179 lines
5.2 KiB
Python
179 lines
5.2 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Mon Mar 4 18:36:31 2024
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@author: ym
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"""
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import numpy as np
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from utils.mergetrack import track_equal_track
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from scipy.spatial.distance import cdist
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from .dotracks import doTracks, ShoppingCart
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from .track_back import backTrack
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class doBackTracks(doTracks):
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def __init__(self, bboxes, TracksDict):
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super().__init__(bboxes, TracksDict)
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self.tracks = [backTrack(b) for b in self.lboxes]
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# self.similar_dict = self.similarity()
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self.shopcart = ShoppingCart(bboxes)
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# =============================================================================
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# def array2list(self):
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# ''' 0, 1, 2, 3, 4, 5, 6, 7, 8
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# bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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# lboxes：[x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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# '''
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#
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# track_ids = set(self.bboxes[:, 4])
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# lboxes = []
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# for t_id in track_ids:
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# idx = np.where(self.bboxes[:, 4] == t_id)[0]
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# box = self.bboxes[idx, :]
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#
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# x = (box[:, 0] + box[:, 2]) / 2
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# y = (box[:, 1] + box[:, 3]) / 2
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#
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# # box: [x, y, w, h, track_id, score, cls, frame_index]
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# box[:, 2] = box[:, 2] - box[:, 0]
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# box[:, 3] = box[:, 3] - box[:, 1]
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# box[:, 0] = x
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# box[:, 1] = y
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#
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# lboxes.append(box)
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#
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#
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# return lboxes
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# =============================================================================
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def classify(self):
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'''
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功能：对 tracks 中元素分类
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'''
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tracks = super().classify()
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# tracks = self.tracks
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# shopcart = self.shopcart
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# # 提取手的frame_id，并和动目标的frame_id 进行关联
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# hand_tracks = [t for t in tracks if t.cls==0]
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# self.Hands.extend(hand_tracks)
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# tracks = self.sub_tracks(tracks, hand_tracks)
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# # 提取小孩的track，并计算状态：left, right, incart
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# kid_tracks = [t for t in tracks if t.cls==9]
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# kid_states = [self.kid_state(t) for t in kid_tracks]
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# self.Kids = [x for x in zip(kid_tracks, kid_states)]
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# tracks = self.sub_tracks(tracks, kid_tracks)
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# static_tracks = [t for t in tracks if t.frnum>1 and t.is_static()]
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# self.Static.extend(static_tracks)
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# '''剔除静止目标后的 tracks'''
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# tracks = self.sub_tracks(tracks, static_tracks)
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'''购物框边界外具有运动状态的干扰目标'''
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out_trcak = [t for t in tracks if t.is_OutTrack()]
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tracks = self.sub_tracks(tracks, out_trcak)
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'''轨迹循环归并'''
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# merged_tracks = self.merge_tracks(tracks)
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merged_tracks = self.merge_tracks_loop(tracks)
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tracks = [t for t in merged_tracks if t.frnum > 1]
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self.Residual = tracks
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def merge_tracks(self, Residual):
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"""
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对不同id，但可能是同一商品的目标进行归并
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"""
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mergedTracks = self.base_merge_tracks(Residual)
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oldtracks, newtracks = [], []
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for tracklist in mergedTracks:
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if len(tracklist) > 1:
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boxes = np.empty((0, 9), dtype=np.float32)
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for i, track in enumerate(tracklist):
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if i==0: ntid, ncls=track.boxes[0, 4], track.boxes[0, 6]
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iboxes = track.boxes.copy()
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iboxes[:, 4], iboxes[:, 6] = ntid, ncls
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boxes = np.concatenate((boxes, iboxes), axis=0)
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oldtracks.append(track)
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fid_indices = np.argsort(boxes[:, 7])
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boxes_fid = boxes[fid_indices]
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newtracks.append(backTrack(boxes_fid))
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elif len(tracklist) == 1:
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oldtracks.append(tracklist[0])
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newtracks.append(tracklist[0])
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redu = self.sub_tracks(Residual, oldtracks)
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merged = self.join_tracks(redu, newtracks)
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return merged
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def kid_state(self, track):
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left_dist = track.cornpoints[:, 2]
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right_dist = 1024 - track.cornpoints[:, 4]
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if np.sum(left_dist<30)/track.frnum>0.8 and np.sum(right_dist>512)/track.frnum>0.7:
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kidstate = "left"
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elif np.sum(left_dist>512)/track.frnum>0.7 and np.sum(right_dist<30)/track.frnum>0.8:
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kidstate = "right"
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else:
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kidstate = "incart"
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return kidstate
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def is_associate_with_hand(self):
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"""
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分析商品和手之间的关联性
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"""
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pass
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def isuptrack(self, track):
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Flag = False
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return Flag
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def isdowntrack(self, track):
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Flag = False
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return Flag
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def isfreetrack(self, track):
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Flag = False
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return Flag |