| from __future__ import print_function
|
| import os
|
| import sys
|
| import cv2
|
| import random
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| import datetime
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| import time
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| import math
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| import argparse
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| import numpy as np
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| import torch
|
|
|
| try:
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| from iou import IOU
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| except BaseException:
|
|
|
| def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2):
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| sa = abs((ax2 - ax1) * (ay2 - ay1))
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| sb = abs((bx2 - bx1) * (by2 - by1))
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| x1, y1 = max(ax1, bx1), max(ay1, by1)
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| x2, y2 = min(ax2, bx2), min(ay2, by2)
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| w = x2 - x1
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| h = y2 - y1
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| if w < 0 or h < 0:
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| return 0.0
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| else:
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| return 1.0 * w * h / (sa + sb - w * h)
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|
|
|
|
| def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh):
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| xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1
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| dx, dy = (xc - axc) / aww, (yc - ayc) / ahh
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| dw, dh = math.log(ww / aww), math.log(hh / ahh)
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| return dx, dy, dw, dh
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|
|
|
|
| def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh):
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| xc, yc = dx * aww + axc, dy * ahh + ayc
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| ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh
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| x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2
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| return x1, y1, x2, y2
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|
|
|
|
| def nms(dets, thresh):
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| if 0 == len(dets):
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| return []
|
| x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4]
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| areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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| order = scores.argsort()[::-1]
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|
|
| keep = []
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| while order.size > 0:
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| i = order[0]
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| keep.append(i)
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| xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]])
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| xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]])
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|
|
| w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1)
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| ovr = w * h / (areas[i] + areas[order[1:]] - w * h)
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|
|
| inds = np.where(ovr <= thresh)[0]
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| order = order[inds + 1]
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|
|
| return keep
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|
|
|
|
| def encode(matched, priors, variances):
|
| """Encode the variances from the priorbox layers into the ground truth boxes
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| 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].
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| priors: (tensor) Prior boxes in center-offset form
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| Shape: [num_priors,4].
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| variances: (list[float]) Variances of priorboxes
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| Return:
|
| encoded boxes (tensor), Shape: [num_priors, 4]
|
| """
|
|
|
|
|
| g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
|
|
|
| g_cxcy /= (variances[0] * priors[:, 2:])
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|
|
| g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
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| g_wh = torch.log(g_wh) / variances[1]
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|
|
| return torch.cat([g_cxcy, g_wh], 1)
|
|
|
|
|
| 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:],
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| priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
|
| boxes[:, :2] -= boxes[:, 2:] / 2
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| boxes[:, 2:] += boxes[:, :2]
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| 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
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| boxes[:, :, 2:] += boxes[:, :, :2]
|
| return boxes
|
|
|
