def testROIpool(sample):
sam = sample['image']
bbs = sample['bb']
bz = bbs.size(0)
num = bbs.size(1)
bbs=bbs.view(-1, 5)
ind = torch.arange(bz,requires_grad=False).view(-1,1)
ind = ind.repeat(1,num).view(-1,1)
bbs[:,0] = ind[:,0]
sam,bbs = sam.to(cfg.DEVICE),bbs.to(cfg.DEVICE)
simg = ROIPool(sam,bbs,(50,50),1)
ther = convertTensor2Img(sam)
bbs = bbs.cpu().detach().numpy()
img = visualizeRP(ther, bbs)
cv2.imshow('raw',img)
# simg = simg.cpu().detach().numpy()
for ind,labels in enumerate(simg):
rois = convertTensor2Img(labels)
cv2.imshow('rrn{}'.format(ind), rois)
cv2.waitKey()
cv2.destroyAllWindows()
def testRRN_Pretrain(sample,pre):
RRN_net = MyRRN()
RRN_net.to(cfg.DEVICE)
RRN_net.load_state_dict(torch.load(pre))
sam = sample['image']
bbb = sample['bb']
tm = sample['tm']
gt = sample['gt']
bz = bbb.size(0)
num = bbb.size(1)
bbb = bbb.view(-1, 5)
ind = torch.arange(bz,requires_grad=False).view(-1,1)
ind = ind.repeat(1,num).view(-1,1)
bbb[:,0] = ind[:,0]
labels_output = resizeThermal(tm, bbb)
# labels_output = labels_output.to(cfg.DEVICE)
sam,bbb,tm = sam.to(cfg.DEVICE), bbb.to(cfg.DEVICE), tm.to(cfg.DEVICE)
out_RRN = RRN_net(sam,bbb)
# out_RRN = out_RRN.cpu().detach().numpy()
bbb = bbb.cpu().detach().numpy()
ther = convertTensor2Img(tm,norm=False)
imgg = visualizeRP(ther, bbb)
# print(imgg.dtype)
cv2.imshow('aa',imgg)
for ind,labels in enumerate(out_RRN[:4]):
# print('output')
p = convertTensor2Img(labels,norm=False)
cv2.imshow('rrn{}'.format(ind), p)
for ind,labels in enumerate(labels_output[:4]):
p = convertTensor2Img(labels,norm=False)
cv2.imshow('bbs{}'.format(ind), p)
cv2.waitKey(0)
cv2.destroyAllWindows()
def testDataset(sample,drawgt=None):
print(sample['img_info'])
sam = sample['image']
tm = sample['tm']
bbs = sample['bb']
gt = sample['gt']
print('sam',sam.size())
print('tm',tm.size())
print('bbs',bbs.size())
print('gt',gt.size())
bbs = bbs.cpu()
bbs = bbs.view(-1,5)
bbs = bbs.detach().numpy()
gt = gt.cpu()
gt = gt.view(-1,5)
gt = gt.detach().numpy()
raw = convertTensor2Img(sam)
ther = convertTensor2Img(tm,norm=False)
# print(ther)
if not drawgt:
gt = None
draw_bbs = visualizeRP(raw, bbs,gt)
# img,bboxes = flipBoundingBox(raw, bbs)
# draw_flip_bbs = visualizeRP(img, bboxes,gt)
print('size of raw',raw.shape)
print('size of ther',ther.shape)
print('size of bbs',draw_bbs.get().shape)
cv2.imshow('raw',raw)
cv2.imshow('thermal', ther)
cv2.imshow('bbs', draw_bbs)
# cv2.imshow('flip_bbs', draw_flip_bbs)
cv2.waitKey(0)
cv2.destroyAllWindows()
def testResizeThermal(sample):
sam = sample['image']
bbb = sample['bb']
tm = sample['tm']
print(sample['img_info'])
gt = sample['gt']
bbb = bbb.cpu()
bz = bbb.size(0)
num = bbb.size(1)
bbb = bbb.view(-1,5)
gt = gt.cpu()
gt = gt.view(-1,5)
gt = gt.detach().numpy()
ind = torch.arange(bz).view(-1,1)
ind = ind.repeat(1,num).view(-1,1)
bbb[:,0] = ind[:,0]
labels_output = resizeThermal(tm, bbb)
print(labels_output.shape)
# labels_output = labels_output.type('torch.ByteTensor')
exit()
ther = convertTensor2Img(tm,norm=False)
bbb = bbb.detach().numpy()
imgg = visualizeRP(ther, bbb[:9])
cv2.imshow('winname', imgg)
for ind,labels in enumerate(labels_output[:9]):
p = convertTensor2Img(labels,norm=False)
cv2.imshow('bbs{}'.format(ind), p)
cv2.waitKey(0)
cv2.destroyAllWindows()
def main():
pre = 'mymodel/RRN/model27_lr_1e-6_bz_6_NBS_128_norm_epoch_9.pth'
# sample = getSampleDataset(train=False,bz=1)
dataiter = getSampleDataset(train=True,bz=1)
for sample in dataiter:
name = sample['img_info']
img = sample['image']
gt = sample['gt']
gt = gt.cpu()
gt = gt.view(-1,5)
gt = gt.detach().numpy()
img = convertTensor2Img(img)
img = visualizeRP(img, bbs=None,gt=gt)
cv2.imshow('A',img)
cv2.waitKey()
# input('Enter to next image')
cv2.destroyAllWindows()