增加了单帧入侵判断及yoloV10
This commit is contained in:
18262620154
@ -27,188 +27,24 @@ Created on Fri Aug 30 17:53:03 2024
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"""
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import numpy as np
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import cv2
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import os
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import sys
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import random
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import pickle
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import json
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import random
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import copy
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import sys
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# import torch
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import time
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# import json
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from pathlib import Path
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from scipy.spatial.distance import cdist
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import matplotlib.pyplot as plt
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import shutil
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from datetime import datetime
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# from openpyxl import load_workbook, Workbook
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# from config import config as conf
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# from model import resnet18 as resnet18
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# from feat_inference import inference_image
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[1] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT))
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sys.path.append(r"D:\DetectTracking")
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from tracking.utils.read_data import extract_data, read_tracking_output, read_similar, read_deletedBarcode_file
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from tracking.utils.plotting import Annotator, colors
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from feat_extract.config import config as conf
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from feat_extract.inference import FeatsInterface
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from utils.event import ShoppingEvent, save_data
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from utils.calsimi import calsimi_vs_stdfeat, calsimi_vs_stdfeat_new
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from utils.tools import get_evtList, init_eventDict
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from utils.databits import data_precision_compare
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from genfeats import gen_bcd_features
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from event_test import calc_simil
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from one2n_contrast import init_eventDict
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def int8_to_ft16(arr_uint8, amin, amax):
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arr_ft16 = (arr_uint8 / 255 * (amax-amin) + amin).astype(np.float16)
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return arr_ft16
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def ft16_to_uint8(arr_ft16):
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# pickpath = r"\\192.168.1.28\share\测试_202406\contrast\std_features_ft32vsft16\6902265587712_ft16.pickle"
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# with open(pickpath, 'rb') as f:
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# edict = pickle.load(f)
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# arr_ft16 = edict['feats']
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amin = np.min(arr_ft16)
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amax = np.max(arr_ft16)
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arr_ft255 = (arr_ft16 - amin) * 255 / (amax-amin)
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arr_uint8 = arr_ft255.astype(np.uint8)
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arr_ft16_ = int8_to_ft16(arr_uint8, amin, amax)
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arrDistNorm = np.linalg.norm(arr_ft16_ - arr_ft16) / arr_ft16_.size
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return arr_uint8, arr_ft16_
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def data_precision_compare(stdfeat, evtfeat, evtMessage, save=True):
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evt, stdbcd, label = evtMessage
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rltdata, rltdata_ft16, rltdata_ft16_ = [], [], []
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matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
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simi_mean = np.mean(matrix)
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simi_max = np.max(matrix)
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stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
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evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
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simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
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rltdata = [label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]]
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##================================================================= float16
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stdfeat_ft16 = stdfeat.astype(np.float16)
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evtfeat_ft16 = evtfeat.astype(np.float16)
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stdfeat_ft16 /= np.linalg.norm(stdfeat_ft16, axis=1)[:, None]
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evtfeat_ft16 /= np.linalg.norm(evtfeat_ft16, axis=1)[:, None]
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matrix_ft16 = 1 - cdist(stdfeat_ft16, evtfeat_ft16, 'cosine')
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simi_mean_ft16 = np.mean(matrix_ft16)
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simi_max_ft16 = np.max(matrix_ft16)
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stdfeatm_ft16 = np.mean(stdfeat_ft16, axis=0, keepdims=True)
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evtfeatm_ft16 = np.mean(evtfeat_ft16, axis=0, keepdims=True)
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simi_mfeat_ft16 = 1- np.maximum(0.0, cdist(stdfeatm_ft16, evtfeatm_ft16, 'cosine'))
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rltdata_ft16 = [label, stdbcd, evt, simi_mean_ft16, simi_max_ft16, simi_mfeat_ft16[0,0]]
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'''****************** uint8 is ok!!!!!! ******************'''
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##=================================================================== uint8
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# stdfeat_uint8, stdfeat_ft16_ = ft16_to_uint8(stdfeat_ft16)
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# evtfeat_uint8, evtfeat_ft16_ = ft16_to_uint8(evtfeat_ft16)
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stdfeat_uint8 = (stdfeat_ft16*128).astype(np.int8)
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evtfeat_uint8 = (evtfeat_ft16*128).astype(np.int8)
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stdfeat_ft16_ = stdfeat_uint8.astype(np.float16)/128
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evtfeat_ft16_ = evtfeat_uint8.astype(np.float16)/128
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absdiff = np.linalg.norm(stdfeat_ft16_ - stdfeat) / stdfeat.size
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matrix_ft16_ = 1 - cdist(stdfeat_ft16_, evtfeat_ft16_, 'cosine')
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simi_mean_ft16_ = np.mean(matrix_ft16_)
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simi_max_ft16_ = np.max(matrix_ft16_)
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stdfeatm_ft16_ = np.mean(stdfeat_ft16_, axis=0, keepdims=True)
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evtfeatm_ft16_ = np.mean(evtfeat_ft16_, axis=0, keepdims=True)
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simi_mfeat_ft16_ = 1- np.maximum(0.0, cdist(stdfeatm_ft16_, evtfeatm_ft16_, 'cosine'))
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rltdata_ft16_ = [label, stdbcd, evt, simi_mean_ft16_, simi_max_ft16_, simi_mfeat_ft16_[0,0]]
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if not save:
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return
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##========================================================= save as float32
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rppath = os.path.join(similPath, f'{evt}_ft32.pickle')
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with open(rppath, 'wb') as f:
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pickle.dump(rltdata, f)
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rtpath = os.path.join(similPath, f'{evt}_ft32.txt')
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with open(rtpath, 'w', encoding='utf-8') as f:
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for result in rltdata:
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part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
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line = ', '.join(part)
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f.write(line + '\n')
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##========================================================= save as float16
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rppath_ft16 = os.path.join(similPath, f'{evt}_ft16.pickle')
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with open(rppath_ft16, 'wb') as f:
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pickle.dump(rltdata_ft16, f)
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rtpath_ft16 = os.path.join(similPath, f'{evt}_ft16.txt')
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with open(rtpath_ft16, 'w', encoding='utf-8') as f:
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for result in rltdata_ft16:
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part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
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line = ', '.join(part)
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f.write(line + '\n')
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##=========================================================== save as uint8
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rppath_uint8 = os.path.join(similPath, f'{evt}_uint8.pickle')
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with open(rppath_uint8, 'wb') as f:
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pickle.dump(rltdata_ft16_, f)
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rtpath_uint8 = os.path.join(similPath, f'{evt}_uint8.txt')
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with open(rtpath_uint8, 'w', encoding='utf-8') as f:
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for result in rltdata_ft16_:
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part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
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line = ', '.join(part)
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f.write(line + '\n')
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def simi_calc(event, stdfeat):
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evtfeat = event.feats_compose
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if isinstance(event.feats_select, list):
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if len(event.feats_select) and len(event.feats_select[0]):
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evtfeat = event.feats_select[0]
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else:
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return None, None, None
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else:
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evtfeat = event.feats_select
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if len(evtfeat)==0 or len(stdfeat)==0:
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return None, None, None
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evtfeat /= np.linalg.norm(evtfeat, axis=1)[:, None]
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stdfeat /= np.linalg.norm(stdfeat, axis=1)[:, None]
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matrix = 1 - cdist(evtfeat, stdfeat, 'cosine')
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matrix[matrix < 0] = 0
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simi_mean = np.mean(matrix)
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simi_max = np.max(matrix)
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stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
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evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
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simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
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return simi_mean, simi_max, simi_mfeat[0,0]
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def build_std_evt_dict():
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@ -218,18 +54,6 @@ def build_std_evt_dict():
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'''
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stdBarcode = [p.stem for p in Path(stdFeaturePath).iterdir() if p.is_file() and (p.suffix=='.json' or p.suffix=='.pickle')]
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'''*********** USearch ***********'''
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# stdFeaturePath = r"D:\contrast\stdlib\v11_test.json"
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# stdBarcode = []
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# stdlib = {}
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# with open(stdFeaturePath, 'r', encoding='utf-8') as f:
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# data = json.load(f)
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# for dic in data['total']:
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# barcode = dic['key']
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# feature = np.array(dic['value'])
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# stdBarcode.append(barcode)
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# stdlib[barcode] = feature
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'''======1. 购物事件列表，该列表中的 Barcode 存在于标准的 stdBarcode 内 ==='''
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evtList = [(p.stem, p.stem.split('_')[-1]) for p in Path(eventDataPath).iterdir()
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@ -259,10 +83,7 @@ def build_std_evt_dict():
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stddata = pickle.load(f)
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feat = stddata["feats_ft32"]
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stdDict[barcode] = feat
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'''*********** USearch ***********'''
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# stdDict = {}
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# for barcode in barcodes:
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@ -282,7 +103,7 @@ def build_std_evt_dict():
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return evtList, evtDict, stdDict
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def one2SN_pr(evtList, evtDict, stdDict):
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def one2SN_pr(evtList, evtDict, stdDict, simType="simple"):
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std_barcodes = set([bcd for _, bcd in evtList])
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@ -312,8 +133,14 @@ def one2SN_pr(evtList, evtDict, stdDict):
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barcodes, similars = [], []
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for stdbcd in bcd_selected:
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stdfeat = stdDict[stdbcd]
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simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat)
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# simi_mean = calc_simil(event, stdfeat)
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if simType=="typea":
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simi_mean, simi_max, simi_mfeat = calsimi_vs_stdfeat(event, stdfeat)
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elif simType=="typeb":
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pass
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else:
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simi_mean, simi_1, simi_2 = calsimi_vs_stdfeat_new(event, stdfeat)
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## 在event.front_feats和event.back_feats同时为空时，此处不需要保护
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# if simi_mean==None:
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@ -376,6 +203,10 @@ def one2SN_pr(evtList, evtDict, stdDict):
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ax.set_xlabel(f"Event Num: {len(tp_events) + len(fn_events)}")
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ax.legend()
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plt.show()
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rltpath = os.path.join(similPath, f'pr_1toSN_{simType}.png')
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plt.savefig(rltpath)
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## ============================= 1:N 展厅 直方图'''
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fig, axes = plt.subplots(2, 2)
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axes[0, 0].hist(tp_simi, bins=60, range=(-0.2, 1), edgecolor='black')
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@ -391,11 +222,14 @@ def one2SN_pr(evtList, evtDict, stdDict):
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axes[1, 1].set_xlim([-0.2, 1])
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axes[1, 1].set_title(f'FN({len(fn_simi)})')
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plt.show()
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rltpath = os.path.join(similPath, f'hist_1toSN_{simType}.png')
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plt.savefig(rltpath)
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def one2one_simi(evtList, evtDict, stdDict):
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def one2one_simi(evtList, evtDict, stdDict, simType):
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barcodes = set([bcd for _, bcd in evtList])
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'''======1 构造 3 个事件对: 扫 A 放 A, 扫 A 放 B, 合并 ===================='''
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@ -421,31 +255,50 @@ def one2one_simi(evtList, evtDict, stdDict):
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continue
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stdfeat = stdDict[stdbcd] # float32
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simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat)
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if simType=="typea":
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simi_mean, simi_1, simi_2 = calsimi_vs_stdfeat_new(event, stdfeat)
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elif simType=="typeb":
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pass
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else:
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simi_mean, simi_1, simi_2 = calsimi_vs_stdfeat(event, stdfeat)
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if simi_mean is None:
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continue
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rltdata.append((label, stdbcd, evtname, simi_mean, simi_max, simi_mfeat))
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rltdata.append((label, stdbcd, evtname, simi_mean, simi_1, simi_2))
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'''================ float32、16、int8 精度比较与存储 ============='''
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# data_precision_compare(stdfeat, evtfeat, mergePairs[i], save=True)
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# data_precision_compare(stdfeat, evtfeat, mergePairs[i], similPath, save=True)
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errorFile_one2one = list(set(errorFile_one2one))
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return rltdata, errorFile_one2one
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def one2one_pr(evtList, evtDict, stdDict):
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def one2one_pr(evtList, evtDict, stdDict, simType="simple"):
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rltdata, errorFile_one2one = one2one_simi(evtList, evtDict, stdDict)
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rltdata, errorFile_one2one = one2one_simi(evtList, evtDict, stdDict, simType)
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Same, Cross = [], []
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for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata:
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if label == "same":
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if simType=="simple" and label == "same":
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Same.append(simi_max)
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if label == "diff":
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if simType=="simple" and label == "diff":
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Cross.append(simi_max)
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if simType=="typea" and label == "same":
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Same.append(simi_mean)
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if simType=="typea" and label == "diff":
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Cross.append(simi_mean)
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# for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata:
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# if label == "same":
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# Same.append(simi_mean)
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# if label == "diff":
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# Cross.append(simi_mean)
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Same = np.array(Same)
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Cross = np.array(Cross)
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@ -508,7 +361,7 @@ def one2one_pr(evtList, evtDict, stdDict):
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ax.legend()
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plt.show()
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rltpath = os.path.join(similPath, 'pr.png')
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rltpath = os.path.join(similPath, f'pr_1to1_{simType}.png')
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plt.savefig(rltpath) # svg, png, pdf
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@ -521,7 +374,7 @@ def one2one_pr(evtList, evtDict, stdDict):
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axes[1].set_xlim([-0.2, 1])
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axes[1].set_title(f'TN({len(Cross)})')
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rltpath = os.path.join(similPath, 'hist.png')
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rltpath = os.path.join(similPath, f'hist_1to1_{simType}.png')
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plt.savefig(rltpath)
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@ -529,158 +382,25 @@ def one2one_pr(evtList, evtDict, stdDict):
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def gen_eventdict(sourcePath, saveimg=True):
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k, errEvents = 0, []
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for source_path in sourcePath:
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evtpath, bname = os.path.split(source_path)
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## 兼容事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出
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if os.path.isfile(source_path):
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bname, ext = os.path.splitext(bname)
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# evt = bname.split("_")
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evt = bname.split('_')
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condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
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if not condt: continue
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# 如果已完成事件生成，则不执行
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pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
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if os.path.isfile(pickpath): continue
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try:
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event = ShoppingEvent(source_path, stype=source_type)
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# save_data(event, resultPath)
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with open(pickpath, 'wb') as f:
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pickle.dump(event, f)
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print(bname)
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except Exception as e:
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errEvents.append(source_path)
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print(e)
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# k += 1
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# if k==1:
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# break
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errfile = os.path.join(resultPath, 'error_events.txt')
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# with open(errfile, 'w', encoding='utf-8') as f:
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# for line in errEvents:
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# f.write(line + '\n')
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# def init_std_evt_dict():
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# '''==== 0. 生成事件列表和对应的 Barcodes列表 ==========='''
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# bcdList, event_spath = [], []
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# for evtname in os.listdir(eventSourcePath):
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# bname, ext = os.path.splitext(evtname)
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# ## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出
|
||||
# fpath = os.path.join(eventSourcePath, evtname)
|
||||
# if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"):
|
||||
# evt = bname.split('_')
|
||||
# elif os.path.isdir(fpath):
|
||||
# evt = evtname.split('_')
|
||||
# else:
|
||||
# continue
|
||||
|
||||
# if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
|
||||
# bcdList.append(evt[-1])
|
||||
# event_spath.append(fpath)
|
||||
|
||||
# '''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ==========='''
|
||||
# bcdSet = set(bcdList)
|
||||
# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
|
||||
# print("stdFeats have generated and saved!")
|
||||
|
||||
# '''==== 2. 生成事件字典, 只需运行一次 ==============='''
|
||||
# gen_eventdict(event_spath)
|
||||
# print("eventList have generated and saved!")
|
||||
|
||||
def get_evtList():
|
||||
|
||||
'''==== 0. 生成事件列表和对应的 Barcodes 集合 ==========='''
|
||||
bcdList, evtpaths = [], []
|
||||
for evtname in os.listdir(eventSourcePath):
|
||||
bname, ext = os.path.splitext(evtname)
|
||||
|
||||
## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出
|
||||
fpath = os.path.join(eventSourcePath, evtname)
|
||||
if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"):
|
||||
evt = bname.split('_')
|
||||
elif os.path.isdir(fpath):
|
||||
evt = evtname.split('_')
|
||||
else:
|
||||
continue
|
||||
|
||||
if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
|
||||
bcdList.append(evt[-1])
|
||||
evtpaths.append(fpath)
|
||||
|
||||
bcdSet = set(bcdList)
|
||||
|
||||
return evtpaths, bcdSet
|
||||
|
||||
|
||||
|
||||
# def init_stdDict():
|
||||
# evtpaths, bcdSet = get_evtList()
|
||||
# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
|
||||
# print("stdFeats have generated and saved!")
|
||||
|
||||
|
||||
# def init_evtDict():
|
||||
# '''==== 0. 生成事件列表和对应的 Barcodes列表 ==========='''
|
||||
# bcdList, event_spath = [], []
|
||||
# for evtname in os.listdir(eventSourcePath):
|
||||
# bname, ext = os.path.splitext(evtname)
|
||||
|
||||
# ## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出
|
||||
# fpath = os.path.join(eventSourcePath, evtname)
|
||||
# if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"):
|
||||
# evt = bname.split('_')
|
||||
# elif os.path.isdir(fpath):
|
||||
# evt = evtname.split('_')
|
||||
# else:
|
||||
# continue
|
||||
|
||||
# if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
|
||||
# bcdList.append(evt[-1])
|
||||
# event_spath.append(fpath)
|
||||
|
||||
# '''==== 2. 生成事件字典, 只需运行一次 ==============='''
|
||||
# gen_eventdict(event_spath)
|
||||
# print("eventList have generated and saved!")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def test_one2one_one2SN():
|
||||
def test_one2one_one2SN(simType):
|
||||
'''1:1性能评估'''
|
||||
|
||||
# evtpaths, bcdSet = get_evtList()
|
||||
# evtpaths, bcdSet = get_evtList(eventSourcePath)
|
||||
|
||||
'''=== 1. 只需运行一次，生成事件对应的标准特征库字典,如已生成，无需运行 ===='''
|
||||
# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, eventSourcePath)
|
||||
|
||||
'''==== 2. 生成事件字典, 只需运行一次 ===================='''
|
||||
|
||||
# date_ = ['2025-3-4_1', '2025-3-5_1', '2025-3-5_2']
|
||||
# for dt in date_:
|
||||
# evtpaths = os.path.join(eventSourcePath, dt)
|
||||
# init_eventDict(evtpaths, eventDataPath, source_type)
|
||||
|
||||
init_eventDict(eventSourcePath, eventDataPath, source_type)
|
||||
|
||||
|
||||
# init_eventDict(eventSourcePath, eventDataPath, source_type)
|
||||
|
||||
'''==== 2. 基于事件barcode集和标准库barcode交集构造事件集合 ========='''
|
||||
'''==== 3. 基于事件barcode集和标准库barcode交集构造事件集合 ========='''
|
||||
evtList, evtDict, stdDict = build_std_evt_dict()
|
||||
|
||||
one2one_pr(evtList, evtDict, stdDict)
|
||||
one2one_pr(evtList, evtDict, stdDict, simType)
|
||||
|
||||
one2SN_pr(evtList, evtDict, stdDict)
|
||||
one2SN_pr(evtList, evtDict, stdDict, simType)
|
||||
|
||||
if __name__ == '__main__':
|
||||
'''
|
||||
@ -694,21 +414,10 @@ if __name__ == '__main__':
|
||||
(7) similPath: 1:1比对结果存储地址(事件级)，在resultPath下
|
||||
'''
|
||||
|
||||
# stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v1.0\比对数据\整理\zhantingBase"
|
||||
# stdBarcodePath = r"D:\exhibition\dataset\bcdpath"
|
||||
# stdFeaturePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\features_json\v11_barcode_11592"
|
||||
stdSamplePath = "/home/wqg/dataset/total_barcode/totalBarcode"
|
||||
stdBarcodePath = "/home/wqg/dataset/total_barcode/bcdpath"
|
||||
stdFeaturePath = "/home/wqg/dataset/test_dataset/total_barcode/features_json/v11_barcode_0304/"
|
||||
|
||||
# eventSourcePath = r'D:\exhibition\images\20241202'
|
||||
# eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1129_展厅模型v801测试组测试"
|
||||
|
||||
# stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v2.0_abroad\比对数据\all_base_二筛"
|
||||
# stdBarcodePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对测试数据20250121_testing\bcdpath"
|
||||
# stdFeaturePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对测试数据20250121_testing\stdfeats"
|
||||
|
||||
stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\totalBarcode"
|
||||
stdBarcodePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing\bcdpath"
|
||||
stdFeaturePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\features_json\v11_barcode_0304"
|
||||
|
||||
if not os.path.exists(stdBarcodePath):
|
||||
os.makedirs(stdBarcodePath)
|
||||
if not os.path.exists(stdFeaturePath):
|
||||
@ -719,18 +428,24 @@ if __name__ == '__main__':
|
||||
"data": 基于事件切分的原 data 文件版本
|
||||
"realtime": 全实时生成的 data 文件
|
||||
'''
|
||||
source_type = 'realtime' # 'source', 'data', 'realtime'
|
||||
eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\基准数据集\2025-3-4_1"
|
||||
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing"
|
||||
source_type = 'source' # 'source', 'data', 'realtime'
|
||||
simType = "typea" # "simple", "typea", "typeb"
|
||||
|
||||
eventDataPath = os.path.join(resultPath, "evtobjs_0304_1")
|
||||
similPath = os.path.join(resultPath, "simidata_0304_1")
|
||||
evttype = "single_event_V10"
|
||||
# evttype = "single_event_V5"
|
||||
# evttype = "performence_V10"
|
||||
# evttype = "performence_V5"
|
||||
eventSourcePath = "/home/wqg/dataset/pipeline/yrt/{}/shopping_pkl".format(evttype)
|
||||
|
||||
resultPath = "/home/wqg/dataset/pipeline/contrast/{}".format(evttype)
|
||||
eventDataPath = os.path.join(resultPath, "evtobjs")
|
||||
similPath = os.path.join(resultPath, "simidata")
|
||||
if not os.path.exists(eventDataPath):
|
||||
os.makedirs(eventDataPath)
|
||||
if not os.path.exists(similPath):
|
||||
os.makedirs(similPath)
|
||||
|
||||
test_one2one_one2SN()
|
||||
test_one2one_one2SN(simType)
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user