130 lines
4.2 KiB
Python
130 lines
4.2 KiB
Python
|
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):
|
||
|
|
if 0 == len(dets):
|
||
|
|
return []
|
||
|
|
x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4]
|
||
|
|
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
|
||
|
|
|
||
|
|
def batch_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])), 2)
|
||
|
|
boxes[:, :, :2] -= boxes[:, :, 2:] / 2
|
||
|
|
boxes[:, :, 2:] += boxes[:, :, :2]
|
||
|
|
return boxes
|