'][distance_array[:, 1], :])
#coners = displaySIFTPatches(mat['positions2'][tlist[:, 1], :], mat['scales2'][tlist[:, 1], :],
# mat['orients2'][tlist[:, 1], :])
for j in range(len(coners)):
bx.plot([coners[j][0][1], coners[j][1][1]],
[coners[j][0][0], coners[j][1][0]],
color='g',
linestyle='-',
linewidth=1)
bx.plot([coners[j][1][1], coners[j][2][1]],
[coners[j][1][0], coners[j][2][0]],
color='g',
linestyle='-',
linewidth=1)
bx.plot([coners[j][2][1], coners[j][3][1]],
[coners[j][2][0], coners[j][3][0]],
color='g',
linestyle='-',
linewidth=1)
bx.plot([coners[j][3][1], coners[j][0][1]],
[coners[j][3][0], coners[j][0][0]],
color='g',
linestyle='-',
linewidth=1)
bx.set_xlim(0, im2.shape[1])
bx.set_ylim(0, im2.shape[0])
plt.gca().invert_yaxis()
bx.imshow(im2)
plt.show()
# load that file
mat = scipy.io.loadmat(os.path.join(siftdir, fname))
num_feats = mat["descriptors"].shape[0]
print(f"{fname} contains {num_feats} features", flush=True)
print(f"feature dimension: {mat['descriptors'].shape[1]}", flush=True)
# read the associated image
im = imageio.imread(os.path.join(framesdir, fname[:-4]))
# display the image and its SIFT features drawn as squares
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(im)
coners = displaySIFTPatches(mat["positions"], mat["scales"], mat["orients"])
for j in range(len(coners)):
ax.plot(
[coners[j][0][1], coners[j][1][1]],
[coners[j][0][0], coners[j][1][0]],
color="g",
linestyle="-",
linewidth=1,
)
ax.plot(
[coners[j][1][1], coners[j][2][1]],
[coners[j][1][0], coners[j][2][0]],
color="g",
linestyle="-",
linewidth=1,
)
