回传数据解析，兼容v5和v10
This commit is contained in:
jiajie555
387
tracking/utils/drawtracks.py
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387
tracking/utils/drawtracks.py
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Jan 15 15:26:38 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 os
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from pathlib import Path
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import matplotlib.pyplot as plt
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from sklearn.decomposition import PCA
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from tracking.utils.annotator import TrackAnnotator
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from tracking.utils.plotting import colors
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def plot_frameID_y2(vts):
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# boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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# 0, 1, 2, 3, 4, 5, 6, 7, 8
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markers = ['o', 'v', '^', '<', '>', 's', 'p', 'P','*', '+', 'x', 'X', 'd', 'D', 'H']
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colors = ['b', 'g', 'c', 'm', 'y', ]
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bboxes = vts.bboxes
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maxfid = max(vts.bboxes[:, 7])
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CART_HIGH_THRESH1 = 430
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TRACK_STATIC_THRESH = 8
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fig = plt.figure(figsize=(16, 12))
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gs = fig.add_gridspec(2, 1, left=0.1, right=0.9, bottom=0.1, top=0.9,
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wspace=0.05, hspace=0.15)
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# ax1, ax2 = axs
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ax1 = fig.add_subplot(gs[0,0])
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ax2 = fig.add_subplot(gs[1,0])
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ax1.plot((0, maxfid+5), (1280-CART_HIGH_THRESH1, 1280-CART_HIGH_THRESH1), 'b--', linewidth=2 )
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ax2.plot((0, maxfid+5), (1280-CART_HIGH_THRESH1, 1280-CART_HIGH_THRESH1), 'b--', linewidth=2 )
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hands = [t for t in vts.Hands if not t.isHandStatic]
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tracks = vts.join_tracks(vts.Residual, hands)
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for i, track in enumerate(vts.tracks):
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boxes = track.boxes
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cls, tid = track.cls, track.tid
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y2, fids = boxes[:, 3], boxes[:, 7]
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if cls==0:
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ax1.scatter(fids, 1280-y2, marker='4', s=50, color=colors[tid%len(colors)], label = f"ID_{tid}")
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else:
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ax1.scatter(fids, 1280-y2, marker=markers[tid%len(markers)], color=colors[tid%len(colors)],
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s=50, label = f"ID_{tid}")
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# hist, bins = np.histogram(1280-y2, bins='auto')
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ax1.set_ylim([-50, 1350])
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for i, track in enumerate(tracks):
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boxes = track.boxes
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cls, tid = track.cls, track.tid
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y2, fids = boxes[:, 3], boxes[:, 7]
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if cls==0:
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ax2.scatter(fids, 1280-y2, marker='4', s=50, color=colors[tid%len(colors)], label = f"ID_{tid}")
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else:
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ax2.scatter(fids, 1280-y2, marker=markers[tid%len(markers)], color=colors[tid%len(colors)],
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s=50, label = f"ID_{tid}")
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# hist, bins = np.histogram(1280-y2, bins='auto')
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ax2.set_ylim([-50, 1350])
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ax1.grid(True), ax1.set_xlim(0, maxfid+5), ax1.set_title('y2')
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ax1.legend()
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ax2.grid(True), ax2.set_xlim(0, maxfid+5), ax2.set_title('y2')
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ax2.legend()
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# plt.show()
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return plt
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def draw_all_trajectories(vts, edgeline, save_dir, file, draw5p=False):
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'''显示四种类型结果'''
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# file, ext = os.path.splitext(filename)
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# edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
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# edgeline2 = edgeline1.copy()
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# edgeline = np.concatenate((edgeline1, edgeline2), exis = 1)
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if not isinstance(save_dir, Path): save_dir = Path(save_dir)
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''' all tracks 中心轨迹'''
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img1, img2 = edgeline.copy(), edgeline.copy()
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img1 = drawTrack(vts.tracks, img1)
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img2 = drawTrack(vts.Residual, img2)
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imgcat = np.concatenate((img1, img2), axis = 1)
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H, W = imgcat.shape[:2]
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cv2.line(imgcat, (int(W/2), 0), (int(W/2), H), (128, 255, 128), 2)
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# imgpth = save_dir.joinpath(f"{file}_show.png")
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# cv2.imwrite(str(imgpth), img)
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if not draw5p:
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return imgcat
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## yi write piclke change
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''' tracks 5点轨迹'''
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# trackpth = save_dir / Path("trajectory")
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# if not trackpth.exists():
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# trackpth.mkdir(parents=True, exist_ok=True)
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# for track in vts.tracks:
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# # if track.cls != 0:
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# img = edgeline.copy()
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# img = draw5points(track, img)
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#
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# pth = trackpth.joinpath(f"{file}_{track.tid}_.png")
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# cv2.imwrite(str(pth), img)
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# for track in vts.Residual:
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# # if track.cls != 0:
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# img = edgeline.copy()
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# img = draw5points(track, img)
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# pth = trackpth.joinpath(f"{file}_{track.tid}_.png")
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# cv2.imwrite(str(pth), img)
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return imgcat
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# =============================================================================
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# '''3. moving tracks 中心轨迹'''
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# filename2 = f"{file}_show_r.png"
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# img = edgeline.copy()
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# img = drawTrack(vts.Residual, img)
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# pth = save_dir.joinpath(filename2)
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# cv2.imwrite(pth, img)
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# =============================================================================
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'''5. tracks 时序：trajmin、trajmax、arearate、incartrate'''
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# plt = drawtracefeat(vts)
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# pth = save_dir.joinpath(f"{file}_x.png")
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# plt.savefig(pth)
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# plt.close('all')
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def drawFeatures(allvts, save_dir):
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# [trajlen_min, trajdist_max, trajlen_rate, trajist_rate]]
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feats = [track.TrajFeat for vts in allvts for track in vts.tracks]
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feats = np.array(feats)
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fig, ax = plt.subplots()
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ax.scatter(feats[:,3], feats[:, 1], s=10)
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# ax.set_xlim(0, 2)
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# ax.set_ylim(0, 100)
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ax.grid(True)
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plt.show()
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pth = save_dir.joinpath("scatter.png")
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plt.savefig(pth)
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plt.close('all')
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def drawtracefeat(vts):
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'''
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需要对曲线进行特征提取和分类
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boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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0 1 2 3 4 5 6 7 8
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'''
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# matplotlib.use('Agg')
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fid = vts.frameid
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fid1, fid2 = min(fid), max(fid)
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fig, axs = plt.subplots(2, 2,figsize=(18, 8))
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kernel = [0.15, 0.7, 0.15]
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for i, track in enumerate(vts.tracks):
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boxes = track.boxes
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tid = int(track.tid)
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cls = int(track.cls)
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posState = track.posState
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if track.frnum>=5:
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x1 = boxes[1:, 7]
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y1 = track.trajmin
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x11 = [i for i in range(int(min(x1)), int(max(x1)+1))]
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y11 = np.interp(x11, x1, y1)
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y11[1:-1] = np.convolve(y11, kernel, 'valid')
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x3 = boxes[1:, 7]
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y3 = track.trajmax
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x33 = [i for i in range(int(min(x3)), int(max(x3)+1))]
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y33 = np.interp(x33, x3, y3)
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y33[1:-1] = np.convolve(y33, kernel, 'valid')
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x2 = boxes[:, 7]
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# y2 = track.Area/max(track.Area) - min(track.Area/max(track.Area))
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y2 = track.Area/max(track.Area)
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x22 = [i for i in range(int(min(x2)), int(max(x2)+1))]
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y22 = np.interp(x22, x2, y2)
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y22[1:-1] = np.convolve(y22, kernel, 'valid')
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x4 = boxes[:, 7]
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y4 = track.incartrates
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x44 = [i for i in range(int(min(x4)), int(max(x4)+1))]
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y44 = np.interp(x44, x4, y4)
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y44[1:-1] = np.convolve(y44, kernel, 'valid')
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elif track.frnum>=2:
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x11 = boxes[1:, 7]
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y11 = track.trajmin
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x33 = boxes[1:, 7]
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y33 = track.trajmax
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x22 = boxes[:, 7]
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# y22 = track.Area/max(track.Area) - min(track.Area/max(track.Area))
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y22 = track.Area/max(track.Area)
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x44 = boxes[:, 7]
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y44 = track.incartrates
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else:
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continue
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# cls!=0, max(y)>20
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if cls!=0 and cls!=9 and posState>=2 and max(y11)>10 and max(y33)>10 and max(y22>0.1):
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axs[0, 0].plot(x11, y11, label=f"ID_{tid}")
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axs[0, 0].legend()
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# axs[0].set_ylim(0, 100)
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axs[0, 1].plot(x22, y22, label=f"ID_{tid}")
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axs[0, 1].legend()
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axs[1, 0].plot(x33, y33, label=f"ID_{tid}")
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axs[1, 0].legend()
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axs[1, 1].plot(x44, y44, label=f"ID_{tid}")
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axs[1, 1].legend()
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axs[0, 0].grid(True), axs[0, 0].set_xlim(fid1, fid2+10), axs[0, 0].set_title('trajmin')
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axs[0, 1].grid(True), axs[0, 1].set_xlim(fid1, fid2+10), axs[0, 1].set_title('arearate')
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axs[1, 0].grid(True), axs[1, 0].set_xlim(fid1, fid2+10), axs[1, 0].set_title('trajmax')
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axs[1, 1].grid(True), axs[1, 1].set_xlim(fid1, fid2+10), axs[1, 1].set_ylim(-0.1, 1.1)
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axs[1, 1].set_title('incartrate')
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# pth = save_dir.joinpath(f"{file}_show_x.png")
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# plt.savefig(pth)
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# plt.savefig(f"./result/cls11_80212_time/{file}_show_x.png")
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# plt.show()
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return plt
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def draw5points(track, img):
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"""
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显示中心点、4角点的轨迹，以及轨迹 features
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"""
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colorx = np.array([[255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255],
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[0, 0, 255], [0, 255, 0], [255, 51, 255], [102, 178, 255], [51, 153, 255],[255, 153, 153],
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[255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102], [51, 255, 51],
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[255, 102, 255], [153, 204, 255], [255, 0, 0], [255, 255, 255]], dtype=np.uint8)
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color = ((0, 0, 255), (255, 128, 0))
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# img = cv2.imread("./shopcart/cart_tempt/edgeline.png")
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boxes = track.boxes
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cornpoints = track.cornpoints
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trajlens = [int(t) for t in track.trajlens]
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trajdist = [int(t) for t in track.trajdist]
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if len(track.trajmin):
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trajstd = np.std(track.trajmin)
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else:
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trajstd = 0
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trajlen_min, trajlen_max, trajdist_min, trajdist_max, trajlen_rate, trajdist_rate = track.TrajFeat
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for i in range(boxes.shape[0]):
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cv2.circle(img, (int(cornpoints[i, 0]), int(cornpoints[i, 1])), 6, (255, 255, 255), 2)
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cv2.circle(img, (int(cornpoints[i, 2]), int(cornpoints[i, 3])), 6, (255, 0, 255), 2)
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cv2.circle(img, (int(cornpoints[i, 4]), int(cornpoints[i, 5])), 6, (0, 255, 0), 2)
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cv2.circle(img, (int(cornpoints[i, 6]), int(cornpoints[i, 7])), 6, (64, 128, 255), 2)
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cv2.circle(img, (int(cornpoints[i, 8]), int(cornpoints[i, 9])), 6, (255, 128, 64), 2)
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label_0 = f"ID: {track.tid}, Class: {track.cls}"
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label_1 = f"trajlens: {trajlens}, trajlen_min: {int(trajlen_min)}"
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label_2 = f"trajdist: {trajdist}: trajdist_max: {int(trajdist_max)}"
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label_3 = "trajlen_min/trajlen_max: {:.2f}/{:.2f} = {:.2f}".format(trajlen_min, trajlen_max, trajlen_rate)
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label_4 = "trajdist_min/mwh : {:.2f}/{:.2f} = {:.2f}".format(trajdist_min, track.mwh, trajdist_rate)
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label_5 = "std(trajmin) : {:.2f}".format(trajstd)
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label_6 = "PCA(variance_ratio) : "
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label_7 = "Rect W&H&Ratio : "
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label_8 = ""
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# label_8 = "IOU of incart/maxbox/minbox: {:.2f}, {:.2f}, {:.2f}".format(
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# track.feature_ious[0], track.feature_ious[3], track.feature_ious[4])
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'''=============== 最小轨迹长度索引 ===================='''
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trajlens = [int(t) for t in track.trajrects_wh]
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if track.isCornpoint:
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idx = 0
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else:
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idx = trajlens.index(min(trajlens))
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'''=============== PCA ===================='''
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if trajlens[idx] > 12:
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X = cornpoints[:, 2*idx:2*(idx+1)]
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pca = PCA()
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pca.fit(X)
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label_6 = "PCA(variance_ratio): {:.2f}".format(pca.explained_variance_ratio_[0])
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# if sum(np.isnan(pca.explained_variance_ratio_)) == 0:
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for i, (comp, var) in enumerate(zip(pca.components_, pca.explained_variance_ratio_)):
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pt1 = (pca.mean_ - comp*var*200).astype(np.int64)
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pt2 = (pca.mean_ + comp*var*200).astype(np.int64)
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cv2.line(img, pt1, pt2, color=color[i], thickness=2)
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'''=============== RECT ===================='''
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rect = track.trajrects[idx]
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box = cv2.boxPoints(rect)
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box = np.int0(box)
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cv2.drawContours(img, [box], 0, (0, 255, 0), 2)
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label_7 = "Rect W&H&Ratio: {}, {}, {:.2f}".format(int(rect[1][0]), int(rect[1][1]), min(rect[1])/(max(rect[1])+0.001))
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'''=============== 显示文字 ===================='''
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# label = [label_0, label_1, label_2, label_3, label_4, label_5, label_6, label_7, label_8]
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# w, h = cv2.getTextSize('abc', 0, fontScale=2, thickness=1)[0]
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# for i in range(len(label)):
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# cv2.putText(img, label[i], (20, int((i+1)*1.1*h)), 0, 1,
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# [int(x) for x in colorx[i]], 2, lineType=cv2.LINE_AA)
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# pth = save_dir.joinpath(f"{file}_{track.tid}.png")
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# cv2.imwrite(pth, img)
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'''撰写专利需要，生成黑白图像'''
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# imgbt = cv2.bitwise_not(img)
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# for i in range(box.shape[0]):
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# cv2.circle(imgbt, (int(cornpoints[i, 0]), int(cornpoints[i, 1])), 14, (0, 0, 0), 2)
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# cv2.drawMarker(imgbt, (int(cornpoints[i, 2]), int(cornpoints[i, 3])), color= (0, 0, 0), markerType=3, markerSize = 30, thickness=2)
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# cv2.drawMarker(imgbt, (int(cornpoints[i, 4]), int(cornpoints[i, 5])), color= (0, 0, 0), markerType=4, markerSize = 30, thickness=2)
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# cv2.drawMarker(imgbt, (int(cornpoints[i, 6]), int(cornpoints[i, 7])), color= (0, 0, 0), markerType=5, markerSize = 30, thickness=2)
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# cv2.drawMarker(imgbt, (int(cornpoints[i, 8]), int(cornpoints[i, 9])), color= (0, 0, 0), markerType=6, markerSize = 30, thickness=2)
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# cv2.imwrite(pth + f"/zhuanli/{file}_{track.tid}.png", imgbt)
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return img
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def drawTrack(tracks, img):
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# img = cv2.imread("./shopcart/cart_tempt/edgeline.png")
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annotator = TrackAnnotator(img, line_width=2)
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for track in tracks:
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if isinstance(track, np.ndarray):
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annotator.plotting_track(track)
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else:
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annotator.plotting_track(track.boxes)
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img = annotator.result()
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# pth = save_dir.joinpath(f"{filename}")
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# cv2.imwrite(pth, img)
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return img
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if __name__ == "__main__":
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y = np.array([5.0, 20, 40, 41, 42, 55, 56])
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Reference in New Issue
Block a user