File size: 3,679 Bytes
8c212a5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
from __future__ import print_function
import os
import sys
import cv2
import random
import datetime
import time
import math
import argparse
import numpy as np
import torch
try:
from iou import IOU
except BaseException:
# IOU cython speedup 10x
def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2):
sa = abs((ax2 - ax1) * (ay2 - ay1))
sb = abs((bx2 - bx1) * (by2 - by1))
x1, y1 = max(ax1, bx1), max(ay1, by1)
x2, y2 = min(ax2, bx2), min(ay2, by2)
w = x2 - x1
h = y2 - y1
if w < 0 or h < 0:
return 0.0
else:
return 1.0 * w * h / (sa + sb - w * h)
def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh):
xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1
dx, dy = (xc - axc) / aww, (yc - ayc) / ahh
dw, dh = math.log(ww / aww), math.log(hh / ahh)
return dx, dy, dw, dh
def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh):
xc, yc = dx * aww + axc, dy * ahh + ayc
ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh
x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2
return x1, y1, x2, y2
def nms(dets, thresh):
# print(dets)
if 0 == len(dets):
return []
x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4]
# print(x1,x2,y1,y2)
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]])
xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]])
w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1)
ovr = w * h / (areas[i] + areas[order[1:]] - w * h)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
def encode(matched, priors, variances):
"""Encode the variances from the priorbox layers into the ground truth boxes
we have matched (based on jaccard overlap) with the prior boxes.
Args:
matched: (tensor) Coords of ground truth for each prior in point-form
Shape: [num_priors, 4].
priors: (tensor) Prior boxes in center-offset form
Shape: [num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
encoded boxes (tensor), Shape: [num_priors, 4]
"""
# dist b/t match center and prior's center
g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
# encode variance
g_cxcy /= (variances[0] * priors[:, 2:])
# match wh / prior wh
g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
g_wh = torch.log(g_wh) / variances[1]
# return target for smooth_l1_loss
return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4]
def decode(loc, priors, variances):
"""Decode locations from predictions using priors to undo
the encoding we did for offset regression at train time.
Args:
loc (tensor): location predictions for loc layers,
Shape: [num_priors,4]
priors (tensor): Prior boxes in center-offset form.
Shape: [num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
decoded bounding box predictions
"""
boxes = torch.cat((
priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
boxes[:, :2] -= boxes[:, 2:] / 2
boxes[:, 2:] += boxes[:, :2]
return boxes
|