# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

"""Fast R-CNN config system.

This file specifies default config options for Fast R-CNN. You should not
change values in this file. Instead, you should write a config file (in yaml)
and use cfg_from_file(yaml_file) to load it and override the default options.

Most tools in $ROOT/tools take a --cfg option to specify an override file.
    - See tools/{train,test}_net.py for example code that uses cfg_from_file()
    - See experiments/cfgs/*.yml for example YAML config override files
"""

import os
import os.path as osp
import numpy as np
import math
# `pip install easydict` if you don't have it
from easydict import EasyDict as edict

__C = edict()
# Consumers can get config by:
#   from fast_rcnn_config import cfg
cfg = __C

# region proposal network (RPN) or not
__C.IS_RPN = True

# multiscale training and testing
__C.IS_MULTISCALE = False
__C.IS_EXTRAPOLATING = True

#
__C.REGION_PROPOSAL = 'RPN'

__C.NET_NAME = 'CaffeNet'
__C.SUBCLS_NAME = 'voxel_exemplars'

#
# Training options
#

__C.TRAIN = edict()

# learning rate
__C.TRAIN.LEARNING_RATE = 0.001
__C.TRAIN.MOMENTUM = 0.9
__C.TRAIN.GAMMA = 0.1
__C.TRAIN.STEPSIZE = 30000

# Scales to compute real features
__C.TRAIN.SCALES_BASE = (0.25, 0.5, 1.0, 2.0, 3.0)

# The number of scales per octave in the image pyramid
# An octave is the set of scales up to half of the initial scale
__C.TRAIN.NUM_PER_OCTAVE = 4

# parameters for ROI generating
__C.TRAIN.SPATIAL_SCALE = 0.0625
__C.TRAIN.KERNEL_SIZE = 5

# Aspect ratio to use during training
__C.TRAIN.ASPECTS = (1, 0.75, 0.5, 0.25)

# Images to use per minibatch
__C.TRAIN.IMS_PER_BATCH = 2

# Minibatch size (number of regions of interest [ROIs])
__C.TRAIN.BATCH_SIZE = 128

# Fraction of minibatch that is labeled foreground (i.e. class > 0)
__C.TRAIN.FG_FRACTION = 0.25

# Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH)
__C.TRAIN.FG_THRESH = (0.5,)

# Overlap threshold for a ROI to be considered background (class = 0 if
# overlap in [LO, HI))
__C.TRAIN.BG_THRESH_HI = (0.5,)
__C.TRAIN.BG_THRESH_LO = (0.1,)

# Use horizontally-flipped images during training?
__C.TRAIN.USE_FLIPPED = True

# Train bounding-box regressors
__C.TRAIN.BBOX_REG = True

# Overlap required between a ROI and ground-truth box in order for that ROI to
# be used as a bounding-box regression training example
__C.TRAIN.BBOX_THRESH = (0.5,)

# Iterations between snapshots
__C.TRAIN.SNAPSHOT_ITERS = 10000

# solver.prototxt specifies the snapshot path prefix, this adds an optional
# infix to yield the path: <prefix>[_<infix>]_iters_XYZ.caffemodel
__C.TRAIN.SNAPSHOT_PREFIX = 'caffenet_fast_rcnn'
__C.TRAIN.SNAPSHOT_INFIX = ''

# Use a prefetch thread in roi_data_layer.layer
# So far I haven't found this useful; likely more engineering work is required
__C.TRAIN.USE_PREFETCH = False

# Train using subclasses
__C.TRAIN.SUBCLS = True

# Train using viewpoint
__C.TRAIN.VIEWPOINT = False

# Threshold of ROIs in training RCNN
__C.TRAIN.ROI_THRESHOLD = 0.1

__C.TRAIN.DISPLAY = 20


# IOU >= thresh: positive example
__C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7
# IOU < thresh: negative example
__C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3
# If an anchor statisfied by positive and negative conditions set to negative
__C.TRAIN.RPN_CLOBBER_POSITIVES = False
# Max number of foreground examples
__C.TRAIN.RPN_FG_FRACTION = 0.5
# Total number of examples
__C.TRAIN.RPN_BATCHSIZE = 256
# NMS threshold used on RPN proposals
__C.TRAIN.RPN_NMS_THRESH = 0.7
# Number of top scoring boxes to keep before apply NMS to RPN proposals
__C.TRAIN.RPN_PRE_NMS_TOP_N = 12000
# Number of top scoring boxes to keep after applying NMS to RPN proposals
__C.TRAIN.RPN_POST_NMS_TOP_N = 2000
# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
__C.TRAIN.RPN_MIN_SIZE = 16
# Deprecated (outside weights)
__C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
# Give the positive RPN examples weight of p * 1 / {num positives}
# and give negatives a weight of (1 - p)
# Set to -1.0 to use uniform example weighting
__C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0

__C.TRAIN.RPN_BASE_SIZE = 16
__C.TRAIN.RPN_ASPECTS = [0.25, 0.5, 0.75, 1, 1.5, 2, 3]  # 7 aspects
__C.TRAIN.RPN_SCALES = [2, 2.82842712, 4, 5.65685425, 8, 11.3137085, 16, 22.627417, 32, 45.254834] # 2**np.arange(1, 6, 0.5), 10 scales

#
# Testing options
#

__C.TEST = edict()

# Scales to compute real features
#__C.TEST.SCALES_BASE = (0.25, 0.5, 1.0, 2.0, 3.0)
__C.TEST.SCALES_BASE = (1.0,)

# The number of scales per octave in the image pyramid
# An octave is the set of scales up to half of the initial scale
__C.TEST.NUM_PER_OCTAVE = 4

# Aspect ratio to use during testing
__C.TEST.ASPECTS = (1, 0.75, 0.5, 0.25)

# parameters for ROI generating
__C.TEST.SPATIAL_SCALE = 0.0625
__C.TEST.KERNEL_SIZE = 5

# Overlap threshold used for non-maximum suppression (suppress boxes with
# IoU >= this threshold)
__C.TEST.NMS = 0.5

# Experimental: treat the (K+1) units in the cls_score layer as linear
# predictors (trained, eg, with one-vs-rest SVMs).
__C.TEST.SVM = False

# Test using bounding-box regressors
__C.TEST.BBOX_REG = True

# Test using subclass
__C.TEST.SUBCLS = True

# Train using viewpoint
__C.TEST.VIEWPOINT = False

# Threshold of ROIs in testing
__C.TEST.ROI_THRESHOLD = 0.1
__C.TEST.ROI_NUM = 2000
__C.TEST.DET_THRESHOLD = 0.0001

## NMS threshold used on RPN proposals
__C.TEST.RPN_NMS_THRESH = 0.7
## Number of top scoring boxes to keep before apply NMS to RPN proposals
__C.TEST.RPN_PRE_NMS_TOP_N = 6000
## Number of top scoring boxes to keep after applying NMS to RPN proposals
__C.TEST.RPN_POST_NMS_TOP_N = 300
# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
__C.TEST.RPN_MIN_SIZE = 16

#
# MISC
#

# The mapping from image coordinates to feature map coordinates might cause
# some boxes that are distinct in image space to become identical in feature
# coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor
# for identifying duplicate boxes.
# 1/16 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16
__C.DEDUP_BOXES = 1./16.

# Pixel mean values (BGR order) as a (1, 1, 3) array
# These are the values originally used for training VGG16
__C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])

# For reproducibility
__C.RNG_SEED = 3

# A small number that's used many times
__C.EPS = 1e-14

# Root directory of project
__C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..'))

# Place outputs under an experiments directory
__C.EXP_DIR = 'default'

# Use GPU implementation of non-maximum suppression
__C.USE_GPU_NMS = True

# Default GPU device id
__C.GPU_ID = 0

def get_output_dir(imdb, net):
    """Return the directory where experimental artifacts are placed.

    A canonical path is built using the name from an imdb and a network
    (if not None).
    """
    path = osp.abspath(osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name))
    if net is None:
        return path
    else:
        return osp.join(path, net)

def _add_more_info(is_train):
    # compute all the scales
    if is_train:
        scales_base = __C.TRAIN.SCALES_BASE
        num_per_octave = __C.TRAIN.NUM_PER_OCTAVE
    else:
        scales_base = __C.TEST.SCALES_BASE
        num_per_octave = __C.TEST.NUM_PER_OCTAVE

    num_scale_base = len(scales_base)
    num = (num_scale_base - 1) * num_per_octave + 1
    scales = []
    for i in xrange(num):
        index_scale_base = i / num_per_octave
        sbase = scales_base[index_scale_base]
        j = i % num_per_octave
        if j == 0: