def reginQueries():
#get kmeans
with open('cluster_question3.pkl', "rb") as f:
kmeans = pickle.load(f)
#get histogram and histogram mapping
with open("histogram.pkl", 'rb') as f:
histogram = pickle.load(f)
histogram = np.array(histogram)
print(histogram.shape)
with open("histogram_mapping.pkl", 'rb') as f:
histogram_mappping = pickle.load(f)
print(len(histogram_mappping))
#get selected region
framesdir = 'frames/'
siftdir = 'sift/'
histogram_choosed=[]
hist_choosed=np.zeros(k)
fnames = glob.glob(siftdir + '*.mat')
fnames = [i[-27:] for i in fnames]
fname = siftdir + fnames[ID]
mat = scipy.io.loadmat(fname, verify_compressed_data_integrity=False)
imname = framesdir + fnames[ID][:-4]
im = imread(imname)
pl.imshow(im)
MyROI = roipoly(roicolor='r')
Ind = MyROI.getIdx(im, mat['positions'])
# get parameters
describtors = mat['descriptors'][Ind, :]
if len(describtors) > 0:
predict_result = kmeans.predict(describtors)
for label in predict_result:
hist_choosed[label] += 1
histogram_choosed.append(hist_choosed)
histogram_choosed = np.array(histogram_choosed)
return histogram,histogram_mappping,histogram_choosed
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()
def regionQueries():
# get the 4 images and sifts
dir = 'regionQueries/'
querySiftNames = glob.glob(dir + '*.mat')
queryImageNames = glob.glob(dir + '*.jpeg')
querySifts = []
queryImages = []
for i in range(len(querySiftNames)):
querySifts.append(scipy.io.loadmat(querySiftNames[i]))
queryImages.append(misc.imread(queryImageNames[i]))
# for each of them
for i in range(len(querySifts)):
image = queryImages[i]
sift = querySifts[i]
# select a region
plt.imshow(image)
MyROI = roipoly(roicolor='r')
Ind = MyROI.getIdx(image, sift['positions'])
# get the descriptors
selectedDescriptors = sift['descriptors'][Ind]
# build histogram
queryHistogram = buildHistogram(selectedDescriptors)
# initialize dictionary
similarityMap = {}
# for each of the images
imagesdir = '100sift/'
imageNames = glob.glob(imagesdir + '*.mat')
for imageName in imageNames:
print imageName
imageSift = scipy.io.loadmat(imageName)
# build histogram
imageHistogram = buildHistogram(imageSift['descriptors'])
# get similarity values
similarity = getSimilarityValue(queryHistogram, imageHistogram)
# put in the dictionary
similarityMap[imageName] = similarity
# sort dictionary
sorted_similarityMap = sorted(similarityMap.items(),
key=operator.itemgetter(1))
sorted_similarityMap.reverse()
# take top 5 to display
top5Images = []
for m in range(5):
name = sorted_similarityMap[m][0]
name = name[-27:-4]
top5Images.append(name)
displayImageList(top5Images)
def gen_regions(query_imnames):
for query_imname in query_imnames:
query_im = Image.open(os.path.join(FRAME_DIR, query_imname))
desc, _ = get_desc_imname(query_imname + ".mat")
pos, _, _ = get_keypoints(query_imname + ".mat")
fig, ax = plt.subplots()
ax.imshow(query_im)
roi_plotter = roipoly(roicolor='r')
fig, ax = plt.subplots()
ax.imshow(query_im)
roi_plotter.displayROI()
plt.savefig(query_imname + "_reg_query.png")
plt.show()
indices = roi_plotter.getIdx(query_im, pos)
np.save(query_imname + '_reg_ind', indices)
import scipy.io
import matplotlib.pyplot as plt
import pylab as pl
from selectRegion import roipoly
from displaySIFTPatches import displaySIFTPatches
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)
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, im.shape[1])
bx.set_ylim(0, im.shape[0])
plt.gca().invert_yaxis()
plt.show()
# 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'
pl.imshow(im)
MyROI = roipoly(roicolor='r')
Ind = MyROI.getIdx(im, mat['positions'])
# Ind contains the indices of the SIFT features whose centers fall
# within the selected region of interest.
# Note that these indices apply to the *rows* of 'descriptors' and
# 'positions', as well as the entries of 'scales' and 'orients'
# now display the same image but only in the polygon.
fig=plt.figure()
bx=fig.add_subplot(111)
bx.imshow(im)
coners = displaySIFTPatches(mat['positions'][Ind,:], mat['scales'][Ind,:], 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)
import numpy as np
import scipy.io
import matplotlib.pyplot as plt
from displaySIFTPatches import displaySIFTPatches
from selectRegion import roipoly
from dist2 import dist2
mat = scipy.io.loadmat('twoFrameData.mat')
im1 = mat['im1']
im2 = mat['im2']
plt.imshow(im1)
MyROI = roipoly(roicolor='r')
ind = MyROI.getIdx(im1, mat['positions1'])
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, :],
if __name__ == '__main__':
contents = sio.loadmat("./twoFrameData.mat")
im1 = contents['im1']
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)
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()
import numpy as np
import scipy.io
import matplotlib.pyplot as plt
from selectRegion import roipoly
import imageio
if __name__ == "__main__":
frame_num = int(input("Frame Num: "))
im = np.array(
imageio.imread("PS4Frames/frames/friends_{}.jpeg".format(
str(frame_num).zfill(10)))).astype(np.uint8)
mat = scipy.io.loadmat("PS4SIFT/sift/friends_{}.jpeg.mat".format(
str(frame_num).zfill(10)))
plt.show()
plt.imshow(im)
plt.title("Select Region of Interest")
roi = roipoly(color="r")
region = np.array((roi.all_x_points, roi.all_y_points)).T
indices = roi.get_indices(im, mat["positions"])
np.save("region-{}.npy".format(frame_num), region)
np.save("points-{}.npy".format(frame_num), indices)