<filename>codes/Baseline Model Example/scoring/matching.py
"""
Copyright 2018 Defense Innovation Unit Experimental
All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

from collections import defaultdict
from rectangle import Rectangle
import numpy as np

class Matching(object):
  """Matching class."""

  def __init__(self, groundtruth_rects, rects):
    """Constructs a Matching instance.

    Args:
      groundtruth_rects: a list of groundtruth rectangles.
      rects: a list of rectangles to be matched against the groundtruth_rects.
    Raises:
      ValueError: if any item inside the groundtruth_rects or rects are not
      Rectangle type.
    """
    for rect in groundtruth_rects:
      if not isinstance(rect, Rectangle):
        raise ValueError('Invalid instance type: should be Rectangle.')
    for rect in rects:
      if not isinstance(rect, Rectangle):
        raise ValueError('Invalid instance type: should be Rectangle.')
    self.groundtruth_rects_ = groundtruth_rects
    self.rects_ = rects
    self._compute_iou_from_rectangle_pairs()

  def _compute_iou_from_rectangle_pairs(self):
    """Computes the iou scores between all pairs of rectangles."""
    #try to extract a matrix nx4 from rects
    m = len(self.groundtruth_rects_)
    n = len(self.rects_)
    self.n = n
    self.m = m

    self.iou_rectangle_pair_indices_ = defaultdict(list)

    if not(n == 0 or m == 0):

      mat2 = np.array( [j.coords for j in self.groundtruth_rects_])
      mat1 = np.array([j.coords for j in self.rects_])
      #i,j axes correspond to #boxes, #coords per rect

      #compute the areas
      w1 = mat1[:,2] - mat1[:,0]
      w2 = mat2[:,2] - mat2[:,0]
      h1 = mat1[:,3] - mat1[:,1]
      h2 = mat2[:,3] - mat2[:,1]
      a1 = np.multiply(h1,w1)
      a2 = np.multiply(h2,w2)
      w_h_matrix = cartesian([a1,a2]).reshape((n,m,2))
      a_matrix = w_h_matrix.sum(axis=2)

      #now calculate the intersection rectangle
      i_xmin = cartesian([mat1[:,0],mat2[:,0]]).reshape((n,m,2))
      i_xmax = cartesian([mat1[:,2],mat2[:,2]]).reshape((n,m,2))
      i_ymin = cartesian([mat1[:,1],mat2[:,1]]).reshape((n,m,2))
      i_ymax = cartesian([mat1[:,3],mat2[:,3]]).reshape((n,m,2))
      i_w = np.min(i_xmax,axis=2) - np.max(i_xmin,axis=2)
      i_h = np.min(i_ymax,axis=2) - np.max(i_ymin,axis=2)
      i_w[i_w < 0] = 0
      i_h[i_h < 0] = 0

      i_a_matrix = np.multiply(i_w,i_h)
      iou_matrix = np.divide(i_a_matrix, (a_matrix - i_a_matrix))
      self.iou_matrix = iou_matrix

    else:
      self.iou_matrix = np.zeros((n,m))

  def greedy_match(self,iou_threshold):

    gt_rects_matched = [False for gt_index in range(self.m)]
    rects_matched = [False for r_index in range(self.n)]

    if self.n == 0:
      return [],[]
    elif self.m == 0:
      return rects_matched, []

    for i,gt_index in enumerate(np.argmax(self.iou_matrix, axis=1)):
      if self.iou_matrix[i, gt_index] >= iou_threshold:
        if gt_rects_matched[gt_index] is False and rects_matched[i] is False:
          rects_matched[i] = True
          gt_rects_matched[gt_index] = True
    return rects_matched, gt_rects_matched


def cartesian(arrays, out=None):
  """Generate a cartesian product of input arrays.

  Parameters
  ----------
  arrays : list of array-like
      1-D arrays to form the cartesian product of.
  out : ndarray
      Array to place the cartesian product in.
  Returns
  -------
  out : ndarray
      2-D array of shape (M, len(arrays)) containing cartesian products
      formed of input arrays.

  """
  arrays = [np.asarray(x) for x in arrays]
  shape = (len(x) for x in arrays)
  dtype = arrays[0].dtype

  ix = np.indices(shape)
  ix = ix.reshape(len(arrays), -1).T

  if out is None:
      out = np.empty_like(ix, dtype=dtype)

  for n, arr in enumerate(arrays):
      out[:, n] = arrays[n][ix[:, n]]

  return out
<filename>codes/Baseline Model Example/scoring/matching.py
"""
Copyright 2018 Defense Innovation Unit Experimental
All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

from collections import defaultdict
from rectangle import Rectangle
import numpy as np

class Matching(object):
  """Matching class."""

  def __init__(self, groundtruth_rects, rects):
    """Constructs a Matching instance.

    Args:
      groundtruth_rects: a list of groundtruth rectangles.
      rects: a list of rectangles to be matched against the groundtruth_rects.
    Raises:
      ValueError: if any item inside the groundtruth_rects or rects are not
      Rectangle type.
    """
    for rect in groundtruth_rects:
      if not isinstance(rect, Rectangle):
        raise ValueError('Invalid instance type: should be Rectangle.')
    for rect in rects:
      if not isinstance(rect, Rectangle):
        raise ValueError('Invalid instance type: should be Rectangle.')
    self.groundtruth_rects_ = groundtruth_rects
    self.rects_ = rects
    self._compute_iou_from_rectangle_pairs()

  def _compute_iou_from_rectangle_pairs(self):
    """Computes the iou scores between all pairs of rectangles."""
    #try to extract a matrix nx4 from rects
    m = len(self.groundtruth_rects_)
    n = len(self.rects_)
    self.n = n
    self.m = m

    self.iou_rectangle_pair_indices_ = defaultdict(list)

    if not(n == 0 or m == 0):

      mat2 = np.array( [j.coords for j in self.groundtruth_rects_])
      mat1 = np.array([j.coords for j in self.rects_])
      #i,j axes correspond to #boxes, #coords per rect

      #compute the areas
      w1 = mat1[:,2] - mat1[:,0]
      w2 = mat2[:,2] - mat2[:,0]
      h1 = mat1[:,3] - mat1[:,1]
      h2 = mat2[:,3] - mat2[:,1]
      a1 = np.multiply(h1,w1)
      a2 = np.multiply(h2,w2)
      w_h_matrix = cartesian([a1,a2]).reshape((n,m,2))
      a_matrix = w_h_matrix.sum(axis=2)

      #now calculate the intersection rectangle
      i_xmin = cartesian([mat1[:,0],mat2[:,0]]).reshape((n,m,2))
      i_xmax = cartesian([mat1[:,2],mat2[:,2]]).reshape((n,m,2))
      i_ymin = cartesian([mat1[:,1],mat2[:,1]]).reshape((n,m,2))
      i_ymax = cartesian([mat1[:,3],mat2[:,3]]).reshape((n,m,2))
      i_w = np.min(i_xmax,axis=2) - np.max(i_xmin,axis=2)
      i_h = np.min(i_ymax,axis=2) - np.max(i_ymin,axis=2)
      i_w[i_w < 0] = 0
      i_h[i_h < 0] = 0

      i_a_matrix = np.multiply(i_w,i_h)
      iou_matrix = np.divide(i_a_matrix, (a_matrix - i_a_matrix))
      self.iou_matrix = iou_matrix

    else:
      self.iou_matrix = np.zeros((n,m))

  def greedy_match(self,iou_threshold):

    gt_rects_matched = [False for gt_index in range(self.m)]
    rects_matched = [False for r_index in range(self.n)]

    if self.n == 0:
      return [],[]
    elif self.m == 0:
      return rects_matched, []

    for i,gt_index in enumerate(np.argmax(self.iou_matrix, axis=1)):
      if self.iou_matrix[i, gt_index] >= iou_threshold:
        if gt_rects_matched[gt_index] is False and rects_matched[i] is False:
          rects_matched[i] = True
          gt_rects_matched[gt_index] = True
    return rects_matched, gt_rects_matched


def cartesian(arrays, out=None):
  """Generate a cartesian product of input arrays.

  Parameters
  ----------
  arrays : list of array-like
      1-D arrays to form the cartesian product of.
  out : ndarray
      Array to place the cartesian product in.
  Returns
  -------
  out : ndarray
      2-D array of shape (M, len(arrays)) containing cartesian products
      formed of input arrays.

  """
  arrays = [np.asarray(x) for x in arrays]
  shape = (len(x) for x in arrays)
  dtype = arrays[0].dtype

  ix = np.indices(shape)
  ix = ix.reshape(len(arrays), -1).T

  if out is None:
      out = np.empty_like(ix, dtype=dtype)

  for n, arr in enumerate(arrays):
      out[:, n] = arrays[n][ix[:, n]]

  return out
Copyright 2018 Defense Innovation Unit Experimental All rights reserved.  Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at  http://www.apache.org/licenses/LICENSE-2.0  Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. Matching class. Constructs a Matching instance.      Args:       groundtruth_rects: a list of groundtruth rectangles.       rects: a list of rectangles to be matched against the groundtruth_rects.     Raises:       ValueError: if any item inside the groundtruth_rects or rects are not       Rectangle type. Computes the iou scores between all pairs of rectangles. #try to extract a matrix nx4 from rects #i,j axes correspond to #boxes, #coords per rect #compute the areas #now calculate the intersection rectangle Generate a cartesian product of input arrays.    Parameters   ----------   arrays : list of array-like       1-D arrays to form the cartesian product of.   out : ndarray       Array to place the cartesian product in.   Returns   -------   out : ndarray       2-D array of shape (M, len(arrays)) containing cartesian products       formed of input arrays.