TryYours-Virtual-Try-On/HR-VITON-main/cp_dataset_test.py

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_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.fine_width, self.fine_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_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 = {}
        #print(self.c_names)
        for key in self.c_names:
            c_name[key] = self.c_names[key][index]
            if key=="paired":
                c[key] = Image.open(osp.join(self.data_path, 'image', c_name[key])).convert('RGB')
            else:
                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])
            if key=="paired":
                cm[key] = Image.open(osp.join(self.data_path, 'image-parse-v3', c_name[key]).replace('.jpg', '.png'))
            else:
                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)
        
        parse_name = im_name.replace('.jpg', '.png')
        parse = Image.open(osp.join(self.data_path, 'image-parse-v3', parse_name))
        parse = transforms.Resize(self.fine_width, interpolation=0)(parse)
        parse_agnostic = self.get_parse_agnostic(parse, pose_data)
        np.set_printoptions(threshold=np.inf, linewidth=np.inf) 
        #print(parse,parse_name)
        parse_agnostic = torch.from_numpy(np.array(parse_agnostic)[None]).long()

        parse_agnostic_map = torch.zeros(20, self.fine_height, self.fine_width, dtype=torch.float)
        #print(parse_agnostic.shape,parse_agnostic_map.shape)
        parse_agnostic_map.scatter_(0, parse_agnostic, 1.0)
        new_parse_agnostic_map = torch.zeros(self.semantic_nc, self.fine_height, self.fine_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]

        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