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| import torch | |
| import torch.nn.functional as F | |
| import os | |
| import sys | |
| import cv2 | |
| import random | |
| import datetime | |
| import math | |
| import argparse | |
| import numpy as np | |
| import scipy.io as sio | |
| import zipfile | |
| from .net_s3fd import s3fd | |
| from .bbox import * | |
| def detect(net, img, device): | |
| img = img - np.array([104, 117, 123]) | |
| img = img.transpose(2, 0, 1) | |
| img = img.reshape((1,) + img.shape) | |
| if 'cuda' in device: | |
| torch.backends.cudnn.benchmark = True | |
| img = torch.from_numpy(img).float().to(device) | |
| BB, CC, HH, WW = img.size() | |
| with torch.no_grad(): | |
| olist = net(img) | |
| bboxlist = [] | |
| for i in range(len(olist) // 2): | |
| olist[i * 2] = F.softmax(olist[i * 2], dim=1) | |
| olist = [oelem.data.cpu() for oelem in olist] | |
| for i in range(len(olist) // 2): | |
| ocls, oreg = olist[i * 2], olist[i * 2 + 1] | |
| FB, FC, FH, FW = ocls.size() # feature map size | |
| stride = 2**(i + 2) # 4,8,16,32,64,128 | |
| anchor = stride * 4 | |
| poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) | |
| for Iindex, hindex, windex in poss: | |
| axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride | |
| score = ocls[0, 1, hindex, windex] | |
| loc = oreg[0, :, hindex, windex].contiguous().view(1, 4) | |
| priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]) | |
| variances = [0.1, 0.2] | |
| box = decode(loc, priors, variances) | |
| x1, y1, x2, y2 = box[0] * 1.0 | |
| # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) | |
| bboxlist.append([x1, y1, x2, y2, score]) | |
| bboxlist = np.array(bboxlist) | |
| if 0 == len(bboxlist): | |
| bboxlist = np.zeros((1, 5)) | |
| return bboxlist | |
| def batch_detect(net, imgs, device): | |
| imgs = imgs - np.array([104, 117, 123]) | |
| imgs = imgs.transpose(0, 3, 1, 2) | |
| if 'cuda' in device: | |
| torch.backends.cudnn.benchmark = True | |
| imgs = torch.from_numpy(imgs).float().to(device) | |
| BB, CC, HH, WW = imgs.size() | |
| with torch.no_grad(): | |
| # print(type(net),type(imgs), device) | |
| olist = net(imgs) | |
| bboxlist = [] | |
| for i in range(len(olist) // 2): | |
| olist[i * 2] = F.softmax(olist[i * 2], dim=1) | |
| # print(olist) | |
| # import pdb; pdb.set_trace() | |
| olist = [oelem.cpu() for oelem in olist] | |
| for i in range(len(olist) // 2): | |
| ocls, oreg = olist[i * 2], olist[i * 2 + 1] | |
| FB, FC, FH, FW = ocls.size() # feature map size | |
| stride = 2**(i + 2) # 4,8,16,32,64,128 | |
| anchor = stride * 4 | |
| poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) | |
| for Iindex, hindex, windex in poss: | |
| axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride | |
| score = ocls[:, 1, hindex, windex] | |
| loc = oreg[:, :, hindex, windex].contiguous().view(BB, 1, 4) | |
| priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]).view(1, 1, 4) | |
| variances = [0.1, 0.2] | |
| box = batch_decode(loc, priors, variances) | |
| box = box[:, 0] * 1.0 | |
| # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) | |
| bboxlist.append(torch.cat([box, score.unsqueeze(1)], 1).cpu().numpy()) | |
| bboxlist = np.array(bboxlist) | |
| if 0 == len(bboxlist): | |
| bboxlist = np.zeros((1, BB, 5)) | |
| return bboxlist | |
| def flip_detect(net, img, device): | |
| img = cv2.flip(img, 1) | |
| b = detect(net, img, device) | |
| bboxlist = np.zeros(b.shape) | |
| bboxlist[:, 0] = img.shape[1] - b[:, 2] | |
| bboxlist[:, 1] = b[:, 1] | |
| bboxlist[:, 2] = img.shape[1] - b[:, 0] | |
| bboxlist[:, 3] = b[:, 3] | |
| bboxlist[:, 4] = b[:, 4] | |
| return bboxlist | |
| def pts_to_bb(pts): | |
| min_x, min_y = np.min(pts, axis=0) | |
| max_x, max_y = np.max(pts, axis=0) | |
| return np.array([min_x, min_y, max_x, max_y]) | |