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Running
on
Zero
Running
on
Zero
File size: 3,769 Bytes
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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():
olist = net(imgs)
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[:, 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])
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