add veriwild

.gitignore

@@ -0,0 +1,5 @@
+examples/EDA/*
+examples/outputs/*
+*.pth
+__pycache__
+*.egg-info

README.md

@@ -8,7 +8,7 @@ This is the official code of article "Parsing-based viewaware embedding network
 ## Install
 Clone this repository. Then excute the following commands
 ```
-cd vehcile_reid.pytorch
+cd PVEN
 pip install -r requirements.txt
 python setup.py install
 ```

examples/parsing/generate_masks.py

@@ -53,7 +53,7 @@ def predict(model, test_dataset, test_dataset_vis, output_path):
         metas = pickle.load(f)
     output_path = Path(args.output_path).absolute()
 
-    for phase in ['train', 'query', 'gallery']:
+    for phase in metas.keys():
         sub_path = output_path / phase
         mkdir_p(str(sub_path))
         dataset = VehicleReIDParsingDataset(metas[phase], augmentation=get_validation_augmentation(),

examples/preprocess_data/preprocess_veriwild.py

@@ -0,0 +1,49 @@
+import pandas as pd
+import os
+import pickle as pkl
+import click
+
+
+@click.command()
+@click.option('--input-path', default='/home/aa/mengdechao/datasets/veriwild')
+@click.option('--output-path', default='../outputs/veriwild.pkl')
+def veriwild(input_path, output_path):
+    PATH = input_path
+
+    images = {}
+
+    images['train']        = open(PATH + '/train_test_split/train_list.txt').read().strip().split('\n')
+    images['query_3000']   = open(PATH + '/train_test_split/test_3000_query.txt').read().strip().split('\n')
+    images['gallery_3000'] = open(PATH + '/train_test_split/test_3000.txt').read().strip().split('\n')
+    images['query_5000']   = open(PATH + '/train_test_split/test_5000_query.txt').read().strip().split('\n')
+    images['gallery_5000'] = open(PATH + '/train_test_split/test_5000.txt').read().strip().split('\n')
+    images['query_10000']  = open(PATH + '/train_test_split/test_10000_query.txt').read().strip().split('\n')
+    images['gallery_10000']= open(PATH + '/train_test_split/test_10000.txt').read().strip().split('\n')
+
+    wild_df = pd.read_csv(f'{PATH}/train_test_split/vehicle_info.txt', sep=';', index_col='id/image')
+    wild_dict = wild_df.to_dict()
+    camid_dict = wild_dict['Camera ID']
+
+    outputs = {}
+    for key, lists in images.items():
+        output = []
+        for img_name in lists:
+            item = {
+                "image_path": f"{PATH}/images/{img_name}.jpg",
+                "name": img_name,
+                "id": img_name.split('/')[0],
+    #             "cam": wild_df.loc[img_name]['Camera ID'] 
+                "cam": camid_dict[img_name]
+            }
+            output.append(item)
+        outputs[key] = output
+
+    base_path = os.path.split(output_path)[0]
+    if base_path != '' and not os.path.exists(base_path):
+        os.makedirs(base_path, exist_ok=True)
+    with open(output_path, 'wb') as f:
+        pkl.dump(outputs, f)
+
+
+if __name__ == "__main__":
+    veriwild()