import torch from torch.autograd import Variable from collections import OrderedDict import numpy as np import os from PIL import Image import util.util as util from .base_model import BaseModel from . import networks class UIModel(BaseModel): def name(self): return 'UIModel' def initialize(self, opt): assert(not opt.isTrain) BaseModel.initialize(self, opt) self.use_features = opt.instance_feat or opt.label_feat netG_input_nc = opt.label_nc if not opt.no_instance: netG_input_nc += 1 if self.use_features: netG_input_nc += opt.feat_num self.netG = networks.define_G(netG_input_nc, opt.output_nc, opt.ngf, opt.netG, opt.n_downsample_global, opt.n_blocks_global, opt.n_local_enhancers, opt.n_blocks_local, opt.norm, gpu_ids=self.gpu_ids) self.load_network(self.netG, 'G', opt.which_epoch) print('---------- Networks initialized -------------') def toTensor(self, img, normalize=False): tensor = torch.from_numpy(np.array(img, np.int32, copy=False)) tensor = tensor.view(1, img.size[1], img.size[0], len(img.mode)) tensor = tensor.transpose(1, 2).transpose(1, 3).contiguous() if normalize: return (tensor.float()/255.0 - 0.5) / 0.5 return tensor.float() def load_image(self, label_path, inst_path, feat_path): opt = self.opt # read label map label_img = Image.open(label_path) if label_path.find('face') != -1: label_img = label_img.convert('L') ow, oh = label_img.size w = opt.loadSize h = int(w * oh / ow) label_img = label_img.resize((w, h), Image.NEAREST) label_map = self.toTensor(label_img) # onehot vector input for label map self.label_map = label_map.cuda() oneHot_size = (1, opt.label_nc, h, w) input_label = self.Tensor(torch.Size(oneHot_size)).zero_() self.input_label = input_label.scatter_(1, label_map.long().cuda(), 1.0) # read instance map if not opt.no_instance: inst_img = Image.open(inst_path) inst_img = inst_img.resize((w, h), Image.NEAREST) self.inst_map = self.toTensor(inst_img).cuda() self.edge_map = self.get_edges(self.inst_map) self.net_input = Variable(torch.cat((self.input_label, self.edge_map), dim=1), volatile=True) else: self.net_input = Variable(self.input_label, volatile=True) self.features_clustered = np.load(feat_path).item() self.object_map = self.inst_map if opt.instance_feat else self.label_map object_np = self.object_map.cpu().numpy().astype(int) self.feat_map = self.Tensor(1, opt.feat_num, h, w).zero_() self.cluster_indices = np.zeros(self.opt.label_nc, np.uint8) for i in np.unique(object_np): label = i if i < 1000 else i//1000 if label in self.features_clustered: feat = self.features_clustered[label] np.random.seed(i+1) cluster_idx = np.random.randint(0, feat.shape[0]) self.cluster_indices[label] = cluster_idx idx = (self.object_map == i).nonzero() self.set_features(idx, feat, cluster_idx) self.net_input_original = self.net_input.clone() self.label_map_original = self.label_map.clone() self.feat_map_original = self.feat_map.clone() if not opt.no_instance: self.inst_map_original = self.inst_map.clone() def reset(self): self.net_input = self.net_input_prev = self.net_input_original.clone() self.label_map = self.label_map_prev = self.label_map_original.clone() self.feat_map = self.feat_map_prev = self.feat_map_original.clone() if not self.opt.no_instance: self.inst_map = self.inst_map_prev = self.inst_map_original.clone() self.object_map = self.inst_map if self.opt.instance_feat else self.label_map def undo(self): self.net_input = self.net_input_prev self.label_map = self.label_map_prev self.feat_map = self.feat_map_prev if not self.opt.no_instance: self.inst_map = self.inst_map_prev self.object_map = self.inst_map if self.opt.instance_feat else self.label_map # get boundary map from instance map def get_edges(self, t): edge = torch.cuda.ByteTensor(t.size()).zero_() edge[:,:,:,1:] = edge[:,:,:,1:] | (t[:,:,:,1:] != t[:,:,:,:-1]) edge[:,:,:,:-1] = edge[:,:,:,:-1] | (t[:,:,:,1:] != t[:,:,:,:-1]) edge[:,:,1:,:] = edge[:,:,1:,:] | (t[:,:,1:,:] != t[:,:,:-1,:]) edge[:,:,:-1,:] = edge[:,:,:-1,:] | (t[:,:,1:,:] != t[:,:,:-1,:]) return edge.float() # change the label at the source position to the label at the target position def change_labels(self, click_src, click_tgt): y_src, x_src = click_src[0], click_src[1] y_tgt, x_tgt = click_tgt[0], click_tgt[1] label_src = int(self.label_map[0, 0, y_src, x_src]) inst_src = self.inst_map[0, 0, y_src, x_src] label_tgt = int(self.label_map[0, 0, y_tgt, x_tgt]) inst_tgt = self.inst_map[0, 0, y_tgt, x_tgt] idx_src = (self.inst_map == inst_src).nonzero() # need to change 3 things: label map, instance map, and feature map if idx_src.shape: # backup current maps self.backup_current_state() # change both the label map and the network input self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt self.net_input[idx_src[:,0], idx_src[:,1] + label_src, idx_src[:,2], idx_src[:,3]] = 0 self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1 # update the instance map (and the network input) if inst_tgt > 1000: # if different instances have different ids, give the new object a new id tgt_indices = (self.inst_map > label_tgt * 1000) & (self.inst_map < (label_tgt+1) * 1000) inst_tgt = self.inst_map[tgt_indices].max() + 1 self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = inst_tgt self.net_input[:,-1,:,:] = self.get_edges(self.inst_map) # also copy the source features to the target position idx_tgt = (self.inst_map == inst_tgt).nonzero() if idx_tgt.shape: self.copy_features(idx_src, idx_tgt[0,:]) self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map)) # add strokes of target label in the image def add_strokes(self, click_src, label_tgt, bw, save): # get the region of the new strokes (bw is the brush width) size = self.net_input.size() h, w = size[2], size[3] idx_src = torch.LongTensor(bw**2, 4).fill_(0) for i in range(bw): idx_src[i*bw:(i+1)*bw, 2] = min(h-1, max(0, click_src[0]-bw//2 + i)) for j in range(bw): idx_src[i*bw+j, 3] = min(w-1, max(0, click_src[1]-bw//2 + j)) idx_src = idx_src.cuda() # again, need to update 3 things if idx_src.shape: # backup current maps if save: self.backup_current_state() # update the label map (and the network input) in the stroke region self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt for k in range(self.opt.label_nc): self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0 self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1 # update the instance map (and the network input) self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt self.net_input[:,-1,:,:] = self.get_edges(self.inst_map) # also update the features if available if self.opt.instance_feat: feat = self.features_clustered[label_tgt] #np.random.seed(label_tgt+1) #cluster_idx = np.random.randint(0, feat.shape[0]) cluster_idx = self.cluster_indices[label_tgt] self.set_features(idx_src, feat, cluster_idx) self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map)) # add an object to the clicked position with selected style def add_objects(self, click_src, label_tgt, mask, style_id=0): y, x = click_src[0], click_src[1] mask = np.transpose(mask, (2, 0, 1))[np.newaxis,...] idx_src = torch.from_numpy(mask).cuda().nonzero() idx_src[:,2] += y idx_src[:,3] += x # backup current maps self.backup_current_state() # update label map self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt for k in range(self.opt.label_nc): self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0 self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1 # update instance map self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt self.net_input[:,-1,:,:] = self.get_edges(self.inst_map) # update feature map self.set_features(idx_src, self.feat, style_id) self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map)) def single_forward(self, net_input, feat_map): net_input = torch.cat((net_input, feat_map), dim=1) fake_image = self.netG.forward(net_input) if fake_image.size()[0] == 1: return fake_image.data[0] return fake_image.data # generate all outputs for different styles def style_forward(self, click_pt, style_id=-1): if click_pt is None: self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map)) self.crop = None self.mask = None else: instToChange = int(self.object_map[0, 0, click_pt[0], click_pt[1]]) self.instToChange = instToChange label = instToChange if instToChange < 1000 else instToChange//1000 self.feat = self.features_clustered[label] self.fake_image = [] self.mask = self.object_map == instToChange idx = self.mask.nonzero() self.get_crop_region(idx) if idx.size(): if style_id == -1: (min_y, min_x, max_y, max_x) = self.crop ### original for cluster_idx in range(self.opt.multiple_output): self.set_features(idx, self.feat, cluster_idx) fake_image = self.single_forward(self.net_input, self.feat_map) fake_image = util.tensor2im(fake_image[:,min_y:max_y,min_x:max_x]) self.fake_image.append(fake_image) """### To speed up previewing different style results, either crop or downsample the label maps if instToChange > 1000: (min_y, min_x, max_y, max_x) = self.crop ### crop _, _, h, w = self.net_input.size() offset = 512 y_start, x_start = max(0, min_y-offset), max(0, min_x-offset) y_end, x_end = min(h, (max_y + offset)), min(w, (max_x + offset)) y_region = slice(y_start, y_start+(y_end-y_start)//16*16) x_region = slice(x_start, x_start+(x_end-x_start)//16*16) net_input = self.net_input[:,:,y_region,x_region] for cluster_idx in range(self.opt.multiple_output): self.set_features(idx, self.feat, cluster_idx) fake_image = self.single_forward(net_input, self.feat_map[:,:,y_region,x_region]) fake_image = util.tensor2im(fake_image[:,min_y-y_start:max_y-y_start,min_x-x_start:max_x-x_start]) self.fake_image.append(fake_image) else: ### downsample (min_y, min_x, max_y, max_x) = [crop//2 for crop in self.crop] net_input = self.net_input[:,:,::2,::2] size = net_input.size() net_input_batch = net_input.expand(self.opt.multiple_output, size[1], size[2], size[3]) for cluster_idx in range(self.opt.multiple_output): self.set_features(idx, self.feat, cluster_idx) feat_map = self.feat_map[:,:,::2,::2] if cluster_idx == 0: feat_map_batch = feat_map else: feat_map_batch = torch.cat((feat_map_batch, feat_map), dim=0) fake_image_batch = self.single_forward(net_input_batch, feat_map_batch) for i in range(self.opt.multiple_output): self.fake_image.append(util.tensor2im(fake_image_batch[i,:,min_y:max_y,min_x:max_x]))""" else: self.set_features(idx, self.feat, style_id) self.cluster_indices[label] = style_id self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map)) def backup_current_state(self): self.net_input_prev = self.net_input.clone() self.label_map_prev = self.label_map.clone() self.inst_map_prev = self.inst_map.clone() self.feat_map_prev = self.feat_map.clone() # crop the ROI and get the mask of the object def get_crop_region(self, idx): size = self.net_input.size() h, w = size[2], size[3] min_y, min_x = idx[:,2].min(), idx[:,3].min() max_y, max_x = idx[:,2].max(), idx[:,3].max() crop_min = 128 if max_y - min_y < crop_min: min_y = max(0, (max_y + min_y) // 2 - crop_min // 2) max_y = min(h-1, min_y + crop_min) if max_x - min_x < crop_min: min_x = max(0, (max_x + min_x) // 2 - crop_min // 2) max_x = min(w-1, min_x + crop_min) self.crop = (min_y, min_x, max_y, max_x) self.mask = self.mask[:,:, min_y:max_y, min_x:max_x] # update the feature map once a new object is added or the label is changed def update_features(self, cluster_idx, mask=None, click_pt=None): self.feat_map_prev = self.feat_map.clone() # adding a new object if mask is not None: y, x = click_pt[0], click_pt[1] mask = np.transpose(mask, (2,0,1))[np.newaxis,...] idx = torch.from_numpy(mask).cuda().nonzero() idx[:,2] += y idx[:,3] += x # changing the label of an existing object else: idx = (self.object_map == self.instToChange).nonzero() # update feature map self.set_features(idx, self.feat, cluster_idx) # set the class features to the target feature def set_features(self, idx, feat, cluster_idx): for k in range(self.opt.feat_num): self.feat_map[idx[:,0], idx[:,1] + k, idx[:,2], idx[:,3]] = feat[cluster_idx, k] # copy the features at the target position to the source position def copy_features(self, idx_src, idx_tgt): for k in range(self.opt.feat_num): val = self.feat_map[idx_tgt[0], idx_tgt[1] + k, idx_tgt[2], idx_tgt[3]] self.feat_map[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = val def get_current_visuals(self, getLabel=False): mask = self.mask if self.mask is not None: mask = np.transpose(self.mask[0].cpu().float().numpy(), (1,2,0)).astype(np.uint8) dict_list = [('fake_image', self.fake_image), ('mask', mask)] if getLabel: # only output label map if needed to save bandwidth label = util.tensor2label(self.net_input.data[0], self.opt.label_nc) dict_list += [('label', label)] return OrderedDict(dict_list)