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| 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 | |