267 lines
8.5 KiB
Python
267 lines
8.5 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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import cv2
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import copy
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from tracking.utils.mergetrack import track_equal_track
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from scipy.spatial.distance import cdist
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from pathlib import Path
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curpath = Path(__file__).resolve().parents[0]
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curpath = Path(curpath)
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parpath = curpath.parent
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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, trackefeats):
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super().__init__(bboxes, trackefeats)
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self.tracks = [backTrack(b, f) for b, f in zip(self.lboxes, self.lfeats)]
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# self.similar_dict = self.similarity()
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# self.shopcart = ShoppingCart(bboxes)
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self.incart = self.getincart()
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def getincart(self):
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img1 = cv2.imread(str(parpath/'shopcart/cart_tempt/incart.png'), cv2.IMREAD_GRAYSCALE)
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img2 = cv2.imread(str(parpath/'shopcart/cart_tempt/cartedge.png'), cv2.IMREAD_GRAYSCALE)
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ret, binary1 = cv2.threshold(img1, 250, 255, cv2.THRESH_BINARY)
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ret, binary2 = cv2.threshold(img2, 250, 255, cv2.THRESH_BINARY)
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binary = cv2.bitwise_or(binary1, binary2)
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return binary
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def classify(self):
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'''功能：对 tracks 中元素分类 '''
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tracks = self.tracks
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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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out_trcak = [t for t in tracks if t.isWholeOutCart]
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tracks = self.sub_tracks(tracks, out_trcak)
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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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tracks_free = [t for t in tracks if t.frnum>1 and t.is_freemove()]
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self.FreeMove.extend(tracks_free)
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tracks = self.sub_tracks(tracks, tracks_free)
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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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[self.associate_with_hand(htrack, gtrack) for htrack in hand_tracks for gtrack in tracks]
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tracks = [t for t in merged_tracks if t.frnum > 1]
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self.merged_tracks = merged_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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tracks = self.sub_tracks(tracks, static_tracks)
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# for gtrack in tracks:
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# for htrack in hand_tracks:
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# hand_ious = self.associate_with_hand(htrack, gtrack)
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# if len(hand_ious):
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# gtrack.Hands.append(htrack)
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# gtrack.HandsIou.append(hand_ious)
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# htrack.Goods.append((gtrack, hand_ious))
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# for htrack in hand_tracks:
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# self.merge_based_hands(htrack)
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self.Residual = tracks
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self.Confirmed = self.confirm_track()
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def confirm_track(self):
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Confirmed = None
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mindist = 0
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for track in self.Residual:
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md = min(track.trajrects_wh)
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if md > mindist:
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mindist = copy.deepcopy(md)
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Confirmed = copy.deepcopy(track)
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if Confirmed is not None:
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return [Confirmed]
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return []
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# def merge_based_hands(self, htrack):
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# gtracks = htrack.Goods
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# if len(gtracks) >= 2:
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# atrack, afious = gtracks[0]
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# btrack, bfious = gtracks[1]
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def associate_with_hand(self, htrack, gtrack):
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'''
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迁移至基类：
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手部 Track、商品 Track 建立关联的依据：
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a. 运动帧的帧索引有交集
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b. 帧索引交集部分iou均大于0
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'''
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assert htrack.cls==0 and gtrack.cls!=0 and gtrack.cls!=9, 'Track cls is Error!'
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hand_ious = []
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hboxes = np.empty(shape=(0, 9), dtype = np.float64)
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gboxes = np.empty(shape=(0, 9), dtype = np.float64)
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# start, end 为索引值，需要 start:(end+1)
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for start, end in htrack.moving_index:
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hboxes = np.concatenate((hboxes, htrack.boxes[start:end+1, :]), axis=0)
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for start, end in gtrack.moving_index:
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gboxes = np.concatenate((gboxes, gtrack.boxes[start:end+1, :]), axis=0)
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hfids, gfids = hboxes[:, 7], gboxes[:, 7]
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fids = sorted(set(hfids).intersection(set(gfids)))
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if len(fids)==0:
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return None
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# print(f"Goods ID: {gtrack.tid}, Hand ID: {htrack.tid}")
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for f in fids:
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h = np.where(hboxes[:,7] == f)[0][0]
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g = np.where(gboxes[:,7] == f)[0][0]
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x11, y11, x12, y12 = hboxes[h, 0:4]
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x21, y21, x22, y22 = gboxes[g, 0:4]
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x1, y1 = max((x11, x21)), max((y11, y21))
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x2, y2 = min((x12, x22)), min((y12, y22))
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union = (x2 - x1).clip(0) * (y2 - y1).clip(0)
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area1 = (x12 - x11) * (y12 - y11)
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area2 = (x22 - x21) * (y22 - y21)
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iou = union / (area1 + area2 - union + 1e-6)
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if iou >= 0.01:
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gtrack.Hands.append((htrack.tid, f, iou))
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return gtrack.Hands
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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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和 dotrack_front.py中函数相同，可以合并，可以合并至基类
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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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feats = np.empty((0, 256), 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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ifeats = track.features.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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feats = np.concatenate((feats, ifeats), 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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feats_fid = feats[fid_indices]
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newtracks.append(backTrack(boxes_fid, feats_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 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 |