import torch import torch.utils.data as data import torchvision.transforms as transforms from PIL import Image, ImageDraw import os.path as osp import numpy as np import json class CPDatasetTest(data.Dataset): """ Test Dataset for CP-VTON. """ def __init__(self, opt): super(CPDatasetTest, self).__init__() # base setting self.opt = opt self.root = opt.dataroot self.datamode = opt.datamode # train or test or self-defined self.data_list = opt.data_list self.fine_height = opt.fine_height self.fine_width = opt.fine_width self.semantic_nc = opt.semantic_nc self.data_path = osp.join(opt.dataroot, opt.datamode) self.transform = transforms.Compose([ \ transforms.ToTensor(), \ transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) # load data list im_names = [] c_names = [] with open(osp.join(opt.dataroot, opt.data_list), 'r') as f: for line in f.readlines(): im_name, c_name = line.strip().split() im_names.append(im_name) c_names.append(c_name) self.im_names = im_names self.c_names = dict() self.c_names['paired'] = im_names self.c_names['unpaired'] = c_names def name(self): return "CPDataset" def get_agnostic(self, im, im_parse, pose_data): parse_array = np.array(im_parse) parse_head = ((parse_array == 4).astype(np.float32) + (parse_array == 13).astype(np.float32)) parse_lower = ((parse_array == 9).astype(np.float32) + (parse_array == 12).astype(np.float32) + (parse_array == 16).astype(np.float32) + (parse_array == 17).astype(np.float32) + (parse_array == 18).astype(np.float32) + (parse_array == 19).astype(np.float32)) agnostic = im.copy() agnostic_draw = ImageDraw.Draw(agnostic) length_a = np.linalg.norm(pose_data[5] - pose_data[2]) length_b = np.linalg.norm(pose_data[12] - pose_data[9]) point = (pose_data[9] + pose_data[12]) / 2 pose_data[9] = point + (pose_data[9] - point) / length_b * length_a pose_data[12] = point + (pose_data[12] - point) / length_b * length_a r = int(length_a / 16) + 1 # mask torso for i in [9, 12]: pointx, pointy = pose_data[i] agnostic_draw.ellipse((pointx-r*3, pointy-r*6, pointx+r*3, pointy+r*6), 'gray', 'gray') agnostic_draw.line([tuple(pose_data[i]) for i in [2, 9]], 'gray', width=r*6) agnostic_draw.line([tuple(pose_data[i]) for i in [5, 12]], 'gray', width=r*6) agnostic_draw.line([tuple(pose_data[i]) for i in [9, 12]], 'gray', width=r*12) agnostic_draw.polygon([tuple(pose_data[i]) for i in [2, 5, 12, 9]], 'gray', 'gray') # mask neck pointx, pointy = pose_data[1] agnostic_draw.rectangle((pointx-r*5, pointy-r*9, pointx+r*5, pointy), 'gray', 'gray') # mask arms agnostic_draw.line([tuple(pose_data[i]) for i in [2, 5]], 'gray', width=r*12) for i in [2, 5]: pointx, pointy = pose_data[i] agnostic_draw.ellipse((pointx-r*5, pointy-r*6, pointx+r*5, pointy+r*6), 'gray', 'gray') for i in [3, 4, 6, 7]: if (pose_data[i-1, 0] == 0.0 and pose_data[i-1, 1] == 0.0) or (pose_data[i, 0] == 0.0 and pose_data[i, 1] == 0.0): continue agnostic_draw.line([tuple(pose_data[j]) for j in [i - 1, i]], 'gray', width=r*10) pointx, pointy = pose_data[i] agnostic_draw.ellipse((pointx-r*5, pointy-r*5, pointx+r*5, pointy+r*5), 'gray', 'gray') for parse_id, pose_ids in [(14, [5, 6, 7]), (15, [2, 3, 4])]: mask_arm = Image.new('L', (768, 1024), 'white') mask_arm_draw = ImageDraw.Draw(mask_arm) pointx, pointy = pose_data[pose_ids[0]] mask_arm_draw.ellipse((pointx-r*5, pointy-r*6, pointx+r*5, pointy+r*6), 'black', 'black') for i in pose_ids[1:]: if (pose_data[i-1, 0] == 0.0 and pose_data[i-1, 1] == 0.0) or (pose_data[i, 0] == 0.0 and pose_data[i, 1] == 0.0): continue mask_arm_draw.line([tuple(pose_data[j]) for j in [i - 1, i]], 'black', width=r*10) pointx, pointy = pose_data[i] if i != pose_ids[-1]: mask_arm_draw.ellipse((pointx-r*5, pointy-r*5, pointx+r*5, pointy+r*5), 'black', 'black') mask_arm_draw.ellipse((pointx-r*4, pointy-r*4, pointx+r*4, pointy+r*4), 'black', 'black') parse_arm = (np.array(mask_arm) / 255) * (parse_array == parse_id).astype(np.float32) agnostic.paste(im, None, Image.fromarray(np.uint8(parse_arm * 255), 'L')) agnostic.paste(im, None, Image.fromarray(np.uint8(parse_head * 255), 'L')) agnostic.paste(im, None, Image.fromarray(np.uint8(parse_lower * 255), 'L')) return agnostic def __getitem__(self, index): im_name = self.im_names[index] c_name = {} c = {} cm = {} for key in self.c_names: c_name[key] = self.c_names[key][index] c[key] = Image.open(osp.join(self.data_path, 'cloth', c_name[key])).convert('RGB') c[key] = transforms.Resize(self.fine_width, interpolation=2)(c[key]) cm[key] = Image.open(osp.join(self.data_path, 'cloth-mask', c_name[key])) cm[key] = transforms.Resize(self.fine_width, interpolation=0)(cm[key]) c[key] = self.transform(c[key]) # [-1,1] cm_array = np.array(cm[key]) cm_array = (cm_array >= 128).astype(np.float32) cm[key] = torch.from_numpy(cm_array) # [0,1] cm[key].unsqueeze_(0) # person image im_pil_big = Image.open(osp.join(self.data_path, 'image', im_name)) im_pil = transforms.Resize(self.fine_width, interpolation=2)(im_pil_big) im = self.transform(im_pil) # load parsing image parse_name = im_name.replace('.jpg', '.png') im_parse_pil_big = Image.open(osp.join(self.data_path, 'image-parse-v3', parse_name)) im_parse_pil = transforms.Resize(self.fine_width, interpolation=0)(im_parse_pil_big) parse = torch.from_numpy(np.array(im_parse_pil)[None]).long() im_parse = self.transform(im_parse_pil.convert('RGB')) labels = { 0: ['background', [0, 10]], 1: ['hair', [1, 2]], 2: ['face', [4, 13]], 3: ['upper', [5, 6, 7]], 4: ['bottom', [9, 12]], 5: ['left_arm', [14]], 6: ['right_arm', [15]], 7: ['left_leg', [16]], 8: ['right_leg', [17]], 9: ['left_shoe', [18]], 10: ['right_shoe', [19]], 11: ['socks', [8]], 12: ['noise', [3, 11]] } parse_map = torch.FloatTensor(20, self.fine_height, self.fine_width).zero_() parse_map = parse_map.scatter_(0, parse, 1.0) new_parse_map = torch.FloatTensor(self.semantic_nc, self.fine_height, self.fine_width).zero_() for i in range(len(labels)): for label in labels[i][1]: new_parse_map[i] += parse_map[label] parse_onehot = torch.FloatTensor(1, self.fine_height, self.fine_width).zero_() for i in range(len(labels)): for label in labels[i][1]: parse_onehot[0] += parse_map[label] * i # load image-parse-agnostic image_parse_agnostic = Image.open(osp.join(self.data_path, 'image-parse-agnostic-v3.2', parse_name)) image_parse_agnostic = transforms.Resize(self.fine_width, interpolation=0)(image_parse_agnostic) parse_agnostic = torch.from_numpy(np.array(image_parse_agnostic)[None]).long() image_parse_agnostic = self.transform(image_parse_agnostic.convert('RGB')) parse_agnostic_map = torch.FloatTensor(20, self.fine_height, self.fine_width).zero_() parse_agnostic_map = parse_agnostic_map.scatter_(0, parse_agnostic, 1.0) new_parse_agnostic_map = torch.FloatTensor(self.semantic_nc, self.fine_height, self.fine_width).zero_() for i in range(len(labels)): for label in labels[i][1]: new_parse_agnostic_map[i] += parse_agnostic_map[label] # parse cloth & parse cloth mask pcm = new_parse_map[3:4] im_c = im * pcm + (1 - pcm) # load pose points pose_name = im_name.replace('.jpg', '_rendered.png') pose_map = Image.open(osp.join(self.data_path, 'openpose_img', pose_name)) pose_map = transforms.Resize(self.fine_width, interpolation=2)(pose_map) pose_map = self.transform(pose_map) # [-1,1] pose_name = im_name.replace('.jpg', '_keypoints.json') with open(osp.join(self.data_path, 'openpose_json', pose_name), 'r') as f: pose_label = json.load(f) pose_data = pose_label['people'][0]['pose_keypoints_2d'] pose_data = np.array(pose_data) pose_data = pose_data.reshape((-1, 3))[:, :2] # load densepose densepose_name = im_name.replace('image', 'image-densepose') densepose_map = Image.open(osp.join(self.data_path, 'image-densepose', densepose_name)) densepose_map = transforms.Resize(self.fine_width, interpolation=2)(densepose_map) densepose_map = self.transform(densepose_map) # [-1,1] agnostic = self.get_agnostic(im_pil_big, im_parse_pil_big, pose_data) agnostic = transforms.Resize(self.fine_width, interpolation=2)(agnostic) agnostic = self.transform(agnostic) result = { 'c_name': c_name, # for visualization 'im_name': im_name, # for visualization or ground truth # intput 1 (clothfloww) 'cloth': c, # for input 'cloth_mask': cm, # for input # intput 2 (segnet) 'parse_agnostic': new_parse_agnostic_map, 'densepose': densepose_map, 'pose': pose_map, # for conditioning # GT 'parse_onehot' : parse_onehot, # Cross Entropy 'parse': new_parse_map, # GAN Loss real 'pcm': pcm, # L1 Loss & vis 'parse_cloth': im_c, # VGG Loss & vis # visualization 'image': im, # for visualization 'agnostic' : agnostic } return result def __len__(self): return len(self.im_names) class CPDataLoader(object): def __init__(self, opt, dataset): super(CPDataLoader, self).__init__() if opt.shuffle : train_sampler = torch.utils.data.sampler.RandomSampler(dataset) else: train_sampler = None self.data_loader = torch.utils.data.DataLoader( dataset, batch_size=opt.batch_size, shuffle=(train_sampler is None), num_workers=opt.workers, pin_memory=True, drop_last=True, sampler=train_sampler) self.dataset = dataset self.data_iter = self.data_loader.__iter__() def next_batch(self): try: batch = self.data_iter.__next__() except StopIteration: self.data_iter = self.data_loader.__iter__() batch = self.data_iter.__next__() return batch