1:n modified
This commit is contained in:
王庆刚
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@ -61,9 +61,9 @@ class Config:
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test_val = "D:/比对/cl"
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# test_val = "./data/test_data_100"
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# test_model = "checkpoints/best_resnet18_v12.pth"
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test_model = "checkpoints/best_20250228.pth"
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# test_model = "checkpoints/zhanting_res_801.pth"
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test_model = "checkpoints/zhanting_res_abroad_8021.pth"
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# test_model = "checkpoints/zhanting_res_abroad_8021.pth"
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58
contrast/feat_infer.py
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58
contrast/feat_infer.py
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@ -0,0 +1,58 @@
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Feb 28 16:27:17 2025
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@author: ym
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"""
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import os
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import time
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import pickle
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import numpy as np
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from PIL import Image
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from scipy.spatial.distance import cdist
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from feat_extract.config import config as conf
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from feat_extract.inference import FeatsInterface #, inference_image
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Encoder = FeatsInterface(conf)
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def main():
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imgpaths = r"D:\全实时\202502\result\Yolos_Tracking\20250228-160049-188_6921168558018_6921168558018\a"
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featDict = {}
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imgs, imgfiles = [], []
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for filename in os.listdir(imgpaths):
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file, ext = os.path.splitext(filename)
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imgpath = os.path.join(imgpaths, filename)
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img = Image.open(imgpath)
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imgs.append(img)
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imgfiles.append(filename)
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feature = Encoder.inference([img])
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feature /= np.linalg.norm(feature, axis=1)[:, None]
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feature_ft32 = feature.astype(np.float32)
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featDict[file] = feature_ft32
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feature = Encoder.inference(imgs)
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feature /= np.linalg.norm(feature, axis=1)[:, None]
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feature_ft32 = feature.astype(np.float32)
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matrix = 1 - cdist(feature, feature, 'cosine')
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print("do")
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if __name__ == '__main__':
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main()
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@ -6,6 +6,7 @@ Created on Wed Dec 18 11:49:01 2024
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"""
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import os
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import pickle
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import copy
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import numpy as np
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from pathlib import Path
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import matplotlib.pyplot as plt
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@ -17,44 +18,47 @@ def init_eventdict(sourcePath, stype="data"):
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'''stype: str,
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'source': 由 videos 或 images 生成的 pickle 文件
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'data': 从 data 文件中读取的现场运行数据
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"realtime": 全实时数据，从 data 文件中读取的现场运行数据
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'''
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k, errEvents = 0, []
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for bname in os.listdir(sourcePath):
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# bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
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source_path = os.path.join(sourcePath, bname)
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if stype=="data" or stype=="realtime":
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if stype=="source" and not os.path.isfile(source_path): continue
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if stype=="data" and os.path.isfile(source_path): continue
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if stype=="realtime" and os.path.isfile(source_path): continue
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if os.path.isdir(source_path):
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pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
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if not os.path.isdir(source_path) or os.path.isfile(pickpath):
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continue
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if stype=="source":
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else:
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pickpath = os.path.join(eventDataPath, bname)
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if not os.path.isfile(source_path) or os.path.isfile(pickpath):
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continue
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if os.path.isfile(pickpath):
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continue
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evt = os.path.splitext(os.path.split(pickpath)[-1])[0].split('_')
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cont = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
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if not cont:
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continue
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# event = ShoppingEvent(source_path, stype)
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try:
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event = ShoppingEvent(source_path, stype)
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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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print(f"Error: {bname}, {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, 'a', 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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errfile = os.path.join(resultPath, 'error_events.txt')
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with open(errfile, 'a', 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 read_eventdict(eventDataPath):
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evtDict = {}
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@ -70,38 +74,38 @@ def read_eventdict(eventDataPath):
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return evtDict
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def simi_calc(event, o2nevt, typee=None):
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if typee == "11":
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boxes1 = event.front_boxes
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boxes2 = o2nevt.front_boxes
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def simi_calc(event, o2nevt, pattern, typee=None):
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if pattern==1 or pattern==2:
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if typee == "11":
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boxes1 = event.front_boxes
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boxes2 = o2nevt.front_boxes
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feat1 = event.front_feats
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feat2 = o2nevt.front_feats
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if typee == "10":
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boxes1 = event.front_boxes
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boxes2 = o2nevt.back_boxes
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feat1 = event.front_feats
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feat2 = o2nevt.front_feats
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if typee == "10":
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boxes1 = event.front_boxes
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boxes2 = o2nevt.back_boxes
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feat1 = event.front_feats
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feat2 = o2nevt.back_feats
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if typee == "00":
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boxes1 = event.back_boxes
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boxes2 = o2nevt.back_boxes
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feat1 = event.front_feats
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feat2 = o2nevt.back_feats
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if typee == "00":
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boxes1 = event.back_boxes
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boxes2 = o2nevt.back_boxes
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feat1 = event.back_feats
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feat2 = o2nevt.back_feats
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if typee == "01":
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boxes1 = event.back_boxes
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boxes2 = o2nevt.front_boxes
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feat1 = event.back_feats
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feat2 = o2nevt.back_feats
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if typee == "01":
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boxes1 = event.back_boxes
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boxes2 = o2nevt.front_boxes
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feat1 = event.back_feats
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feat2 = o2nevt.front_feats
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feat1 = event.back_feats
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feat2 = o2nevt.front_feats
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'''自定义事件特征选择'''
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if typee==3 and len(event.feats_compose) and len(o2nevt.feats_compose):
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if pattern==3 and len(event.feats_compose) and len(o2nevt.feats_compose):
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feat1 = [event.feats_compose]
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feat2 = [o2nevt.feats_compose]
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if len(feat1) and len(feat2):
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matrix = 1 - cdist(feat1[0], feat2[0], 'cosine')
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simi = np.mean(matrix)
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@ -114,48 +118,51 @@ def one2n_pr(evtDicts, pattern=1):
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'''
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pattern:
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1: process.data 中记录的相似度
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2: 根据 process.data 中标记的 type 选择特征计算相似度
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3: 以其它方式选择特征计算相似度
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2: 根据 process.data 中标记的 type 选择特征组合方式计算相似度
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3: 利用 process.data 中的轨迹特征，以其它方式计算相似度
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'''
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tpevents, fnevents, fpevents, tnevents = [], [], [], []
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tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], []
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one2nFile, errorFile_one2n = [], []
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errorFile_one2n_ = []
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evts_output = []
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for evtname, event in evtDicts.items():
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evt_names, evt_barcodes, evt_similars, evt_types = [], [], [], []
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if len(event.barcode)==0:
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if len(event.one2n)==0 or len(event.barcode)==0:
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continue
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evts_output.append(evtname)
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for ndict in event.one2n:
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nname = ndict["event"]
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barcode = ndict["barcode"]
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similar = ndict["similar"]
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typee = ndict["type"].strip()
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if len(barcode)==0:
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continue
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if typee.find(",") >=0:
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typee = typee.split(",")[-1]
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if pattern==1:
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evt_similars.append(similar)
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if pattern==2 or pattern==3:
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o2n_evt = [evt for name, evt in evtDicts.items() if name.find(nname[:15])==0]
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if len(o2n_evt)!=1:
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continue
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simival = simi_calc(event, o2n_evt[0], pattern, typee)
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if simival==None:
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continue
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evt_similars.append(simival)
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evt_names.append(nname)
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evt_barcodes.append(barcode)
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evt_types.append(typee)
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if pattern==1:
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evt_similars.append(similar)
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if pattern==2 or pattern==3:
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o2n_evt = [evt for name, evt in evtDicts.items() if name.find(nname[:15])==0]
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if len(o2n_evt)==1:
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o2nevt = o2n_evt[0]
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else:
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continue
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if pattern==2:
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simival = simi_calc(event, o2nevt, typee)
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if pattern==3:
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simival = simi_calc(event, o2nevt, typee=pattern)
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if simival==None:
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continue
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evt_similars.append(simival)
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# if evtname == "20250226-170321-327_6903244678377":
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# print("evtname")
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## process.data的oneTon的各项中，均不包括当前事件的barcode
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if event.barcode not in evt_barcodes:
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@ -164,14 +171,11 @@ def one2n_pr(evtDicts, pattern=1):
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else:
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one2nFile.append(evtname)
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if len(evt_names)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \
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and len(evt_similars)==len(evt_types) and len(evt_names)>0:
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if len(evt_names)==len(evt_barcodes)==len(evt_similars)==len(evt_types) and len(evt_names)>0:
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# maxsim = evt_similars[evt_similars.index(max(evt_similars))]
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maxsim = max(evt_similars)
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for i in range(len(evt_names)):
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bcd, simi = evt_barcodes[i], evt_similars[i]
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if bcd==event.barcode and simi==maxsim:
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tpsimi.append(simi)
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tpevents.append(evtname)
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@ -185,14 +189,11 @@ def one2n_pr(evtDicts, pattern=1):
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fpsimi.append(simi)
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fpevents.append(evtname)
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else:
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errorFile_one2n.append(evtname)
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errorFile_one2n_.append(evtname)
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''' 1:n 数据存储，需根据相似度排序'''
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PPrecise, PRecall = [], []
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NPrecise, NRecall = [], []
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Thresh = np.linspace(-0.2, 1, 100)
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for th in Thresh:
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'''============================= 1:n 计算'''
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@ -202,9 +203,9 @@ def one2n_pr(evtDicts, pattern=1):
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TN = sum(np.array(tnsimi) < th)
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PPrecise.append(TP/(TP+FP+1e-6))
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PRecall.append(TP/(len(one2nFile)+1e-6))
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PRecall.append(TP/(TP+FN+1e-6))
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NPrecise.append(TN/(TN+FN+1e-6))
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NRecall.append(TN/(len(tnsimi)+len(fpsimi)+1e-6))
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NRecall.append(TN/(TN+FP+1e-6))
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'''4. ============================= 1:n 曲线,'''
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@ -239,10 +240,8 @@ def one2n_pr(evtDicts, pattern=1):
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return fpevents
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def main():
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'''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 '''
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init_eventdict(eventSourcePath, stype="source") # 'source', 'data', 'realtime'
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init_eventdict(eventSourcePath, stype="realtime") # 'source', 'data', 'realtime'
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# for pfile in os.listdir(eventDataPath):
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# evt = os.path.splitext(pfile)[0].split('_')
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@ -250,13 +249,12 @@ def main():
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# if not cont:
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# continue
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'''2. 读取事件字典 '''
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evtDicts = read_eventdict(eventDataPath)
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'''3. 1:n 比对事件评估 '''
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fpevents = one2n_pr(evtDicts, pattern=2)
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fpevents = one2n_pr(evtDicts, pattern=1)
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fpErrFile = str(Path(resultPath).joinpath("one2n_fp_Error.txt"))
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with open(fpErrFile, "w") as file:
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@ -266,10 +264,10 @@ def main():
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if __name__ == '__main__':
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eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\result_V12\ShoppingDict_pkfile"
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eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-27"
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resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing"
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eventDataPath = os.path.join(resultPath, "evtobjs_data")
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eventDataPath = os.path.join(resultPath, "evtobjs_wang")
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if not os.path.exists(eventDataPath):
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os.makedirs(eventDataPath)
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@ -507,7 +507,7 @@ def contrast_pr(paths):
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if __name__ == "__main__":
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evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-21\比对\video"
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evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-26_2"
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contrast_pr(evtpaths)
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@ -266,16 +266,16 @@ def main():
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函数：pipeline()，遍历事件文件夹，选择类型 image 或 video,
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'''
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parmDict = {}
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evtdir = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-21\比对\video"
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evtdir = r"D:\全实时\202502"
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parmDict["SourceType"] = "video" # video, image
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parmDict["savepath"] = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\result_V12"
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parmDict["savepath"] = r"D:\全实时\202502\result"
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parmDict["weights"] = r'D:\DetectTracking\ckpts\best_cls10_0906.pt'
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evtdir = Path(evtdir)
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k, errEvents = 0, []
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for item in evtdir.iterdir():
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if item.is_dir():
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item = evtdir/Path("20250221-160936-893_6942506204855_6942506204855")
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item = evtdir/Path("20250228-160049-188_6921168558018_6921168558018")
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parmDict["eventpath"] = item
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# pipeline(**parmDict)
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@ -279,7 +279,7 @@ def yolo_resnet_tracker(
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color = colors(int(id), True)
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else:
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color = colors(19, True) # 19为调色板的最后一个元素
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annotator.box_label(xyxy, label, color=color)
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# annotator.box_label(xyxy, label, color=color)
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'''====== Save results (image and video) ======'''
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# save_path = str(save_dir / Path(path).name) # 带有后缀名
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@ -24,6 +24,8 @@ from dotrack.dotracks_back import doBackTracks
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from dotrack.dotracks_front import doFrontTracks
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from utils.drawtracks import draw5points, drawTrack, drawtracefeat, plot_frameID_y2, drawFeatures, draw_all_trajectories
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from utils.read_data import extract_data_realtime, read_tracking_output_realtime
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# from datetime import datetime
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# from utils.proBoxes import boxes_add_fid
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# from utils.plotting import boxing_img #, Annotator, colors,
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@ -80,20 +82,38 @@ def save_subimgs(vts, file, TracksDict):
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cv2.imwrite(str(imgdir) + f"/{tid}_{fid}_{bid}.png", img)
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def have_tracked():
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trackdict = r'./data/trackdicts'
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alltracks = []
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# trackdict = r'./data/trackdicts'
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||||
trackdict = r'D:\全实时\202502\20250228-152846-438_6901668934727_6901668934727'
|
||||
|
||||
bboxes, alltracks = [], []
|
||||
k = 0
|
||||
gt = Profile()
|
||||
for filename in os.listdir(trackdict):
|
||||
filename = '153112511_0_seek_105.pkl'
|
||||
# filename = '153112511_0_seek_105.pkl'
|
||||
|
||||
file, ext = os.path.splitext(filename)
|
||||
filepath = os.path.join(trackdict, filename)
|
||||
TracksDict = np.load(filepath, allow_pickle=True)
|
||||
|
||||
bboxes = TracksDict['TrackBoxes']
|
||||
if file.split('_')[0]=='0' or file.find("back") >= 0:
|
||||
CamerType = "back"
|
||||
if file.split('_')[0]=='1' or file.find("front") >= 0:
|
||||
CamerType = "front"
|
||||
|
||||
## 1. 加载 tracker 输出的 pickle 文件
|
||||
if ext in ['.pkl', '.pickle']:
|
||||
filepath = os.path.join(trackdict, filename)
|
||||
TracksDict = np.load(filepath, allow_pickle=True)
|
||||
bboxes = TracksDict['TrackBoxes']
|
||||
|
||||
## 2. 加载 data 文件
|
||||
if filename.find('tracker.data')>0:
|
||||
bboxes, TracksDict = extract_data_realtime(filepath)
|
||||
|
||||
if len(bboxes)==0:
|
||||
continue
|
||||
|
||||
with gt:
|
||||
if filename.find("front") >= 0:
|
||||
if CamerType == "front":
|
||||
vts = doFrontTracks(bboxes, TracksDict)
|
||||
|
||||
Intrude = vts.isintrude()
|
||||
@ -112,7 +132,7 @@ def have_tracked():
|
||||
edgeline = cv2.imread("./shopcart/cart_tempt/board_ftmp_line.png")
|
||||
img_tracking = draw_all_trajectories(vts, edgeline, save_dir, file, draw5p=True)
|
||||
|
||||
else:
|
||||
if CamerType == "back":
|
||||
vts = doBackTracks(bboxes, TracksDict)
|
||||
|
||||
Intrude = vts.isintrude()
|
||||
|
||||
Binary file not shown.
@ -205,10 +205,13 @@ def extract_data_realtime(datapath):
|
||||
if line.endswith(','):
|
||||
line = line[:-1]
|
||||
ftlist = [float(x) for x in line.split()]
|
||||
if len(ftlist) != 265: continue
|
||||
|
||||
if len(ftlist) != 265:
|
||||
continue
|
||||
boxes.append(ftlist[:9])
|
||||
feats.append(ftlist[9:])
|
||||
|
||||
|
||||
trackerboxes = np.array(boxes)
|
||||
trackerfeats = np.array(feats)
|
||||
|
||||
@ -381,14 +384,21 @@ def read_similar(filePath):
|
||||
if Flag_1ton:
|
||||
label = line.split(':')[0].strip()
|
||||
value = line.split(':')[1].strip()
|
||||
Dict['barcode'] = ''
|
||||
if label.find("_") > 0:
|
||||
bcd = label.split('_')[-1]
|
||||
if len(bcd)>=10 and bcd.isdigit():
|
||||
Dict['barcode'] = bcd
|
||||
|
||||
|
||||
bcd = label.split('_')[-1]
|
||||
if len(bcd)<8: continue
|
||||
|
||||
Dict['event'] = label
|
||||
Dict['barcode'] = bcd
|
||||
Dict['similar'] = float(value.split(',')[0])
|
||||
Dict['type'] = value.split(',')[1]
|
||||
|
||||
if value.find("=")>0:
|
||||
Dict['type'] = value.split('=')[-1]
|
||||
else:
|
||||
Dict['type'] = value.split(',')[-1]
|
||||
one2n_list.append(Dict)
|
||||
|
||||
if len(one2one_list): SimiDict['one2one'] = one2one_list
|
||||
|
||||
Reference in New Issue
Block a user