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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import numpy as np
import torch
import torchvision
import torch.nn.functional as F
__all__ = [
"filter_box",
"postprocess",
"bboxes_iou",
"matrix_iou",
"adjust_box_anns",
"xyxy2xywh",
"xyxy2cxcywh",
]
def filter_box(output, scale_range):
"""
output: (N, 5+class) shape
"""
min_scale, max_scale = scale_range
w = output[:, 2] - output[:, 0]
h = output[:, 3] - output[:, 1]
keep = (w * h > min_scale * min_scale) & (w * h < max_scale * max_scale)
return output[keep]
def postprocess(prediction, num_classes, conf_thre=0.7, nms_thre=0.45):
box_corner = prediction.new(prediction.shape)
box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2
box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2
box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2
box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2
prediction[:, :, :4] = box_corner[:, :, :4]
output = [None for _ in range(len(prediction))]
for i, image_pred in enumerate(prediction):
# If none are remaining => process next image
if not image_pred.size(0):
continue
# Get score and class with highest confidence
class_conf, class_pred = torch.max(
image_pred[:, 5 : 5 + num_classes], 1, keepdim=True
)
conf_mask = (image_pred[:, 4] * class_conf.squeeze() >= conf_thre).squeeze()
# _, conf_mask = torch.topk((image_pred[:, 4] * class_conf.squeeze()), 1000)
# Detections ordered as (x1, y1, x2, y2, obj_conf, class_conf, class_pred)
detections = torch.cat((image_pred[:, :5], class_conf, class_pred.float()), 1)
detections = detections[conf_mask]
if not detections.size(0):
continue
nms_out_index = torchvision.ops.batched_nms(
detections[:, :4],
detections[:, 4] * detections[:, 5],
detections[:, 6],
nms_thre,
)
detections = detections[nms_out_index]
if output[i] is None:
output[i] = detections
else:
output[i] = torch.cat((output[i], detections))
return output
def bboxes_iou(bboxes_a, bboxes_b, xyxy=True):
if bboxes_a.shape[1] != 4 or bboxes_b.shape[1] != 4:
raise IndexError
if xyxy:
tl = torch.max(bboxes_a[:, None, :2], bboxes_b[:, :2])
br = torch.min(bboxes_a[:, None, 2:], bboxes_b[:, 2:])
area_a = torch.prod(bboxes_a[:, 2:] - bboxes_a[:, :2], 1)
area_b = torch.prod(bboxes_b[:, 2:] - bboxes_b[:, :2], 1)
else:
tl = torch.max(
(bboxes_a[:, None, :2] - bboxes_a[:, None, 2:] / 2),
(bboxes_b[:, :2] - bboxes_b[:, 2:] / 2),
)
br = torch.min(
(bboxes_a[:, None, :2] + bboxes_a[:, None, 2:] / 2),
(bboxes_b[:, :2] + bboxes_b[:, 2:] / 2),
)
area_a = torch.prod(bboxes_a[:, 2:], 1)
area_b = torch.prod(bboxes_b[:, 2:], 1)
en = (tl < br).type(tl.type()).prod(dim=2)
area_i = torch.prod(br - tl, 2) * en # * ((tl < br).all())
return area_i / (area_a[:, None] + area_b - area_i)
def matrix_iou(a, b):
"""
return iou of a and b, numpy version for data augenmentation
"""
lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
return area_i / (area_a[:, np.newaxis] + area_b - area_i + 1e-12)
def adjust_box_anns(bbox, scale_ratio, padw, padh, w_max, h_max):
#bbox[:, 0::2] = np.clip(bbox[:, 0::2] * scale_ratio + padw, 0, w_max)
#bbox[:, 1::2] = np.clip(bbox[:, 1::2] * scale_ratio + padh, 0, h_max)
bbox[:, 0::2] = bbox[:, 0::2] * scale_ratio + padw
bbox[:, 1::2] = bbox[:, 1::2] * scale_ratio + padh
return bbox
def xyxy2xywh(bboxes):
bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 0]
bboxes[:, 3] = bboxes[:, 3] - bboxes[:, 1]
return bboxes
def xyxy2cxcywh(bboxes):
bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 0]
bboxes[:, 3] = bboxes[:, 3] - bboxes[:, 1]
bboxes[:, 0] = bboxes[:, 0] + bboxes[:, 2] * 0.5
bboxes[:, 1] = bboxes[:, 1] + bboxes[:, 3] * 0.5
return bboxes
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