new scripts

scripts/get_error_hood.py

@@ -0,0 +1,84 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+
+import importlib
+import argparse
+parser = argparse.ArgumentParser(description='')
+parser.add_argument('--name', type=str)
+parser.add_argument('--config', type=str)
+parser.add_argument('--debug', action='store_true')
+parser.add_argument('--iteration', type=str, default='')
+parser.add_argument('-g', '--gpu', type=str)
+parser.add_argument('-n', '--normal', action='store_true')
+parser.add_argument('-e', '--edge', action='store_true')
+parser.add_argument('-cr', '--collision_ratio', action='store_true')
+parser.add_argument('-a', '--all', action='store_true')
+args = vars(parser.parse_args())
+
+if args['debug']:
+    from configs.iccvw.pyr_64_128_256_512_cape_f_e2e import config as config_1
+else:
+    config_module = importlib.import_module(args['config'])
+    config_1 = config_module.config
+
+
+import time
+import argparse
+import tqdm
+import random
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as ex
+from data.utils import Frame
+
+
+
+
+def calculate(type=''):
+    errors = dict()
+    errors_mean = dict()
+
+    with open(f'{folder}/{hood_csv}', 'r') as f:
+        lines_hood = f.readlines()
+    with open(f'{folder}/{ours_csv}', 'r') as f:
+        lines_ours = f.readlines()
+
+    for line in lines_hood:
+        line = line.split(',')
+        category = line[2]
+
+        if category not in errors:
+            errors[category] = []
+        errors[category].append(float(line[3]))
+
+    for category in errors:
+        errors_mean[category] = round(np.mean(errors[category]), 5)
+    print(errors_mean) 
+
+    mean = round(np.mean(list(errors_mean.values())), 5)
+    print(f'Final HOOD error{type}: {mean}')
+
+    for line in lines_ours:
+        line = line.split(',')
+        category = line[2]
+
+        if category not in errors:
+            errors[category] = []
+        errors[category].append(float(line[3]))
+
+    for category in errors:
+        errors_mean[category] = round(np.mean(errors[category]), 5)
+    print(errors_mean) 
+
+    mean = round(np.mean(list(errors_mean.values())), 5)
+    print(f'Final OURS error{type}: {mean}')
+
+
+if __name__ == '__main__':
+
+    folder = '/data-local/127_2/results/hood'
+    hood_csv = 'hood_2_hood.csv'
+    ours_csv = 'hood_2_ours.csv'
+
+    calculate()