38 lines
1.4 KiB
Python
38 lines
1.4 KiB
Python
import os
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ENV = "dev" # [dev/test/prod]
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PROJECT_DIR = os.path.dirname(os.path.abspath(__file__))
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print("PROJECT_DIR:\t", PROJECT_DIR)
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DATA_FOLDER = os.path.join(PROJECT_DIR, 'data')
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MODELS_FOLDER = os.path.join(PROJECT_DIR, 'models')
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LOGS_FOLDER = os.path.join(PROJECT_DIR, 'logs')
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if ENV == "dev":
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print("DATA_FOLDER:\t", DATA_FOLDER)
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print("MODELS_FOLDER:\t", MODELS_FOLDER)
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print("LOGS_FOLDER:\t", LOGS_FOLDER)
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MODEL_WEIGHTS_PATH = os.path.join(MODELS_FOLDER, "bmi_model_weights.h5")
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GENDER_MODEL_PATH = os.path.join(MODELS_FOLDER, "simple_CNN.81-0.96.hdf5")
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AGE_TRAINED_MODEL_PATH = os.path.join(MODELS_FOLDER, "age_only_resnet50_weights.061-3.300-4.410.hdf5")
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CNN_FACE_DETECTOR_MODEL_PATH = os.path.join(MODELS_FOLDER, "mmod_human_face_detector.dat")
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BEAUTY_MODEL_WEIGHTS_PATH = os.path.join(MODELS_FOLDER, "beauty_model-ldl-resnet.h5")
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RESNET50_DEFAULT_IMG_WIDTH = 224
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MARGIN = .1
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TRAIN_BATCH_SIZE = 16
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VALIDATION_SIZE = 100
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ORIGINAL_IMGS_DIR = 'images'
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ORIGINAL_IMGS_INFO_FILE = 'data.csv'
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#AGE_TRAINED_WEIGHTS_FILE = 'age_only_resnet50_weights.061-3.300-4.410.hdf5'
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CROPPED_IMGS_DIR = 'normalized_images'
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CROPPED_IMGS_INFO_FILE = 'normalized_data.csv'
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TOP_LAYER_LOG_DIR = 'logs/top_layer'
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ALL_LAYERS_LOG_DIR = 'logs/all_layers'
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#The minimize percentage size of the targeted face to be considered for metadata extraction
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MIN_FACE_SIZE_PERCENTAGE = 0.05
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MIN_FACE_SIZE_PIXELS = (75 * 75)
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