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# Copyright (c) 2022 PaddlePaddle Authors. 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.
import numpy as np
import cv2
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 = np.argsort(scores)
indexes = indexes[-candidate_size:]
while len(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]
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 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 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]
class PPYOLOEPostProcess(object):
"""
Args:
input_shape (int): network input image size
scale_factor (float): scale factor of ori image
"""
def __init__(self,
score_threshold=0.4,
nms_threshold=0.5,
nms_top_k=10000,
keep_top_k=300):
self.score_threshold = score_threshold
self.nms_threshold = nms_threshold
self.nms_top_k = nms_top_k
self.keep_top_k = keep_top_k
def _non_max_suppression(self, prediction, scale_factor):
batch_size = prediction.shape[0]
out_boxes_list = []
box_num_list = []
for batch_id in range(batch_size):
bboxes, confidences = prediction[batch_id][..., :4], prediction[
batch_id][..., 4:]
# nms
picked_box_probs = []
picked_labels = []
for class_index in range(0, confidences.shape[1]):
probs = confidences[:, class_index]
mask = probs > self.score_threshold
probs = probs[mask]
if probs.shape[0] == 0:
continue
subset_boxes = bboxes[mask, :]
box_probs = np.concatenate(
[subset_boxes, probs.reshape(-1, 1)], axis=1)
box_probs = hard_nms(
box_probs,
iou_threshold=self.nms_threshold,
top_k=self.nms_top_k)
picked_box_probs.append(box_probs)
picked_labels.extend([class_index] * box_probs.shape[0])
if len(picked_box_probs) == 0:
out_boxes_list.append(np.empty((0, 4)))
else:
picked_box_probs = np.concatenate(picked_box_probs)
# resize output boxes
picked_box_probs[:, 0] /= scale_factor[batch_id][1]
picked_box_probs[:, 2] /= scale_factor[batch_id][1]
picked_box_probs[:, 1] /= scale_factor[batch_id][0]
picked_box_probs[:, 3] /= scale_factor[batch_id][0]
# clas score box
out_box = np.concatenate(
[
np.expand_dims(
np.array(picked_labels), axis=-1), np.expand_dims(
picked_box_probs[:, 4], axis=-1),
picked_box_probs[:, :4]
],
axis=1)
if out_box.shape[0] > self.keep_top_k:
out_box = out_box[out_box[:, 1].argsort()[::-1]
[:self.keep_top_k]]
out_boxes_list.append(out_box)
box_num_list.append(out_box.shape[0])
out_boxes_list = np.concatenate(out_boxes_list, axis=0)
box_num_list = np.array(box_num_list)
return out_boxes_list, box_num_list
def __call__(self, outs, scale_factor):
out_boxes_list, box_num_list = self._non_max_suppression(outs,
scale_factor)
return {'bbox': out_boxes_list, 'bbox_num': box_num_list}
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