aleo1 commited on
Commit
e5818de
1 Parent(s): ecd4a3e
app.py CHANGED
@@ -1,8 +1,8 @@
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  import time
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  import os
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-
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-
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  os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 
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  from PIL import Image
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  import numpy as np
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  import argparse
@@ -40,17 +40,17 @@ def get_parser():
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  cfg = config.merge_cfg_from_list(cfg, args.opts)
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  return cfg
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  args = get_parser()
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- data_dir = './LuojiaHOG(demo).json'
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- imgs_folder = './image/'
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  # image_id = 'sample44_1641.jpg'
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  # model_path = './rsvit.pth'
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- with open('image_features.pkl', 'rb') as f:
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  image_dict = pickle.load(f)
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  image_feat = np.array(list(image_dict.values()))
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  f.close()
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- with open('text_features.pkl', 'rb') as f:
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  text_dict = pickle.load(f)
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  text_feat = np.array(list(text_dict.values()))
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  f.close()
@@ -169,8 +169,20 @@ def search(text_query, image_query, top_k: int = 10):
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  # {"Score": [torch.round(torch.tensor(value)*100)/100 for value in top_k_scores], "Title": top_k_titles, "Text": top_k_texts}
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  # )
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  # 获取图像
 
 
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  if text_query != None:
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- image_output = [Image.open(imgs_folder + img.replace('_','/')) for img in info]
 
 
 
 
 
 
 
 
 
 
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  else:
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  image_output = []
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@@ -282,7 +294,6 @@ def txt_search(text_query, top_k: int = 10):
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  # query_embedding = model.encode(query)
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  text = tokenize(text_query, 328)
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-
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  query_vector = model.text_encode(text)
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  index = text_index
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  import time
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  import os
 
 
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  os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
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+ import zipfile
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+ from io import BytesIO
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  from PIL import Image
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  import numpy as np
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  import argparse
 
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  cfg = config.merge_cfg_from_list(cfg, args.opts)
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  return cfg
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  args = get_parser()
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+ data_dir = './LuojiaHOG(best)_.json'
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+ imgs_folder = 'image/'
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  # image_id = 'sample44_1641.jpg'
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  # model_path = './rsvit.pth'
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+ with open('image_features_best.pkl', 'rb') as f:
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  image_dict = pickle.load(f)
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  image_feat = np.array(list(image_dict.values()))
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  f.close()
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+ with open('text_features_best.pkl', 'rb') as f:
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  text_dict = pickle.load(f)
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  text_feat = np.array(list(text_dict.values()))
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  f.close()
 
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  # {"Score": [torch.round(torch.tensor(value)*100)/100 for value in top_k_scores], "Title": top_k_titles, "Text": top_k_texts}
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  # )
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  # 获取图像
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+
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+
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  if text_query != None:
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+ # image_output = [Image.open(imgs_folder + img.replace('_','/')) for img in info]
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+ image_output = []
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+ for img in info:
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+ sample_name = img.split('_')[0]
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+ image_path = imgs_folder + sample_name + '.zip'
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+ with zipfile.ZipFile(image_path, 'r') as zip_ref:
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+ # 读取图像文件
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+ with zip_ref.open(img.replace('_', '/')) as image_file:
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+ # 将读取的字节流转换为图像
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+ image = Image.open(BytesIO(image_file.read()))
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+ image_output.append(image)
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  else:
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  image_output = []
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  # query_embedding = model.encode(query)
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  text = tokenize(text_query, 328)
 
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  query_vector = model.text_encode(text)
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  index = text_index
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