"""
Programmer  :   EOF
File        :   config.py
Date        :   2016.01.06
E-mail      :   jasonleaster@163.com
License     :   MIT License

Description :
    This is a configure file for this project.

"""
import os


def get_project_rootpath():
    """
    获取项目根目录。此函数的能力体现在，不论当前module被import到任何位置，都可以正确获取项目根目录
    :return:
    """
    path = os.path.realpath(os.curdir)
    while True:
        # PyCharm项目中，'.idea'是必然存在的，且名称唯一
        if '.idea' in os.listdir(path):
            path = str(path).replace('\\', '/')
            path += '/'
            return path
        path = os.path.dirname(path)


rpath = get_project_rootpath()
DEBUG_MODEL = True
USING_CASCADE = False

# training set directory for face and non-face images
# TRAINING_FACE = os.path.normpath(os.path.join(rpath, "FaceDetection/TrainingImages/FACES/"))
# TRAINING_NONFACE = os.path.normpath(os.path.join(rpath, "FaceDetection/TrainingImages/NFACES/"))
TRAINING_FACE = "./TrainingImages/FACES/"
TRAINING_NONFACE = "./TrainingImages/NFACES/"


# test set directory for face and non-face images
TEST_FACE = "./TrainingImages/FACES/"
TEST_NONFACE = "./TrainingImages/NFACES/"

# single image for testing
# TEST_IMG = "./Test/soccer.gif"
TEST_IMG = "./Test/nens.png"

FEATURE_FILE_TRAINING = "./features/features_train.cache"
FEATURE_FILE_TESTING = "./features/features_test.cache"

FEATURE_FILE_SUBSET = "./features/features_train_subset"
FEATURE_FILE_SUBSET_0 = "./features/features_train_subset0.cache"
FEATURE_FILE_SUBSET_1 = "./features/features_train_subset1.cache"

# For parallel
PROCESS_NUM = 2

ADABOOST_CACHE_FILE = "./model/adaboost_classifier.cache"
ROC_FILE = "./model/roc.cache"

FIGURES = "./figure/"

# image size in the training set 19 * 19
TRAINING_IMG_HEIGHT = 19
TRAINING_IMG_WIDTH = 19

# How many different types of  Haar-feature
FEATURE_TYPE_NUM = 5
# How many number of features that a single training image have
FEATURE_NUM = 37862
# FEATURE_NUM         = 16373
# FEATURE_NUM         = 49608

# number of positive and negative sample will be used in the training process
# POSITIVE_SAMPLE = 4800
# NEGATIVE_SAMPLE = 9000
POSITIVE_SAMPLE = 240
NEGATIVE_SAMPLE = 450

SAMPLE_NUM = POSITIVE_SAMPLE + NEGATIVE_SAMPLE

TESTING_POSITIVE_SAMPLE = 20
TESTING_NEGATIVE_SAMPLE = 20

TESTING_SAMPLE_NUM = TESTING_NEGATIVE_SAMPLE + TESTING_POSITIVE_SAMPLE

LABEL_POSITIVE = +1
LABEL_NEGATIVE = -1

WHITE = 255
BLACK = 0

EXPECTED_TPR = 0.999
EXPECTED_FPR = 0.0005

# for CASCADE
EXPECTED_FPR_PRE_LAYYER = 0.1
EXPECTED_TPR_PRE_LAYYER = 0.999

# the threshold range of adaboost. (from -inf to +inf)
AB_TH_MIN = -15
AB_TH_MAX = +15

HAAR_FEATURE_TYPE_I = "I"
HAAR_FEATURE_TYPE_II = "II"
HAAR_FEATURE_TYPE_III = "III"
HAAR_FEATURE_TYPE_IV = "IV"
HAAR_FEATURE_TYPE_V = "V"

AB_TH = -3.
OVER_LAP_TH = 0.1

MAX_WEAK_NUM = 12

CASACADE_LIMIT = 3
ADABOOST_LIMIT = 150

SEARCH_WIN_STEP = 4

DETECT_START = 1.
DETECT_END = 2.
DETECT_STEP = 0.2