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global_defs.py
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/
global_defs.py
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#!/usr/bin/env python3
"""
script including
class object with global settings
"""
class CONFIG:
#------------------#
# select or define #
#------------------#
img_types = ["kitti", "mot"]
model_names = ["mask_rcnn", "yolact"]
classification_models = ["LR_L1", "GB", "NN_L2"]
regression_models = ["LR", "LR_L1", "LR_L2", "GB", "NN_L1", "NN_L2"]
IMG_TYPE = img_types[0]
MODEL_NAME = model_names[0]
CLASSIFICATION_MODEL = classification_models[0]
REGRESSION_MODEL = regression_models[0]
#---------------------#
# set necessary path #
#---------------------#
my_io_path = "/home/user/object_tracking_io/" + IMG_TYPE + "/"
#--------------------------------------------------------------------#
# select tasks to be executed by setting boolean variable True/False #
#--------------------------------------------------------------------#
COMPUTE_TIME_SERIES_INSTANCES = False
PLOT_TIME_SERIES_INSTANCES = False
ANALYZE_TRACKING = False
COMPUTE_TIME_SERIES_METRICS = False
VISUALIZE_METRICS = False
COMPUTE_MEAN_AP = False
COMPUTE_MEAN_AP_METRICS = False
PLOT_MEAN_AP = False
VISUALIZE_REGRESSION = False
VISUALIZE_CLASSIFICATION = False
ANALYZE_METRICS = False
PLOT_ANALYZE_METRICS = False
#-----------#
# optionals #
#-----------#
SCORE_THRESHOLD = '00'
MAP_THRESHOLD = '00'
NUM_CORES = 10
EPS_MATCHING = 100
NUM_REG_MATCHING = 5
CLASS_COMPONENT = "car" # car, person
METRICS_COMPONENT = ("E", "S", "iou")
NUM_PREV_FRAMES = 5
NUM_RESAMPLING = 10
FLAG_CLASSIF = 1
FLAG_OBJ_SEG = 1 # 0: object detection, 1: segmentation
FLAG_NEW_METRICS = 2 # 0: U^i, 1: U^is plus score and ratio, 2: V^i
if IMG_TYPE == "kitti":
NUM_IMAGES = 2981
CLASSES = [1,2]
elif IMG_TYPE == "mot":
if MODEL_NAME == "mask_rcnn":
NUM_IMAGES = 2862
elif MODEL_NAME == "yolact":
NUM_IMAGES = 2562
NUM_RESAMPLING = min(NUM_RESAMPLING, 4)
CLASSES = [2]
IMG_DIR = my_io_path + "inputimages/val/"
GT_DIR = my_io_path + "groundtruth/val/"
HELPER_DIR = my_io_path + "helpers/" + MODEL_NAME + str(SCORE_THRESHOLD) + "/"
PRED_DIR = my_io_path + "pred_instance/" + MODEL_NAME + str(SCORE_THRESHOLD) + "/"
SOFTMAX_DIR = my_io_path + "pred_softmax/" + MODEL_NAME + str(SCORE_THRESHOLD) + "/"
INSTANCES_SMALL_DIR = my_io_path + "instances_small/" + MODEL_NAME + str(SCORE_THRESHOLD) + "/"
SOFTMAX_SMALL_DIR = my_io_path + "softmax_small/" + MODEL_NAME + str(SCORE_THRESHOLD) + "/"
TIME_SERIES_INST_DIR = my_io_path + "time_series_instances/" + MODEL_NAME + str(SCORE_THRESHOLD) + "/"
IMG_TIME_SERIES_DIR = my_io_path + "img_time_series_instances/" + MODEL_NAME + str(SCORE_THRESHOLD) + "/"
ANALYZE_TRACKING_DIR = my_io_path + "results_tracking/" + MODEL_NAME + str(SCORE_THRESHOLD) + "/"
METRICS_DIR = my_io_path + "metrics/" + MODEL_NAME + str(SCORE_THRESHOLD) + "_os" + str(FLAG_OBJ_SEG) + "/"
IMG_METRICS_DIR = my_io_path + "img_metrics/" + MODEL_NAME + str(SCORE_THRESHOLD) + "_os" + str(FLAG_OBJ_SEG) + "/"
MEAN_AP_DIR = my_io_path + "mean_ap/" + MODEL_NAME + str(MAP_THRESHOLD) + "/"
MEAN_AP_METRICS_DIR = my_io_path + "mean_ap_metrics/" + MODEL_NAME + str(MAP_THRESHOLD) + "_os" + str(FLAG_OBJ_SEG) + "/"
IMG_IOU_INST_DIR = my_io_path + "img_iou_instances/" + MODEL_NAME + str(SCORE_THRESHOLD) + "_os" + str(FLAG_OBJ_SEG) + "/npf" + str(NUM_PREV_FRAMES) + "_" + REGRESSION_MODEL + "_nm" + str(FLAG_NEW_METRICS) + "/"
IMG_IOU0_INST_DIR = my_io_path + "img_iou0_instances/" + MODEL_NAME + str(SCORE_THRESHOLD) + "_os" + str(FLAG_OBJ_SEG) + "/npf" + str(NUM_PREV_FRAMES) + "_" + CLASSIFICATION_MODEL + "_nm" + str(FLAG_NEW_METRICS) + "/"
ANALYZE_DIR = my_io_path + "results_analyze/" + MODEL_NAME + str(SCORE_THRESHOLD) + "_os" + str(FLAG_OBJ_SEG) + "/npf" + str(NUM_PREV_FRAMES) + "_runs" + str(NUM_RESAMPLING) + "_nm" + str(FLAG_NEW_METRICS) + "/"
IMG_ANALYZE_DIR = my_io_path + "img_results_analyze/" + MODEL_NAME + str(SCORE_THRESHOLD) + "_os" + str(FLAG_OBJ_SEG) + "/npf" + str(NUM_PREV_FRAMES) + "_runs" + str(NUM_RESAMPLING) + "_nm" + str(FLAG_NEW_METRICS) + "/"
"""
In case of problems, feel free to contact: Kira Maag, kmaag@uni-wuppertal.de
"""