add type aware method and use it

app/service/outfit_matcher/outfit_evaluator.py

@@ -1,19 +1,51 @@
-import os
 import requests
-import json
+
 from PIL import Image
 import cv2
 import numpy as np
 import tritonclient.http as httpclient
 import torch
 from matplotlib import pyplot as plt, image as mpimg
+from torchvision import transforms
 
 from foco import extract_main_colors
 
-TRITON_IP_DEFAULT = "0.0.0.0"
 
+class OutfitMatcher(object):
+    def __init__(self):
+        self.tritonclient = httpclient.InferenceServerClient(url="10.1.1.240:10010")
 
-def imnormalize(img, mean, std, to_rgb=True):
+    @staticmethod
+    def pad_array(input_value, value=0):
+        """pad List of Array into same batch size
+
+        Args:
+            input_value: List of numpy arrary need to be padded
+
+        Returns:
+            Tensor: [batch_dim, max_dim, original_tensor_size]
+        """
+        max_dim = max([len(x) for x in input_value])
+        mask = np.zeros((len(input_value), max_dim), dtype=np.float32)
+
+        # Pad each array
+        padded_arrays = []
+        for i, array in enumerate(input_value):
+            # Compute padding amount along the pad dimension
+            pad_dim = max_dim - array.shape[0]
+            consistent_shape = array.shape[1:]
+            pad_widths = [(0, pad_dim)] + [(0, 0)] * len(consistent_shape)
+            padded_array = np.pad(array, pad_widths, mode='constant', constant_values=value)
+            padded_arrays.append(padded_array)
+
+            mask[i, array.shape[0]:] = float("-inf")
+
+        # Stack the padded arrays and change the dimension
+        batched_arrays = np.stack(padded_arrays, axis=0)
+        return batched_arrays, mask
+
+    @staticmethod
+    def imnormalize(img, mean, std, to_rgb=True):
         """Normalize an image with mean and std.
 
         Args:
@@ -34,152 +66,7 @@ def imnormalize(img, mean, std, to_rgb=True):
         cv2.subtract(img, mean, img)  # inplace
         cv2.multiply(img, stdinv, img)  # inplace
         return img
-
+    def visualize(self, outfits, scores, topk=5, best=True, output_path=None):
-
-def load_image(img_path):
-    if 'http' in img_path:
-        file = requests.get(img_path)
-        image = cv2.imdecode(np.fromstring(file.content, np.uint8), 1)
-        image = Image.fromarray(image.astype('uint8'), 'RGB')
-    else:
-        image = Image.open(img_path).convert('RGB')
-    return np.array(image)
-
-
-def resize_image(img):
-    """
-    Args:
-        img: ndarray (height, width, channel)
-    """
-    resized_img = cv2.resize(img, (224, 224), dst=None, interpolation=1)
-    return resized_img
-
-
-def pad_array(input_value):
-    """pad List of Array into same batch size
-
-    Args:
-        input_value: List of numpy arrary need to be padded
-
-    Returns:
-        Tensor: [batch_dim, max_dim, original_tensor_size]
-    """
-    max_dim = max([len(x) for x in input_value])
-    mask = np.zeros((len(input_value), max_dim), dtype=np.float32)
-
-    # Pad each array
-    padded_arrays = []
-    for i, array in enumerate(input_value):
-        # Compute padding amount along the pad dimension
-        pad_dim = max_dim - array.shape[0]
-        consistent_shape = array.shape[1:]
-        pad_widths = [(0, pad_dim)] + [(0, 0)] * len(consistent_shape)
-        padded_array = np.pad(array, pad_widths, mode='constant', constant_values=0)
-        padded_arrays.append(padded_array)
-
-        mask[i, array.shape[0]:] = float("-inf")
-
-    # Stack the padded arrays and change the dimension
-    batched_arrays = np.stack(padded_arrays, axis=0)
-    return batched_arrays, mask
-
-
-def extract_color(image, img_path):
-    # TODO: replace to vector database
-    relative_path, filename = os.path.split(img_path)
-    basename = filename.split(".")[0]
-    color_file = os.path.join(r"D:\PhD_Study\trinity_client\application\outfit_matcher\color",
-                              basename + ".npy")
-    if os.path.exists(color_file):
-        color = np.load(color_file)
-    else:
-        color = extract_main_colors(image)
-        np.save(color_file, color)
-    return color
-
-
-def preprocess(outfits):
-    outfit_images = []
-    outfit_colors = []
-    for outfit in outfits:
-        images = []
-        colors = []
-        for item in outfit:
-            image = load_image(item["image_path"])
-            image = resize_image(image)
-            normalized_image = imnormalize(image,
-                                           mean=np.array([208.32996145, 201.28227452, 198.47047691], dtype=np.float32),
-                                           std=np.array([75.48939648, 80.47423057, 82.21144189], dtype=np.float32))
-            images.append(normalized_image.transpose(2, 0, 1))
-            color = extract_color(image, item["image_path"])
-            colors.append(color)
-        images = np.stack(images, axis=0)
-        outfit_images.append(images)  # List[(items, 3, 224, 224)]
-        colors = np.stack(colors, axis=0)
-        outfit_colors.append(colors)
-    outfit_images, mask = pad_array(outfit_images)
-    outfit_colors, _ = pad_array(outfit_colors)
-    return outfit_images, outfit_colors, mask
-
-
-def evaluate_outfits(outfits):
-    """Input outfits structure and output scores.
-    Args:
-        outfits: outfits to be evaluated.
-        Example:
-            [
-              [
-                {
-                    "item_name": "MSE_57987",
-                    "semantic_category": "BOTTOM/PANTS",
-                    "image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_57987.jpg",
-                    "mapped_cate": "bottoms"
-                },
-                {
-                    "item_name": "MPO_SP7712",
-                    "semantic_category": "TOP/TANK",
-                    "image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7712.jpg",
-                    "mapped_cate": "tops"
-                },
-                {
-                    "item_name": "MWSS27195",
-                    "semantic_category": "OUTERWEAR/GILET",
-                    "image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27195.jpg",
-                    "mapped_cate": "outerwear"
-                }
-              ],
-              ...
-            ]
-    Returns:
-        scores: List of float
-    """
-
-    # start = time.time()
-    image, color, mask = preprocess(outfits)
-    # print(start - time.time())
-    client = httpclient.InferenceServerClient(url="localhost:8000")
-    # transformed_img = image.astype(np.float32)
-    # 输入集
-    inputs = [
-        httpclient.InferInput("input__0", image.shape, datatype="FP32"),
-        httpclient.InferInput("input__1", color.shape, datatype="FP32"),
-        httpclient.InferInput("input__2", mask.shape, datatype="FP32"),
-    ]
-    inputs[0].set_data_from_numpy(image.astype(np.float32), binary_data=True)
-    inputs[1].set_data_from_numpy(color.astype(np.float32), binary_data=True)
-    inputs[2].set_data_from_numpy(mask.astype(np.float32), binary_data=True)
-    # 输出集
-    outputs = [
-        httpclient.InferRequestedOutput("output__0", binary_data=True),
-    ]
-    results = client.infer(model_name="outfit_matcher_hon", inputs=inputs, outputs=outputs)
-    # 推理
-    # 取结果
-    scores = torch.from_numpy(results.as_numpy("output__0"))
-    return scores  # Shape (N, 1)
-
-
-def visualize(outfits, scores, topk=5, best=True, output_path=None):
         # 将outfits和scores按照scores的值进行排序
         sorted_indices = np.argsort(-scores.flatten() if best else scores.flatten())[:topk]  # 使用负号进行降序排序
         outfits = [outfits[i] for i in sorted_indices]
@@ -224,8 +111,183 @@ def visualize(outfits, scores, topk=5, best=True, output_path=None):
             plt.show()
 
 
-if __name__ == '__main__':
+class OutfitMatcherHon(OutfitMatcher):
-    with open("test_input.json", "r") as f:
+    def __init__(self):
-        outfits = json.load(f)
+        super().__init__()
-    scores = evaluate_outfits(outfits)
+
-    print(scores)
+    @staticmethod
+    def load_image(img_path):
+        if 'http' in img_path:
+            file = requests.get(img_path)
+            image = cv2.imdecode(np.fromstring(file.content, np.uint8), 1)
+            image = Image.fromarray(image.astype('uint8'), 'RGB')
+        else:
+            image = Image.open(img_path).convert('RGB')
+        return np.array(image)
+
+    @staticmethod
+    def resize_image(img):
+        """
+        Args:
+            img: ndarray (height, width, channel)
+        """
+        resized_img = cv2.resize(img, (224, 224), dst=None, interpolation=1)
+        return resized_img
+
+    def preprocess(self, outfits):
+        outfit_images = []
+        outfit_colors = []
+        for outfit in outfits:
+            images = []
+            colors = []
+            for item in outfit:
+                image = self.load_image(item["image_path"])
+                image = self.resize_image(image)
+                normalized_image = self.imnormalize(image,
+                                                    mean=np.array([208.32996145, 201.28227452, 198.47047691],
+                                                                  dtype=np.float32),
+                                                    std=np.array([75.48939648, 80.47423057, 82.21144189],
+                                                                 dtype=np.float32))
+                images.append(normalized_image.transpose(2, 0, 1))
+                color = extract_main_colors(image)
+                colors.append(color)
+            images = np.stack(images, axis=0)
+            outfit_images.append(images)  # List[(items, 3, 224, 224)]
+            colors = np.stack(colors, axis=0)
+            outfit_colors.append(colors)
+        outfit_images, mask = self.pad_array(outfit_images)
+        outfit_colors, _ = self.pad_array(outfit_colors)
+        return outfit_images, outfit_colors, mask
+
+    def get_result(self, outfits):
+        # start = time.time()
+        image, color, mask = self.preprocess(outfits)
+        # print(start - time.time())
+        # transformed_img = image.astype(np.float32)
+        # 输入集
+        inputs = [
+            httpclient.InferInput("input__0", image.shape, datatype="FP32"),
+            httpclient.InferInput("input__1", color.shape, datatype="FP32"),
+            httpclient.InferInput("input__2", mask.shape, datatype="FP32"),
+        ]
+        inputs[0].set_data_from_numpy(image.astype(np.float32), binary_data=True)
+        inputs[1].set_data_from_numpy(color.astype(np.float32), binary_data=True)
+        inputs[2].set_data_from_numpy(mask.astype(np.float32), binary_data=True)
+        # 输出集
+        outputs = [
+            httpclient.InferRequestedOutput("output__0", binary_data=True),
+        ]
+        results = self.tritonclient.infer(model_name="outfit_matcher_hon", inputs=inputs, outputs=outputs)
+        # 推理
+        # 取结果
+        inference_output1 = torch.from_numpy(results.as_numpy("output__0"))
+        return inference_output1  # Shape (N, 1)
+
+
+class OutfitMaterTypeAware(OutfitMatcher):
+    base_fashion_categories = [
+        'accessories', 'all-body', 'bags', 'bottoms', 'hats', 'jewellery',
+        'outerwear', 'scarves', 'shoes', 'sunglasses', 'tops'
+    ]
+
+    def __init__(self):
+        super().__init__()
+
+    @staticmethod
+    def load_image(img_path):
+        if 'http' in img_path:
+            file = requests.get(img_path)
+            image = cv2.imdecode(np.fromstring(file.content, np.uint8), 1)
+            image = Image.fromarray(image.astype('uint8'), 'RGB')
+        else:
+            image = Image.open(img_path).convert('RGB')
+        return image
+
+    @staticmethod
+    def resize_image(img):
+        """
+        Args:
+            img: ndarray (height, width, channel)
+        """
+        image_transforms = transforms.Compose([
+            transforms.Resize(112),
+            transforms.CenterCrop(112),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                                 std=[0.229, 0.224, 0.225]),
+        ])
+        resized_img = image_transforms(img).numpy()
+        return resized_img
+
+    def preprocess(self, outfits):
+        outfit_images = []
+        outfit_categories = []
+        for outfit in outfits:
+            images = []
+            categories = []
+            for item in outfit:
+                image = self.load_image(item["image_path"])
+                image = self.resize_image(image)
+                images.append(image)
+
+                category = self.base_fashion_categories.index(item["mapped_cate"])
+                categories.append(category)
+            images = np.stack(images, axis=0)
+            outfit_images.append(images)  # List[(items, 3, 224, 224)]
+            categories = np.array(categories)
+            outfit_categories.append(categories)  # List[(items)]
+        outfit_images, mask = self.pad_array(outfit_images, value=0)
+        outfit_categories, _ = self.pad_array(outfit_categories, value=len(self.base_fashion_categories))
+        return outfit_images, outfit_categories, mask
+
+    def get_result(self, outfits):
+        """Input outfits structure and output scores.
+        Args:
+            outfits: outfits to be evaluated.
+            Example:
+                [
+                  [
+                    {
+                        "item_name": "MSE_57987",
+                        "semantic_category": "BOTTOM/PANTS",
+                        "image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_57987.jpg",
+                        "mapped_cate": "bottoms"
+                    },
+                    {
+                        "item_name": "MPO_SP7712",
+                        "semantic_category": "TOP/TANK",
+                        "image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7712.jpg",
+                        "mapped_cate": "tops"
+                    },
+                    {
+                        "item_name": "MWSS27195",
+                        "semantic_category": "OUTERWEAR/GILET",
+                        "image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27195.jpg",
+                        "mapped_cate": "outerwear"
+                    }
+                  ],
+                  ...
+                ]
+        Returns:
+            scores: List of float
+        """
+        image, category, mask = self.preprocess(outfits)
+        client = httpclient.InferenceServerClient(url="localhost:8000")
+        # 输入集
+        inputs = [
+            httpclient.InferInput("input__0", image.shape, datatype="FP32"),
+            httpclient.InferInput("input__1", category.shape, datatype="INT16"),
+            httpclient.InferInput("input__2", mask.shape, datatype="FP32"),
+        ]
+        inputs[0].set_data_from_numpy(image.astype(np.float32), binary_data=True)
+        inputs[1].set_data_from_numpy(category.astype(np.int16), binary_data=True)
+        inputs[2].set_data_from_numpy(mask.astype(np.float32), binary_data=True)
+        # 输出集
+        outputs = [
+            httpclient.InferRequestedOutput("output__0", binary_data=True),
+        ]
+        results = client.infer(model_name="outfit_matcher_type_aware", inputs=inputs, outputs=outputs)
+        # 推理
+        # 取结果
+        scores = torch.from_numpy(results.as_numpy("output__0"))
+        return scores  # Shape (N, 1)

app/service/outfit_matcher/service.py

@@ -1,160 +1,25 @@
 import json
-import os
-
-import torch
-import torch.nn.functional as F
-import tritonclient.http as httpclient
-import requests
-import cv2
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
 
 from app.service.outfit_matcher.dataset import FashionDataset
-from app.service.outfit_matcher.foco import extract_main_colors
+from app.service.outfit_matcher.outfit_evaluator import OutfitMaterTypeAware
-from app.service.outfit_matcher.outfit_evaluator import evaluate_outfits, visualize
-
-
-class OutfitMatcherHon:
-    def __init__(self, outfits):
-        self.outfits = outfits
-        self.tritonclient = httpclient.InferenceServerClient(url="10.1.1.240:10010")
-
-    @staticmethod
-    def imnormalize(img, mean, std, to_rgb=True):
-        """Normalize an image with mean and std.
-
-        Args:
-            img (ndarray): Image to be normalized.
-            mean (ndarray): The mean to be used for normalize.
-            std (ndarray): The std to be used for normalize.
-            to_rgb (bool): Whether to convert to rgb.
-
-        Returns:
-            ndarray: The normalized image.
-        """
-        img = img.copy().astype(np.float32)
-        assert img.dtype != np.uint8
-        mean = np.float64(mean.reshape(1, -1))
-        stdinv = 1 / np.float64(std.reshape(1, -1))
-        if to_rgb:
-            cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)  # inplace
-        cv2.subtract(img, mean, img)  # inplace
-        cv2.multiply(img, stdinv, img)  # inplace
-        return img
-
-    @staticmethod
-    def load_image(img_path):
-        if 'http' in img_path:
-            file = requests.get(img_path)
-            image = cv2.imdecode(np.fromstring(file.content, np.uint8), 1)
-            image = Image.fromarray(image.astype('uint8'), 'RGB')
-        else:
-            image = Image.open(img_path).convert('RGB')
-        return np.array(image)
-
-    @staticmethod
-    def resize_image(img):
-        """
-        Args:
-            img: ndarray (height, width, channel)
-        """
-        resized_img = cv2.resize(img, (224, 224), dst=None, interpolation=1)
-        return resized_img
-
-    @staticmethod
-    def pad_array(input_value):
-        """pad List of Array into same batch size
-
-        Args:
-            input_value: List of numpy arrary need to be padded
-
-        Returns:
-            Tensor: [batch_dim, max_dim, original_tensor_size]
-        """
-        max_dim = max([len(x) for x in input_value])
-        mask = np.zeros((len(input_value), max_dim), dtype=np.float32)
-
-        # Pad each array
-        padded_arrays = []
-        for i, array in enumerate(input_value):
-            # Compute padding amount along the pad dimension
-            pad_dim = max_dim - array.shape[0]
-            consistent_shape = array.shape[1:]
-            pad_widths = [(0, pad_dim)] + [(0, 0)] * len(consistent_shape)
-            padded_array = np.pad(array, pad_widths, mode='constant', constant_values=0)
-            padded_arrays.append(padded_array)
-
-            mask[i, array.shape[0]:] = float("-inf")
-
-        # Stack the padded arrays and change the dimension
-        batched_arrays = np.stack(padded_arrays, axis=0)
-        return batched_arrays, mask
-
-    def preprocess(self):
-        outfit_images = []
-        outfit_colors = []
-        for outfit in self.outfits:
-            images = []
-            colors = []
-            for item in outfit:
-                image = self.load_image(item["image_path"])
-                image = self.resize_image(image)
-                normalized_image = self.imnormalize(image,
-                                                    mean=np.array([208.32996145, 201.28227452, 198.47047691], dtype=np.float32),
-                                                    std=np.array([75.48939648, 80.47423057, 82.21144189], dtype=np.float32))
-                images.append(normalized_image.transpose(2, 0, 1))
-                color = extract_main_colors(image)
-                colors.append(color)
-            images = np.stack(images, axis=0)
-            outfit_images.append(images)  # List[(items, 3, 224, 224)]
-            colors = np.stack(colors, axis=0)
-            outfit_colors.append(colors)
-        outfit_images, mask = self.pad_array(outfit_images)
-        outfit_colors, _ = self.pad_array(outfit_colors)
-        return outfit_images, outfit_colors, mask
-
-    def get_result(self):
-        # start = time.time()
-        image, color, mask = self.preprocess()
-        # print(start - time.time())
-        # transformed_img = image.astype(np.float32)
-        # 输入集
-        inputs = [
-            httpclient.InferInput("input__0", image.shape, datatype="FP32"),
-            httpclient.InferInput("input__1", color.shape, datatype="FP32"),
-            httpclient.InferInput("input__2", mask.shape, datatype="FP32"),
-        ]
-        inputs[0].set_data_from_numpy(image.astype(np.float32), binary_data=True)
-        inputs[1].set_data_from_numpy(color.astype(np.float32), binary_data=True)
-        inputs[2].set_data_from_numpy(mask.astype(np.float32), binary_data=True)
-        # 输出集
-        outputs = [
-            httpclient.InferRequestedOutput("output__0", binary_data=True),
-        ]
-        results = self.tritonclient.infer(model_name="outfit_matcher_hon", inputs=inputs, outputs=outputs)
-        # 推理
-        # 取结果
-        inference_output1 = torch.from_numpy(results.as_numpy("output__0"))
-        return inference_output1  # Shape (N, 1)
 
 
 if __name__ == '__main__':
     with open("./test_param/recommendation_test.json", "r") as f:
         param = json.load(f)
     fashion_dataset = FashionDataset(param["database"])
-    for item in tqdm(param["query"]):
+    for item in param["query"]:
         outfits = fashion_dataset.generate_outfit(item, param["topk"], param["max_outfits"])
-        service = OutfitMatcherHon(outfits=outfits)
+        service = OutfitMaterTypeAware()
-        scores = service.get_result()
+        scores = service.get_result(outfits)
-        visualize(outfits, scores, param["topk"], best=True,
+        print(scores)
-                  output_path=os.path.join(
+        # service.visualize(outfits, scores, param["topk"], best=True,
-                      r"E:\workspace\outfit_matcher\2024 SS Outfit",
+        #           output_path=os.path.join(
-                      f"{item['item_name']}_best_{param['topk']}.png"
+        #               r"E:\workspace\outfit_matcher\2024 SS Outfit",
-                  ))
+        #               f"{item['item_name']}_best_{param['topk']}.png"
-        visualize(outfits, scores, param["topk"], best=False,
+        #           ))
-                  output_path=os.path.join(
+        # service.visualize(outfits, scores, param["topk"], best=False,
-                      r"E:\workspace\outfit_matcher\2024 SS Outfit",
+        #           output_path=os.path.join(
-                      f"{item['item_name']}_worst_{param['topk']}.png"
+        #               r"E:\workspace\outfit_matcher\2024 SS Outfit",
-                  ))
+        #               f"{item['item_name']}_worst_{param['topk']}.png"
-        a = 1
+        #           ))

app/service/outfit_matcher/test_param/recommendation_test_pkc.json

@@ -0,0 +1,849 @@
+{
+	"topk": 5,
+	"max_outfits": 100,
+	"query": [
+		{
+			"item_name": "MSE_58107",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58107.jpg"
+		},
+		{
+			"item_name": "MKTS27047",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27047.jpg"
+		},
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+			"semantic_category": "OUTERWEAR/BLAZER",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58057.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58495.jpg"
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+		},
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+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27047.jpg"
+		},
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+			"semantic_category": "BOTTOM/PANTS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27000.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27001.jpg"
+		},
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+			"semantic_category": "KNIT/CARDIGAN",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MZOS27178.jpg"
+		},
+		{
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+			"semantic_category": "KNIT/KNIT TOP",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MZOS27179.jpg"
+		},
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+		},
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+			"semantic_category": "OUTERWEAR/TWIN SET",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27191.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27193.jpg"
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+		},
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+		},
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+		},
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+		},
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+		},
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+			"semantic_category": "ONE PIECE/TWIN SET",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27014.jpg"
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+			"semantic_category": "OUTERWEAR/GILET",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27015.jpg"
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27016.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27027.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27028.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27029.jpg"
+		},
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+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27030.jpg"
+		},
+		{
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+			"semantic_category": "BOTTOM/SKIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27031.jpg"
+		},
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+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27035.jpg"
+		},
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+			"semantic_category": "TOP/TOP",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27038.jpg"
+		},
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+			"semantic_category": "TOP/TOP",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27039.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27040.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27045.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27046.jpg"
+		},
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+			"semantic_category": "TOP/TOP",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27050.jpg"
+		},
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+			"semantic_category": "TOP/TEE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27059.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27061.jpg"
+		},
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+			"semantic_category": "TOP/BLOUSE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27062.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27066.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27067.jpg"
+		},
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+			"semantic_category": "ONE PIECE/TWIN SET",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27068.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27002.jpg"
+		},
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+			"semantic_category": "OUTERWEAR/GILET",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27003.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27004.jpg"
+		},
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+			"semantic_category": "TOP/VEST",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27011.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27018.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27019.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27058.jpg"
+		},
+		{
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27101.jpg"
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27102.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27103.jpg"
+		},
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+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27107.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27109.jpg"
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27110.jpg"
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+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27157.jpg"
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27160.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27161.jpg"
+		},
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+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27167.jpg"
+		},
+		{
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+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27173.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27174.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27175.jpg"
+		},
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27176.jpg"
+		},
+		{
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+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27073.jpg"
+		},
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+			"semantic_category": "BOTTOM/SKIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27226.jpg"
+		},
+		{
+			"item_name": "MPO_SP7685",
+			"semantic_category": "TOP/BLOUSE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7685.jpg"
+		},
+		{
+			"item_name": "MPO_SP7686",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7686.jpg"
+		},
+		{
+			"item_name": "MPO_SP7687",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7687.jpg"
+		},
+		{
+			"item_name": "MPO_SP7692",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7692.jpg"
+		},
+		{
+			"item_name": "MPO_SP7693",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7693.jpg"
+		},
+		{
+			"item_name": "MPO_SP7694",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7694.jpg"
+		},
+		{
+			"item_name": "MPO_SP7696",
+			"semantic_category": "BOTTOM/PANTS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7696.jpg"
+		},
+		{
+			"item_name": "MPO_SP7697",
+			"semantic_category": "JEANS/JEANS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7697.jpg"
+		},
+		{
+			"item_name": "MPO_SP7704",
+			"semantic_category": "OUTERWEAR/BLAZER",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7704.jpg"
+		},
+		{
+			"item_name": "MPO_SP7705",
+			"semantic_category": "OUTERWEAR/JACKET",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7705.jpg"
+		},
+		{
+			"item_name": "MPO_SP7706",
+			"semantic_category": "JEANS/JEANS JACKET",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7706.jpg"
+		},
+		{
+			"item_name": "MPO_SP7711",
+			"semantic_category": "TOP/VEST",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7711.jpg"
+		},
+		{
+			"item_name": "MPO_SP7712",
+			"semantic_category": "TOP/TANK",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7712.jpg"
+		},
+		{
+			"item_name": "MPO_SP7717",
+			"semantic_category": "TOP/TEE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7717.jpg"
+		},
+		{
+			"item_name": "MPO_SP7722",
+			"semantic_category": "TOP/TOP",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7722.jpg"
+		},
+		{
+			"item_name": "MPO_SP7723",
+			"semantic_category": "TOP/BLOUSE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7723.jpg"
+		},
+		{
+			"item_name": "MPO_SP7726",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7726.jpg"
+		},
+		{
+			"item_name": "MPO_SP7729",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7729.jpg"
+		},
+		{
+			"item_name": "MPO_SP7731",
+			"semantic_category": "TOP/BLOUSE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7731.jpg"
+		},
+		{
+			"item_name": "MPO_SP7732",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7732.jpg"
+		},
+		{
+			"item_name": "MPO_SP7735",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7735.jpg"
+		},
+		{
+			"item_name": "MSE_58197",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58197.jpg"
+		},
+		{
+			"item_name": "MSE_58198",
+			"semantic_category": "TOP/BLOUSE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58198.jpg"
+		},
+		{
+			"item_name": "MSE_58199",
+			"semantic_category": "BOTTOM/PANTS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58199.jpg"
+		},
+		{
+			"item_name": "MSE_58112",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58112.jpg"
+		},
+		{
+			"item_name": "MSE_58114",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58114.jpg"
+		},
+		{
+			"item_name": "MSE_58241",
+			"semantic_category": "TOP/BLOUSE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58241.jpg"
+		},
+		{
+			"item_name": "MSE_57987",
+			"semantic_category": "BOTTOM/PANTS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_57987.jpg"
+		},
+		{
+			"item_name": "MSE_57988",
+			"semantic_category": "OUTERWEAR/BLAZER",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_57988.jpg"
+		},
+		{
+			"item_name": "MSE_58203",
+			"semantic_category": "OUTERWEAR/BLAZER",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58203.jpg"
+		},
+		{
+			"item_name": "MSE_58106",
+			"semantic_category": "BOTTOM/PANTS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58106.jpg"
+		},
+		{
+			"item_name": "MSE_58107",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58107.jpg"
+		},
+		{
+			"item_name": "MSE_58132",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58132.jpg"
+		},
+		{
+			"item_name": "MSE_58133",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58133.jpg"
+		},
+		{
+			"item_name": "MSE_58057",
+			"semantic_category": "OUTERWEAR/BLAZER",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58057.jpg"
+		},
+		{
+			"item_name": "MSE_58058",
+			"semantic_category": "BOTTOM/PANTS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58058.jpg"
+		},
+		{
+			"item_name": "MSE_58222",
+			"semantic_category": "BOTTOM/SKIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58222.jpg"
+		},
+		{
+			"item_name": "MSE_58317",
+			"semantic_category": "TOP/BLOUSE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58317.jpg"
+		},
+		{
+			"item_name": "MSE_58045",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58045.jpg"
+		},
+		{
+			"item_name": "MSE_58275",
+			"semantic_category": "JEANS/JEANS DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58275.jpg"
+		},
+		{
+			"item_name": "MSE_58276",
+			"semantic_category": "JEANS/JEANS JACKET",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58276.jpg"
+		},
+		{
+			"item_name": "MSE_58277",
+			"semantic_category": "JEANS/JEANS SKIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58277.jpg"
+		},
+		{
+			"item_name": "MSE_58183",
+			"semantic_category": "TOP/BLOUSE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58183.jpg"
+		},
+		{
+			"item_name": "MSE_58184",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58184.jpg"
+		},
+		{
+			"item_name": "MSE_58185",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58185.jpg"
+		},
+		{
+			"item_name": "MSE_58188",
+			"semantic_category": "BOTTOM/SKIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58188.jpg"
+		},
+		{
+			"item_name": "MSE_54385",
+			"semantic_category": "BOTTOM/PANTS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_54385.jpg"
+		},
+		{
+			"item_name": "MSE_56720",
+			"semantic_category": "OUTERWEAR/BLAZER",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_56720.jpg"
+		},
+		{
+			"item_name": "MSE_58174",
+			"semantic_category": "TOP/TEE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58174.jpg"
+		},
+		{
+			"item_name": "MSE_58044",
+			"semantic_category": "OUTERWEAR/JACKET",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58044.jpg"
+		},
+		{
+			"item_name": "MSE_58361",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58361.jpg"
+		},
+		{
+			"item_name": "MSE_58495",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58495.jpg"
+		},
+		{
+			"item_name": "MSE_58536",
+			"semantic_category": "ACCESSORY/BAG",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58536.jpg"
+		},
+		{
+			"item_name": "MSE_58653",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58653.jpg"
+		},
+		{
+			"item_name": "MSE_58287",
+			"semantic_category": "BOTTOM/SHORTS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58287.jpg"
+		},
+		{
+			"item_name": "MSE_58289",
+			"semantic_category": "OUTERWEAR/BLAZER",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58289.jpg"
+		},
+		{
+			"item_name": "MSE_58323",
+			"semantic_category": "TOP/BLOUSE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58323.jpg"
+		},
+		{
+			"item_name": "MSE_58421",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58421.jpg"
+		},
+		{
+			"item_name": "MSE_58451",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58451.jpg"
+		},
+		{
+			"item_name": "MSE_58473",
+			"semantic_category": "KNIT/KNIT TOP",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58473.jpg"
+		},
+		{
+			"item_name": "MSE_58498",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58498.jpg"
+		},
+		{
+			"item_name": "MSE_58499",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58499.jpg"
+		},
+		{
+			"item_name": "MSE_58510",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58510.jpg"
+		},
+		{
+			"item_name": "MSE_58516",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58516.jpg"
+		},
+		{
+			"item_name": "MSE_58518",
+			"semantic_category": "BOTTOM/SKIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58518.jpg"
+		},
+		{
+			"item_name": "MSE_58530",
+			"semantic_category": "ONE PIECE/DRESS",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58530.jpg"
+		},
+		{
+			"item_name": "MSE_58540",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58540.jpg"
+		},
+		{
+			"item_name": "MSE_58547",
+			"semantic_category": "TOP/TEE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58547.jpg"
+		},
+		{
+			"item_name": "MSE_58618",
+			"semantic_category": "TOP/BLOUSE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58618.jpg"
+		},
+		{
+			"item_name": "MSE_58655",
+			"semantic_category": "TOP/SHIRT",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58655.jpg"
+		},
+		{
+			"item_name": "MSE_58658",
+			"semantic_category": "TOP/TEE",
+			"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58658.jpg"
+		}
+	]
+}