add type aware method and use it
This commit is contained in:
pangkaicheng
@@ -1,231 +1,293 @@
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import os
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import requests
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import json
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from PIL import Image
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import cv2
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import numpy as np
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import tritonclient.http as httpclient
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import torch
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from matplotlib import pyplot as plt, image as mpimg
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from torchvision import transforms
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from foco import extract_main_colors
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TRITON_IP_DEFAULT = "0.0.0.0"
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class OutfitMatcher(object):
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def __init__(self):
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self.tritonclient = httpclient.InferenceServerClient(url="10.1.1.240:10010")
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@staticmethod
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def pad_array(input_value, value=0):
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"""pad List of Array into same batch size
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Args:
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input_value: List of numpy arrary need to be padded
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Returns:
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Tensor: [batch_dim, max_dim, original_tensor_size]
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"""
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max_dim = max([len(x) for x in input_value])
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mask = np.zeros((len(input_value), max_dim), dtype=np.float32)
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# Pad each array
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padded_arrays = []
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for i, array in enumerate(input_value):
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# Compute padding amount along the pad dimension
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pad_dim = max_dim - array.shape[0]
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consistent_shape = array.shape[1:]
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pad_widths = [(0, pad_dim)] + [(0, 0)] * len(consistent_shape)
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padded_array = np.pad(array, pad_widths, mode='constant', constant_values=value)
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padded_arrays.append(padded_array)
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mask[i, array.shape[0]:] = float("-inf")
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# Stack the padded arrays and change the dimension
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batched_arrays = np.stack(padded_arrays, axis=0)
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return batched_arrays, mask
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@staticmethod
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def imnormalize(img, mean, std, to_rgb=True):
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"""Normalize an image with mean and std.
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Args:
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img (ndarray): Image to be normalized.
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mean (ndarray): The mean to be used for normalize.
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std (ndarray): The std to be used for normalize.
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to_rgb (bool): Whether to convert to rgb.
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Returns:
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ndarray: The normalized image.
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"""
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img = img.copy().astype(np.float32)
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assert img.dtype != np.uint8
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mean = np.float64(mean.reshape(1, -1))
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stdinv = 1 / np.float64(std.reshape(1, -1))
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if to_rgb:
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cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) # inplace
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cv2.subtract(img, mean, img) # inplace
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cv2.multiply(img, stdinv, img) # inplace
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return img
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def visualize(self, outfits, scores, topk=5, best=True, output_path=None):
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# 将outfits和scores按照scores的值进行排序
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sorted_indices = np.argsort(-scores.flatten() if best else scores.flatten())[:topk] # 使用负号进行降序排序
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outfits = [outfits[i] for i in sorted_indices]
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scores = scores[sorted_indices]
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# 设置子图的行列数
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num_rows = len(outfits)
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num_cols = max([len(x) for x in outfits]) + 1 # 一个是图片，一个是分数
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# 创建一个新的图像，并指定子图的行列数
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fig, axes = plt.subplots(num_rows, num_cols, figsize=(8, 15))
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title = f"Best {topk} Outfits" if best else f"Worst {topk} Outfits"
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fig.suptitle(title, fontsize=16)
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# 遍历每套outfit并将其显示在对应的子图中
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for i, (outfit, score) in enumerate(zip(outfits, scores)):
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# 显示分数
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axes[i, 0].text(0.1, 0.5, f"Score: {score[0]:.4f}", fontsize=12)
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axes[i, 0].axis("off")
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# 显示图片
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for j, item in enumerate(outfit):
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img = mpimg.imread(item['image_path']) # 读取图片
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axes[i, j + 1].imshow(img) # 在对应的子图中显示图片
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axes[i, j + 1].axis('off') # 关闭坐标轴
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axes[i, j + 1].set_title(item["semantic_category"], fontsize=10)
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for j in range(len(outfit), num_cols):
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axes[i, j].axis("off")
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# 在每一行的底部添加一条横线
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axes[i, 0].axhline(y=0, color='black', linewidth=1)
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# 隐藏最后一行的横线
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axes[-1, 0].axhline(y=0, color='white', linewidth=1)
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# 调整布局
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plt.subplots_adjust(wspace=0.1, hspace=0.1)
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plt.tight_layout()
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if output_path:
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plt.savefig(output_path)
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else:
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plt.show()
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def imnormalize(img, mean, std, to_rgb=True):
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"""Normalize an image with mean and std.
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class OutfitMatcherHon(OutfitMatcher):
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def __init__(self):
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super().__init__()
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Args:
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img (ndarray): Image to be normalized.
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mean (ndarray): The mean to be used for normalize.
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std (ndarray): The std to be used for normalize.
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to_rgb (bool): Whether to convert to rgb.
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@staticmethod
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def load_image(img_path):
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if 'http' in img_path:
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file = requests.get(img_path)
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image = cv2.imdecode(np.fromstring(file.content, np.uint8), 1)
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image = Image.fromarray(image.astype('uint8'), 'RGB')
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else:
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image = Image.open(img_path).convert('RGB')
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return np.array(image)
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Returns:
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ndarray: The normalized image.
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"""
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img = img.copy().astype(np.float32)
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assert img.dtype != np.uint8
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mean = np.float64(mean.reshape(1, -1))
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stdinv = 1 / np.float64(std.reshape(1, -1))
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if to_rgb:
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cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) # inplace
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cv2.subtract(img, mean, img) # inplace
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cv2.multiply(img, stdinv, img) # inplace
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return img
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@staticmethod
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def resize_image(img):
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"""
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Args:
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img: ndarray (height, width, channel)
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"""
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resized_img = cv2.resize(img, (224, 224), dst=None, interpolation=1)
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return resized_img
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def preprocess(self, outfits):
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outfit_images = []
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outfit_colors = []
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for outfit in outfits:
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images = []
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colors = []
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for item in outfit:
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image = self.load_image(item["image_path"])
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image = self.resize_image(image)
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normalized_image = self.imnormalize(image,
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mean=np.array([208.32996145, 201.28227452, 198.47047691],
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dtype=np.float32),
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std=np.array([75.48939648, 80.47423057, 82.21144189],
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dtype=np.float32))
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images.append(normalized_image.transpose(2, 0, 1))
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color = extract_main_colors(image)
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colors.append(color)
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images = np.stack(images, axis=0)
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outfit_images.append(images) # List[(items, 3, 224, 224)]
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colors = np.stack(colors, axis=0)
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outfit_colors.append(colors)
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outfit_images, mask = self.pad_array(outfit_images)
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outfit_colors, _ = self.pad_array(outfit_colors)
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return outfit_images, outfit_colors, mask
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def get_result(self, outfits):
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# start = time.time()
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image, color, mask = self.preprocess(outfits)
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# print(start - time.time())
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# transformed_img = image.astype(np.float32)
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# 输入集
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inputs = [
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httpclient.InferInput("input__0", image.shape, datatype="FP32"),
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httpclient.InferInput("input__1", color.shape, datatype="FP32"),
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httpclient.InferInput("input__2", mask.shape, datatype="FP32"),
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]
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inputs[0].set_data_from_numpy(image.astype(np.float32), binary_data=True)
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inputs[1].set_data_from_numpy(color.astype(np.float32), binary_data=True)
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inputs[2].set_data_from_numpy(mask.astype(np.float32), binary_data=True)
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# 输出集
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outputs = [
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httpclient.InferRequestedOutput("output__0", binary_data=True),
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]
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results = self.tritonclient.infer(model_name="outfit_matcher_hon", inputs=inputs, outputs=outputs)
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# 推理
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# 取结果
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inference_output1 = torch.from_numpy(results.as_numpy("output__0"))
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return inference_output1 # Shape (N, 1)
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def load_image(img_path):
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if 'http' in img_path:
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file = requests.get(img_path)
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image = cv2.imdecode(np.fromstring(file.content, np.uint8), 1)
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image = Image.fromarray(image.astype('uint8'), 'RGB')
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else:
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image = Image.open(img_path).convert('RGB')
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return np.array(image)
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def resize_image(img):
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"""
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Args:
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img: ndarray (height, width, channel)
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"""
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resized_img = cv2.resize(img, (224, 224), dst=None, interpolation=1)
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return resized_img
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def pad_array(input_value):
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"""pad List of Array into same batch size
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Args:
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input_value: List of numpy arrary need to be padded
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Returns:
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Tensor: [batch_dim, max_dim, original_tensor_size]
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"""
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max_dim = max([len(x) for x in input_value])
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mask = np.zeros((len(input_value), max_dim), dtype=np.float32)
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# Pad each array
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padded_arrays = []
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for i, array in enumerate(input_value):
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# Compute padding amount along the pad dimension
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pad_dim = max_dim - array.shape[0]
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consistent_shape = array.shape[1:]
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pad_widths = [(0, pad_dim)] + [(0, 0)] * len(consistent_shape)
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padded_array = np.pad(array, pad_widths, mode='constant', constant_values=0)
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padded_arrays.append(padded_array)
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mask[i, array.shape[0]:] = float("-inf")
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# Stack the padded arrays and change the dimension
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batched_arrays = np.stack(padded_arrays, axis=0)
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return batched_arrays, mask
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def extract_color(image, img_path):
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# TODO: replace to vector database
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relative_path, filename = os.path.split(img_path)
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basename = filename.split(".")[0]
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color_file = os.path.join(r"D:\PhD_Study\trinity_client\application\outfit_matcher\color",
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basename + ".npy")
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if os.path.exists(color_file):
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color = np.load(color_file)
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else:
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color = extract_main_colors(image)
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np.save(color_file, color)
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return color
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def preprocess(outfits):
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outfit_images = []
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outfit_colors = []
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for outfit in outfits:
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images = []
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colors = []
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for item in outfit:
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image = load_image(item["image_path"])
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image = resize_image(image)
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normalized_image = imnormalize(image,
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mean=np.array([208.32996145, 201.28227452, 198.47047691], dtype=np.float32),
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std=np.array([75.48939648, 80.47423057, 82.21144189], dtype=np.float32))
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images.append(normalized_image.transpose(2, 0, 1))
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color = extract_color(image, item["image_path"])
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colors.append(color)
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images = np.stack(images, axis=0)
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outfit_images.append(images) # List[(items, 3, 224, 224)]
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colors = np.stack(colors, axis=0)
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outfit_colors.append(colors)
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outfit_images, mask = pad_array(outfit_images)
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outfit_colors, _ = pad_array(outfit_colors)
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return outfit_images, outfit_colors, mask
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def evaluate_outfits(outfits):
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"""Input outfits structure and output scores.
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Args:
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outfits: outfits to be evaluated.
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Example:
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[
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[
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{
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"item_name": "MSE_57987",
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"semantic_category": "BOTTOM/PANTS",
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"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_57987.jpg",
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"mapped_cate": "bottoms"
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},
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{
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"item_name": "MPO_SP7712",
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"semantic_category": "TOP/TANK",
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"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7712.jpg",
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"mapped_cate": "tops"
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},
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{
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"item_name": "MWSS27195",
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"semantic_category": "OUTERWEAR/GILET",
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"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27195.jpg",
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"mapped_cate": "outerwear"
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}
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],
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...
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]
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Returns:
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scores: List of float
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"""
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# start = time.time()
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image, color, mask = preprocess(outfits)
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# print(start - time.time())
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client = httpclient.InferenceServerClient(url="localhost:8000")
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# transformed_img = image.astype(np.float32)
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# 输入集
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inputs = [
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httpclient.InferInput("input__0", image.shape, datatype="FP32"),
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httpclient.InferInput("input__1", color.shape, datatype="FP32"),
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httpclient.InferInput("input__2", mask.shape, datatype="FP32"),
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class OutfitMaterTypeAware(OutfitMatcher):
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base_fashion_categories = [
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'accessories', 'all-body', 'bags', 'bottoms', 'hats', 'jewellery',
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'outerwear', 'scarves', 'shoes', 'sunglasses', 'tops'
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]
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inputs[0].set_data_from_numpy(image.astype(np.float32), binary_data=True)
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inputs[1].set_data_from_numpy(color.astype(np.float32), binary_data=True)
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inputs[2].set_data_from_numpy(mask.astype(np.float32), binary_data=True)
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# 输出集
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outputs = [
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httpclient.InferRequestedOutput("output__0", binary_data=True),
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]
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results = client.infer(model_name="outfit_matcher_hon", inputs=inputs, outputs=outputs)
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# 推理
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# 取结果
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scores = torch.from_numpy(results.as_numpy("output__0"))
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return scores # Shape (N, 1)
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def __init__(self):
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super().__init__()
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def visualize(outfits, scores, topk=5, best=True, output_path=None):
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# 将outfits和scores按照scores的值进行排序
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sorted_indices = np.argsort(-scores.flatten() if best else scores.flatten())[:topk] # 使用负号进行降序排序
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outfits = [outfits[i] for i in sorted_indices]
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scores = scores[sorted_indices]
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@staticmethod
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def load_image(img_path):
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if 'http' in img_path:
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file = requests.get(img_path)
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image = cv2.imdecode(np.fromstring(file.content, np.uint8), 1)
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image = Image.fromarray(image.astype('uint8'), 'RGB')
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else:
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image = Image.open(img_path).convert('RGB')
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return image
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# 设置子图的行列数
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num_rows = len(outfits)
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num_cols = max([len(x) for x in outfits]) + 1 # 一个是图片，一个是分数
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@staticmethod
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def resize_image(img):
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"""
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Args:
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img: ndarray (height, width, channel)
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"""
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image_transforms = transforms.Compose([
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transforms.Resize(112),
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transforms.CenterCrop(112),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225]),
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])
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resized_img = image_transforms(img).numpy()
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return resized_img
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# 创建一个新的图像，并指定子图的行列数
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fig, axes = plt.subplots(num_rows, num_cols, figsize=(8, 15))
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def preprocess(self, outfits):
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outfit_images = []
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outfit_categories = []
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for outfit in outfits:
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images = []
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categories = []
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for item in outfit:
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image = self.load_image(item["image_path"])
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image = self.resize_image(image)
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images.append(image)
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title = f"Best {topk} Outfits" if best else f"Worst {topk} Outfits"
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fig.suptitle(title, fontsize=16)
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category = self.base_fashion_categories.index(item["mapped_cate"])
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categories.append(category)
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images = np.stack(images, axis=0)
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outfit_images.append(images) # List[(items, 3, 224, 224)]
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categories = np.array(categories)
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outfit_categories.append(categories) # List[(items)]
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outfit_images, mask = self.pad_array(outfit_images, value=0)
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outfit_categories, _ = self.pad_array(outfit_categories, value=len(self.base_fashion_categories))
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return outfit_images, outfit_categories, mask
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# 遍历每套outfit并将其显示在对应的子图中
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for i, (outfit, score) in enumerate(zip(outfits, scores)):
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# 显示分数
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axes[i, 0].text(0.1, 0.5, f"Score: {score[0]:.4f}", fontsize=12)
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axes[i, 0].axis("off")
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# 显示图片
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for j, item in enumerate(outfit):
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img = mpimg.imread(item['image_path']) # 读取图片
|
||||
axes[i, j + 1].imshow(img) # 在对应的子图中显示图片
|
||||
axes[i, j + 1].axis('off') # 关闭坐标轴
|
||||
axes[i, j + 1].set_title(item["semantic_category"], fontsize=10)
|
||||
for j in range(len(outfit), num_cols):
|
||||
axes[i, j].axis("off")
|
||||
|
||||
# 在每一行的底部添加一条横线
|
||||
axes[i, 0].axhline(y=0, color='black', linewidth=1)
|
||||
# 隐藏最后一行的横线
|
||||
axes[-1, 0].axhline(y=0, color='white', linewidth=1)
|
||||
|
||||
# 调整布局
|
||||
plt.subplots_adjust(wspace=0.1, hspace=0.1)
|
||||
plt.tight_layout()
|
||||
|
||||
if output_path:
|
||||
plt.savefig(output_path)
|
||||
else:
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
with open("test_input.json", "r") as f:
|
||||
outfits = json.load(f)
|
||||
scores = evaluate_outfits(outfits)
|
||||
print(scores)
|
||||
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)
|
||||
@@ -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 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)
|
||||
from app.service.outfit_matcher.outfit_evaluator import OutfitMaterTypeAware
|
||||
|
||||
|
||||
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)
|
||||
scores = service.get_result()
|
||||
visualize(outfits, scores, param["topk"], best=True,
|
||||
output_path=os.path.join(
|
||||
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(
|
||||
r"E:\workspace\outfit_matcher\2024 SS Outfit",
|
||||
f"{item['item_name']}_worst_{param['topk']}.png"
|
||||
))
|
||||
a = 1
|
||||
service = OutfitMaterTypeAware()
|
||||
scores = service.get_result(outfits)
|
||||
print(scores)
|
||||
# service.visualize(outfits, scores, param["topk"], best=True,
|
||||
# output_path=os.path.join(
|
||||
# r"E:\workspace\outfit_matcher\2024 SS Outfit",
|
||||
# f"{item['item_name']}_best_{param['topk']}.png"
|
||||
# ))
|
||||
# service.visualize(outfits, scores, param["topk"], best=False,
|
||||
# output_path=os.path.join(
|
||||
# r"E:\workspace\outfit_matcher\2024 SS Outfit",
|
||||
# f"{item['item_name']}_worst_{param['topk']}.png"
|
||||
# ))
|
||||
|
||||
@@ -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"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27028",
|
||||
"semantic_category": "OUTERWEAR/JACKET",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27028.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_58495",
|
||||
"semantic_category": "TOP/SHIRT",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_58495.jpg"
|
||||
}
|
||||
],
|
||||
"database":
|
||||
[
|
||||
{
|
||||
"item_name": "MKTS27017",
|
||||
"semantic_category": "OUTERWEAR/WINDBREAKER",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27017.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27047",
|
||||
"semantic_category": "ONE PIECE/DRESS",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27047.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27000",
|
||||
"semantic_category": "BOTTOM/PANTS",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27000.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27001",
|
||||
"semantic_category": "BOTTOM/SHORTS",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27001.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MZOS27178",
|
||||
"semantic_category": "KNIT/CARDIGAN",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MZOS27178.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MZOS27179",
|
||||
"semantic_category": "KNIT/KNIT TOP",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MZOS27179.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MWSS27184",
|
||||
"semantic_category": "TOP/TEE",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27184.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MWSS27191",
|
||||
"semantic_category": "OUTERWEAR/TWIN SET",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27191.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MWSS27193",
|
||||
"semantic_category": "OUTERWEAR/JACKET",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27193.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MWSS27195",
|
||||
"semantic_category": "OUTERWEAR/GILET",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27195.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MWSS27200",
|
||||
"semantic_category": "KNIT/VEST",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27200.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MWSS27209",
|
||||
"semantic_category": "ONE PIECE/DRESS",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27209.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MWSS27210",
|
||||
"semantic_category": "ONE PIECE/DRESS",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27210.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MWSS27211",
|
||||
"semantic_category": "ONE PIECE/DRESS",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27211.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MWSS27212",
|
||||
"semantic_category": "TOP/BLOUSE",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27212.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27008",
|
||||
"semantic_category": "OUTERWEAR/BLAZER",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27008.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27009",
|
||||
"semantic_category": "BOTTOM/PANTS",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27009.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27010",
|
||||
"semantic_category": "OUTERWEAR/BLAZER",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27010.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27012",
|
||||
"semantic_category": "OUTERWEAR/JACKET",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27012.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27013",
|
||||
"semantic_category": "BOTTOM/SHORTS",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27013.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27014",
|
||||
"semantic_category": "ONE PIECE/TWIN SET",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27014.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27015",
|
||||
"semantic_category": "OUTERWEAR/GILET",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27015.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27016",
|
||||
"semantic_category": "BOTTOM/SHORTS",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27016.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27027",
|
||||
"semantic_category": "BOTTOM/PANTS",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27027.jpg"
|
||||
},
|
||||
{
|
||||
"item_name": "MKTS27028",
|
||||
"semantic_category": "OUTERWEAR/JACKET",
|
||||
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MKTS27028.jpg"
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|
||||
"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"
|
||||
}
|
||||
]
|
||||
}
|
||||
Reference in New Issue
Block a user