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from __future__ import print_function |
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import os |
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import sys |
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import cv2 |
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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 |
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try: |
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from iou import IOU |
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except BaseException: |
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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 [] |
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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): |
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"""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. |
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Args: |
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matched: (tensor) Coords of ground truth for each prior in point-form |
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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: |
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encoded boxes (tensor), Shape: [num_priors, 4] |
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""" |
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g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2] |
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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) |
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def decode(loc, priors, variances): |
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"""Decode locations from predictions using priors to undo |
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the encoding we did for offset regression at train time. |
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Args: |
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loc (tensor): location predictions for loc layers, |
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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: |
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decoded bounding box predictions |
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""" |
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boxes = torch.cat(( |
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priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], |
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priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) |
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boxes[:, :2] -= boxes[:, 2:] / 2 |
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boxes[:, 2:] += boxes[:, :2] |
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return boxes |
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def batch_decode(loc, priors, variances): |
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"""Decode locations from predictions using priors to undo |
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the encoding we did for offset regression at train time. |
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Args: |
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loc (tensor): location predictions for loc layers, |
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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: |
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decoded bounding box predictions |
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""" |
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boxes = torch.cat(( |
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priors[:, :, :2] + loc[:, :, :2] * variances[0] * priors[:, :, 2:], |
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priors[:, :, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2) |
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boxes[:, :, :2] -= boxes[:, :, 2:] / 2 |
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boxes[:, :, 2:] += boxes[:, :, :2] |
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return boxes |
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