Python ClassifyMainの例

コード例 #1

def ProDeeplearningMain(folder_test = '', folder_train = '', analysis_type = dict()):

    if analysis_type["analysis_type"] == 0:
        SegmentMain.segmentMain(folder_test, folder_train, analysis_type)
    elif analysis_type["analysis_type"] == 1:
        DetectionMain.detectionMain(folder_test, folder_train, analysis_type)
    elif analysis_type["analysis_type"] == 2:
        # detection + classify
        # DetectionMain.detectionMain(folder_test, folder_train, analysis_type)
        ClassifyMain.classifyMain(folder_test, folder_train, analysis_type)
    else:
        raise ValueError("Analysis Mapping Wrong")

    print("Test folder: ", folder_test)
    print("Analysis type: ", analysis_type)
    print("complete")

コード例 #2

ファイル: app.py プロジェクト: jessab/ML

def classify(anklePath, hipPath, options):
    print("---- Classification ---- \n")
    print("Calculating features...")
    features = getFeatures(anklePath, hipPath)
    print("Predicting classification...")
    data = fm.main(True, options['p'])
    result = cm.predict(data, features,
                        options['a'], options['c'], options['f'])
    print(result)

コード例 #3

def ProDeeplearningMain(folder_test='', folder_train='', analysis_type=dict()):

    # convert 1024*1024 or 512*512 to 256*256
    folder_test = cropImg(analysis_type, folder_test)

    if analysis_type["analysis_type"] == 0:
        SegmentMain.segmentMain(folder_test, folder_train, analysis_type)
    elif analysis_type["analysis_type"] == 1:
        DetectionMain.detectionMain(folder_test, folder_train, analysis_type)
    elif analysis_type["analysis_type"] == 2:
        # detection + classify
        DetectionMain.detectionMain(folder_test, folder_train, analysis_type)
        ClassifyMain.classifyMain(folder_test, folder_train, analysis_type)
    else:
        raise ValueError("Analysis Mapping Wrong")

    print("Test folder: ", folder_test)
    print("Analysis type: ", analysis_type)
    print("complete")

コード例 #4

ファイル: app.py プロジェクト: jessab/ML

def experiment(options):
    data = fm.main(True, options['p'])
    cm.main(data, options['f'])