回传数据解析，兼容v5和v10
This commit is contained in:
jiajie555
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realtime/__pycache__/event_time_specify.cpython-312.pyc
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realtime/__pycache__/event_time_specify.cpython-312.pyc
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realtime/__pycache__/event_time_specify.cpython-39.pyc
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realtime/__pycache__/event_time_specify.cpython-39.pyc
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realtime/__pycache__/intrude_detect.cpython-312.pyc
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realtime/__pycache__/intrude_detect.cpython-312.pyc
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realtime/event_time_specify.py
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realtime/event_time_specify.py
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Oct 10 11:01:39 2024
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@author: ym
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"""
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import os
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import numpy as np
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# from matplotlib.pylab import mpl
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# mpl.use('Qt5Agg')
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import matplotlib.pyplot as plt
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import sys
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sys.path.append(r"D:\DetectTracking")
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from move_detect import MoveDetect
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# from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output, read_weight_timeConsuming
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from tracking.utils.read_data import read_weight_timeConsuming
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def str_to_float_arr(s):
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# 移除字符串末尾的逗号（如果存在）
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if s.endswith(','):
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s = s[:-1]
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# 使用split()方法分割字符串，然后将每个元素转化为float
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float_array = [float(x) for x in s.split(",")]
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return float_array
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def find_samebox_in_array(arr, target):
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for i, st in enumerate(arr):
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if st[:4] == target[:4]:
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return i
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return -1
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def array2frame(bboxes):
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frameID = np.sort(np.unique(bboxes[:, 7].astype(int)))
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# frame_ids = bboxes[:, frameID].astype(int)
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fboxes, ttamps = [], []
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for fid in frameID:
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idx = np.where(bboxes[:, 7] == fid)[0]
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box = bboxes[idx, :]
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fboxes.append(box)
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ttamps.append(int(box[0, 9]))
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frameTstamp = np.concatenate((frameID[:,None], np.array(ttamps)[:,None]), axis=1)
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return fboxes, frameTstamp
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def extract_data_1(datapath):
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'''
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要求每一帧（包括最后一帧）输出数据后有一空行作为分割行，该分割行为标志行
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'''
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trackerboxes = np.empty((0, 10), dtype=np.float64)
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trackerfeats = np.empty((0, 256), dtype=np.float64)
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boxes, feats, tboxes, tfeats = [], [], [], []
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timestamp = -1
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newframe = False
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with open(datapath, 'r', encoding='utf-8') as lines:
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for line in lines:
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if line.find("CameraId")>=0:
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newframe = True
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timestamp, frameId = [int(ln.split(":")[1]) for ln in line.split(",")[1:]]
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# boxes, feats, tboxes, tfeats = [], [], [], []
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if line.find("box:") >= 0 and line.find("output_box:") < 0:
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line = line.strip()
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box = line[line.find("box:") + 4:].strip()
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# if len(box)==6:
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boxes.append(str_to_float_arr(box))
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if line.find("feat:") >= 0:
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line = line.strip()
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feat = line[line.find("feat:") + 5:].strip()
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# if len(feat)==256:
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feats.append(str_to_float_arr(feat))
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if line.find("output_box:") >= 0:
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line = line.strip()
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# 确保 boxes 和 feats 一一对应，并可以保证 tboxes 和 tfeats 一一对应
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if len(boxes)==0 or len(boxes)!=len(feats):
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continue
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box = str_to_float_arr(line[line.find("output_box:") + 11:].strip())
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box.append(timestamp)
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index = find_samebox_in_array(boxes, box)
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if index >= 0:
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tboxes.append(box) # 去掉'output_box:'并去除可能的空白字符
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# feat_f = str_to_float_arr(input_feats[index])
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feat_f = feats[index]
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norm_f = np.linalg.norm(feat_f)
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feat_f = feat_f / norm_f
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tfeats.append(feat_f)
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'''标志行(空行)判断'''
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condt = line.find("timestamp")<0 and line.find("box:")<0 and line.find("feat:")<0
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if condt and newframe:
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if len(tboxes) and len(tfeats):
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trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))
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trackerfeats = np.concatenate((trackerfeats, np.array(tfeats)))
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timestamp = -1
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boxes, feats, tboxes, tfeats = [], [], [], []
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newframe = False
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return trackerboxes, trackerfeats
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def devide_motion_state(tboxes, width):
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'''frameTstamp: 用于标记当前相机视野内用购物车运动状态变化
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Hand状态：
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0: 不存在
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1: 手部存在
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2: 手部存在且处于某种状态（静止）
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'''
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fboxes, frameTstamp = array2frame(tboxes)
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fnum = len(frameTstamp)
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state = np.zeros((fnum, 2), dtype=np.int64)
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frameState = np.concatenate((frameTstamp, state), axis = 1).astype(np.int64)
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handState = np.concatenate((frameTstamp, state), axis = 1).astype(np.int64)
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if fnum < width:
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return frameState, handState
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mtrackFid = {}
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handFid = {}
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'''frameState 标记由图像判断的购物车状态：0: 静止，1: 运动'''
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for idx in range(width, fnum+1):
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idx0 = idx-width
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# if idx == 40:
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# print("123")
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lboxes = np.concatenate(fboxes[idx0:idx], axis = 0)
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md = MoveDetect(lboxes)
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md.classify()
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## track.during 二元素组, 表征在该时间片段内，轨迹 track 的起止时间，数值用 boxes[:, 7]
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for track in md.track_motion:
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f1, f2 = track.during
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# if track.cls == 0: continue
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idx1 = set(np.where(frameState[:,0] >= f1)[0])
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idx2 = set(np.where(frameState[:,0] <= f2)[0])
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idx3 = list(idx1.intersection(idx2))
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if track.tid not in mtrackFid:
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mtrackFid[track.tid] = set(idx3)
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else:
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mtrackFid[track.tid] = mtrackFid[track.tid].union(set(idx3))
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frameState[idx-1, 3] = 1
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frameState[idx3, 2] = 1
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for track in md.hand_tracks:
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f11, f22 = track.during
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idx11 = set(np.where(handState[:,0] >= f11)[0])
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idx22 = set(np.where(handState[:,0] <= f22)[0])
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idx33 = list(idx11.intersection(idx22))
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'''手部存在标记'''
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handState[idx33, 2] = 1
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'''未来改进方向：is_static 可以用手部状态判断的函数代替'''
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if track.is_static(70) and len(idx33)>1:
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idx11 = set(np.where(handState[:,0] >= f11)[0])
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idx22 = set(np.where(handState[:,0] <= f22)[0])
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idx33 = list(idx11.intersection(idx22))
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'''手部静止标记'''
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handState[idx33, 2] = 2
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'''状态变化输出'''
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for tid, fid in mtrackFid.items():
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fstate = np.zeros((fnum, 1), dtype=np.int64)
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fstate[list(fid), 0] = tid
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frameState = np.concatenate((frameState, fstate), axis = 1).astype(np.int64)
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return frameState, handState
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def state_measure(periods, weights, hands, spath=None):
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'''两种状态：static、motion,
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(t0, t1)
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t0: static ----> motion
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t1: motion ----> static
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'''
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PrevState = 'static'
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CuurState = 'static'
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camtype_0, frstate_0 = periods[0]
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camtype_1, frstate_1 = periods[1]
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'''计算总时间区间: tmin, tmax, during'''
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tmin_w, tmax_w = np.min(weights[:, 0]), np.max(weights[:, 0])
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tmin_0, tmax_0 = np.min(frstate_0[:, 1]), np.max(frstate_0[:, 1])
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tmin_1, tmax_1 = np.min(frstate_1[:, 1]), np.max(frstate_1[:, 1])
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tmin = min([tmin_w, tmin_0, tmin_1])
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tmax = max([tmax_w, tmax_0, tmax_1])
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# for ctype, tboxes, _ in tracker_boxes:
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# t_min, t_max = np.min(tboxes[:, 9]), np.max(tboxes[:, 9])
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# if t_min<tmin:
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# tmin = t_min
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# if t_max>tmax:
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# tmax = t_max
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# during = tmax - tmin
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fig, (ax1, ax2, ax3) = plt.subplots(3, 1)
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ax1.plot(weights[:, 0] - tmin, weights[:, 1], 'bo-', linewidth=1, markersize=4)
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# ax1.set_xlim([0, during])
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ax1.set_title('Weight (g)')
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ax2.plot(frstate_0[:, 1] - tmin, frstate_0[:, 2], 'rx-', linewidth=1, markersize=8)
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ax2.plot(frstate_0[:, 1] - tmin, frstate_0[:, 3], 'bo-', linewidth=1, markersize=4)
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# ax2.set_xlim([0, during])
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ax2.set_title(f'Camera: {int(camtype_0)}')
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ax3.plot(frstate_1[:, 1] - tmin, frstate_1[:, 2], 'rx-', linewidth=1, markersize=8)
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ax3.plot(frstate_1[:, 1] - tmin, frstate_1[:, 3], 'bo-', linewidth=1, markersize=4)
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ax3.set_title(f'Camera: {int(camtype_1)}')
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if spath:
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plt.savefig(spath)
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plt.show()
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def read_yolo_weight_data(eventdir):
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filepaths = []
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for filename in os.listdir(eventdir):
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file, ext = os.path.splitext(filename)
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if ext =='.data':
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filepath = os.path.join(eventdir, filename)
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filepaths.append(filepath)
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if len(filepaths) != 5:
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return
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tracker_boxes = []
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WeightDict, SensorDict, ProcessTimeDict = {}, {}, {}
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for filepath in filepaths:
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filename = os.path.basename(filepath)
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if filename.find('_track.data')>0:
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CamerType = filename.split('_')[0]
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trackerboxes, trackerfeats = extract_data_1(filepath)
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tracker_boxes.append((CamerType, trackerboxes, trackerfeats))
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if filename.find('process.data')==0:
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WeightDict, SensorDict, ProcessTimeDict = read_weight_timeConsuming(filepath)
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'''====================重力信号处理===================='''
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weights = [(float(t), w) for t, w in WeightDict.items()]
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weights = np.array(weights)
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return tracker_boxes, weights
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def main():
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eventdir = r"\\192.168.1.28\share\测试_202406\0819\images\20240817-192549-6940120c-634c-481b-97a6-65042729f86b_null"
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tracker_boxes, weights = read_yolo_weight_data(eventdir)
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'''====================图像运动分析===================='''
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win_width = 12
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periods, hands = [], []
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for ctype, tboxes, _ in tracker_boxes:
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period, handState = devide_motion_state(tboxes, win_width)
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periods.append((ctype, period))
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hands.append((ctype, handState))
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print('done!')
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'''===============重力、图像信息融合==================='''
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state_measure(periods, weights, hands)
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if __name__ == "__main__":
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main()
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138
realtime/full_realtime.py
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realtime/full_realtime.py
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Dec 25 09:17:32 2024
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@author: ym
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"""
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import os
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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from datetime import datetime
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sys.path.append(r"D:\DetectTracking")
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from contrast.utils.event import ShoppingEvent
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from tracking.utils.read_data import read_weight_sensor, extract_data_realtime, read_tracking_output_realtime
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from tracking.utils.read_data import read_process
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def time_std2stamp(custom_time):
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date_part = custom_time.split('-')[0]
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time_part = custom_time.split('-')[1]
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ms_part = int(custom_time.split('-')[2])
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standard_time = f"{date_part} {time_part[:2]}:{time_part[2:4]}:{time_part[4:]}"
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dt = datetime.strptime(standard_time, "%Y%m%d %H:%M:%S")
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timestamp = int(dt.timestamp() * 1000) + ms_part
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return timestamp
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def time_stamp2std(timestamp):
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if isinstance(timestamp, float) or isinstance(timestamp, str):
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timestamp = int(timestamp)
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ms = timestamp%1000
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times = timestamp//1000
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std_time = datetime.fromtimestamp(times)
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stdtime = std_time.strftime("%Y%m%d-%H%M%S") + '-' +str(ms)
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return stdtime
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def get_timeduring_weight(procpath):
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eventStart, eventEnd, weightValue = None, None, None
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if os.path.isfile(procpath):
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timeDict = read_process(procpath)
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if "eventStart" in timeDict.keys():
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eventStart = timeDict["eventStart"]
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if "eventEnd" in timeDict.keys():
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eventEnd = timeDict["eventEnd"]
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if "weightValue" in timeDict.keys():
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weightValue = timeDict["weightValue"]
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return eventStart, eventEnd, weightValue
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def event_devide(wpath):
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'''
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基于重力时序数据 _weight.data 进行事件切分
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'''
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# wpath = r'D:\全实时\source_data\2024122416\20241224-162658370_weight.data'
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tpath, _ = os.path.split(wpath)
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wsdata = read_weight_sensor(wpath)
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times, weights = wsdata[:, 0], wsdata[:, 1]
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Start, End = times[0], times[-1]
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evtpaths, evtTimeWeight = [], []
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for filename in os.listdir(tpath):
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filelist = filename.split('_')
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custom_time = filelist[0]
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evtpath = os.path.join(tpath, filename)
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if os.path.isdir(evtpath):
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stamp = time_std2stamp(custom_time)
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if stamp >= Start and stamp <= End:
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evtpaths.append(evtpath)
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for evtpath in evtpaths:
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evtname = os.path.basename(evtpath)
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event = ShoppingEvent(evtpath, stype = "realtime")
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# try:
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# event = ShoppingEvent(evtpath, stype = "realtime")
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# except Exception as e:
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# print(f"Error is: {e}", evtname)
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'''读取事件的起止时间、重力变化值'''
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propath = os.path.join(evtpath, "process.data")
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evtStart, evtEnd, wgtValue = get_timeduring_weight(propath)
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evtTimeWeight.append((evtStart, evtEnd, wgtValue))
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'''重力变化曲线、事件起止区间'''
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fig, ax1 = plt.subplots(figsize=(16, 9), dpi=100)
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ax1.plot(times-Start, weights, 'bo-', linewidth=1, markersize=3)
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ax1.set_title('Weight (gram)')
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for t0, t1, w in evtTimeWeight:
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min_diff = float('inf')
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index = None
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for i, t in enumerate(times):
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diff = abs(t0 - t)
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if diff < min_diff:
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min_diff = diff
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index = i
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w0 = weights[index]
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w1 = w0 + w
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ax1.plot((t0, t0) - Start, (w0, w1), 'r*-', linewidth=1, markersize=6)
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ax1.plot((t1, t1) - Start, (w0, w1), 'r*-', linewidth=1, markersize=6)
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ax1.plot((t0, t1) - Start, (w1, w1), 'r*-', linewidth=1, markersize=6)
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ax1.grid(True)
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plt.show()
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return plt
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def main():
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tpath = r"D:\全实时\source_data\2024122416"
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rltpath = r"D:\全实时\result"
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for filename in os.listdir(tpath):
|
||||
bname = filename.split("_")[0]
|
||||
if filename.find("_weight.data") <= 0:
|
||||
continue
|
||||
wpath = os.path.join(tpath, filename)
|
||||
|
||||
plt = event_devide(wpath)
|
||||
|
||||
plt.savefig(os.path.join(rltpath, f'{bname}.png' )) # svg, png, pdf
|
||||
|
||||
print(filename)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
print("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
420
realtime/intrude_detect.py
Normal file
420
realtime/intrude_detect.py
Normal file
@ -0,0 +1,420 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Tue Apr 8 10:07:17 2025
|
||||
|
||||
@author: wqg
|
||||
"""
|
||||
|
||||
import csv
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
import pickle
|
||||
import cv2
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.pyplot as plt
|
||||
from typing import List, Tuple
|
||||
from scipy.spatial.distance import cdist
|
||||
from scipy.spatial import ConvexHull
|
||||
from shapely.geometry import Point, Polygon
|
||||
|
||||
##################################################### for method: run_yrt()
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1]
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.insert(0, str(ROOT))
|
||||
|
||||
from track_reid import yolov10_resnet_tracker
|
||||
from event_time_specify import devide_motion_state
|
||||
|
||||
|
||||
def cross(o: Tuple[float, float], a: Tuple[float, float], b: Tuple[float, float]) -> float:
|
||||
""" 计算向量 OA × OB 的叉积 """
|
||||
return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0])
|
||||
|
||||
def compute_convex_hull(points: List[Tuple[float, float]]) -> List[Tuple[float, float]]:
|
||||
""" 使用 Andrew's Monotone Chain 算法求二维点集的凸包 """
|
||||
points = sorted(set(points)) # 排序并去重
|
||||
if len(points) <= 1:
|
||||
return points
|
||||
|
||||
lower = []
|
||||
for p in points:
|
||||
while len(lower) >= 2 and cross(lower[-2], lower[-1], p) <= 0:
|
||||
lower.pop()
|
||||
lower.append(p)
|
||||
|
||||
upper = []
|
||||
for p in reversed(points):
|
||||
while len(upper) >= 2 and cross(upper[-2], upper[-1], p) <= 0:
|
||||
upper.pop()
|
||||
upper.append(p)
|
||||
|
||||
# 去掉重复的连接点
|
||||
return lower[:-1] + upper[:-1]
|
||||
|
||||
def is_point_in_convex_hull(point: Tuple[float, float], hull: List[Tuple[float, float]]) -> bool:
|
||||
""" 判断一个点是否在凸包（含边界）内 """
|
||||
n = len(hull)
|
||||
if n < 3:
|
||||
# 对于点或线段，直接判断是否共线或在线段上
|
||||
if n == 1:
|
||||
return point == hull[0]
|
||||
if n == 2:
|
||||
a, b = hull
|
||||
return abs(cross(a, b, point)) < 1e-10 and min(a[0], b[0]) <= point[0] <= max(a[0], b[0]) and min(a[1], b[1]) <= point[1] <= max(a[1], b[1])
|
||||
return False
|
||||
|
||||
for i in range(n):
|
||||
a = hull[i]
|
||||
b = hull[(i + 1) % n]
|
||||
if cross(a, b, point) < -1e-10: # 必须全部在左边或边上
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
|
||||
def plot_convex_hull(points: List[Tuple[float, float]], hull: List[Tuple[float, float]], test_points: List[Tuple[float, float]] = None):
|
||||
x_all, y_all = zip(*points)
|
||||
fig, ax = plt.subplots()
|
||||
|
||||
ax.set_xlim(0, 1024)
|
||||
ax.set_ylim(1280, 0)
|
||||
|
||||
ax.plot(x_all, y_all, 'o', label='Points')
|
||||
|
||||
# 凸包闭环线
|
||||
hull_loop = hull + [hull[0]]
|
||||
hx, hy = zip(*hull_loop)
|
||||
ax.plot(hx, hy, 'r-', linewidth=2, label='Convex Hull')
|
||||
|
||||
# 如果有测试点
|
||||
if test_points:
|
||||
for pt in test_points:
|
||||
color = 'green' if is_point_in_convex_hull(pt, hull) else 'black'
|
||||
ax.plot(pt[0], pt[1], 's', color=color, markersize=8)
|
||||
ax.text(pt[0] + 0.05, pt[1], f'{pt}', fontsize=9)
|
||||
|
||||
ax.legend()
|
||||
ax.grid(True)
|
||||
plt.title("Convex Hull Visualization")
|
||||
plt.show()
|
||||
|
||||
|
||||
def convex_scipy():
|
||||
points = np.array([
|
||||
[0, 0],
|
||||
[2, 0],
|
||||
[1, 1],
|
||||
[2, 2],
|
||||
[0, 2],
|
||||
[1, 0.5]])
|
||||
hull = ConvexHull(points)
|
||||
|
||||
# 凸包顶点的索引
|
||||
print("凸包顶点索引:{}".format(hull.vertices))
|
||||
print("凸包顶点坐标:")
|
||||
for i in hull.vertices:
|
||||
print(points[i])
|
||||
|
||||
|
||||
# 将凸包坐标构造成 Polygon
|
||||
hull_points = points[hull.vertices]
|
||||
polygon = Polygon(hull_points)
|
||||
|
||||
# 判断一个点是否在凸包内
|
||||
p = Point(1, 1) # 示例点
|
||||
print("是否在凸包内:", polygon.contains(p)) # True or False
|
||||
|
||||
|
||||
def test_convex():
|
||||
# 测试数据
|
||||
sample_points = [(0, 0), (1, 1), (2, 2), (2, 0), (0, 2), (1, 0.5)]
|
||||
convex_hull = compute_convex_hull(sample_points)
|
||||
|
||||
# 测试点在凸包内
|
||||
test_point_inside = (1, 1)
|
||||
test_point_outside = (3, 3)
|
||||
test_point_on_edge = (1, 0)
|
||||
|
||||
inside = is_point_in_convex_hull(test_point_inside, convex_hull)
|
||||
outside = is_point_in_convex_hull(test_point_outside, convex_hull)
|
||||
on_edge = is_point_in_convex_hull(test_point_on_edge, convex_hull)
|
||||
|
||||
convex_hull, inside, outside, on_edge
|
||||
|
||||
# 展示图像
|
||||
plot_convex_hull(sample_points, convex_hull, [test_point_inside, test_point_outside, test_point_on_edge])
|
||||
|
||||
def array2frame(tboxes):
|
||||
"tboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]"
|
||||
idx = np.where(tboxes[:, 6] != 0)[0]
|
||||
bboxes = tboxes[idx, :]
|
||||
frameID = np.sort(np.unique(bboxes[:, 7].astype(int)))
|
||||
fboxes = []
|
||||
for fid in frameID:
|
||||
idx = np.where(bboxes[:, 7] == fid)[0]
|
||||
box = bboxes[idx, :]
|
||||
fboxes.append(box)
|
||||
return fboxes
|
||||
|
||||
def convex_based(tboxes, width, TH=40):
|
||||
fboxes = array2frame(tboxes)
|
||||
fnum = len(fboxes)
|
||||
|
||||
fids = np.array([i+1 for i in range(fnum)])[:, np.newaxis]
|
||||
state = np.zeros((fnum, 1), dtype=np.int64)
|
||||
frameState = np.concatenate((fids, state), axis = 1).astype(np.int64)
|
||||
if fnum < width:
|
||||
return frameState
|
||||
|
||||
for idx1 in range(width, fnum+1):
|
||||
idx0 = idx1 - width
|
||||
idx = idx1 - width//2 - 1
|
||||
|
||||
iboxes = fboxes[:idx]
|
||||
cboxes = fboxes[idx][:, 0:4]
|
||||
|
||||
cur_xy = np.zeros((len(cboxes), 2))
|
||||
cur_xy[:, 0] = (fboxes[idx][:, 0]+fboxes[idx][:, 2])/2
|
||||
cur_xy[:, 1] = (fboxes[idx][:, 1]+fboxes[idx][:, 3])/2
|
||||
for i in range(width//2):
|
||||
x1, y1, x2, y2 = iboxes[i][:, 0], iboxes[i][:, 1], iboxes[i][:, 2], iboxes[i][:, 3]
|
||||
|
||||
boxes = np.array([(x1, y1), (x1, y2), (x2, y1), (x2, y2)]).transpose(0, 2, 1).reshape(-1, 2)
|
||||
box1 = [(x, y) for x, y in boxes]
|
||||
convex_hull = compute_convex_hull(box1)
|
||||
|
||||
for pt in cur_xy:
|
||||
inside = is_point_in_convex_hull(pt, convex_hull)
|
||||
|
||||
if not inside:
|
||||
break
|
||||
if not inside:
|
||||
break
|
||||
|
||||
# Based on the distance between the four corners of the current frame boxes
|
||||
# and adjacent frame boxes
|
||||
iboxes = fboxes[idx0:idx] + fboxes[idx+1:idx1]
|
||||
cboxes = fboxes[idx][:, 0:4]
|
||||
cx1, cy1, cx2, cy2 = cboxes[:, 0], cboxes[:, 1], cboxes[:, 2], cboxes[:, 3]
|
||||
cxy = np.array([(cx1, cy1), (cx1, cy2), (cx2, cy1), (cx2, cy2)]).transpose(0, 2, 1).reshape(-1, 2)
|
||||
|
||||
iiboxes = np.concatenate(iboxes, axis=0)
|
||||
ix1, iy1, ix2, iy2 = iiboxes[:, 0], iiboxes[:, 1], iiboxes[:, 2], iiboxes[:, 3]
|
||||
ixy = np.array([(ix1, iy1), (ix1, iy2), (ix2, iy1), (ix2, iy2)]).transpose(0, 2, 1).reshape(-1, 2)
|
||||
|
||||
Dist = cdist(cxy, ixy).round(2)
|
||||
max_dist = np.max(np.min(Dist, axis=1))
|
||||
if max_dist > TH and not inside:
|
||||
frameState[idx, 1] = 1
|
||||
# plot_convex_hull(boxes, convex_hull, [pt])
|
||||
frameState[idx, 1] = 1
|
||||
|
||||
return frameState
|
||||
|
||||
|
||||
def single_point(tboxes, width, TH=60):
|
||||
"""width: window width, >=2"""
|
||||
|
||||
|
||||
fboxes = array2frame(tboxes)
|
||||
fnum = len(fboxes)
|
||||
|
||||
fids = np.array([i+1 for i in range(fnum)])[:, np.newaxis]
|
||||
state = np.zeros((fnum, 1), dtype=np.int64)
|
||||
frameState = np.concatenate((fids, state), axis = 1).astype(np.int64)
|
||||
|
||||
|
||||
if fnum < width:
|
||||
return frameState
|
||||
|
||||
for idx1 in range(width, fnum+1):
|
||||
idx0 = idx1 - width
|
||||
idx = idx1 - width//2 - 1
|
||||
|
||||
iboxe1 = fboxes[idx0:idx]
|
||||
iboxe2 = fboxes[idx+1:idx1]
|
||||
iboxes = fboxes[idx0:idx] + fboxes[idx+1:idx1]
|
||||
|
||||
cboxes = fboxes[idx][:, 0:4]
|
||||
cur_xy = np.zeros((len(cboxes), 2))
|
||||
cur_xy[:, 0] = (fboxes[idx][:, 0]+fboxes[idx][:, 2])/2
|
||||
cur_xy[:, 1] = (fboxes[idx][:, 1]+fboxes[idx][:, 3])/2
|
||||
Dist = np.empty((len(cboxes), 0))
|
||||
for i in range(width-1):
|
||||
boxes = iboxes[i][:, 0:4]
|
||||
|
||||
box_xy = np.zeros((len(boxes), 2))
|
||||
box_xy[:, 0] = (boxes[:, 0]+boxes[:, 2])/2
|
||||
box_xy[:, 1] = (boxes[:, 1]+boxes[:, 3])/2
|
||||
dist2 = cdist(cur_xy, box_xy).round(2)
|
||||
|
||||
Dist = np.concatenate((Dist, dist2), axis=1)
|
||||
|
||||
max_dist = np.max(np.min(Dist, axis=1))
|
||||
|
||||
if max_dist > TH:
|
||||
frameState[idx, 1] = 1
|
||||
|
||||
return frameState
|
||||
|
||||
|
||||
|
||||
def intrude():
|
||||
pkpath = Path("/home/wqg/dataset/small-goods/pkfiles")
|
||||
savepath = Path("/home/wqg/dataset/small-goods/illustration_convex")
|
||||
|
||||
if not savepath.exists():
|
||||
savepath.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
err_trail, err_single, err_all = [], [], []
|
||||
num = 0
|
||||
for pth in pkpath.iterdir():
|
||||
# item = r"69042386_20250407-145737_front_returnGood_b82d28427666_15_17700000001.pickle"
|
||||
# pth = pkpath/item
|
||||
|
||||
with open(str(pth), 'rb') as f:
|
||||
yrt = pickle.load(f)
|
||||
|
||||
evtname = pth.stem
|
||||
|
||||
bboxes = []
|
||||
trackerboxes = np.empty((0, 10), dtype=np.float64)
|
||||
for frameDict in yrt:
|
||||
boxes = frameDict["bboxes"]
|
||||
tboxes = frameDict["tboxes"]
|
||||
tboxes = np.concatenate((tboxes, tboxes[:,7][:, None]), axis=1)
|
||||
|
||||
bboxes.append(boxes)
|
||||
|
||||
trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)), axis=0)
|
||||
|
||||
'''single-points based for intrusion detection'''
|
||||
# wd =5
|
||||
# fstate1 = single_point(trackerboxes, wd)
|
||||
|
||||
'''convex-based '''
|
||||
width = 5
|
||||
fstate = convex_based(trackerboxes, width, TH=60)
|
||||
|
||||
# fstate = np.zeros(fstate1.shape)
|
||||
# fstate[:, 0] = fstate1[:, 0]
|
||||
# fstate[:, 1] = fstate1[:, 1] * fstate2[:, 1]
|
||||
|
||||
'''trajectory based for intrusion detection
|
||||
period: 0 1 2 3
|
||||
fid timestamp(fid) 基于滑动窗的tid扩展 滑动窗覆盖的运动区间
|
||||
'''
|
||||
win_width = 12
|
||||
period, handState = devide_motion_state(trackerboxes, win_width)
|
||||
|
||||
num += 1
|
||||
if np.all(period[:,2:4]==0):
|
||||
err_trail.append(evtname)
|
||||
if np.all(fstate[:,1]==0):
|
||||
err_single.append(evtname)
|
||||
if np.all(period[:,2:4]==0) and np.all(fstate[:,1]==0):
|
||||
err_all.append(evtname)
|
||||
|
||||
fig, (ax1, ax2) = plt.subplots(2, 1)
|
||||
ax1.plot(period[:, 1], period[:, 2], 'bo-', linewidth=1, markersize=4)
|
||||
ax1.plot(period[:, 1], period[:, 3], 'rx-', linewidth=1, markersize=8)
|
||||
|
||||
ax2.plot(fstate[:, 0], fstate[:, 1], 'rx-', linewidth=1, markersize=8)
|
||||
plt.savefig(os.path.join(str(savepath), f"{evtname}.png"))
|
||||
|
||||
|
||||
plt.close()
|
||||
# if num==1:
|
||||
# break
|
||||
|
||||
rate_trail = 1 - len(err_trail)/num
|
||||
rate_single = 1 - len(err_single)/num
|
||||
rate_all = 1 - len(err_all)/num
|
||||
|
||||
print(f"rate_trail: {rate_trail}")
|
||||
print(f"rate_single: {rate_single}")
|
||||
print(f"rate_all: {rate_all}")
|
||||
|
||||
txtpath = savepath.parents[0] / "error.txt"
|
||||
with open(str(txtpath), "w") as f:
|
||||
f.write(f"rate_trail: {rate_trail}" + "\n")
|
||||
f.write(f"rate_single: {rate_single}" + "\n")
|
||||
f.write(f"rate_all: {rate_all}" + "\n")
|
||||
|
||||
f.write("\n" + "err_trail" + "\n")
|
||||
for line in err_trail:
|
||||
f.write(line + "\n")
|
||||
|
||||
f.write("\n" + "err_single" + "\n")
|
||||
for line in err_single:
|
||||
f.write(line + "\n")
|
||||
|
||||
f.write("\n" + "err_all" + "\n")
|
||||
for line in err_all:
|
||||
f.write(line + "\n")
|
||||
print("Done!")
|
||||
|
||||
|
||||
|
||||
|
||||
def run_yrt():
|
||||
datapath = Path("/home/wqg/dataset/small-goods/videos/")
|
||||
savepath = Path("/home/wqg/dataset/small-goods/result/")
|
||||
pkpath = Path("/home/wqg/dataset/small-goods/pkfiles/")
|
||||
|
||||
if not savepath.exists():
|
||||
savepath.mkdir(parents=True, exist_ok=True)
|
||||
if not pkpath.exists():
|
||||
pkpath.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
optdict = {}
|
||||
optdict["weights"] = ROOT / 'ckpts/best_v10s_width0375_1205.pt'
|
||||
optdict["is_save_img"] = False
|
||||
optdict["is_save_video"] = True
|
||||
|
||||
k = 0
|
||||
for pth in datapath.iterdir():
|
||||
item = "69042386_20250407-145819_back_returnGood_b82d28427666_15_17700000001.mp4"
|
||||
pth = pth.parents[0] /item
|
||||
|
||||
optdict["source"] = pth
|
||||
optdict["save_dir"] = savepath
|
||||
|
||||
# try:
|
||||
yrtOut = yolov10_resnet_tracker(**optdict)
|
||||
|
||||
pkpath_ = pkpath / f"{Path(pth).stem}.pickle"
|
||||
with open(str(pkpath_), 'wb') as f:
|
||||
pickle.dump(yrtOut, f)
|
||||
|
||||
k += 1
|
||||
if k==1:
|
||||
break
|
||||
# except Exception as e:
|
||||
# print("abc")
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# run_yrt()
|
||||
|
||||
intrude()
|
||||
|
||||
# test_convex()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
544
realtime/time_devide.py
Normal file
544
realtime/time_devide.py
Normal file
@ -0,0 +1,544 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed Oct 16 17:37:07 2024
|
||||
|
||||
@author: ym
|
||||
"""
|
||||
|
||||
# import csv
|
||||
import os
|
||||
# import platform
|
||||
# import sys
|
||||
from pathlib import Path
|
||||
import glob
|
||||
import numpy as np
|
||||
import copy
|
||||
import matplotlib.pyplot as plt
|
||||
from collections import OrderedDict
|
||||
|
||||
from event_time_specify import devide_motion_state #, state_measure
|
||||
|
||||
import sys
|
||||
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
from imgs_inference import run_yolo
|
||||
from tracking.utils.read_data import read_weight_sensor
|
||||
|
||||
# IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes
|
||||
# VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes
|
||||
|
||||
def filesort(p):
|
||||
'''
|
||||
需将图像文件名标准化
|
||||
'''
|
||||
|
||||
|
||||
files = []
|
||||
files.extend(sorted(glob.glob(os.path.join(p, '*.jpg'))))
|
||||
# images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
|
||||
|
||||
tamps_0, tamps_1 = [], []
|
||||
files_0, files_1 = [], []
|
||||
for file in files:
|
||||
basename = os.path.basename(file)
|
||||
# if basename.find('frameId')<0: continue
|
||||
|
||||
|
||||
f, ext = os.path.splitext(basename)
|
||||
_, camer, tamp = f.split('_')
|
||||
|
||||
if camer == '0':
|
||||
tamps_0.append(int(tamp))
|
||||
files_0.append(file)
|
||||
|
||||
if camer == '1':
|
||||
tamps_1.append(int(tamp))
|
||||
files_1.append(file)
|
||||
|
||||
idx0 = sorted(range(len(tamps_0)), key=lambda k: tamps_0[k])
|
||||
files0 = [files_0[i] for i in idx0]
|
||||
|
||||
idx1 = sorted(range(len(tamps_1)), key=lambda k: tamps_1[k])
|
||||
files1 = [files_1[i] for i in idx1]
|
||||
|
||||
|
||||
return (files0, files1)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def rename(filePath, tmin):
|
||||
"""
|
||||
重命名函数fun1
|
||||
输入：文件夹路径
|
||||
功能：对文件夹中的全部文件进行随机命名
|
||||
"""
|
||||
suffix = '.png' # 设置后缀，筛选特定文件以更改名称
|
||||
for file in os.listdir(filePath):
|
||||
if file.endswith(suffix):
|
||||
name = file.split('.')[0]
|
||||
|
||||
tamp = int(name.split('_')[2])-tmin
|
||||
|
||||
suffix = file.split('.')[1]
|
||||
|
||||
newname = name +f'_{int(tamp)}.'+suffix
|
||||
|
||||
os.rename(os.path.join(filePath, file), os.path.join(filePath, newname))
|
||||
|
||||
|
||||
def rerename(filePath=None):
|
||||
"""
|
||||
重命名函数fun1
|
||||
输入：文件夹路径
|
||||
功能：对文件夹中的全部文件进行随机命名
|
||||
"""
|
||||
|
||||
suffix = '.png' # 设置后缀，筛选特定文件以更改名称
|
||||
for file in os.listdir(filePath):
|
||||
if file.endswith(suffix):
|
||||
name = file.split('.')[0]
|
||||
names = name.split('_')
|
||||
if len(names)>=6:
|
||||
newname = "_".join(names[0:5])+'.png'
|
||||
os.rename(os.path.join(filePath, file), os.path.join(filePath, newname))
|
||||
|
||||
|
||||
def state_measure(periods, weights, hands, spath=None):
|
||||
'''
|
||||
数据类型
|
||||
后摄: 0, 前摄: 1, CV综合: 2, 重力: 9
|
||||
frstate_0/1:
|
||||
帧ID 时间戳 单摄状态1 单摄状态2 活动轨迹标记
|
||||
0 1 2 3 4:end
|
||||
|
||||
|
||||
|
||||
time_stream = np.concatenate((tstream, weights))
|
||||
time_stream:
|
||||
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
|
||||
0 1 2 3 4 5 6 7
|
||||
|
||||
单摄状态1：基于运动轨迹的起止点确定的运动区间
|
||||
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
|
||||
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
|
||||
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
|
||||
方案：
|
||||
前后摄状态进行或运算
|
||||
CV与重力状态进行与运算
|
||||
'''
|
||||
|
||||
# BackType = 0 # 后摄数据类型
|
||||
# FrontType = 1 # 前摄数据类型
|
||||
CameraType = 2 # CV数据综合类型
|
||||
HandType = 3 # 手部类型
|
||||
WeightType = 9 # 重力数据类型
|
||||
WeightStableThresh = 7.5 # 单位：g，重力稳定状态下的最大波动范围
|
||||
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
|
||||
CameraTimeInterval = 100 # 单位：ms，前后摄状态进行或运算时的时间间隔阈值
|
||||
InputFrontNum = 10 # 重力增加（放入）向前延拓的重力数据点数
|
||||
InputBackNum = 0 # 重力增加（放入）向后延拓的重力数据点数
|
||||
OutputFrontNum = 2 # 重力减少（取出）向前延拓的重力数据点数
|
||||
OutputBackNum = 10 # 重力减少（取出）向前延拓的重力数据点数
|
||||
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
|
||||
|
||||
|
||||
'''==================== 1.1 Weight 数据综合并排序 ======================='''
|
||||
nw = len(weights)
|
||||
widx = np.array([k for k in range(0, nw)])[:, None]
|
||||
wtype = WeightType * np.ones((nw, 1))
|
||||
wstate = np.zeros((nw, 4))
|
||||
weights = np.concatenate((widx, wtype, weights, wstate), axis=1).astype(np.int64)
|
||||
weights[:, 6] = WeightType
|
||||
|
||||
weights = weights[np.argsort(weights[:, 2]), :]
|
||||
|
||||
'''=================== 1.2 基确Weight的状态切割 ========================='''
|
||||
w_max = np.max(weights[:, 3])
|
||||
# i0=0
|
||||
for i2 in range(0, nw):
|
||||
i1 = max(i2 - WeightWinWidth, 0)
|
||||
wvalue = weights[i1:i2+1, 3]
|
||||
wi2 = weights[i2, 3]
|
||||
wmin = np.min(wvalue)
|
||||
wmax = np.max(wvalue)
|
||||
|
||||
|
||||
'''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'''
|
||||
if wmax - wmin > WeightStableThresh:
|
||||
weights[i2, 4] = w_max
|
||||
|
||||
if i2==0:
|
||||
i0=0
|
||||
wi0 = weights[i0, 3]
|
||||
elif i2>0 and weights[i2-1, 4]==0:
|
||||
i0 = copy.deepcopy(i2)
|
||||
wi0 = weights[i0, 3]
|
||||
|
||||
|
||||
if i2>0 and weights[i2-1, 4]!=0 and weights[i2, 4]==0:
|
||||
# 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
|
||||
if wi2-wi0 > WeightStableThresh:
|
||||
i00 = max(i0 - InputFrontNum, 0)
|
||||
i22 = min(i2 + InputBackNum, nw)
|
||||
elif wi2-wi0 < -1*WeightStableThresh:
|
||||
i00 = max(i0 - OutputFrontNum, 0)
|
||||
i22 = min(i2 + OutputBackNum, nw)
|
||||
else:
|
||||
i00 = max(i0 - max(InputFrontNum, OutputFrontNum), 0)
|
||||
i22 = min(i2 + max(InputBackNum, OutputBackNum), nw)
|
||||
|
||||
weights[i00:i22, 5] = w_max + 100
|
||||
|
||||
|
||||
'''===================== 2.1 CV 数据综合并排序 =========================='''
|
||||
BackType, frstate_0 = periods[0]
|
||||
FrontType, frstate_1 = periods[1]
|
||||
|
||||
|
||||
n0, n1 = len(frstate_0), len(frstate_1)
|
||||
idx0 = np.array([i for i in range(0, n0)], dtype=np.int64)[:, None]
|
||||
idx1 = np.array([i for i in range(0, n1)], dtype=np.int64)[:, None]
|
||||
ctype0 = BackType * np.ones((n0, 1), dtype=np.int64)
|
||||
ctype1 = FrontType * np.ones((n1, 1), dtype=np.int64)
|
||||
tstamp0 = frstate_0[:,1][:, None]
|
||||
tstamp1 = frstate_1[:,1][:, None]
|
||||
state0 = frstate_0[:,2][:, None]
|
||||
state00 = frstate_0[:,3][:, None]
|
||||
|
||||
state1 = frstate_1[:,2][:, None]
|
||||
state11 = frstate_1[:,3][:, None]
|
||||
|
||||
|
||||
|
||||
'''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
|
||||
0 1 2 3 4 5 6 7
|
||||
'''
|
||||
tstream0 = np.concatenate((idx0, ctype0, tstamp0, state0, state00), axis=1)
|
||||
tstream1 = np.concatenate((idx1, ctype1, tstamp1, state1, state11), axis=1)
|
||||
tstream = np.concatenate((tstream0, tstream1), axis=0)
|
||||
tstream = np.concatenate((tstream, np.zeros((len(tstream), 3), dtype=np.int64)), axis=1)
|
||||
tstream[:, 6] = CameraType
|
||||
tstream = tstream[np.argsort(tstream[:, 2]), :]
|
||||
|
||||
'''=============== 2.2 基于前后摄运动轨迹起止点确定CV综合状态 ============'''
|
||||
for i in range(0, len(tstream)):
|
||||
idx, ctype, stamp, state = tstream[i, :4]
|
||||
if i==0:
|
||||
tstream[i, 5] = state
|
||||
if i>0:
|
||||
j = i-1
|
||||
idx0, ctype0, stamp0, state0 = tstream[j, :4]
|
||||
while stamp-stamp0 < CameraTimeInterval and ctype == ctype0 and j>0:
|
||||
j -= 1
|
||||
idx0, ctype0, stamp0, state0 = tstream[j, :4]
|
||||
|
||||
'''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
|
||||
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
|
||||
'''
|
||||
if ctype != ctype0 and state0==1:
|
||||
tstream[i, 5] = state0
|
||||
else:
|
||||
tstream[i, 5] = state
|
||||
|
||||
|
||||
'''================ 3.1 CV、Wweight 数据综合并排序 ======================'''
|
||||
time_stream = np.concatenate((tstream, weights), axis=0, dtype=np.int64)
|
||||
time_stream = time_stream[np.argsort(time_stream[:, 2]), :]
|
||||
tmin = np.min(time_stream[:, 2])
|
||||
time_stream[:, 2] = time_stream[:, 2] - tmin
|
||||
|
||||
'''============== 3.2 基于 CV 和 Weight 确定 Cart 的综合状态 ============'''
|
||||
for i in range(0, len(time_stream)):
|
||||
idx, _, stamp, value, _, state, ctype = time_stream[i, :7]
|
||||
state = min(state, 1)
|
||||
|
||||
if i==0:
|
||||
time_stream[i, 7] = state
|
||||
|
||||
if i>0:
|
||||
j = i-1
|
||||
idx0, _, stamp0, value0, _, state0, ctype0 = time_stream[j, :7]
|
||||
while stamp-stamp0 < CompTimeInterval and ctype == ctype0 and j>0:
|
||||
j -= 1
|
||||
idx0, _, stamp0, value0, _, state0, ctype0 = time_stream[j, :7]
|
||||
|
||||
'''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
|
||||
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
|
||||
'''
|
||||
if ctype != ctype0 and state !=0 and state0 !=0:
|
||||
time_stream[i, 7] = 1
|
||||
|
||||
MotionSlice, motion_slice = [], []
|
||||
t0 = time_stream[0, 7]
|
||||
for i in range(1, len(time_stream)):
|
||||
f0 = time_stream[i-1, 7]
|
||||
f1 = time_stream[i, 7]
|
||||
if f0==0 and f1==1:
|
||||
t0 = time_stream[i, 2]
|
||||
elif f0==1 and f1==0:
|
||||
t1 = time_stream[i, 2]
|
||||
if t1-t0>100: #ms
|
||||
MotionSlice.append((t0+tmin, t1+tmin))
|
||||
motion_slice.append((t0, t1))
|
||||
else:
|
||||
print(f"T0: {t0}, T1: {t1}")
|
||||
|
||||
|
||||
|
||||
|
||||
'''===================== 4. Hands数据综合并排序 =========================='''
|
||||
BackType, hdstate_0 = hands[0]
|
||||
FrontType, hdstate_1 = hands[1]
|
||||
n0, n1 = len(hdstate_0), len(hdstate_1)
|
||||
idx0 = np.array([i for i in range(0, n0)], dtype=np.int64)[:, None]
|
||||
idx1 = np.array([i for i in range(0, n1)], dtype=np.int64)[:, None]
|
||||
ctype0 = BackType * np.ones((n0, 1), dtype=np.int64)
|
||||
ctype1 = FrontType * np.ones((n1, 1), dtype=np.int64)
|
||||
hstamp0 = hdstate_0[:,1][:, None]
|
||||
hstamp1 = hdstate_1[:,1][:, None]
|
||||
state0 = hdstate_0[:,2][:, None]
|
||||
state1 = hdstate_1[:,2][:, None]
|
||||
|
||||
'''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
|
||||
0 1 2 3 4 5 6 7
|
||||
'''
|
||||
hstream0 = np.concatenate((idx0, ctype0, hstamp0, state0), axis=1)
|
||||
hstream1 = np.concatenate((idx1, ctype1, hstamp1, state1), axis=1)
|
||||
hstream = np.concatenate((hstream0, hstream1), axis=0)
|
||||
hstream = np.concatenate((hstream, np.zeros((len(hstream), 4), dtype=np.int64)), axis=1)
|
||||
hstream[:, 6] = HandType
|
||||
hstream = hstream[np.argsort(hstream[:, 2]), :]
|
||||
|
||||
for i in range(0, len(hstream)):
|
||||
idx, ctype, stamp, state = hstream[i, :4]
|
||||
if i==0:
|
||||
hstream[i, 4] = state
|
||||
if i>0:
|
||||
j = i-1
|
||||
idx0, ctype0, stamp0, state0 = hstream[j, :4]
|
||||
while stamp-stamp0 < CameraTimeInterval and ctype == ctype0 and j>0:
|
||||
j -= 1
|
||||
idx0, ctype0, stamp0, state0 = hstream[j, :4]
|
||||
|
||||
'''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
|
||||
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
|
||||
'''
|
||||
if ctype != ctype0 and state0==2:
|
||||
hstream[i, 4] = state0
|
||||
elif ctype != ctype0 and state0==1:
|
||||
hstream[i, 4] = state0
|
||||
else:
|
||||
hstream[i, 4] = state
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
'''========================== 5 结果显示 ================================'''
|
||||
frstate_0[:, 1] = frstate_0[:, 1]-tmin
|
||||
frstate_1[:, 1] = frstate_1[:, 1]-tmin
|
||||
tstream[:, 2] = tstream[:, 2]-tmin
|
||||
|
||||
fig, (ax1, ax2, ax3, ax4, ax5, ax6) = plt.subplots(6, 1)
|
||||
during = np.max(time_stream[:, 2])
|
||||
|
||||
ax1.plot(weights[:, 2]-tmin, weights[:, 3], 'bo-', linewidth=1, markersize=4)
|
||||
ax1.plot(weights[:, 2]-tmin, weights[:, 4], 'mx-', linewidth=1, markersize=4)
|
||||
ax1.plot(weights[:, 2]-tmin, weights[:, 5], 'gx-', linewidth=1, markersize=4)
|
||||
ax1.set_xlim([0, during])
|
||||
ax1.set_title('Weight (gram)')
|
||||
|
||||
ax2.plot(frstate_0[:, 1], frstate_0[:, 3], 'rx-', linewidth=1, markersize=8)
|
||||
ax2.plot(frstate_0[:, 1], frstate_0[:, 2], 'bo-', linewidth=1, markersize=4)
|
||||
ax2.set_xlim([0, during])
|
||||
ax2.set_title('Back Camera')
|
||||
|
||||
ax3.plot(frstate_1[:, 1], frstate_1[:, 3], 'rx-', linewidth=1, markersize=8)
|
||||
ax3.plot(frstate_1[:, 1], frstate_1[:, 2], 'bo-', linewidth=1, markersize=4)
|
||||
ax3.set_xlim([0, during])
|
||||
ax3.set_title('Front Camera')
|
||||
|
||||
ax4.plot(tstream[:, 2], tstream[:, 5], 'bx-', linewidth=1, markersize=4)
|
||||
ax4.set_xlim([0, during])
|
||||
ax4.set_title('CV State')
|
||||
|
||||
ax5.plot(time_stream[:, 2], time_stream[:, 7], 'gx-', linewidth=1, markersize=4)
|
||||
ax5.set_xlim([0, during])
|
||||
ax5.set_title('Cart State')
|
||||
|
||||
ax6.plot(hstream[:, 2]-tmin, hstream[:, 4], 'gx-', linewidth=1, markersize=4)
|
||||
ax6.set_xlim([0, during])
|
||||
ax6.set_title('Hand State')
|
||||
|
||||
|
||||
plt.show()
|
||||
if spath:
|
||||
plt.savefig(spath)
|
||||
|
||||
return tmin, MotionSlice
|
||||
|
||||
def splitevent(imgpath, MotionSlice):
|
||||
suffix = '.png'
|
||||
|
||||
imgfiles = [f for f in os.listdir(imgpath) if f.endswith(suffix)]
|
||||
timestamp = np.array([int(f.split('_')[2]) for f in imgfiles])
|
||||
|
||||
indexes = []
|
||||
k = 0
|
||||
for t0, t1 in MotionSlice:
|
||||
idx0 = set(np.where(timestamp >= t0)[0])
|
||||
idx1 = set(np.where(timestamp <= t1)[0])
|
||||
idx2 = list(idx0.intersection(idx1))
|
||||
files = [imgfiles[i] for i in idx2]
|
||||
|
||||
for filename in files:
|
||||
file, ext = os.path.splitext(filename)
|
||||
newname = file + f'_{k}.png'
|
||||
os.rename(os.path.join(imgpath, filename), os.path.join(imgpath, newname))
|
||||
k += 1
|
||||
|
||||
print("Done!")
|
||||
|
||||
|
||||
|
||||
def runyolo():
|
||||
eventdirs = r"\\192.168.1.28\share\个人文件\wqg\realtime\eventdata"
|
||||
savedir = r"\\192.168.1.28\share\个人文件\wqg\realtime\result"
|
||||
|
||||
k = 0
|
||||
for edir in os.listdir(eventdirs):
|
||||
edir = "1731316835560"
|
||||
source = os.path.join(eventdirs, edir)
|
||||
files = filesort(source)
|
||||
for flist in files:
|
||||
run_yolo(flist, savedir)
|
||||
|
||||
k += 1
|
||||
if k==1:
|
||||
break
|
||||
|
||||
def run_tracking(trackboxes, MotionSlice):
|
||||
pass
|
||||
|
||||
|
||||
def read_wsensor(filepath):
|
||||
WeightDict = OrderedDict()
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
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)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
281
realtime/tracker_test.py
Normal file
281
realtime/tracker_test.py
Normal file
@ -0,0 +1,281 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Sun Mar 2 14:15:57 2025
|
||||
|
||||
@author: ym
|
||||
"""
|
||||
import numpy as np
|
||||
import cv2
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import sys
|
||||
sys.path.append(r"D:\DetectTracking")
|
||||
|
||||
# from tracking.utils.read_data import extract_data_realtime, read_tracking_output_realtime
|
||||
from tracking.utils.plotting import Annotator, colors
|
||||
from tracking.utils import Boxes, IterableSimpleNamespace, yaml_load, boxes_add_fid
|
||||
from tracking.trackers import BOTSORT, BYTETracker
|
||||
from tracking.utils.showtrack import drawtracks
|
||||
from hands.hand_inference import hand_pose
|
||||
from tracking.utils.read_data import read_weight_sensor, extract_data_realtime, read_tracking_output_realtime
|
||||
from contrast.feat_extract.config import config as conf
|
||||
from contrast.feat_extract.inference import FeatsInterface
|
||||
from tracking.utils.drawtracks import drawTrack
|
||||
|
||||
|
||||
ReIDEncoder = FeatsInterface(conf)
|
||||
|
||||
|
||||
W, H = 1024, 1280
|
||||
Mode = 'front' #'back'
|
||||
ImgFormat = ['.jpg', '.jpeg', '.png', '.bmp']
|
||||
|
||||
|
||||
'''调用tracking()函数，利用本地跟踪算法获取各目标轨迹，可以比较本地跟踪算法与现场跟踪算法的区别。'''
|
||||
def init_tracker(tracker_yaml = None, bs=1):
|
||||
"""
|
||||
Initialize tracker for object tracking during prediction.
|
||||
"""
|
||||
TRACKER_MAP = {'bytetrack': BYTETracker, 'botsort': BOTSORT}
|
||||
cfg = IterableSimpleNamespace(**yaml_load(tracker_yaml))
|
||||
|
||||
tracker = TRACKER_MAP[cfg.tracker_type](args=cfg, frame_rate=30)
|
||||
|
||||
return tracker
|
||||
|
||||
|
||||
def init_trackers(tracker_yaml = None, bs=1):
|
||||
"""
|
||||
Initialize trackers for object tracking during prediction.
|
||||
"""
|
||||
# tracker_yaml = r"./tracking/trackers/cfg/botsort.yaml"
|
||||
|
||||
TRACKER_MAP = {'bytetrack': BYTETracker, 'botsort': BOTSORT}
|
||||
|
||||
cfg = IterableSimpleNamespace(**yaml_load(tracker_yaml))
|
||||
trackers = []
|
||||
for _ in range(bs):
|
||||
tracker = TRACKER_MAP[cfg.tracker_type](args=cfg, frame_rate=30)
|
||||
trackers.append(tracker)
|
||||
|
||||
return trackers
|
||||
|
||||
|
||||
def draw_box(img, tracks):
|
||||
annotator = Annotator(img.copy(), line_width=2)
|
||||
# for *xyxy, conf, cls in reversed(tracks):
|
||||
# name = f'{int(cls)} {conf:.2f}'
|
||||
# color = colors(int(cls), True)
|
||||
# annotator.box_label(xyxy, name, color=color)
|
||||
|
||||
for *xyxy, id, conf, cls, fid, bid in reversed(tracks):
|
||||
name = f'ID:{int(id)} {int(cls)} {conf:.2f}'
|
||||
color = colors(int(cls), True)
|
||||
annotator.box_label(xyxy, name, color=color)
|
||||
|
||||
|
||||
|
||||
im0 = annotator.result()
|
||||
|
||||
return im0
|
||||
|
||||
|
||||
|
||||
|
||||
def tracking(bboxes, ffeats):
|
||||
tracker_yaml = "./tracking/trackers/cfg/botsort.yaml"
|
||||
|
||||
tracker = init_tracker(tracker_yaml)
|
||||
|
||||
TrackBoxes = np.empty((0, 9), dtype = np.float32)
|
||||
TracksDict = {}
|
||||
|
||||
frmIds = []
|
||||
'''========================== 执行跟踪处理 ============================='''
|
||||
# dets 与 feats 应保持严格对应
|
||||
k=0
|
||||
for dets, feats in zip(bboxes, ffeats):
|
||||
|
||||
frmIds.append(np.unique(dets[:, 6]).astype(np.int64)[0])
|
||||
|
||||
boxes = dets[:, :6]
|
||||
det_tracking = Boxes(boxes).cpu().numpy()
|
||||
tracks, outfeats = tracker.update(det_tracking, features=feats)
|
||||
'''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
|
||||
0 1 2 3 4 5 6 7 8
|
||||
这里，frame_index 也可以用视频的 帧ID 代替, box_index 保持不变
|
||||
'''
|
||||
k += 1
|
||||
imgpath = r"D:\全实时\202502\tracker\Yolos_Tracking\tracker\1_1740891284792\1_1740891284792_{}.png".format(int(k))
|
||||
img = cv2.imread(imgpath)
|
||||
|
||||
im0 = draw_box(img, tracks)
|
||||
savepath = r"D:\全实时\202502\tracker\Yolos_Tracking\tracker\1_1740891284792\b\1_1740891284792_{}_b.png".format(k)
|
||||
cv2.imwrite(savepath, im0)
|
||||
|
||||
|
||||
if len(tracks):
|
||||
TrackBoxes = np.concatenate([TrackBoxes, tracks], axis=0)
|
||||
|
||||
# =============================================================================
|
||||
# FeatDict = {}
|
||||
# for track in tracks:
|
||||
# tid = int(track[8])
|
||||
# FeatDict.update({tid: feats[tid, :]})
|
||||
#
|
||||
# frameID = tracks[0, 7]
|
||||
#
|
||||
# # print(f"frameID: {int(frameID)}")
|
||||
# assert len(tracks) == len(FeatDict), f"Please check the func: tracker.update() at frameID({int(frameID)})"
|
||||
#
|
||||
# TracksDict[f"frame_{int(frameID)}"] = {"feats":FeatDict}
|
||||
# =============================================================================
|
||||
|
||||
|
||||
return TrackBoxes, TracksDict
|
||||
|
||||
def dotrack():
|
||||
|
||||
datapath = r"D:\全实时\202502\tracker\1_tracker_in.data"
|
||||
|
||||
bboxes, ffeats = extract_data_realtime(datapath)
|
||||
trackerboxes, tracker_feat_dict = tracking(bboxes, ffeats)
|
||||
|
||||
print("done!")
|
||||
|
||||
|
||||
# def plotbox():
|
||||
|
||||
# fpath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-3-3\20250303-103833-338_6928804010091_6928804010091\1_tracking_output.data"
|
||||
# imgpath = r"D:\全实时\202502\result\Yolos_Tracking\20250303-103833-338_6928804010091_6928804010091\1_1740969517953"
|
||||
# trackingboxes, trackingfeats, tracking_outboxes, tracking_outfeats = read_tracking_output_realtime(fpath)
|
||||
|
||||
# for *xyxy, id, conf, cls, fid, bid in tracking_outboxes[0]:
|
||||
# imgname = f"1_1740969517953_{int(fid)}.png"
|
||||
|
||||
# img_path = os.path.join(imgpath, imgname)
|
||||
# img = cv2.imread(img_path)
|
||||
# annotator = Annotator(img.copy(), line_width=2)
|
||||
|
||||
# name = f'ID:{int(id)} {int(cls)} {conf:.2f}'
|
||||
# color = colors(int(cls), True)
|
||||
# annotator.box_label(xyxy, name, color=color)
|
||||
# im0 = annotator.result()
|
||||
# cv2.imwrite(os.path.join(imgpath, f"1_1740969517953_{int(fid)}_.png"), im0)
|
||||
|
||||
# print(f"1_1740969676295_{int(fid)}_.png")
|
||||
|
||||
# print("done")
|
||||
|
||||
def video2imgs(videopath):
|
||||
cap = cv2.VideoCapture(str(videopath))
|
||||
k = 0
|
||||
while True:
|
||||
ret, frame = cap.read()
|
||||
if frame is None:
|
||||
break
|
||||
k += 1
|
||||
imgpath = videopath.parent / f"{videopath.stem}_{k}.png"
|
||||
cv2.imwrite(str(imgpath), frame)
|
||||
|
||||
|
||||
|
||||
def extract_evtimgs(evtpath):
|
||||
vidpaths = [v for v in evtpath.iterdir() if v.suffix == '.mp4']
|
||||
for vidpath in vidpaths:
|
||||
video2imgs(vidpath)
|
||||
|
||||
stamps = [name.stem.split('_')[1] for name in vidpaths]
|
||||
|
||||
if len(set(stamps)==1):
|
||||
return stamps[0]
|
||||
return None
|
||||
|
||||
def draw_tracking_boxes(evtpath, stamp):
|
||||
for datapath in evtpath.iterdir():
|
||||
if datapath.name.find('_tracking_output.data')<=0:
|
||||
continue
|
||||
|
||||
camera = datapath.stem.split('_')[0]
|
||||
trackingboxes, trackingfeats, tracking_outboxes, tracking_outfeats = read_tracking_output_realtime(str(datapath))
|
||||
|
||||
## 该模块先读取轨迹数据，再根据帧ID读取相应图像
|
||||
for *xyxy, id, conf, cls, fid, bid in tracking_outboxes[0]:
|
||||
imgpath = evtpath / f"{camera}_{stamp}_{int(fid)}.png"
|
||||
|
||||
img = cv2.imread(str(imgpath))
|
||||
annotator = Annotator(img.copy(), line_width=2)
|
||||
|
||||
name = f'ID:{int(id)} {int(cls)} {conf:.2f}'
|
||||
color = colors(int(cls), True)
|
||||
annotator.box_label(xyxy, name, color=color)
|
||||
im0 = annotator.result()
|
||||
cv2.imwrite(imgpath, im0)
|
||||
|
||||
print(datapath.name)
|
||||
|
||||
def draw_traj(evtpath):
|
||||
for datapath in evtpath.iterdir():
|
||||
if datapath.name.find('_tracking_output.data')<=0:
|
||||
continue
|
||||
|
||||
fname = datapath.name
|
||||
trackingboxes, trackingfeats, tracking_outboxes, tracking_outfeats = read_tracking_output_realtime(datapath)
|
||||
|
||||
CamerType = fname.split('_')[0]
|
||||
if CamerType == '1':
|
||||
edgeline = cv2.imread("./CartTemp/board_ftmp_line.png")
|
||||
if CamerType == '0':
|
||||
edgeline = cv2.imread("./CartTemp/edgeline.png")
|
||||
edgeline = drawTrack(tracking_outboxes, edgeline)
|
||||
|
||||
|
||||
imgpath = datapath.parent / f"{datapath.stem}.png"
|
||||
cv2.imwrite(str(imgpath), edgeline)
|
||||
|
||||
|
||||
def main():
|
||||
|
||||
path = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-3-3\20250303-104225-381_6920459958674"
|
||||
evtpaths = [p for p in Path(path).iterdir() if p.is_dir()]
|
||||
for evtpath in evtpaths:
|
||||
#1. 从事件的前后摄视频提取图像
|
||||
stamp = extract_evtimgs(evtpath)
|
||||
|
||||
#2. 根据 0/1_tracking_output.data 中提取的轨迹在img中绘制box
|
||||
draw_tracking_boxes(evtpath, stamp)
|
||||
|
||||
#3. 根据 0/1_tracking_output.data 中提取的轨迹在edgeline中绘制box
|
||||
draw_traj(evtpath)
|
||||
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# dotrack()
|
||||
# plotbox()
|
||||
|
||||
vpath = r"D:\datasets\ym\VID_20250307_105606"
|
||||
extract_evtimgs(Path(vpath))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user