def generate_key_frame(indexs): # for index in xrange(len(lists)): keyframes = {} files = getFiles() for k in xrange(len(indexs)): print '@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@' nums = indexs[k].shape[0] keyframes[nums] = [] for j in xrange(nums): if j == 0: temp_matrixs = files[0:indexs[k][j]] else: temp_matrixs = files[indexs[k][j-1]:indexs[k][j]] caculate_key_frame(temp_matrixs)
def get_sift_size(): filelists = getFiles() datasets = [] for index, url in enumerate(filelists): im = np.array(Image.open(url).convert('L')) process_image(url, 'aurora' + str(index) + '.sift') l1, d1 = read_feature_from_file('aurora' + str(index) + '.sift') if l1.shape[0] == 1 and l1.shape[1] == 0: print '@@@@@@@@@@@@@@@@@@@@' print url datasets.append(0) continue else: datasets.append(l1.shape[0]) return datasets
def get_sift_size(): filelists = getFiles() datasets = [] for index, url in enumerate(filelists): im = np.array(Image.open(url).convert('L')) process_image(url, 'aurora'+str(index)+'.sift') l1, d1 = read_feature_from_file('aurora'+str(index)+'.sift') if l1.shape[0] == 1 and l1.shape[1] == 0: print '@@@@@@@@@@@@@@@@@@@@' print url datasets.append(0) continue else: datasets.append(l1.shape[0]) return datasets
#encoding:UTF-8 __author__ = 'auroua' __version__ = 0.1 import numpy as np import seaborn as sns import imgutil as img if __name__=='__main__': filenames = img.getFiles('/home/auroua/workspace/PycharmProjects/data/N20040103G') # filenames = img.getFiles('/home/auroua/workspace/PycharmProjects/data/picture11') # filenames = getFiles('/home/auroua/workspace/PycharmProjects/data/N20040103G') distance = [] # img1 = img.getImg(filenames[0]) for i,fn in enumerate(filenames): # print i,fn img1 = img.getImg(filenames[0]) hist_img = img.hist(img1) try: url = filenames[i+1] img2 = img.getImg(url) hist_img2 = img.hist(img2) except IndexError as ie: print 'end for loop' break distance.append(np.sum(np.abs(hist_img-hist_img2))) # print distance np_distance = np.array(distance,dtype=np.double) # np_distance = preprocessing.scale(np_distance) print np_distance
def dsift_filelist(url): imlists = getFiles(url) return imlists
#encoding:UTF-8 __author__ = 'auroua' __version__ = 0.1 import numpy as np import seaborn as sns import imgutil as img if __name__ == '__main__': filenames = img.getFiles( '/home/auroua/workspace/PycharmProjects/data/N20040103G') # filenames = getFiles('/home/auroua/workspace/PycharmProjects/data/N20040103G') distance = [] # img1 = img.getImg(filenames[0]) for i, fn in enumerate(filenames): # print i,fn img1 = img.getImg(filenames[0]) avg_img = img.avg_channel(img1) # avg_img = img1 try: url = filenames[i + 1] img2 = img.getImg(url) avg_img2 = img.avg_channel(img2) except IndexError as ie: print 'end for loop' break distance.append(np.sum(np.abs(avg_img - avg_img2))) # print distance np_distance = np.array(distance, dtype=np.double) # np_distance = preprocessing.scale(np_distance) print np_distance