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"""
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Programmer : EOF
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File : config.py
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Date : 2016.01.06
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E-mail : jasonleaster@163.com
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License : MIT License
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Description :
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This is a configure file for this project.
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"""
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import os
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def get_project_rootpath():
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"""
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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
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:return:
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"""
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path = os.path.realpath(os.curdir)
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while True:
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# PyCharm项目中，'.idea'是必然存在的，且名称唯一
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if '.idea' in os.listdir(path):
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path = str(path).replace('\\', '/')
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path += '/'
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return path
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path = os.path.dirname(path)
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rpath = get_project_rootpath()
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DEBUG_MODEL = True
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USING_CASCADE = False
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# training set directory for face and non-face images
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# TRAINING_FACE = os.path.normpath(os.path.join(rpath, "FaceDetection/TrainingImages/FACES/"))
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# TRAINING_NONFACE = os.path.normpath(os.path.join(rpath, "FaceDetection/TrainingImages/NFACES/"))
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TRAINING_FACE = "./TrainingImages/FACES/"
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TRAINING_NONFACE = "./TrainingImages/NFACES/"
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# test set directory for face and non-face images
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TEST_FACE = "./TrainingImages/FACES/"
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TEST_NONFACE = "./TrainingImages/NFACES/"
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# single image for testing
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# TEST_IMG = "./Test/soccer.gif"
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TEST_IMG = "./Test/nens.png"
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FEATURE_FILE_TRAINING = "./features/features_train.cache"
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FEATURE_FILE_TESTING = "./features/features_test.cache"
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FEATURE_FILE_SUBSET = "./features/features_train_subset"
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FEATURE_FILE_SUBSET_0 = "./features/features_train_subset0.cache"
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FEATURE_FILE_SUBSET_1 = "./features/features_train_subset1.cache"
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# For parallel
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PROCESS_NUM = 2
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ADABOOST_CACHE_FILE = "./model/adaboost_classifier.cache"
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ROC_FILE = "./model/roc.cache"
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FIGURES = "./figure/"
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# image size in the training set 19 * 19
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TRAINING_IMG_HEIGHT = 19
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TRAINING_IMG_WIDTH = 19
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# How many different types of Haar-feature
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FEATURE_TYPE_NUM = 5
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# How many number of features that a single training image have
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FEATURE_NUM = 37862
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# FEATURE_NUM = 16373
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# FEATURE_NUM = 49608
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# number of positive and negative sample will be used in the training process
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# POSITIVE_SAMPLE = 4800
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# NEGATIVE_SAMPLE = 9000
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POSITIVE_SAMPLE = 240
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NEGATIVE_SAMPLE = 450
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SAMPLE_NUM = POSITIVE_SAMPLE + NEGATIVE_SAMPLE
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TESTING_POSITIVE_SAMPLE = 20
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TESTING_NEGATIVE_SAMPLE = 20
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TESTING_SAMPLE_NUM = TESTING_NEGATIVE_SAMPLE + TESTING_POSITIVE_SAMPLE
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LABEL_POSITIVE = +1
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LABEL_NEGATIVE = -1
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WHITE = 255
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BLACK = 0
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EXPECTED_TPR = 0.999
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EXPECTED_FPR = 0.0005
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# for CASCADE
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EXPECTED_FPR_PRE_LAYYER = 0.1
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EXPECTED_TPR_PRE_LAYYER = 0.999
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# the threshold range of adaboost. (from -inf to +inf)
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AB_TH_MIN = -15
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AB_TH_MAX = +15
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HAAR_FEATURE_TYPE_I = "I"
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HAAR_FEATURE_TYPE_II = "II"
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HAAR_FEATURE_TYPE_III = "III"
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HAAR_FEATURE_TYPE_IV = "IV"
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HAAR_FEATURE_TYPE_V = "V"
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AB_TH = -3.
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OVER_LAP_TH = 0.1
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MAX_WEAK_NUM = 12
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CASACADE_LIMIT = 3
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ADABOOST_LIMIT = 150
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SEARCH_WIN_STEP = 4
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DETECT_START = 1.
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DETECT_END = 2.
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DETECT_STEP = 0.2
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