315 lines
10 KiB
Python
315 lines
10 KiB
Python
# -*- 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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