コード例 #1
0
ファイル: classify.py プロジェクト: viperk/CS426
def test_func(img_file_name):
    print("---------------------")
    print("## extract Sift features")
    all_files = []
    all_files_labels = {}
    all_features = {}
    '''args = parse_arguments()
    model_file = args.m
    codebook_file = args.c
    fnames = args.input_images'''
    model_file = 'traintrainingdata.svm.model'
    codebook_file = 'traincodebook.file'
    fnames = []
    fnames.append(img_file_name)
    all_features = extractSift(fnames)
    for i in fnames:
        all_files_labels[i] = 0  # label is unknown

    print("---------------------")
    print("## loading codebook from " + codebook_file)
    with open(codebook_file, 'rb') as f:
        codebook = load(f)

    print("---------------------")
    print("## computing visual word histograms")
    all_word_histgrams = {}
    for imagefname in all_features:
        word_histgram = computeHistograms(codebook, all_features[imagefname])
        all_word_histgrams[imagefname] = word_histgram

    print("---------------------")
    print("## write the histograms to file to pass it to the svm")
    nclusters = codebook.shape[0]
    writeHistogramsToFile(nclusters, all_files_labels, fnames,
                          all_word_histgrams, HISTOGRAMS_FILE)

    print("---------------------")
    print("## test data with svm")
    result = libsvm.test(HISTOGRAMS_FILE, model_file)
    print(result)
    return result
コード例 #2
0
ファイル: classify.py プロジェクト: navinpai/CS706
def predict_image(model_file, codebook_file, fnames):
    print str(fnames[0]) + ".sift"
    try:
        os.remove(str(fnames[0]) + ".sift")
    except OSError:
        pass
    # extract Sift features for each individual image"
    all_files = []
    all_files_labels = {}
    all_features = {}

    print fnames
    try:
        all_features = extractSift(fnames)
        for i in fnames:
            all_files_labels[i] = 0  # label is unknown

        # loading codebook from codebook_file
        # default codebookfile: codebook.file
        with open(codebook_file, 'rb') as f:
            codebook = load(f)

        # computing visual word histograms"
        all_word_histgrams = {}
        for imagefname in all_features:
            word_histgram = computeHistograms(codebook,
                                              all_features[imagefname])
            all_word_histgrams[imagefname] = word_histgram

        # write the histograms to file to pass it to the svm
        nclusters = codebook.shape[0]
        writeHistogramsToFile(nclusters, all_files_labels, fnames,
                              all_word_histgrams, HISTOGRAMS_FILE)

        # test data with svm"
        print libsvm.test(HISTOGRAMS_FILE, model_file)
    except Exception as e:
        pass  #Incase of error with sift extraction, just try again on next stroke
コード例 #3
0
ファイル: multi_classify.py プロジェクト: luciamr/tesina
    
    print "---------------------"
    print "## extract Sift features"
    all_files = []
    all_files_labels = {}
    all_features = {}
    
    for cat_path in files.keys():
        cat_files = [cat_path + '/' + name for name in files[cat_path][set][1]]
        all_files.extend(cat_files)        

    datasetpath = DATASETPATH
    model_file = datasetpath + str(set) + '.' + MODEL_FILE
    codebook_file = datasetpath + str(set) + '.' + CODEBOOK_FILE
    fnames = all_files
    all_features = extractSift(fnames)

    for i in fnames:
        all_files_labels[i] = 0  # label is unknown

    print "---------------------"
    print "## loading codebook from " + codebook_file
    with open(codebook_file, 'rb') as f:
        codebook = load(f)

    print "---------------------"
    print "## computing visual word histograms"
    all_word_histgrams = {}
    for imagefname in all_features:
        word_histgram = computeHistograms(codebook, all_features[imagefname])
        all_word_histgrams[imagefname] = word_histgram
コード例 #4
0
ファイル: classify.py プロジェクト: trainsn/cbir4RS
dataset_path = dataset_path[2:len(dataset_path) - 2]
codebook_file = dataset_path + 'codebook.file'

f = open(dataset_path + 'filename.txt', 'r')
idx = 0
for line in f.readlines():
    imgfilename = line.split()[0]

    label = line.split()[1]
    all_files.append(imgfilename)
    all_files_labels[imgfilename] = int(label)
    idx = idx + 1

    if (idx % 1500 == 0 or idx == 36707):
        if (idx > 34500):
            all_features = extractSift(all_files)
            #for i in fnames:
            #    all_files_labels[i] = 0  # label is unknown

            print "---------------------"
            print "## loading codebook from " + codebook_file
            with open(codebook_file, 'rb') as f:
                codebook = load(f)

            print "---------------------"
            print "## computing visual word histograms"
            all_word_histgrams = {}
            for imagefname in all_features:
                word_histgram = computeHistograms(codebook,
                                                  all_features[imagefname])
                all_word_histgrams[imagefname] = word_histgram
コード例 #5
0
all_files = []
all_files_labels = {}
all_features = {}

args = parse_arguments()
model_file = args.m
codebook_file = args.c
farr=[]
val=[]
fol = args.input_images
fl=os.listdir(fol[0])
fl.sort()
for f in fl:
    fnames=[fol[0]+'/'+f]
    farr.append(f[0])
    all_features = extractSift(fnames)
    for i in fnames:
        all_files_labels[i] = 0  # label is unknown

    print "---------------------"
    print "## loading codebook from " + codebook_file
    with open(codebook_file, 'rb') as f:
        codebook = load(f)

    print "---------------------"
    print "## computing visual word histograms"
    all_word_histgrams = {}
    for imagefname in all_features:
        word_histgram = computeHistograms(codebook, all_features[imagefname])
        all_word_histgrams[imagefname] = word_histgram
コード例 #6
0
ファイル: classify.py プロジェクト: ratatatata/ML-OSM
 with open(SVM_MODEL_PATH, 'rb') as fid:
     crf = cPickle.load(fid)
         
 
 print "---------------------"
 print "## extract Sift features"
 all_files = []
 all_files_labels = {}
 all_features = {}
 
 model_file = SVM_MODEL_PATH
 codebook_file = CODEBOOK_FILE
 
 
 files = learn.get_imgfiles(DATASETPATH)
 all_features = extractSift(files)
 for i in files:
     all_files_labels[i] = 0  # label is unknown
 
 print "---------------------"
 print "## loading codebook from " + codebook_file
 with open(codebook_file, 'rb') as f:
     codebook = load(f)
 
 print "---------------------"
 print "## computing visual word histograms"
 all_word_histgrams = {}
 for imagefname in all_features:
     word_histgram = computeHistograms(codebook, all_features[imagefname])
     all_word_histgrams[imagefname] = word_histgram