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cp_dataset_test.py

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+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