diff --git a/app/core/config.py b/app/core/config.py index 5b1ef5a..f4b7583 100644 --- a/app/core/config.py +++ b/app/core/config.py @@ -27,16 +27,16 @@ MINIO_SECURE = False MINIO_ACCESS = "e8zc55mzDOh4IzRrZ9Oa" MINIO_SECRET = "uHfqJ7UkwA1PTDGfnA44Hp9ux5YkZTkzZLjeOYhE" -OM_TRITON_IP = "10.1.1.150" -OM_TRITON_PORT = "9000" +OM_TRITON_IP = "10.1.1.240" +OM_TRITON_PORT = "10010" ATT_TRITON_IP = "10.1.1.240" ATT_TRITON_PORT = "10020" # service env -# LOGS_PATH = "app/logs/errors.log" -# FASHION_CATEGORIES = "app/service/outfit_matcher/config/fashion_categories.json" -# FASHION_CATEGORIES_MAPPING = "app/service/outfit_matcher/config/fashion_category_mapping.json" +LOGS_PATH = "app/logs/errors.log" +FASHION_CATEGORIES = "app/service/outfit_matcher/config/fashion_categories.json" +FASHION_CATEGORIES_MAPPING = "app/service/outfit_matcher/config/fashion_category_mapping.json" # pycharm debug # LOGS_PATH = "logs/errors.log" @@ -44,6 +44,6 @@ ATT_TRITON_PORT = "10020" # FASHION_CATEGORIES_MAPPING = "service/outfit_matcher/config/fashion_category_mapping.json" -LOGS_PATH = "app/logs/errors.log" -FASHION_CATEGORIES = "./config/fashion_categories.json" -FASHION_CATEGORIES_MAPPING = "./config/fashion_category_mapping.json" \ No newline at end of file +# LOGS_PATH = "app/logs/errors.log" +# FASHION_CATEGORIES = "./config/fashion_categories.json" +# FASHION_CATEGORIES_MAPPING = "./config/fashion_category_mapping.json" \ No newline at end of file diff --git a/app/logs/errors.log b/app/logs/errors.log index 3619c22..500d6f5 100644 --- a/app/logs/errors.log +++ b/app/logs/errors.log @@ -1,3 +1,267 @@ +2024-03-25 17:26:03,804 decorator.py [line:11] INFO function：【load_image】,runtime：【0.13544344902038574】s +2024-03-25 17:26:03,804 decorator.py [line:11] INFO function：【load_image】,runtime：【0.13544344902038574】s +2024-03-25 17:26:03,901 decorator.py [line:11] INFO function：【load_image】,runtime：【0.07622218132019043】s +2024-03-25 17:26:03,901 decorator.py [line:11] INFO 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function：【load_image】,runtime：【0.06318235397338867】s +2024-03-27 11:29:00,322 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06962013244628906】s +2024-03-27 11:29:00,322 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06962013244628906】s +2024-03-27 11:29:00,405 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06734132766723633】s +2024-03-27 11:29:00,405 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06734132766723633】s +2024-03-27 11:29:00,481 decorator.py [line:11] INFO function：【load_image】,runtime：【0.05873560905456543】s +2024-03-27 11:29:00,481 decorator.py [line:11] INFO function：【load_image】,runtime：【0.05873560905456543】s +2024-03-27 11:29:00,560 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06250882148742676】s +2024-03-27 11:29:00,560 decorator.py [line:11] INFO function：【load_image】,runtime：【0.06250882148742676】s +2024-03-27 11:29:00,638 decorator.py [line:11] INFO function：【load_image】,runtime：【0.0628812313079834】s +2024-03-27 11:29:00,638 decorator.py [line:11] INFO function：【load_image】,runtime：【0.0628812313079834】s +2024-03-27 11:29:00,679 decorator.py [line:11] INFO function：【get_result】,runtime：【0.5070171356201172】s +2024-03-27 11:29:00,679 decorator.py [line:11] INFO function：【get_result】,runtime：【0.5070171356201172】s +2024-03-27 11:29:00,680 api_outfit_matcher.py [line:43] INFO run time is : 9.467643737792969 +2024-03-27 11:29:00,680 api_outfit_matcher.py [line:43] INFO run time is : 9.467643737792969 diff --git a/app/schemas/similar_match.py b/app/schemas/similar_match.py index 5deb058..e5fb063 100644 --- a/app/schemas/similar_match.py +++ b/app/schemas/similar_match.py @@ -3,3 +3,4 @@ from pydantic import BaseModel class SimilarMatchMItem(BaseModel): image_path: str + result_number: int diff --git a/app/service/outfit_matcher/outfit_evaluator.py b/app/service/outfit_matcher/outfit_evaluator.py index 2404454..0392414 100644 --- a/app/service/outfit_matcher/outfit_evaluator.py +++ b/app/service/outfit_matcher/outfit_evaluator.py @@ -8,7 +8,7 @@ from matplotlib import pyplot as plt, image as mpimg from minio import Minio from torchvision import transforms -from app.core.config import MINIO_IP, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, MINIO_PORT, OM_TRITON_PORT, OM_TRITON_IP +from app.core.config import * from app.service.outfit_matcher.foco import extract_main_colors from app.service.utils.decorator import RunTime diff --git a/app/service/outfit_matcher/test_param/test.json b/app/service/outfit_matcher/test_param/test.json new file mode 100644 index 0000000..a62fd97 --- /dev/null +++ b/app/service/outfit_matcher/test_param/test.json @@ -0,0 +1,19 @@ +{ + "topk": 1, + "max_outfits": 5, + "is_best": true, + "query": [ + { + "image_path": "mi-tu/26/BOTTOM/PANTS/MKTS27000_0BLK.jpg/3f4676db-98a1-44d4-947f-9d1f59828629.jpg", + "item_name": "MKTS27000", + "semantic_category": "BOTTOM/PANTS" + } + ], + "database": [ + { + "image_path": "mi-tu/26/TOP/BLOUSE/MKTS27002_0WHT.jpg/131cc29e-8f70-4134-a0e8-82f826b00058.jpg", + "item_name": "MKTS27002", + "semantic_category": "TOP/BLOUSE" + } + ] +} \ No newline at end of file diff --git a/app/service/similar_match/service.py b/app/service/similar_match/service.py index dae4af9..a071461 100644 --- a/app/service/similar_match/service.py +++ b/app/service/similar_match/service.py @@ -10,6 +10,8 @@ from pymilvus import MilvusClient from app.core.config import * from torchvision import transforms +from app.service.utils.decorator import RunTime + class SimilarMatch: def __init__(self): @@ -77,19 +79,20 @@ class SimilarMatch: features = results.as_numpy("output__1") # Shape (N, 64) return features + @RunTime def match_features(self, features): # 连接milvus # 连接milvus client = MilvusClient(uri="http://10.1.1.240:19530", db_name="mixi") try: - res = client.search( + search_response = client.search( collection_name="mixi_outfit", # Replace with the actual name of your collection # Replace with your query vector data=[features[0]], limit=5, # Max. number of search results to return output_fields=["id", "image_path"], # Search parameters ) - return res + return search_response finally: client.close() @@ -97,6 +100,6 @@ class SimilarMatch: if __name__ == '__main__': service = SimilarMatch() features = service.get_features(img_path="test/2024 SS/MKTS27000.jpg") - res = service.match_features(features) + search_response = service.match_features(features) - print(json.dumps(res, indent=4)) + print(json.dumps(search_response, indent=4)) diff --git a/app/service/utils/decorator.py b/app/service/utils/decorator.py index 294b54b..2e563fc 100644 --- a/app/service/utils/decorator.py +++ b/app/service/utils/decorator.py @@ -9,6 +9,7 @@ def RunTime(func): t2 = time.time() if t2 - t1 > 0.05: logging.info(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s") + print(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s") return res return wrapper diff --git a/requirements.txt b/requirements.txt index 084644b..0eb1ed8 100644 Binary files a/requirements.txt and b/requirements.txt differ diff --git a/test/attribute/infer_test.py b/test/attribute/infer_test.py new file mode 100644 index 0000000..e01b948 --- /dev/null +++ b/test/attribute/infer_test.py @@ -0,0 +1,110 @@ +import cv2 +import mmcv +import numpy as np +import tritonclient.http as httpclient +import torch + + +def preprocess(img): + img = mmcv.imread(img) + ori_shape = img.shape[:2] + img_scale = (224, 224) + scale_factor = [] + img, x, y = mmcv.imresize(img, img_scale, return_scale=True) + scale_factor.append(x) + scale_factor.append(y) + img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True) + preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0) + return preprocessed_img, ori_shape + + +def get_attribute(model_save_name, sample): + triton_client = httpclient.InferenceServerClient(url=f"10.1.1.240:10020") + inputs = [ + httpclient.InferInput("input__0", sample.shape, datatype="FP32") + ] + inputs[0].set_data_from_numpy(sample, binary_data=True) + results = triton_client.infer(model_name=model_save_name, inputs=inputs) + inference_output = torch.from_numpy(results.as_numpy(f"output__0")) + scores = inference_output.detach().numpy() + print(f"{model} is ok") + + +image, shape = preprocess(cv2.imread("8365b04b-69d0-4f6d-a61b-a8cc136256263422b10ff45d4ae4e10be3fc25a9c36b.jpg")) +# get_attribute("attr_retrieve_D_sleeve_shape", image) + +model_list = ['top_length', + 'top_type', + 'top_Sleeve_length', + 'top_Sleeve_shape', + 'top_Sleeve_shoulder', + 'top_Neckline', + 'top_print', + 'top_material', + 'top_Softness', + 'top_Design', + 'top_optype', + 'top_Silhouette', + 'top_Collar', + 'bottom_sub-Type', + 'bottom_length', + 'bottom_print', + 'bottom_material', + 'bottom_Softness_B', + 'bottom_Silhouette_B', + 'bottom_OPType_B', + 'bottom_design', + 'outwear_outer_length', + # 'outwear_2_outer_type', + 'outwear_outer_sleeve_length', + 'outwear_outer_sleeve_shape', + 'outwear_outer_sleeve_shoulder', + 'outwear_outer_collar', + 'outwear_print', + 'outwear_material', + 'outwear_outer_softness', + 'outwear_outer_design', + # 'outwear_11_outer_opening', + 'outwear_outer_optype', + 'outwear_outer_silhouette', + 'jumpsuit_length', + 'jumpsuit_sleeve_length', + 'jumpsuit_sleeve_shape', + 'jumpsuit_sleeve_shoulder', + 'jumpsuit_neckline', + 'jumpsuit_collar', + 'jumpsuit_print', + 'jumpsuit_material', + 'jumpsuit_softness', + 'jumpsuit_design', + 'jumpsuit_optype', + 'jumpsuit_subtype', + 'dress_length', + 'dress_sleeve_length', + 'dress_sleeve_shape', + 'dress_sleeve_shoulder', + 'dress_neckline', + 'dress_print', + 'dress_collar', + 'dress_material', + 'dress_design', + 'dress_softness', + 'dress_silohouette12', + # 'dress_' + 'dress_type' + ] +except_model = [] +# 'attr_recong_B_optype', 'attr_recong_D_length', 'attr_recong_O_length', 'attr_recong_O_optype', 'attr_recong_material', +# 'attr_recong_print', 'attr_retrieve_D_collar', 'attr_retrieve_D_design', 'attr_retrieve_D_neckline', 'attr_retrieve_D_silohouette', +# 'attr_retrieve_D_sleeve_shape', 'attr_retrieve_D_sleeve_shoulder', 'attr_retrieve_D_type', 'attr_retrieve_T_length', 'attr_retrieve_T_optype' + +# for model in model_list: +# get_attribute(model, image) + +for model in model_list: + try: + get_attribute(model, image) + except Exception as e: + print(e) + except_model.append(model) +print(except_model) diff --git a/test/attribute/model.py b/test/attribute/model.py new file mode 100644 index 0000000..1c7ac6a --- /dev/null +++ b/test/attribute/model.py @@ -0,0 +1,56 @@ +var = ["bottom_OPType_B", + "bottom_Silhouette_B", + "bottom_Softness_B", + "bottom_design", + "bottom_length", + "bottom_material", + "bottom_print", + "bottom_sub-Type", + "category", + "dress_collar", + "dress_design", + "dress_length", + "dress_material", + "dress_neckline", + "dress_print", + "dress_silohouette2", + "dress_sleeve_length", + "dress_sleeve_shape", + "dress_sleeve_shoulder", + "dress_softness", + "dress_type", + "jumpsuit_collar", + "jumpsuit_design", + "jumpsuit_length", + "jumpsuit_material", + "jumpsuit_optype", + "jumpsuit_print", + "jumpsuit_sleeve_length", + "jumpsuit_sleeve_shape", + "jumpsuit_sleeve_shoulder", + "jumpsuit_softness", + "jumpsuit_subtype", + "outwear_material", + "outwear_outear_length", + "outwear_outer_collar", + "outwear_outer_design", + "outwear_outer_optype", + "outwear_outer_silhouette", + "outwear_outer_sleeve_length", + "outwear_outer_sleeve_shape", + "outwear_outer_sleeve_shoulder", + "outwear_outer_softness", + "outwear_print", + "top_Collar", + "top_Design", + "top_Neckline", + "top_Silhouette", + "top_Sleeve_length", + "top_Sleeve_shape", + "top_Sleeve_shoulder", + "top_Softness", + "top_length", + "top_material", + "top_optype", + "top_print", + "top_type", ] diff --git a/test/attribute_recognition_model_infer/attribute_recognition_model_infer.py b/test/attribute_recognition_model_infer/attribute_recognition_model_infer.py new file mode 100644 index 0000000..67a6c0b --- /dev/null +++ b/test/attribute_recognition_model_infer/attribute_recognition_model_infer.py @@ -0,0 +1,51 @@ +from pprint import pprint + +import cv2 +import mmcv +import numpy as np +import tritonclient.http as httpclient +import torch + +from app.core.config import ATT_TRITON_PORT, ATT_TRITON_IP + +model_list = ['bottom_design', 'bottom_length', 'bottom_material', 'bottom_OPType_B', 'bottom_print', 'bottom_Silhouette_B', 'bottom_Softness_B', 'bottom_sub-Type', 'category', 'dress_collar', 'dress_design', 'dress_length', 'dress_material', 'dress_neckline', 'dress_print', 'dress_silohouette12', 'dress_sleeve_length', 'dress_sleeve_shape', 'dress_sleeve_shoulder', 'dress_softness', 'dress_type', 'jumpsuit_collar', 'jumpsuit_design', 'jumpsuit_length', 'jumpsuit_material', 'jumpsuit_optype', + 'jumpsuit_print', 'jumpsuit_sleeve_length', 'jumpsuit_sleeve_shape', 'jumpsuit_sleeve_shoulder', 'jumpsuit_softness', 'jumpsuit_subtype', 'outwear_material', 'outwear_outear_length', 'outwear_outer_collar', 'outwear_outer_design', 'outwear_outer_optype', 'outwear_outer_silhouette', 'outwear_outer_sleeve_length', 'outwear_outer_sleeve_shape', 'outwear_outer_sleeve_shoulder', 'outwear_outer_softness', 'outwear_print', 'top_Collar', 'top_Design', 'top_length', 'top_material', + 'top_Neckline', 'top_optype', 'top_print', 'top_Silhouette', 'top_Sleeve_length', 'top_Sleeve_shape', 'top_Sleeve_shoulder', 'top_Softness', 'top_type'] + + +def preprocess(img): + img = mmcv.imread(img) + ori_shape = img.shape[:2] + img_scale = (224, 224) + scale_factor = [] + img, x, y = mmcv.imresize(img, img_scale, return_scale=True) + scale_factor.append(x) + scale_factor.append(y) + img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True) + preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0) + return preprocessed_img, ori_shape + + +def get_attribute(model_save_name, sample): + triton_client = httpclient.InferenceServerClient(url=f"{ATT_TRITON_IP}:{ATT_TRITON_PORT}") + inputs = [ + httpclient.InferInput("input__0", sample.shape, datatype="FP32") + ] + inputs[0].set_data_from_numpy(sample, binary_data=True) + results = triton_client.infer(model_name=model_save_name, inputs=inputs) + inference_output = torch.from_numpy(results.as_numpy(f"output__0")) + scores = inference_output.detach().numpy() + pprint(scores) + print(f"{model_save_name} is ok") + + +image, shape = preprocess(cv2.imread("test_top1.jpg")) +except_model = [] + +for model in model_list: + try: + get_attribute(model, image) + except Exception as e: + print(e) + except_model.append(model) +print(except_model) diff --git a/test/json/outfit_response.py b/test/json/outfit_response.py new file mode 100644 index 0000000..c834f65 --- /dev/null +++ b/test/json/outfit_response.py @@ -0,0 +1,273 @@ +a = [{'best_outfits': [({'image_path': 'test/2024 SS/MKTS27047.jpg', + 'item_name': 'MKTS27047', + 'mapped_cate': 'all-body', + 'semantic_category': 'ONE PIECE/DRESS'}, + {'image_path': 'test/2024 SS/MLSS27156.jpg', + 'item_name': 'MLSS27156', + 'mapped_cate': 'outerwear', + 'semantic_category': 'TOP/VEST'}), + ({'image_path': 'test/2024 SS/MKTS27047.jpg', + 'item_name': 'MKTS27047', + 'mapped_cate': 'all-body', + 'semantic_category': 'ONE PIECE/DRESS'}, + {'image_path': 'test/2024 SS/MLSS27157.jpg', + 'item_name': 'MLSS27157', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/WINDBREAKER'}), + ({'image_path': 'test/2024 SS/MKTS27047.jpg', + 'item_name': 'MKTS27047', + 'mapped_cate': 'all-body', + 'semantic_category': 'ONE PIECE/DRESS'}, + {'image_path': 'test/2024 SS/MLSS27150.jpg', + 'item_name': 'MLSS27150', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/JACKET'}), + ({'image_path': 'test/2024 SS/MKTS27047.jpg', + 'item_name': 'MKTS27047', + 'mapped_cate': 'all-body', + 'semantic_category': 'ONE PIECE/DRESS'}, + {'image_path': 'test/2024 SS/MSE_56720.jpg', + 'item_name': 'MSE_56720', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/BLAZER'}), + ({'image_path': 'test/2024 SS/MKTS27047.jpg', + 'item_name': 'MKTS27047', + 'mapped_cate': 'all-body', + 'semantic_category': 'ONE PIECE/DRESS'}, + {'image_path': 'test/2024 SS/MLSS27109.jpg', + 'item_name': 'MLSS27109', + 'mapped_cate': 'outerwear', + 'semantic_category': 'JEANS/JEANS JACKET'})], + 'best_scores': [-0.111208215, + -0.120505564, + -0.1213615, + -0.12190802, + -0.12419219]}, + {'best_outfits': [({'image_path': 'test/2024 SS/MKTS27028.jpg', + 'item_name': 'MKTS27028', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/JACKET'}, + {'image_path': 'test/2024 SS/MSE_58107.jpg', + 'item_name': 'MSE_58107', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MLSS27133.jpg', + 'item_name': 'MLSS27133', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/SKIRT'}), + ({'image_path': 'test/2024 SS/MKTS27028.jpg', + 'item_name': 'MKTS27028', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/JACKET'}, + {'image_path': 'test/2024 SS/MLSS27132.jpg', + 'item_name': 'MLSS27132', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/TOP'}, + {'image_path': 'test/2024 SS/MLSS27159.jpg', + 'item_name': 'MLSS27159', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/PANTS'}), + ({'image_path': 'test/2024 SS/MKTS27028.jpg', + 'item_name': 'MKTS27028', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/JACKET'}, + {'image_path': 'test/2024 SS/MLSS27132.jpg', + 'item_name': 'MLSS27132', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/TOP'}, + {'image_path': 'test/2024 SS/MLSS27133.jpg', + 'item_name': 'MLSS27133', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/SKIRT'}), + ({'image_path': 'test/2024 SS/MKTS27028.jpg', + 'item_name': 'MKTS27028', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/JACKET'}, + {'image_path': 'test/2024 SS/MKTS27039.jpg', + 'item_name': 'MKTS27039', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/TOP'}, + {'image_path': 'test/2024 SS/MLSS27226.jpg', + 'item_name': 'MLSS27226', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/SKIRT'}), + ({'image_path': 'test/2024 SS/MKTS27028.jpg', + 'item_name': 'MKTS27028', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/JACKET'}, + {'image_path': 'test/2024 SS/MLSS27146.jpg', + 'item_name': 'MLSS27146', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/TOP'}, + {'image_path': 'test/2024 SS/MSE_58518.jpg', + 'item_name': 'MSE_58518', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/SKIRT'})], + 'best_scores': [-0.111208215, + -0.11449928, + -0.11642833, + -0.120505564, + -0.1213615]}, + {'best_outfits': [({'image_path': 'test/2024 SS/MSE_58057.jpg', + 'item_name': 'MSE_58057', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/BLAZER'}, + {'image_path': 'test/2024 SS/MKTS27046.jpg', + 'item_name': 'MKTS27046', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MSE_58518.jpg', + 'item_name': 'MSE_58518', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/SKIRT'}), + ({'image_path': 'test/2024 SS/MSE_58057.jpg', + 'item_name': 'MSE_58057', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/BLAZER'}, + {'image_path': 'test/2024 SS/MSE_58473.jpg', + 'item_name': 'MSE_58473', + 'mapped_cate': 'tops', + 'semantic_category': 'KNIT/KNIT TOP'}, + {'image_path': 'test/2024 SS/MKTS27031.jpg', + 'item_name': 'MKTS27031', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/SKIRT'}), + ({'image_path': 'test/2024 SS/MSE_58057.jpg', + 'item_name': 'MSE_58057', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/BLAZER'}, + {'image_path': 'test/2024 SS/MPO_SP7687.jpg', + 'item_name': 'MPO_SP7687', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MLSS27107.jpg', + 'item_name': 'MLSS27107', + 'mapped_cate': 'bottoms', + 'semantic_category': 'JEANS/JEANS'}), + ({'image_path': 'test/2024 SS/MSE_58057.jpg', + 'item_name': 'MSE_58057', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/BLAZER'}, + {'image_path': 'test/2024 SS/MLSS27142.jpg', + 'item_name': 'MLSS27142', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MLSS27159.jpg', + 'item_name': 'MLSS27159', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/PANTS'}), + ({'image_path': 'test/2024 SS/MSE_58057.jpg', + 'item_name': 'MSE_58057', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/BLAZER'}, + {'image_path': 'test/2024 SS/MLSS27142.jpg', + 'item_name': 'MLSS27142', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MLSS27226.jpg', + 'item_name': 'MLSS27226', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/SKIRT'})], + 'best_scores': [-0.111208215, + -0.11449928, + -0.11642833, + -0.120505564, + -0.1213615]}, + {'best_outfits': [({'image_path': 'test/2024 SS/MSE_58107.jpg', + 'item_name': 'MSE_58107', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MLSS27159.jpg', + 'item_name': 'MLSS27159', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/PANTS'}, + {'image_path': 'test/2024 SS/MSE_58289.jpg', + 'item_name': 'MSE_58289', + 'mapped_cate': 'outerwear', + 'semantic_category': 'OUTERWEAR/BLAZER'}), + ({'image_path': 'test/2024 SS/MSE_58107.jpg', + 'item_name': 'MSE_58107', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MLSS27111.jpg', + 'item_name': 'MLSS27111', + 'mapped_cate': 'bottoms', + 'semantic_category': 'JEANS/JEANS PANTS'}), + ({'image_path': 'test/2024 SS/MSE_58107.jpg', + 'item_name': 'MSE_58107', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MLSS27104.jpg', + 'item_name': 'MLSS27104', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/SHORTS'}), + ({'image_path': 'test/2024 SS/MSE_58107.jpg', + 'item_name': 'MSE_58107', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MLSS27159.jpg', + 'item_name': 'MLSS27159', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/PANTS'}), + ({'image_path': 'test/2024 SS/MSE_58107.jpg', + 'item_name': 'MSE_58107', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MLSS27160.jpg', + 'item_name': 'MLSS27160', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/PANTS'})], + 'best_scores': [-0.111208215, + -0.11449928, + -0.120505564, + -0.1213615, + -0.12190802]}, + {'best_outfits': [({'image_path': 'test/2024 SS/MSE_58495.jpg', + 'item_name': 'MSE_58495', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MLSS27159.jpg', + 'item_name': 'MLSS27159', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/PANTS'}), + ({'image_path': 'test/2024 SS/MSE_58495.jpg', + 'item_name': 'MSE_58495', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MLSS27160.jpg', + 'item_name': 'MLSS27160', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/PANTS'}), + ({'image_path': 'test/2024 SS/MSE_58495.jpg', + 'item_name': 'MSE_58495', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MLSS27159.jpg', + 'item_name': 'MLSS27159', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/PANTS'}, + {'image_path': 'test/2024 SS/MLSS27109.jpg', + 'item_name': 'MLSS27109', + 'mapped_cate': 'outerwear', + 'semantic_category': 'JEANS/JEANS JACKET'}), + ({'image_path': 'test/2024 SS/MSE_58495.jpg', + 'item_name': 'MSE_58495', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MLSS27119.jpg', + 'item_name': 'MLSS27119', + 'mapped_cate': 'bottoms', + 'semantic_category': 'JEANS/JEANS SKIRT'}), + ({'image_path': 'test/2024 SS/MSE_58495.jpg', + 'item_name': 'MSE_58495', + 'mapped_cate': 'tops', + 'semantic_category': 'TOP/SHIRT'}, + {'image_path': 'test/2024 SS/MKTS27000.jpg', + 'item_name': 'MKTS27000', + 'mapped_cate': 'bottoms', + 'semantic_category': 'BOTTOM/PANTS'})], + 'best_scores': [-0.111208215, + -0.11449928, + -0.120505564, + -0.1213615, + -0.12190802]}] diff --git a/test/minio_client_test/minio_client.py b/test/minio_client_test/minio_client.py new file mode 100644 index 0000000..48f041c --- /dev/null +++ b/test/minio_client_test/minio_client.py @@ -0,0 +1,12 @@ +from minio import Minio + +from app.core.config import MINIO_PORT, MINIO_IP, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE + +minio_client = Minio( + f"{MINIO_IP}:{MINIO_PORT}", + access_key=MINIO_ACCESS, + secret_key=MINIO_SECRET, + secure=MINIO_SECURE) + +for i in minio_client.list_objects('test'): + print(i.object_name) diff --git a/test/outfit_matcher/triton_infer.py b/test/outfit_matcher/triton_infer.py new file mode 100644 index 0000000..e69de29