533 lines
19 KiB
Python
533 lines
19 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Wed Oct 16 17:37:07 2024
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@author: ym
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"""
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# import csv
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import os
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# import platform
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# import sys
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from pathlib import Path
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import glob
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import numpy as np
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import copy
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import matplotlib.pyplot as plt
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from collections import OrderedDict
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from event_time_specify import devide_motion_state #, state_measure
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import sys
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sys.path.append(r"D:\DetectTracking")
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from imgs_inference import run_yolo
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from tracking.utils.read_data import read_weight_sensor
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# IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes
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# VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes
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def filesort(p):
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'''
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需将图像文件名标准化
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'''
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files = []
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files.extend(sorted(glob.glob(os.path.join(p, '*.jpg'))))
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# images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
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tamps_0, tamps_1 = [], []
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files_0, files_1 = [], []
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for file in files:
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basename = os.path.basename(file)
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# if basename.find('frameId')<0: continue
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f, ext = os.path.splitext(basename)
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_, camer, tamp = f.split('_')
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if camer == '0':
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tamps_0.append(int(tamp))
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files_0.append(file)
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if camer == '1':
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tamps_1.append(int(tamp))
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files_1.append(file)
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idx0 = sorted(range(len(tamps_0)), key=lambda k: tamps_0[k])
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files0 = [files_0[i] for i in idx0]
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idx1 = sorted(range(len(tamps_1)), key=lambda k: tamps_1[k])
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files1 = [files_1[i] for i in idx1]
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return (files0, files1)
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def rename(filePath, tmin):
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"""
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重命名函数fun1
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输入：文件夹路径
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功能：对文件夹中的全部文件进行随机命名
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"""
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suffix = '.png' # 设置后缀，筛选特定文件以更改名称
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for file in os.listdir(filePath):
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if file.endswith(suffix):
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name = file.split('.')[0]
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tamp = int(name.split('_')[2])-tmin
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suffix = file.split('.')[1]
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newname = name +f'_{int(tamp)}.'+suffix
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os.rename(os.path.join(filePath, file), os.path.join(filePath, newname))
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def rerename(filePath=None):
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"""
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重命名函数fun1
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输入：文件夹路径
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功能：对文件夹中的全部文件进行随机命名
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"""
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suffix = '.png' # 设置后缀，筛选特定文件以更改名称
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for file in os.listdir(filePath):
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if file.endswith(suffix):
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name = file.split('.')[0]
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names = name.split('_')
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if len(names)>=6:
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newname = "_".join(names[0:5])+'.png'
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os.rename(os.path.join(filePath, file), os.path.join(filePath, newname))
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def state_measure(periods, weights, hands, spath=None):
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'''
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数据类型
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后摄: 0, 前摄: 1, CV综合: 2, 重力: 9
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frstate_0/1:
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帧ID 时间戳 单摄状态1 单摄状态2 活动轨迹标记
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0 1 2 3 4:end
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time_stream = np.concatenate((tstream, weights))
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time_stream:
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序列索引号 数据类型 时间戳 单摄状态1/重力值 单摄状态2/重力(t0, t1) CV状态/重力(t0', t1') 综合数据类型 综合状态
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0 1 2 3 4 5 6 7
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单摄状态1：基于运动轨迹的起止点确定的运动区间
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单摄状态2: 基于滑动窗口的起止点(窗口终点)确定的运动区间
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重力(t0, t1): 重力波动的精确时间区间，基于重力波动的起止点，而不是仅依赖重力稳定时间
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重力(t0', t1'): 根据加退购对重力波动窗口进行扩展，扩展应该涵盖购物事件的发生过程
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方案：
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前后摄状态进行或运算
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CV与重力状态进行与运算
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'''
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# BackType = 0 # 后摄数据类型
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# FrontType = 1 # 前摄数据类型
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CameraType = 2 # CV数据综合类型
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HandType = 3 # 手部类型
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WeightType = 9 # 重力数据类型
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WeightStableThresh = 7.5 # 单位：g，重力稳定状态下的最大波动范围
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WeightWinWidth = 10 # 单位：重力数据点数，该值和采样间隔关联，重力稳定时间设定为500ms = WeightWinWidth * 采样间隔
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CameraTimeInterval = 100 # 单位：ms，前后摄状态进行或运算时的时间间隔阈值
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InputFrontNum = 10 # 重力增加（放入）向前延拓的重力数据点数
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InputBackNum = 0 # 重力增加（放入）向后延拓的重力数据点数
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OutputFrontNum = 2 # 重力减少（取出）向前延拓的重力数据点数
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OutputBackNum = 10 # 重力减少（取出）向前延拓的重力数据点数
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CompTimeInterval = 150 # 单位：ms，CV状态和重力状态进行与运算时的时间间隔阈值
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'''==================== 1.1 Weight 数据综合并排序 ======================='''
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nw = len(weights)
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widx = np.array([k for k in range(0, nw)])[:, None]
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wtype = WeightType * np.ones((nw, 1))
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wstate = np.zeros((nw, 4))
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weights = np.concatenate((widx, wtype, weights, wstate), axis=1).astype(np.int64)
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weights[:, 6] = WeightType
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weights = weights[np.argsort(weights[:, 2]), :]
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'''=================== 1.2 基确Weight的状态切割 ========================='''
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w_max = np.max(weights[:, 3])
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# i0=0
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for i2 in range(0, nw):
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i1 = max(i2 - WeightWinWidth, 0)
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wvalue = weights[i1:i2+1, 3]
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wi2 = weights[i2, 3]
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wmin = np.min(wvalue)
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wmax = np.max(wvalue)
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'''对重力波动区间进行标记，并标记最新一次重力稳定值的索引和相应重力值'''
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if wmax - wmin > WeightStableThresh:
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weights[i2, 4] = w_max
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if i2==0:
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i0=0
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wi0 = weights[i0, 3]
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elif i2>0 and weights[i2-1, 4]==0:
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i0 = copy.deepcopy(i2)
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wi0 = weights[i0, 3]
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if i2>0 and weights[i2-1, 4]!=0 and weights[i2, 4]==0:
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# 当前稳定状态下的重力值和前一次重力稳定值的差值，确定放入还是取出
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if wi2-wi0 > WeightStableThresh:
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i00 = max(i0 - InputFrontNum, 0)
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i22 = min(i2 + InputBackNum, nw)
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elif wi2-wi0 < -1*WeightStableThresh:
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i00 = max(i0 - OutputFrontNum, 0)
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i22 = min(i2 + OutputBackNum, nw)
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else:
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i00 = max(i0 - max(InputFrontNum, OutputFrontNum), 0)
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i22 = min(i2 + max(InputBackNum, OutputBackNum), nw)
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weights[i00:i22, 5] = w_max + 100
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'''===================== 2.1 CV 数据综合并排序 =========================='''
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BackType, frstate_0 = periods[0]
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FrontType, frstate_1 = periods[1]
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n0, n1 = len(frstate_0), len(frstate_1)
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idx0 = np.array([i for i in range(0, n0)], dtype=np.int64)[:, None]
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idx1 = np.array([i for i in range(0, n1)], dtype=np.int64)[:, None]
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ctype0 = BackType * np.ones((n0, 1), dtype=np.int64)
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ctype1 = FrontType * np.ones((n1, 1), dtype=np.int64)
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tstamp0 = frstate_0[:,1][:, None]
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tstamp1 = frstate_1[:,1][:, None]
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state0 = frstate_0[:,2][:, None]
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state00 = frstate_0[:,3][:, None]
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state1 = frstate_1[:,2][:, None]
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state11 = frstate_1[:,3][:, None]
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'''序列索引号， 相机类型，时间戳, 单摄状态1、单摄状态2、CV综合状态、综合数据类型、综合状态
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0 1 2 3 4 5 6 7
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'''
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tstream0 = np.concatenate((idx0, ctype0, tstamp0, state0, state00), axis=1)
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tstream1 = np.concatenate((idx1, ctype1, tstamp1, state1, state11), axis=1)
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tstream = np.concatenate((tstream0, tstream1), axis=0)
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tstream = np.concatenate((tstream, np.zeros((len(tstream), 3), dtype=np.int64)), axis=1)
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tstream[:, 6] = CameraType
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tstream = tstream[np.argsort(tstream[:, 2]), :]
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'''=============== 2.2 基于前后摄运动轨迹起止点确定CV综合状态 ============'''
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for i in range(0, len(tstream)):
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idx, ctype, stamp, state = tstream[i, :4]
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if i==0:
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tstream[i, 5] = state
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if i>0:
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j = i-1
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idx0, ctype0, stamp0, state0 = tstream[j, :4]
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while stamp-stamp0 < CameraTimeInterval and ctype == ctype0 and j>0:
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j -= 1
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idx0, ctype0, stamp0, state0 = tstream[j, :4]
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'''两摄像头状态的或运算. 由于前后摄图像不同时，如何构造或运算，关键在于选择不同摄像头的对齐点
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i时刻摄像头(ctype)状态state，另一摄像头(ctype0 != ctype)距 i 最近最近时刻 j 的状态state0
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'''
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if ctype != ctype0 and state0==1:
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tstream[i, 5] = state0
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else:
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tstream[i, 5] = state
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'''================ 3.1 CV、Wweight 数据综合并排序 ======================'''
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time_stream = np.concatenate((tstream, weights), axis=0, dtype=np.int64)
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time_stream = time_stream[np.argsort(time_stream[:, 2]), :]
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tmin = np.min(time_stream[:, 2])
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time_stream[:, 2] = time_stream[:, 2] - tmin
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'''============== 3.2 基于 CV 和 Weight 确定 Cart 的综合状态 ============'''
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for i in range(0, len(time_stream)):
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idx, _, stamp, value, _, state, ctype = time_stream[i, :7]
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state = min(state, 1)
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if i==0:
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time_stream[i, 7] = state
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if i>0:
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j = i-1
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idx0, _, stamp0, value0, _, state0, ctype0 = time_stream[j, :7]
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while stamp-stamp0 < CompTimeInterval and ctype == ctype0 and j>0:
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j -= 1
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idx0, _, stamp0, value0, _, state0, ctype0 = time_stream[j, :7]
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'''CV与Weight的与运算. 由于CV与Weight不同时，如何构造与运算，关键在于选择不同数据源的对齐点
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i时数据类型(ctype)状态state，另一数据类型(ctype0 != ctype)距 i 最近最近时刻 j 的状态state0
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'''
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if ctype != ctype0 and state !=0 and state0 !=0:
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time_stream[i, 7] = 1
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MotionSlice, motion_slice = [], []
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t0 = time_stream[0, 7]
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for i in range(1, len(time_stream)):
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f0 = time_stream[i-1, 7]
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f1 = time_stream[i, 7]
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if f0==0 and f1==1:
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t0 = time_stream[i, 2]
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elif f0==1 and f1==0:
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t1 = time_stream[i, 2]
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if t1-t0>100: #ms
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MotionSlice.append((t0+tmin, t1+tmin))
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motion_slice.append((t0, t1))
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else:
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print(f"T0: {t0}, T1: {t1}")
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'''===================== 4. Hands数据综合并排序 =========================='''
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BackType, hdstate_0 = hands[0]
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FrontType, hdstate_1 = hands[1]
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n0, n1 = len(hdstate_0), len(hdstate_1)
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idx0 = np.array([i for i in range(0, n0)], dtype=np.int64)[:, None]
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idx1 = np.array([i for i in range(0, n1)], dtype=np.int64)[:, None]
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ctype0 = BackType * np.ones((n0, 1), dtype=np.int64)
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ctype1 = FrontType * np.ones((n1, 1), dtype=np.int64)
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hstamp0 = hdstate_0[:,1][:, None]
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hstamp1 = hdstate_1[:,1][:, None]
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state0 = hdstate_0[:,2][:, None]
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state1 = hdstate_1[:,2][:, None]
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'''序列索引号， 相机类型，时间戳, 单摄手部状态、手部综合状态、保留位2、综合数据类型、综合状态
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0 1 2 3 4 5 6 7
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'''
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hstream0 = np.concatenate((idx0, ctype0, hstamp0, state0), axis=1)
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hstream1 = np.concatenate((idx1, ctype1, hstamp1, state1), axis=1)
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hstream = np.concatenate((hstream0, hstream1), axis=0)
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hstream = np.concatenate((hstream, np.zeros((len(hstream), 4), dtype=np.int64)), axis=1)
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hstream[:, 6] = HandType
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hstream = hstream[np.argsort(hstream[:, 2]), :]
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for i in range(0, len(hstream)):
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idx, ctype, stamp, state = hstream[i, :4]
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if i==0:
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hstream[i, 4] = state
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if i>0:
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j = i-1
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idx0, ctype0, stamp0, state0 = hstream[j, :4]
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while stamp-stamp0 < CameraTimeInterval and ctype == ctype0 and j>0:
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j -= 1
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idx0, ctype0, stamp0, state0 = hstream[j, :4]
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'''两摄像头状态的或运算. 由于前后摄图像不同时，如何构造或运算，关键在于选择不同摄像头的对齐点
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i时刻摄像头(ctype)状态state，另一摄像头(ctype0 != ctype)距 i 最近最近时刻 j 的状态state0
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'''
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if ctype != ctype0 and state0==2:
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hstream[i, 4] = state0
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elif ctype != ctype0 and state0==1:
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hstream[i, 4] = state0
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else:
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hstream[i, 4] = state
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'''========================== 5 结果显示 ================================'''
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frstate_0[:, 1] = frstate_0[:, 1]-tmin
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frstate_1[:, 1] = frstate_1[:, 1]-tmin
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tstream[:, 2] = tstream[:, 2]-tmin
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fig, (ax1, ax2, ax3, ax4, ax5, ax6) = plt.subplots(6, 1)
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during = np.max(time_stream[:, 2])
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ax1.plot(weights[:, 2]-tmin, weights[:, 3], 'bo-', linewidth=1, markersize=4)
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ax1.plot(weights[:, 2]-tmin, weights[:, 4], 'mx-', linewidth=1, markersize=4)
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ax1.plot(weights[:, 2]-tmin, weights[:, 5], 'gx-', linewidth=1, markersize=4)
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ax1.set_xlim([0, during])
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ax1.set_title('Weight (gram)')
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ax2.plot(frstate_0[:, 1], frstate_0[:, 3], 'rx-', linewidth=1, markersize=8)
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ax2.plot(frstate_0[:, 1], frstate_0[:, 2], 'bo-', linewidth=1, markersize=4)
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ax2.set_xlim([0, during])
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ax2.set_title('Back Camera')
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ax3.plot(frstate_1[:, 1], frstate_1[:, 3], 'rx-', linewidth=1, markersize=8)
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ax3.plot(frstate_1[:, 1], frstate_1[:, 2], 'bo-', linewidth=1, markersize=4)
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ax3.set_xlim([0, during])
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ax3.set_title('Front Camera')
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ax4.plot(tstream[:, 2], tstream[:, 5], 'bx-', linewidth=1, markersize=4)
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ax4.set_xlim([0, during])
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ax4.set_title('CV State')
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ax5.plot(time_stream[:, 2], time_stream[:, 7], 'gx-', linewidth=1, markersize=4)
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ax5.set_xlim([0, during])
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ax5.set_title('Cart State')
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ax6.plot(hstream[:, 2]-tmin, hstream[:, 4], 'gx-', linewidth=1, markersize=4)
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ax6.set_xlim([0, during])
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ax6.set_title('Hand State')
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plt.show()
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if spath:
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plt.savefig(spath)
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return tmin, MotionSlice
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def splitevent(imgpath, MotionSlice):
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suffix = '.png'
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imgfiles = [f for f in os.listdir(imgpath) if f.endswith(suffix)]
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timestamp = np.array([int(f.split('_')[2]) for f in imgfiles])
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indexes = []
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k = 0
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for t0, t1 in MotionSlice:
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idx0 = set(np.where(timestamp >= t0)[0])
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idx1 = set(np.where(timestamp <= t1)[0])
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idx2 = list(idx0.intersection(idx1))
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files = [imgfiles[i] for i in idx2]
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for filename in files:
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file, ext = os.path.splitext(filename)
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newname = file + f'_{k}.png'
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os.rename(os.path.join(imgpath, filename), os.path.join(imgpath, newname))
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k += 1
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print("Done!")
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def runyolo():
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eventdirs = r"\\192.168.1.28\share\个人文件\wqg\realtime\eventdata"
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savedir = r"\\192.168.1.28\share\个人文件\wqg\realtime\result"
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||
|
||
k = 0
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for edir in os.listdir(eventdirs):
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edir = "1731316835560"
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source = os.path.join(eventdirs, edir)
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||
files = filesort(source)
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for flist in files:
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run_yolo(flist, savedir)
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||
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k += 1
|
||
if k==1:
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||
break
|
||
|
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def run_tracking(trackboxes, MotionSlice):
|
||
pass
|
||
|
||
|
||
def read_wsensor(filepath):
|
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WeightDict = OrderedDict()
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with open(filepath, 'r', encoding='utf-8') as f:
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lines = f.readlines()
|
||
clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
|
||
for i, line in enumerate(clean_lines):
|
||
line = line.strip()
|
||
|
||
line = line.strip()
|
||
|
||
if line.find(':') < 0: continue
|
||
# if line.find("Weight") >= 0:
|
||
# label = "Weight"
|
||
# continue
|
||
|
||
|
||
keyword = line.split(':')[0]
|
||
value = line.split(':')[1]
|
||
|
||
# if label == "Weight":
|
||
if len(keyword) and len(value):
|
||
vdata = [float(s) for s in value.split(',') if len(s)]
|
||
WeightDict[keyword] = vdata[-1]
|
||
|
||
|
||
weights = [(float(t), w) for t, w in WeightDict.items()]
|
||
weights = np.array(weights).astype(np.int64)
|
||
|
||
return weights
|
||
|
||
|
||
def show_seri():
|
||
datapath = r"\\192.168.1.28\share\个人文件\wqg\realtime\eventdata\1731316835560"
|
||
savedir = r"\\192.168.1.28\share\个人文件\wqg\realtime\1"
|
||
|
||
|
||
imgdir = datapath.split('\\')[-2] + "_" + datapath.split('\\')[-1]
|
||
imgpath = os.path.join(savedir, imgdir)
|
||
|
||
eventname = Path(datapath).stem
|
||
|
||
datafiles = sorted(glob.glob(os.path.join(datapath, '*.npy')))
|
||
|
||
periods, trackboxes, hands = [], [], []
|
||
win_width = 12
|
||
for npypath in datafiles:
|
||
CameraType = Path(npypath).stem.split('_')[-1]
|
||
tkboxes = np.load(npypath)
|
||
|
||
trackboxes.append((CameraType, tkboxes))
|
||
|
||
period, handState = devide_motion_state(tkboxes, win_width)
|
||
periods.append((int(CameraType), period))
|
||
hands.append((int(CameraType), handState))
|
||
|
||
|
||
|
||
'''===============读取重力信号数据==================='''
|
||
seneorfile = os.path.join(datapath, 'sensor.txt')
|
||
weights = read_wsensor(seneorfile)
|
||
|
||
# weights = [(float(t), w) for t, w in WeightDict.items()]
|
||
# weights = np.array(weights)
|
||
|
||
|
||
'''===============重力、图像信息融合==================='''
|
||
spath = os.path.join(savedir, f"{eventname}.png" )
|
||
tmin, MotionSlice = state_measure(periods, weights, hands, spath)
|
||
|
||
# 第一次运行时用于更改图像文件名
|
||
# rerename(imgpath)
|
||
# rename(imgpath, tmin)
|
||
|
||
# splitevent(imgpath, MotionSlice)
|
||
|
||
|
||
|
||
|
||
def main():
|
||
# runyolo()
|
||
show_seri()
|
||
|
||
if __name__ == '__main__':
|
||
main()
|
||
|
||
# imgpaths = r"\\192.168.1.28\share\realtime\result\eventdata_1728978106733"
|
||
# rerename(imgpaths)
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|