import json from os import path as osp import numpy as np from PIL import Image, ImageDraw import torch from torch.utils import data from torchvision import transforms class VITONDataset(data.Dataset): def __init__(self, opt): super(VITONDataset, self).__init__() self.load_height = opt.load_height self.load_width = opt.load_width self.semantic_nc = opt.semantic_nc self.data_path = osp.join(opt.dataset_dir, opt.dataset_mode) self.transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) # load data list img_names = [] c_names = [] with open(osp.join(opt.dataset_dir, opt.dataset_list), 'r') as f: for line in f.readlines(): img_name, c_name = line.strip().split() img_names.append(img_name) c_names.append(c_name) self.img_names = img_names self.c_names = dict() self.c_names['unpaired'] = c_names def get_parse_agnostic(self, parse, pose_data): parse_array = np.array(parse) parse_upper = ((parse_array == 5).astype(np.float32) + (parse_array == 6).astype(np.float32) + (parse_array == 7).astype(np.float32)) parse_neck = (parse_array == 10).astype(np.float32) r = 10 agnostic = parse.copy() # mask arms for parse_id, pose_ids in [(14, [2, 5, 6, 7]), (15, [5, 2, 3, 4])]: mask_arm = Image.new('L', (self.load_width, self.load_height), 'black') mask_arm_draw = ImageDraw.Draw(mask_arm) i_prev = pose_ids[0] for i in pose_ids[1:]: if (pose_data[i_prev, 0] == 0.0 and pose_data[i_prev, 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_prev, i]], 'white', width=r*10) pointx, pointy = pose_data[i] radius = r*4 if i == pose_ids[-1] else r*15 mask_arm_draw.ellipse((pointx-radius, pointy-radius, pointx+radius, pointy+radius), 'white', 'white') i_prev = i parse_arm = (np.array(mask_arm) / 255) * (parse_array == parse_id).astype(np.float32) agnostic.paste(0, None, Image.fromarray(np.uint8(parse_arm * 255), 'L')) # mask torso & neck agnostic.paste(0, None, Image.fromarray(np.uint8(parse_upper * 255), 'L')) agnostic.paste(0, None, Image.fromarray(np.uint8(parse_neck * 255), 'L')) return agnostic def get_img_agnostic(self, img, parse, pose_data): parse_array = np.array(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)) r = 20 agnostic = img.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 # mask arms agnostic_draw.line([tuple(pose_data[i]) for i in [2, 5]], 'gray', width=r*10) for i in [2, 5]: pointx, pointy = pose_data[i] agnostic_draw.ellipse((pointx-r*5, pointy-r*5, pointx+r*5, pointy+r*5), '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') # 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*7, pointy-r*7, pointx+r*7, pointy+r*7), 'gray', 'gray') agnostic.paste(img, None, Image.fromarray(np.uint8(parse_head * 255), 'L')) agnostic.paste(img, None, Image.fromarray(np.uint8(parse_lower * 255), 'L')) return agnostic def __getitem__(self, index): img_name = self.img_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.load_width, interpolation=2)(c[key]) cm[key] = Image.open(osp.join(self.data_path, 'cloth-mask', c_name[key])) cm[key] = transforms.Resize(self.load_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) # load pose image pose_name = img_name.replace('.jpg', '_rendered.png') pose_rgb = Image.open(osp.join(self.data_path, 'openpose-img', pose_name)) pose_rgb = transforms.Resize(self.load_width, interpolation=2)(pose_rgb) pose_rgb = self.transform(pose_rgb) # [-1,1] pose_name = img_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 parsing image parse_name = img_name.replace('.jpg', '.png') parse = Image.open(osp.join(self.data_path, 'image-parse', parse_name)) parse = transforms.Resize(self.load_width, interpolation=0)(parse) parse_agnostic = self.get_parse_agnostic(parse, pose_data) parse_agnostic = torch.from_numpy(np.array(parse_agnostic)[None]).long() 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_agnostic_map = torch.zeros(20, self.load_height, self.load_width, dtype=torch.float) parse_agnostic_map.scatter_(0, parse_agnostic, 1.0) new_parse_agnostic_map = torch.zeros(self.semantic_nc, self.load_height, self.load_width, dtype=torch.float) for i in range(len(labels)): for label in labels[i][1]: new_parse_agnostic_map[i] += parse_agnostic_map[label] # load person image img = Image.open(osp.join(self.data_path, 'image', img_name)) img = transforms.Resize(self.load_width, interpolation=2)(img) img_agnostic = self.get_img_agnostic(img, parse, pose_data) img = self.transform(img) img_agnostic = self.transform(img_agnostic) # [-1,1] result = { 'img_name': img_name, 'c_name': c_name, 'img': img, 'img_agnostic': img_agnostic, 'parse_agnostic': new_parse_agnostic_map, 'pose': pose_rgb, 'cloth': c, 'cloth_mask': cm, } return result def __len__(self): return len(self.img_names) class VITONDataLoader: def __init__(self, opt, dataset): super(VITONDataLoader, self).__init__() if opt.shuffle: train_sampler = data.sampler.RandomSampler(dataset) else: train_sampler = None self.data_loader = 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