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 requests
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import json
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from PIL import Image
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from PIL import Image
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import cv2
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import cv2
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import numpy as np
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import numpy as np
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import tritonclient.http as httpclient
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import tritonclient.http as httpclient
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import torch
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import torch
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from matplotlib import pyplot as plt, image as mpimg
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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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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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class OutfitMatcherHon(OutfitMatcher):
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"""Normalize an image with mean and std.
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def __init__(self):
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super().__init__()
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Args:
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@staticmethod
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img (ndarray): Image to be normalized.
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def load_image(img_path):
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mean (ndarray): The mean to be used for normalize.
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if 'http' in img_path:
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std (ndarray): The std to be used for normalize.
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file = requests.get(img_path)
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to_rgb (bool): Whether to convert to rgb.
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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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@staticmethod
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ndarray: The normalized image.
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def resize_image(img):
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"""
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"""
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img = img.copy().astype(np.float32)
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Args:
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assert img.dtype != np.uint8
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img: ndarray (height, width, channel)
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mean = np.float64(mean.reshape(1, -1))
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"""
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stdinv = 1 / np.float64(std.reshape(1, -1))
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resized_img = cv2.resize(img, (224, 224), dst=None, interpolation=1)
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if to_rgb:
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return resized_img
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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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def preprocess(self, outfits):
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cv2.multiply(img, stdinv, img) # inplace
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outfit_images = []
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return img
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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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class OutfitMaterTypeAware(OutfitMatcher):
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if 'http' in img_path:
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base_fashion_categories = [
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file = requests.get(img_path)
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'accessories', 'all-body', 'bags', 'bottoms', 'hats', 'jewellery',
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image = cv2.imdecode(np.fromstring(file.content, np.uint8), 1)
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'outerwear', 'scarves', 'shoes', 'sunglasses', 'tops'
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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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]
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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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@staticmethod
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# 将outfits和scores按照scores的值进行排序
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def load_image(img_path):
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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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if 'http' in img_path:
|
||||||
outfits = [outfits[i] for i in sorted_indices]
|
file = requests.get(img_path)
|
||||||
scores = scores[sorted_indices]
|
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
|
||||||
|
|
||||||
# 设置子图的行列数
|
@staticmethod
|
||||||
num_rows = len(outfits)
|
def resize_image(img):
|
||||||
num_cols = max([len(x) for x in outfits]) + 1 # 一个是图片，一个是分数
|
"""
|
||||||
|
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
|
||||||
|
|
||||||
# 创建一个新的图像，并指定子图的行列数
|
def preprocess(self, outfits):
|
||||||
fig, axes = plt.subplots(num_rows, num_cols, figsize=(8, 15))
|
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)
|
||||||
|
|
||||||
title = f"Best {topk} Outfits" if best else f"Worst {topk} Outfits"
|
category = self.base_fashion_categories.index(item["mapped_cate"])
|
||||||
fig.suptitle(title, fontsize=16)
|
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
|
||||||
|
|
||||||
# 遍历每套outfit并将其显示在对应的子图中
|
def get_result(self, outfits):
|
||||||
for i, (outfit, score) in enumerate(zip(outfits, scores)):
|
"""Input outfits structure and output scores.
|
||||||
# 显示分数
|
Args:
|
||||||
axes[i, 0].text(0.1, 0.5, f"Score: {score[0]:.4f}", fontsize=12)
|
outfits: outfits to be evaluated.
|
||||||
axes[i, 0].axis("off")
|
Example:
|
||||||
# 显示图片
|
[
|
||||||
for j, item in enumerate(outfit):
|
[
|
||||||
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
|
"item_name": "MSE_57987",
|
||||||
axes[i, j + 1].axis('off') # 关闭坐标轴
|
"semantic_category": "BOTTOM/PANTS",
|
||||||
axes[i, j + 1].set_title(item["semantic_category"], fontsize=10)
|
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MSE_57987.jpg",
|
||||||
for j in range(len(outfit), num_cols):
|
"mapped_cate": "bottoms"
|
||||||
axes[i, j].axis("off")
|
},
|
||||||
|
{
|
||||||
# 在每一行的底部添加一条横线
|
"item_name": "MPO_SP7712",
|
||||||
axes[i, 0].axhline(y=0, color='black', linewidth=1)
|
"semantic_category": "TOP/TANK",
|
||||||
# 隐藏最后一行的横线
|
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MPO_SP7712.jpg",
|
||||||
axes[-1, 0].axhline(y=0, color='white', linewidth=1)
|
"mapped_cate": "tops"
|
||||||
|
},
|
||||||
# 调整布局
|
{
|
||||||
plt.subplots_adjust(wspace=0.1, hspace=0.1)
|
"item_name": "MWSS27195",
|
||||||
plt.tight_layout()
|
"semantic_category": "OUTERWEAR/GILET",
|
||||||
|
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MWSS27195.jpg",
|
||||||
if output_path:
|
"mapped_cate": "outerwear"
|
||||||
plt.savefig(output_path)
|
}
|
||||||
else:
|
],
|
||||||
plt.show()
|
...
|
||||||
|
]
|
||||||
|
Returns:
|
||||||
if __name__ == '__main__':
|
scores: List of float
|
||||||
with open("test_input.json", "r") as f:
|
"""
|
||||||
outfits = json.load(f)
|
image, category, mask = self.preprocess(outfits)
|
||||||
scores = evaluate_outfits(outfits)
|
client = httpclient.InferenceServerClient(url="localhost:8000")
|
||||||
print(scores)
|
# 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 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.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__':
|
if __name__ == '__main__':
|
||||||
with open("./test_param/recommendation_test.json", "r") as f:
|
with open("./test_param/recommendation_test.json", "r") as f:
|
||||||
param = json.load(f)
|
param = json.load(f)
|
||||||
fashion_dataset = FashionDataset(param["database"])
|
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"])
|
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
|
# ))
|
||||||
|
|||||||
@@ -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",
|
||||||
|
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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|
||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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|
||||||
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||||||
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||||||
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|
||||||
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||||||
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|
||||||
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||||||
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|
||||||
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|
||||||
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||||||
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||||||
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||||||
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||||||
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|
||||||
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||||||
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||||||
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||||||
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|
||||||
|
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|
||||||
|
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||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
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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": "JEANS/JEANS SHORTS",
|
||||||
|
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27129.jpg"
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27132.jpg"
|
||||||
|
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|
||||||
|
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|
||||||
|
"item_name": "MLSS27133",
|
||||||
|
"semantic_category": "BOTTOM/SKIRT",
|
||||||
|
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27133.jpg"
|
||||||
|
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|
||||||
|
{
|
||||||
|
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|
||||||
|
"semantic_category": "ONE PIECE/DRESS",
|
||||||
|
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27136.jpg"
|
||||||
|
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|
||||||
|
{
|
||||||
|
"item_name": "MLSS27137",
|
||||||
|
"semantic_category": "TOP/SHIRT",
|
||||||
|
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27137.jpg"
|
||||||
|
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|
||||||
|
{
|
||||||
|
"item_name": "MLSS27140",
|
||||||
|
"semantic_category": "OUTERWEAR/JACKET",
|
||||||
|
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27140.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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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27145.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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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"image_path": "D:\\PhD_Study\\MIXI\\mitu\\image\\2024 SS\\MLSS27147.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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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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||||||
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
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||||||
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
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||||||
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|
||||||
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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|
||||||
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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|
||||||
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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||||||
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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|
||||||
|
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|
||||||
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||||||
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|
||||||
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||||||
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|
||||||
|
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|
||||||
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||||||
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|
||||||
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|
||||||
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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|
||||||
|
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|
||||||
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|
||||||
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||||||
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||||||
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|
||||||
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|
||||||
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||||||
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||||||
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||||||
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||||||
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|
||||||
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|
||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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|
||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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|
||||||
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||||||
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||||||
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||||||
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||||||
|
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|
||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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|
||||||
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||||||
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||||||
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||||||
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|
||||||
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||||||
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||||||
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||||||
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||||||
|
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|
||||||
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||||||
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||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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||||||
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|
||||||
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||||||
|
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|
||||||
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||||||
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||||||
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||||||
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||||||
|
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|
||||||
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||||||
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||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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||||||
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||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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||||||
|
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|
||||||
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||||||
|
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|
||||||
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||||||
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
{
|
||||||
|
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|
||||||
|
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|
||||||
|
"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