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搭配服务修改

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zhouchengrong
2024-03-28 17:22:51 +08:00
parent 39dae92ea0
commit eb9351dc87
5 changed files with 117 additions and 79 deletions
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74
app/service/outfit_matcher/outfit_evaluator.py
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@@ -197,43 +197,45 @@ class OutfitMatcher(object):
outfits = [outfits[i] for i in sorted_indices] # 最好或最差的五个
scores = scores[sorted_indices] # 这五个的分数
# 设置子图的行列数
num_rows = len(outfits)
num_cols = max([len(x) for x in outfits]) + 1 # 一个是图片，一个是分数
return outfits, scores.tolist()
# 创建一个新的图像，并指定子图的行列数
fig, axes = plt.subplots(num_rows, num_cols, figsize=(8, 15))
title = f"Best {topk} Outfits" if best else f"Worst {topk} Outfits"
fig.suptitle(title, fontsize=16)
# 遍历每套outfit并将其显示在对应的子图中
for i, (outfit, score) in enumerate(zip(outfits, scores)):
# 显示分数
axes[i, 0].text(0.1, 0.5, f"Score: {score:.4f}", fontsize=12)
axes[i, 0].axis("off")
# 显示图片
for j, item in enumerate(outfit):
img = self.load_image(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()
# # 设置子图的行列数
# num_rows = len(outfits)
# num_cols = max([len(x) for x in outfits]) + 1 # 一个是图片，一个是分数
#
# # 创建一个新的图像，并指定子图的行列数
# fig, axes = plt.subplots(num_rows, num_cols, figsize=(8, 15))
#
# title = f"Best {topk} Outfits" if best else f"Worst {topk} Outfits"
# fig.suptitle(title, fontsize=16)
#
# # 遍历每套outfit并将其显示在对应的子图中
# for i, (outfit, score) in enumerate(zip(outfits, scores)):
# # 显示分数
# axes[i, 0].text(0.1, 0.5, f"Score: {score:.4f}", fontsize=12)
# axes[i, 0].axis("off")
# # 显示图片
# for j, item in enumerate(outfit):
# img = self.load_image(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()
class OutfitMatcherHon(OutfitMatcher):

66
app/service/outfit_matcher/service.py
View File

@@ -1,37 +1,57 @@
import json
import os
from pprint import pprint
import numpy as np
from app.service.outfit_matcher.dataset import FashionDataset
from app.service.outfit_matcher.outfit_evaluator import OutfitMaterTypeAware
from app.service.outfit_matcher.outfit_evaluator import OutfitMaterTypeAware, Backbone
if __name__ == '__main__':
with open("./test_param/recommendation_test.json", "r") as f:
param = json.load(f)
fashion_dataset = FashionDataset(param["database"])
backbone_service = Backbone()
service = OutfitMaterTypeAware()
best_list = []
bad_list = []
# read feature from vector database
all_items = param["query"] + param["database"]
unextracted_item = []
prepared_feature = {}
# 拿到所有需要提取特征的图片
for item in all_items:
if f'{item["item_name"]}.npy' not in os.listdir("feature"):
unextracted_item.append(item)
if len(unextracted_item) > 0:
# 通过backbone模型提取图片特征
extracted_features = backbone_service.get_result(unextracted_item)
for i, item in enumerate(unextracted_item):
# save features
# 链接milvus
# TODO
np.save(f'feature/{item["item_name"]}.npy', extracted_features[i])
# 存入数据库
# 关闭链接
# TODO 读取本次任务需要的图片特征
for item in all_items:
if item["item_name"] not in prepared_feature.keys():
prepared_feature[item["item_name"]] = np.load(f'feature/{item["item_name"]}.npy')
# 开始服装搭配任务
for item in param["query"]:
# 根据一定规则生成outfit
outfits = fashion_dataset.generate_outfit(item, param["topk"], param["max_outfits"])
scores, features = service.get_result(outfits)
# save features
# 根据模型对生成的outfit打分
scores = service.get_result(outfits, prepared_feature)
# 对评分排序，拿到最好的topk个outfit输出
sorted_indices = np.argsort(scores)[:param["topk"]] # type-aware
best_outfits = [outfits[i] for i in sorted_indices] # 最好的五个
# 链接milvus
# 存入数据库
# 关闭链接
# print(scores)
# print(len(scores))
best_outfits, best_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")
)
bad_outfits, bad_scores = 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")
)
best_list.append({"best_outfits": best_outfits, "best_scores": best_scores})
bad_list.append({"bad_outfits": bad_outfits, "bad_scores": bad_scores})
pprint(best_list)
pprint(bad_list)
# 结果可视化
# service.visualize(outfits, scores, param["topk"], best=True,
# output_path=os.path.join(r"D:\PhD_Study\MIXI\mitu\image\123",
# f"{item['item_name']}_best_{param['topk']}.png"))
# service.visualize(outfits, scores, param["topk"], best=False,
# output_path=os.path.join(r"D:\PhD_Study\MIXI\mitu\image\123",
# f"{item['item_name']}_worst_{param['topk']}.png"))