#!/usr/bin/env python
# ******************************************************************************
# Copyright 2023 Brainchip Holdings Ltd.
#
# 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.
# ******************************************************************************
"""
This module provides some utils to handle boxes and calculate the jaccard overlap score.
"""
__all__ = ["xywh_to_xyxy", "xyxy_to_xywh", "compute_center_xy",
"compute_center_wh", "compute_overlap"]
import numpy as np
import tensorflow as tf
from .data_utils import Coord
[docs]
def xywh_to_xyxy(boxes):
"""Convert a set of boxes from format xywh to xyxy, where each format represent:
* 'xywh': format of ('cx', 'xy', 'w', 'h'), also called 'centroids' and
* 'xyxy': format of ('x_min', 'y_min', 'x_max', 'y_max'), also called 'corners'.
Args:
boxes (tf.Tensor or np.ndarray): tensor with shape (N, 4)
Returns:
tf.Tensor or np.ndarray: tensor with new format
"""
assert boxes.shape[-1] == 4, "Expected 4 as last dimension."
x1y1 = boxes[..., :2] - 0.5 * boxes[..., -2:]
x2y2 = boxes[..., :2] + 0.5 * boxes[..., -2:]
if isinstance(boxes, tf.Tensor):
return tf.concat([x1y1, x2y2], axis=-1)
return np.concatenate([x1y1, x2y2], axis=-1)
[docs]
def xyxy_to_xywh(boxes):
"""Convert a set of boxes from format xyxy to xywh, where each format represent:
* 'xyxy': format of ('x_min', 'y_min', 'x_max', 'y_max'), also called 'corners' and
* 'xywh': format of ('cx', 'xy', 'w', 'h'), also called 'centroids'.
Args:
boxes (tf.Tensor): tensor with shape (N, 4)
Returns:
tf.Tensor: tensor with new format
"""
assert boxes.shape[-1] == 4, "Expected 4 as last dimension."
tf.debugging.assert_greater_equal(boxes[..., -2:], boxes[..., :2],
"x2y2 must be greater than x1y1")
wh = boxes[..., -2:] - boxes[..., :2]
xy = boxes[..., :2] + 0.5 * wh
if isinstance(boxes, tf.Tensor):
return tf.concat([xy, wh], axis=-1)
return np.concatenate([xy, wh], axis=-1)
[docs]
def compute_center_xy(bbox, grid_size):
"""
Computes the center coordinates (x, y) of a bounding box relative to the grid.
Args:
bbox (tf.Tensor): Bounding box coordinates (ymin, xmin, ymax, xmax).
grid_size (tuple): The grid size in the format (h, w).
Returns:
tuple: A tuple containing the center coordinates (center_x, center_y).
"""
center_x = .5 * (bbox[Coord.x1] + bbox[Coord.x2])
center_x = center_x * grid_size[1]
center_y = .5 * (bbox[Coord.y1] + bbox[Coord.y2])
center_y = center_y * grid_size[0]
return center_x, center_y
[docs]
def compute_center_wh(bbox, grid_size):
"""
Computes the width and height of a bounding box relative to a grid.
Args:
bbox (tf.Tensor): Bounding box coordinates (ymin, xmin, ymax, xmax).
grid_size (tuple): The grid size in the format (h, w).
Returns:
tuple: The width and height of the bounding box.
"""
center_h = (bbox[Coord.y2] - bbox[Coord.y1]) * grid_size[0]
center_w = (bbox[Coord.x2] - bbox[Coord.x1]) * grid_size[1]
return center_w, center_h
[docs]
def compute_overlap(a1, a2, mode="element_wise", box_format="xywh"):
"""Calculate ious between a1, a2 in two different modes:
* element_wise: compute iou element-by-element, returning 1D array tensor,
* outer_product: compute cross iou with all possible combination between inputs.
Args:
a1 (tf.Tensor or np.ndarray): set of boxes, with shape at least equal to (N, 4).
a2 (tf.Tensor or np.ndarray): set of boxes, with compatible broadcast-shape
(in 'element_wise' mode) or shape at least equal to (N, 4) (in 'outer_product' mode).
mode (str, optional): the mode to use. 'element_wise' or 'outer_product'. Defaults to
"element_wise".
box_format (str, optional): format of both inputs. Defaults to 'xywh'.
Returns:
tf.Tensor or np.ndarray: IoU between inputs with shape (N,) in 'element_wise',
otherwise (N, M).
"""
format_choices = ["xywh", "xyxy"]
mode_choises = ["element_wise", "outer_product"]
assert box_format in format_choices, f"box format must be one of {format_choices}"
assert mode in mode_choises, f"mode must be one of {mode_choises}"
def process_boxes(a):
# Decompose the input set of boxes into three parts:
# [(x_min, y_min), (x_max, y_max), (w, h)]
if box_format == "xywh":
a_wh = a[..., 2:4]
a = xywh_to_xyxy(a[..., :4])
else:
a_wh = a[..., 2:4] - a[..., :2]
return a[..., :2], a[..., 2:4], a_wh
# Check inputs are not empty
a1_num_boxes = a1.shape[0]
a2_num_boxes = a2.shape[0]
is_tf_tensor = isinstance(a1, tf.Tensor)
if a1_num_boxes == 0 or a2_num_boxes == 0:
if is_tf_tensor:
return tf.zeros((a1_num_boxes, a2_num_boxes))
return np.zeros((a1_num_boxes, a2_num_boxes))
# In outer product, we need expand tensors. Broadcast will do the rest of the job.
if mode == "outer_product":
a1 = a1[:, None]
a2 = a2[None]
# Process two sets
a2_mins, a2_maxes, a2_wh = process_boxes(a2)
a1_mins, a1_maxes, a1_wh = process_boxes(a1)
# Intersection as min((a2_maxes, a1_maxes) - max(a2_mins, a1_mins)
intersect_mins = tf.math.maximum(a2_mins, a1_mins)
intersect_maxes = tf.math.minimum(a2_maxes, a1_maxes)
# Getting the intersections in the xy (aka the width, height intersection)
intersect_wh = tf.math.maximum(intersect_maxes - intersect_mins, 0)
# Multiply to get intersecting area
intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
# Values for the single sets
true_areas = a1_wh[..., 0] * a1_wh[..., 1]
pred_areas = a2_wh[..., 0] * a2_wh[..., 1]
# Compute union for the IoU
add_areas = tf.convert_to_tensor(pred_areas + true_areas, dtype=intersect_areas.dtype)
union_areas = add_areas - intersect_areas
iou = intersect_areas / union_areas
if is_tf_tensor:
return iou
return iou.numpy()