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from .box_utils import SSDSpec
from typing import List
import itertools
import math
import numpy as np
def generate_ssd_priors(specs: List[SSDSpec], image_size, clamp=True):
"""Generate SSD Prior Boxes.
It returns the center, height and width of the priors. The values are relative to the image size
Args:
specs: SSDSpecs about the shapes of sizes of prior boxes. i.e.
specs = [
SSDSpec(38, 8, SSDBoxSizes(30, 60), [2]),
SSDSpec(19, 16, SSDBoxSizes(60, 111), [2, 3]),
SSDSpec(10, 32, SSDBoxSizes(111, 162), [2, 3]),
SSDSpec(5, 64, SSDBoxSizes(162, 213), [2, 3]),
SSDSpec(3, 100, SSDBoxSizes(213, 264), [2]),
SSDSpec(1, 300, SSDBoxSizes(264, 315), [2])
]
image_size: image size.
clamp: if true, clamp the values to make fall between [0.0, 1.0]
Returns:
priors (num_priors, 4): The prior boxes represented as [[center_x, center_y, w, h]]. All the values
are relative to the image size.
"""
priors = []
for spec in specs:
scale = image_size / spec.shrinkage
for j, i in itertools.product(range(spec.feature_map_size), repeat=2):
x_center = (i + 0.5) / scale
y_center = (j + 0.5) / scale
# small sized square box
size = spec.box_sizes.min
h = w = size / image_size
priors.append([
x_center,
y_center,
w,
h
])
# big sized square box
size = math.sqrt(spec.box_sizes.max * spec.box_sizes.min)
h = w = size / image_size
priors.append([
x_center,
y_center,
w,
h
])
# change h/w ratio of the small sized box
size = spec.box_sizes.min
h = w = size / image_size
for ratio in spec.aspect_ratios:
ratio = math.sqrt(ratio)
priors.append([
x_center,
y_center,
w * ratio,
h / ratio
])
priors.append([
x_center,
y_center,
w / ratio,
h * ratio
])
priors = np.array(priors, dtype=np.float32)
if clamp:
np.clip(priors, 0.0, 1.0, out=priors)
return priors
def convert_locations_to_boxes(locations, priors, center_variance,
size_variance):
"""Convert regressional location results of SSD into boxes in the form of (center_x, center_y, h, w).
The conversion:
$$predicted\_center * center_variance = \frac {real\_center - prior\_center} {prior\_hw}$$
$$exp(predicted\_hw * size_variance) = \frac {real\_hw} {prior\_hw}$$
We do it in the inverse direction here.
Args:
locations (batch_size, num_priors, 4): the regression output of SSD. It will contain the outputs as well.
priors (num_priors, 4) or (batch_size/1, num_priors, 4): prior boxes.
center_variance: a float used to change the scale of center.
size_variance: a float used to change of scale of size.
Returns:
boxes: priors: [[center_x, center_y, h, w]]. All the values
are relative to the image size.
"""
# priors can have one dimension less.
if len(priors.shape) + 1 == len(locations.shape):
priors = np.expand_dims(priors, 0)
return np.concatenate([
locations[..., :2] * center_variance * priors[..., 2:] + priors[..., :2],
np.exp(locations[..., 2:] * size_variance) * priors[..., 2:]
], axis=len(locations.shape) - 1)
def convert_boxes_to_locations(center_form_boxes, center_form_priors, center_variance, size_variance):
# priors can have one dimension less
if len(center_form_priors.shape) + 1 == len(center_form_boxes.shape):
center_form_priors = np.expand_dims(center_form_priors, 0)
return np.concatenate([
(center_form_boxes[..., :2] - center_form_priors[..., :2]) / center_form_priors[..., 2:] / center_variance,
np.log(center_form_boxes[..., 2:] / center_form_priors[..., 2:]) / size_variance
], axis=len(center_form_boxes.shape) - 1)
def area_of(left_top, right_bottom):
"""Compute the areas of rectangles given two corners.
Args:
left_top (N, 2): left top corner.
right_bottom (N, 2): right bottom corner.
Returns:
area (N): return the area.
"""
hw = np.clip(right_bottom - left_top, 0.0, None)
return hw[..., 0] * hw[..., 1]
def iou_of(boxes0, boxes1, eps=1e-5):
"""Return intersection-over-union (Jaccard index) of boxes.
Args:
boxes0 (N, 4): ground truth boxes.
boxes1 (N or 1, 4): predicted boxes.
eps: a small number to avoid 0 as denominator.
Returns:
iou (N): IoU values.
"""
overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2])
overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:])
overlap_area = area_of(overlap_left_top, overlap_right_bottom)
area0 = area_of(boxes0[..., :2], boxes0[..., 2:])
area1 = area_of(boxes1[..., :2], boxes1[..., 2:])
return overlap_area / (area0 + area1 - overlap_area + eps)
def center_form_to_corner_form(locations):
return np.concatenate([locations[..., :2] - locations[..., 2:]/2,
locations[..., :2] + locations[..., 2:]/2], len(locations.shape) - 1)
def corner_form_to_center_form(boxes):
return np.concatenate([
(boxes[..., :2] + boxes[..., 2:]) / 2,
boxes[..., 2:] - boxes[..., :2]
], len(boxes.shape) - 1)
def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
"""
Args:
box_scores (N, 5): boxes in corner-form and probabilities.
iou_threshold: intersection over union threshold.
top_k: keep top_k results. If k <= 0, keep all the results.
candidate_size: only consider the candidates with the highest scores.
Returns:
picked: a list of indexes of the kept boxes
"""
scores = box_scores[:, -1]
boxes = box_scores[:, :-1]
picked = []
#_, indexes = scores.sort(descending=True)
indexes = np.argsort(scores)
#indexes = indexes[:candidate_size]
indexes = indexes[-candidate_size:]
while len(indexes) > 0:
#current = indexes[0]
current = indexes[-1]
picked.append(current)
if 0 < top_k == len(picked) or len(indexes) == 1:
break
current_box = boxes[current, :]
#indexes = indexes[1:]
indexes = indexes[:-1]
rest_boxes = boxes[indexes, :]
iou = iou_of(
rest_boxes,
np.expand_dims(current_box, axis=0),
)
indexes = indexes[iou <= iou_threshold]
return box_scores[picked, :]
# def nms(box_scores, nms_method=None, score_threshold=None, iou_threshold=None,
# sigma=0.5, top_k=-1, candidate_size=200):
# if nms_method == "soft":
# return soft_nms(box_scores, score_threshold, sigma, top_k)
# else:
# return hard_nms(box_scores, iou_threshold, top_k, candidate_size=candidate_size)
#
# def soft_nms(box_scores, score_threshold, sigma=0.5, top_k=-1):
# """Soft NMS implementation.
#
# References:
# https://arxiv.org/abs/1704.04503
# https://github.com/facebookresearch/Detectron/blob/master/detectron/utils/cython_nms.pyx
#
# Args:
# box_scores (N, 5): boxes in corner-form and probabilities.
# score_threshold: boxes with scores less than value are not considered.
# sigma: the parameter in score re-computation.
# scores[i] = scores[i] * exp(-(iou_i)^2 / simga)
# top_k: keep top_k results. If k <= 0, keep all the results.
# Returns:
# picked_box_scores (K, 5): results of NMS.
# """
# picked_box_scores = []
# while box_scores.size(0) > 0:
# max_score_index = torch.argmax(box_scores[:, 4])
# cur_box_prob = torch.tensor(box_scores[max_score_index, :])
# picked_box_scores.append(cur_box_prob)
# if len(picked_box_scores) == top_k > 0 or box_scores.size(0) == 1:
# break
# cur_box = cur_box_prob[:-1]
# box_scores[max_score_index, :] = box_scores[-1, :]
# box_scores = box_scores[:-1, :]
# ious = iou_of(cur_box.unsqueeze(0), box_scores[:, :-1])
# box_scores[:, -1] = box_scores[:, -1] * torch.exp(-(ious * ious) / sigma)
# box_scores = box_scores[box_scores[:, -1] > score_threshold, :]
# if len(picked_box_scores) > 0:
# return torch.stack(picked_box_scores)
# else:
# return torch.tensor([])
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