def initProcess(params, source, editedSource, difference):
w, h = source.shape[0], source.shape[1]
tgtRegion = np.ones(w * h).reshape(w, h)
srcRegion = np.ones(w * h).reshape(w, h)
fillFront = np.ones(w * h).reshape(w, h)
# Force size_patch to be odd
assert params['sizePatch'] % 2 == 1, "sizePatch must be odd"
log.debug('sizePatch:%s'% params['sizePatch'] )
# Fill targerRegion
for y in range(h):
for x in range(w):
tgtRegion[x,y] = 1 if difference[x,y,:].any() else 0
nbPoint = np.sum(tgtRegion)
log.info('nbPoints: %d'%nbPoint)
# # Initialisation of source_region
log.debug('tgtRegion.dtype: %s'% tgtRegion.dtype)
sizeDilate = params['sizeDilate']
sizeErode = params['sizeErode']
log.debug('sizeDilate:%s '% sizeDilate)
structureDilate = np.ones((sizeDilate, sizeDilate))
structureErode = np.ones((sizeErode, sizeErode))
tgtRegionDilated = ndimage.binary_dilation(tgtRegion, structure=structureDilate).astype(tgtRegion.dtype)
tgtRegionEroded = ndimage.binary_erosion(tgtRegion , structure=structureErode ).astype(tgtRegion.dtype)
fillFront = tgtRegionDilated - tgtRegionEroded
utils.saveOutput(syntheticsDirPath, tgtRegion, 'tgtRegion')
utils.saveOutput(syntheticsDirPath, tgtRegionDilated, 'tgtRegionDilated')
utils.saveOutput(syntheticsDirPath, tgtRegionEroded, 'tgtRegionEroded')
utils.saveOutput(syntheticsDirPath, fillFront, 'fillFront')
return fillFront, tgtRegion, srcRegion, nbPoint
def predictFlow(np_arr, model):
input_data = InputData(np_arr, removePOffset=True, makeDimLess=True)
input_arr, target_arr = input_data.input, input_data.target
input_tr = torch.tensor(input_arr)
input_batch = input_tr.unsqueeze(0)
print('Running inference')
output_batch = model(input_batch.float())
output_tr = output_batch.squeeze(0)
output_arr = output_tr.detach().numpy()
print('Saving output')
saveOutput(output_arr, target_arr)
return 1
net.eval()
with torch.no_grad():
for batch_idx, blob in enumerate(valid_loader):
img = blob['img'].cuda()
img = img.view(-1, *img.size()[2:])
# shuf = torch.randperm(len(img))
shuf = torch.arange(len(img)).view(
-1, crop_num).t().contiguous().view(-1)
targ = blob['target'].cuda().repeat(1, crop_num).view(-1, C)[shuf]
pred = net(img[shuf])
img = []
valid_loss.append(lossfunc(pred, targ).cpu().numpy())
now = batch_idx * batch_size
valid_pred[now:now + batch_size] = pred
valid_targ[now:now + batch_size] = targ
myOutput(batch_idx, len(valid_pred), valid_loss, valid_pred,
valid_targ)
print("epoch:{}/{}".format(epoch, epoches))
print("Train: ", end='')
myOutput(-1, len(train_pred), train_loss, train_pred, train_targ)
print("Valid: ", end='')
myOutput(-1, len(valid_pred), valid_loss, valid_pred, valid_targ)
if parallel:
torch.save(net.module.state_dict(), pt_name.format(epoch))
else:
torch.save(net.state_dict(), pt_name.format(epoch))
saveOutput(save_name, epoch)
print("Time:", int(time.time() - t), '(s)')
print("---")
def saveOutputs(outDir, diff=None):
if not diff==None:
plt.imsave(os.path.join(syntheticsDirPath, 'diff.png'), diff)
return 0
"""Main fonction
This is the main
"""
if __name__ == '__main__':
logging.info('Start main')
source, editedSource = getInputs()
difference = process.run(params, source, editedSource)
utils.saveOutput(syntheticsDirPath, difference, 'difference')
logging.info('End main')
##Heart of C code.
# void Pima::run(const char * outputName)
# {
# unsigned int IPS(0),JPS(0),IS(0),JS(0),cpt(0);
# double Cprec(0.0);
# float cpt2(0.9);
# CImg<unsigned char> SRC_prec(srcToFillRGB);
# SRC_prec.fill(0);
# initialize_Pp();
# my_display(cpt);
# while(nb_points!=0)
# {
# cpt++;
