350 lines
12 KiB
Python
350 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Mon Dec 16 18:56:18 2024
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@author: ym
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"""
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import os
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import cv2
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import json
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib import rcParams
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from matplotlib.font_manager import FontProperties
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from scipy.spatial.distance import cdist
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from utils.event import ShoppingEvent, save_data
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rcParams['font.sans-serif'] = ['SimHei'] # 用黑体显示中文
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rcParams['axes.unicode_minus'] = False # 正确显示负号
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'''*********** USearch ***********'''
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def read_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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return stdlib
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def get_eventlist():
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'''
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读取一次测试中的错误事件
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'''
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evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
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text1 = "one2n_Error.txt"
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text2 = "one2SN_Error.txt"
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events = []
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text = (text1, text2)
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for txt in text:
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txtfile = os.path.join(evtpaths, txt)
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with open(txtfile, "r") as f:
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lines = f.readlines()
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for i, line in enumerate(lines):
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line = line.strip()
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if line:
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fpath=os.path.join(evtpaths, line)
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events.append(fpath)
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events = list(set(events))
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return events
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def single_event():
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events = get_eventlist()
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'''定义当前事件存储地址及生成相应文件件'''
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resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\single_event"
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for evtpath in events:
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event = ShoppingEvent(evtpath)
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save_data(event, resultPath)
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print(event.evtname)
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def get_topk_percent(data, k):
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"""
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获取数据中最大的 k% 的元素
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"""
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# 将数据转换为 NumPy 数组
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if isinstance(data, list):
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data = np.array(data)
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percentile = np.percentile(data, 100-k)
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top_k_percent = data[data >= percentile]
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return top_k_percent
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def cluster(data, thresh=0.15):
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# data = np.array([0.1, 0.13, 0.7, 0.2, 0.8, 0.52, 0.3, 0.7, 0.85, 0.58])
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# data = np.array([0.1, 0.13, 0.2, 0.3])
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# data = np.array([0.1])
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if isinstance(data, list):
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data = np.array(data)
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data1 = np.sort(data)
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cluter, Cluters, = [data1[0]], []
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for i in range(1, len(data1)):
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if data1[i] - data1[i-1]< thresh:
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cluter.append(data1[i])
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else:
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Cluters.append(cluter)
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cluter = [data1[i]]
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Cluters.append(cluter)
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clt_center = []
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for clt in Cluters:
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## 是否应该在此处限制一个聚类中的最小轨迹样本数，应该将该因素放在轨迹分析中
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# if len(clt)>=3:
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# clt_center.append(np.mean(clt))
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clt_center.append(np.mean(clt))
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# print(clt_center)
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return clt_center
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def calc_simil(event, stdfeat):
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def calsiml(feat1, feat2):
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'''轨迹样本和标准特征集样本相似度的选择策略'''
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matrix = 1 - cdist(feat1, feat2, 'cosine')
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simi_max = []
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for i in range(len(matrix)):
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sim = np.mean(get_topk_percent(matrix[i, :], 75))
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simi_max.append(sim)
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cltc_max = cluster(simi_max)
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Simi = max(cltc_max)
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## cltc_max为空属于编程考虑不周，应予以排查解决
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# if len(cltc_max):
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# Simi = max(cltc_max)
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# else:
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# Simi = 0 #不应该走到该处
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return Simi
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front_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容
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front_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
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for i in range(len(event.front_boxes)):
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front_boxes = np.concatenate((front_boxes, event.front_boxes[i]), axis=0)
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front_feats = np.concatenate((front_feats, event.front_feats[i]), axis=0)
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back_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容
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back_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
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for i in range(len(event.back_boxes)):
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back_boxes = np.concatenate((back_boxes, event.back_boxes[i]), axis=0)
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back_feats = np.concatenate((back_feats, event.back_feats[i]), axis=0)
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if len(front_feats):
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front_simi = calsiml(front_feats, stdfeat)
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if len(back_feats):
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back_simi = calsiml(back_feats, stdfeat)
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'''前后摄相似度融合策略'''
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if len(front_feats) and len(back_feats):
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diff_simi = abs(front_simi - back_simi)
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if diff_simi>0.15:
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Similar = max([front_simi, back_simi])
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else:
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Similar = (front_simi+back_simi)/2
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elif len(front_feats) and len(back_feats)==0:
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Similar = front_simi
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elif len(front_feats)==0 and len(back_feats):
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Similar = back_simi
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else:
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Similar = None # 在event.front_feats和event.back_feats同时为空时
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return Similar
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def simi_matrix():
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resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\single_event"
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stdlib = read_usearch()
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events = get_eventlist()
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for evtpath in events:
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evtname = os.path.basename(evtpath)
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_, barcode = evtname.split("_")
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# 生成事件与相应标准特征集
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event = ShoppingEvent(evtpath)
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stdfeat = stdlib[barcode]
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Similar = calc_simil(event, stdfeat)
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# 构造 boxes 子图存储路径
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subimgpath = os.path.join(resultPath, f"{event.evtname}", "subimg")
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if not os.path.exists(subimgpath):
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os.makedirs(subimgpath)
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histpath = os.path.join(resultPath, "simi_hist")
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if not os.path.exists(histpath):
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os.makedirs(histpath)
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mean_values, max_values = [], []
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cameras = ('front', 'back')
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fig, ax = plt.subplots(2, 3, figsize=(16, 9), dpi=100)
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kpercent = 25
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for camera in cameras:
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boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容
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evtfeat = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
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if camera == 'front':
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for i in range(len(event.front_boxes)):
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boxes = np.concatenate((boxes, event.front_boxes[i]), axis=0)
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evtfeat = np.concatenate((evtfeat, event.front_feats[i]), axis=0)
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imgpaths = event.front_imgpaths
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else:
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for i in range(len(event.back_boxes)):
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boxes = np.concatenate((boxes, event.back_boxes[i]), axis=0)
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evtfeat = np.concatenate((evtfeat, event.back_feats[i]), axis=0)
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imgpaths = event.back_imgpaths
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assert len(boxes)==len(evtfeat), f"Please check the Event: {evtname}"
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if len(boxes)==0: continue
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print(evtname)
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matrix = 1 - cdist(evtfeat, stdfeat, 'cosine')
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simi_1d = matrix.flatten()
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simi_mean = np.mean(matrix, axis=1)
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# simi_max = np.max(matrix, axis=1)
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'''以相似度矩阵每一行最大的 k% 的相似度做均值计算'''
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simi_max = []
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for i in range(len(matrix)):
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sim = np.mean(get_topk_percent(matrix[i, :], kpercent))
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simi_max.append(sim)
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mean_values.append(np.mean(matrix))
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max_values.append(np.mean(simi_max))
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diff_max_mean = np.mean(simi_max) - np.mean(matrix)
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'''相似度统计特性图示'''
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k =0
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if camera == 'front': k = 1
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'''********************* 相似度全体数据 *********************'''
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ax[k, 0].hist(simi_1d, bins=60, range=(-0.2, 1), edgecolor='black')
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ax[k, 0].set_xlim([-0.2, 1])
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ax[k, 0].set_title(camera)
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_, y_max = ax[k, 0].get_ylim() # 获取y轴范围
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'''相似度变动范围'''
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ax[k, 0].text(-0.1, 0.15*y_max, f"rng:{max(simi_1d)-min(simi_1d):.3f}", fontsize=18, color='b')
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'''********************* 均值********************************'''
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ax[k, 1].hist(simi_mean, bins=24, range=(-0.2, 1), edgecolor='black')
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ax[k, 1].set_xlim([-0.2, 1])
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ax[k, 1].set_title("mean")
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_, y_max = ax[k, 1].get_ylim() # 获取y轴范围
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'''相似度变动范围'''
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ax[k, 1].text(-0.1, 0.15*y_max, f"rng:{max(simi_mean)-min(simi_mean):.3f}", fontsize=18, color='b')
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'''********************* 最大值 ******************************'''
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ax[k, 2].hist(simi_max, bins=24, range=(-0.2, 1), edgecolor='black')
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ax[k, 2].set_xlim([-0.2, 1])
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ax[k, 2].set_title("max")
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_, y_max = ax[k, 2].get_ylim() # 获取y轴范围
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'''相似度变动范围'''
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ax[k, 2].text(-0.1, 0.15*y_max, f"rng:{max(simi_max)-min(simi_max):.3f}", fontsize=18, color='b')
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'''绘制聚类中心'''
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cltc_mean = cluster(simi_mean)
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for value in cltc_mean:
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ax[k, 1].axvline(x=value, color='m', linestyle='--', linewidth=3)
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cltc_max = cluster(simi_max)
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for value in cltc_max:
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ax[k, 2].axvline(x=value, color='m', linestyle='--', linewidth=3)
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'''绘制相似度均值与最大值均值'''
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ax[k, 1].axvline(x=np.mean(matrix), color='r', linestyle='-', linewidth=3)
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ax[k, 2].axvline(x=np.mean(simi_max), color='g', linestyle='-', linewidth=3)
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'''绘制相似度最大值均值 - 均值'''
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_, y_max = ax[k, 2].get_ylim() # 获取y轴范围
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ax[k, 2].text(-0.1, 0.05*y_max, f"g-r={diff_max_mean:.3f}", fontsize=18, color='m')
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plt.show()
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# for i, box in enumerate(boxes):
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# x1, y1, x2, y2, tid, score, cls, fid, bid = box
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# imgpath = imgpaths[int(fid-1)]
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# image = cv2.imread(imgpath)
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# subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :]
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# camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_')
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# subimgName = f"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID})_{simi_mean[i]:.3f}.png"
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# imgpairs.append((subimgName, subimg))
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# spath = os.path.join(subimgpath, subimgName)
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# cv2.imwrite(spath, subimg)
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# oldname = f"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID}).png"
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# oldpath = os.path.join(subimgpath, oldname)
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# if os.path.exists(oldpath):
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# os.remove(oldpath)
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if len(mean_values)==2:
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mean_diff = abs(mean_values[1]-mean_values[0])
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ax[0, 1].set_title(f"mean diff: {mean_diff:.3f}")
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if len(max_values)==2:
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max_values = abs(max_values[1]-max_values[0])
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ax[0, 2].set_title(f"max diff: {max_values:.3f}")
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try:
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fig.suptitle(f"Similar: {Similar:.3f}", fontsize=16)
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except Exception as e:
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print(e)
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print(f"Similar: {Similar}")
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pltpath = os.path.join(subimgpath, f"hist_max_{kpercent}%_.png")
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plt.savefig(pltpath)
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pltpath1 = os.path.join(histpath, f"{evtname}_.png")
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plt.savefig(pltpath1)
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plt.close()
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def main():
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simi_matrix()
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if __name__ == "__main__":
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main()
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# cluster()
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