def sift_pan_desc_generator(path='/home/aurora/hdd/workspace/PycharmProjects/data/N20040103G/'):
filelists = getFiles(path)
feature = []
for index, file in enumerate(filelists):
sift.process_image(file, 'pan'+str(index)+'.sift')
feature.append('pan'+str(index)+'.sift')
return feature
def sift_aurora_desc_generator(path, des):
filelists = getFiles(path)
feature = []
for index, file in enumerate(filelists):
sift.process_image(file, des+str(index)+'.sift')
feature.append(des+str(index)+'.sift')
return feature
def sift_pan_desc_generator(path='/home/aurora/hdd/workspace/PycharmProjects/data/N20040103G/'):
filelists = getFiles(path)
feature = []
for index, file in enumerate(filelists):
sift.process_image(file, 'pan'+str(index)+'.sift')
feature.append('pan'+str(index)+'.sift')
return feature
def sift_matrix():
featurelist = sift_pan_desc_generator('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/panoimages/')
imlist = getFiles('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/panoimages/')
nbr_images = len(imlist)
matchscores = np.zeros((nbr_images, nbr_images))
for i in range(nbr_images):
for j in range(i, nbr_images):
print 'comparing ', imlist[i], imlist[j]
l1, d1 = sift.read_feature_from_file(featurelist[i])
l2, d2 = sift.read_feature_from_file(featurelist[j])
matches = sift.match_twosided(d1, d2)
nbr_matches = sum(matches > 0)
print 'number of matches = ', nbr_matches
matchscores[i, j] = nbr_matches
for i in range(nbr_images):
for j in range(i + 1, nbr_images):
matchscores[j, i] = matchscores[i, j]
np.save('pan_img_matchscore', matchscores)
def sift_matrix():
featurelist = sift_pan_desc_generator('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/panoimages/')
imlist = getFiles('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/panoimages/')
nbr_images = len(imlist)
matchscores = np.zeros((nbr_images, nbr_images))
for i in range(nbr_images):
for j in range(i, nbr_images):
print 'comparing ', imlist[i], imlist[j]
l1, d1 = sift.read_feature_from_file(featurelist[i])
l2, d2 = sift.read_feature_from_file(featurelist[j])
matches = sift.match_twosided(d1, d2)
nbr_matches = sum(matches > 0)
print 'number of matches = ', nbr_matches
matchscores[i, j] = nbr_matches
for i in range(nbr_images):
for j in range(i + 1, nbr_images):
matchscores[j, i] = matchscores[i, j]
np.save('pan_img_matchscore', matchscores)
filename = str(i) + '.png'
im.save(filename) # 需要一定大小的临时文件
g.add_node(
pydot.Node(str(i),
fontcolor='transparent',
shape='rectangle',
image=path + filename))
# 图像对中的第二副图像
im = Image.open(imlist[j])
im.thumbnail((100, 100))
filename = str(j) + '.png'
im.save(filename) # 需要一定大小的临时文件
g.add_node(
pydot.Node(str(j),
fontcolor='transparent',
shape='rectangle',
image=path + filename))
g.add_edge(pydot.Edge(str(i), str(j)))
g.write_png('whitehouse.png')
if __name__ == '__main__':
# sift_matrix()
matchscores = np.load('pan_img_matchscore.npy')
imlist = getFiles(
'/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/panoimages/')
nbr_images = len(imlist)
path = '/home/aurora/workspace/PycharmProjects/aurora_detection/img_proc/'
generat_graph(matchscores, imlist, nbr_images, path)
threshold = 2
# not using the default DAG
g = pydot.Dot(graph_type='graph')
for i in range(nbr_images):
for j in range(i + 1, nbr_images):
if matchscores[i, j] > threshold:
# 图像对中的第一幅图像
im = Image.open(imlist[i])
im.thumbnail((100, 100))
filename = str(i) + '.png'
im.save(filename) # 需要一定大小的临时文件
g.add_node(pydot.Node(str(i), fontcolor='transparent', shape='rectangle', image=path + filename))
# 图像对中的第二副图像
im = Image.open(imlist[j])
im.thumbnail((100, 100))
filename = str(j) + '.png'
im.save(filename) # 需要一定大小的临时文件
g.add_node(pydot.Node(str(j), fontcolor='transparent', shape='rectangle', image=path + filename))
g.add_edge(pydot.Edge(str(i), str(j)))
g.write_png('whitehouse.png')
if __name__=='__main__':
# sift_matrix()
matchscores = np.load('pan_img_matchscore.npy')
imlist = getFiles('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/panoimages/')
nbr_images = len(imlist)
path = '/home/aurora/workspace/PycharmProjects/aurora_detection/img_proc/'
generat_graph(matchscores, imlist, nbr_images, path)
