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更新 detacttracking

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2025-01-22 13:16:44 +08:00
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# -*- coding: utf-8 -*-
"""
Created on Mon Jan 15 15:26:38 2024
@author: ym
"""
import numpy as np
import cv2
import os
from pathlib import Path
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from tracking.utils.annotator import TrackAnnotator
from tracking.utils.plotting import colors
def plot_frameID_y2(vts):
# boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
# 0, 1, 2, 3, 4, 5, 6, 7, 8
markers = ['o', 'v', '^', '<', '>', 's', 'p', 'P','*', '+', 'x', 'X', 'd', 'D', 'H']
colors = ['b', 'g', 'c', 'm', 'y', ]
bboxes = vts.bboxes
maxfid = max(vts.bboxes[:, 7])
CART_HIGH_THRESH1 = 430
TRACK_STATIC_THRESH = 8
fig = plt.figure(figsize=(16, 12))
gs = fig.add_gridspec(2, 1, left=0.1, right=0.9, bottom=0.1, top=0.9,
wspace=0.05, hspace=0.15)
# ax1, ax2 = axs
ax1 = fig.add_subplot(gs[0,0])
ax2 = fig.add_subplot(gs[1,0])
ax1.plot((0, maxfid+5), (1280-CART_HIGH_THRESH1, 1280-CART_HIGH_THRESH1), 'b--', linewidth=2 )
ax2.plot((0, maxfid+5), (1280-CART_HIGH_THRESH1, 1280-CART_HIGH_THRESH1), 'b--', linewidth=2 )
hands = [t for t in vts.Hands if not t.isHandStatic]
tracks = vts.join_tracks(vts.Residual, hands)
for i, track in enumerate(vts.tracks):
boxes = track.boxes
cls, tid = track.cls, track.tid
y2, fids = boxes[:, 3], boxes[:, 7]
if cls==0:
ax1.scatter(fids, 1280-y2, marker='4', s=50, color=colors[tid%len(colors)], label = f"ID_{tid}")
else:
ax1.scatter(fids, 1280-y2, marker=markers[tid%len(markers)], color=colors[tid%len(colors)],
s=50, label = f"ID_{tid}")
# hist, bins = np.histogram(1280-y2, bins='auto')
ax1.set_ylim([-50, 1350])
for i, track in enumerate(tracks):
boxes = track.boxes
cls, tid = track.cls, track.tid
y2, fids = boxes[:, 3], boxes[:, 7]
if cls==0:
ax2.scatter(fids, 1280-y2, marker='4', s=50, color=colors[tid%len(colors)], label = f"ID_{tid}")
else:
ax2.scatter(fids, 1280-y2, marker=markers[tid%len(markers)], color=colors[tid%len(colors)],
s=50, label = f"ID_{tid}")
# hist, bins = np.histogram(1280-y2, bins='auto')
ax2.set_ylim([-50, 1350])
ax1.grid(True), ax1.set_xlim(0, maxfid+5), ax1.set_title('y2')
ax1.legend()
ax2.grid(True), ax2.set_xlim(0, maxfid+5), ax2.set_title('y2')
ax2.legend()
# plt.show()
return plt
def draw_all_trajectories(vts, edgeline, save_dir, file, draw5p=False):
'''显示四种类型结果'''
# file, ext = os.path.splitext(filename)
# edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
# edgeline2 = edgeline1.copy()
# edgeline = np.concatenate((edgeline1, edgeline2), exis = 1)
if not isinstance(save_dir, Path): save_dir = Path(save_dir)
''' all tracks 中心轨迹'''
img1, img2 = edgeline.copy(), edgeline.copy()
img1 = drawTrack(vts.tracks, img1)
img2 = drawTrack(vts.Residual, img2)
imgcat = np.concatenate((img1, img2), axis = 1)
H, W = imgcat.shape[:2]
cv2.line(imgcat, (int(W/2), 0), (int(W/2), H), (128, 255, 128), 2)
# imgpth = save_dir.joinpath(f"{file}_show.png")
# cv2.imwrite(str(imgpth), img)
if not draw5p:
return imgcat
''' tracks 5点轨迹'''
trackpth = save_dir / Path("trajectory")
if not trackpth.exists():
trackpth.mkdir(parents=True, exist_ok=True)
for track in vts.tracks:
# if track.cls != 0:
img = edgeline.copy()
img = draw5points(track, img)
pth = trackpth.joinpath(f"{file}_{track.tid}_.png")
cv2.imwrite(str(pth), img)
# for track in vts.Residual:
# # if track.cls != 0:
# img = edgeline.copy()
# img = draw5points(track, img)
# pth = trackpth.joinpath(f"{file}_{track.tid}_.png")
# cv2.imwrite(str(pth), img)
return imgcat
# =============================================================================
# '''3. moving tracks 中心轨迹'''
# filename2 = f"{file}_show_r.png"
# img = edgeline.copy()
# img = drawTrack(vts.Residual, img)
# pth = save_dir.joinpath(filename2)
# cv2.imwrite(pth, img)
# =============================================================================
'''5. tracks 时序：trajmin、trajmax、arearate、incartrate'''
# plt = drawtracefeat(vts)
# pth = save_dir.joinpath(f"{file}_x.png")
# plt.savefig(pth)
# plt.close('all')
def drawFeatures(allvts, save_dir):
# [trajlen_min, trajdist_max, trajlen_rate, trajist_rate]]
feats = [track.TrajFeat for vts in allvts for track in vts.tracks]
feats = np.array(feats)
fig, ax = plt.subplots()
ax.scatter(feats[:,3], feats[:, 1], s=10)
# ax.set_xlim(0, 2)
# ax.set_ylim(0, 100)
ax.grid(True)
plt.show()
pth = save_dir.joinpath("scatter.png")
plt.savefig(pth)
plt.close('all')
def drawtracefeat(vts):
'''
需要对曲线进行特征提取和分类
boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
0 1 2 3 4 5 6 7 8
'''
# matplotlib.use('Agg')
fid = vts.frameid
fid1, fid2 = min(fid), max(fid)
fig, axs = plt.subplots(2, 2,figsize=(18, 8))
kernel = [0.15, 0.7, 0.15]
for i, track in enumerate(vts.tracks):
boxes = track.boxes
tid = int(track.tid)
cls = int(track.cls)
posState = track.posState
if track.frnum>=5:
x1 = boxes[1:, 7]
y1 = track.trajmin
x11 = [i for i in range(int(min(x1)), int(max(x1)+1))]
y11 = np.interp(x11, x1, y1)
y11[1:-1] = np.convolve(y11, kernel, 'valid')
x3 = boxes[1:, 7]
y3 = track.trajmax
x33 = [i for i in range(int(min(x3)), int(max(x3)+1))]
y33 = np.interp(x33, x3, y3)
y33[1:-1] = np.convolve(y33, kernel, 'valid')
x2 = boxes[:, 7]
# y2 = track.Area/max(track.Area) - min(track.Area/max(track.Area))
y2 = track.Area/max(track.Area)
x22 = [i for i in range(int(min(x2)), int(max(x2)+1))]
y22 = np.interp(x22, x2, y2)
y22[1:-1] = np.convolve(y22, kernel, 'valid')
x4 = boxes[:, 7]
y4 = track.incartrates
x44 = [i for i in range(int(min(x4)), int(max(x4)+1))]
y44 = np.interp(x44, x4, y4)
y44[1:-1] = np.convolve(y44, kernel, 'valid')
elif track.frnum>=2:
x11 = boxes[1:, 7]
y11 = track.trajmin
x33 = boxes[1:, 7]
y33 = track.trajmax
x22 = boxes[:, 7]
# y22 = track.Area/max(track.Area) - min(track.Area/max(track.Area))
y22 = track.Area/max(track.Area)
x44 = boxes[:, 7]
y44 = track.incartrates
else:
continue
# cls!=0, max(y)>20
if cls!=0 and cls!=9 and posState>=2 and max(y11)>10 and max(y33)>10 and max(y22>0.1):
axs[0, 0].plot(x11, y11, label=f"ID_{tid}")
axs[0, 0].legend()
# axs[0].set_ylim(0, 100)
axs[0, 1].plot(x22, y22, label=f"ID_{tid}")
axs[0, 1].legend()
axs[1, 0].plot(x33, y33, label=f"ID_{tid}")
axs[1, 0].legend()
axs[1, 1].plot(x44, y44, label=f"ID_{tid}")
axs[1, 1].legend()
axs[0, 0].grid(True), axs[0, 0].set_xlim(fid1, fid2+10), axs[0, 0].set_title('trajmin')
axs[0, 1].grid(True), axs[0, 1].set_xlim(fid1, fid2+10), axs[0, 1].set_title('arearate')
axs[1, 0].grid(True), axs[1, 0].set_xlim(fid1, fid2+10), axs[1, 0].set_title('trajmax')
axs[1, 1].grid(True), axs[1, 1].set_xlim(fid1, fid2+10), axs[1, 1].set_ylim(-0.1, 1.1)
axs[1, 1].set_title('incartrate')
# pth = save_dir.joinpath(f"{file}_show_x.png")
# plt.savefig(pth)
# plt.savefig(f"./result/cls11_80212_time/{file}_show_x.png")
# plt.show()
return plt
def draw5points(track, img):
"""
显示中心点、4角点的轨迹，以及轨迹 features
"""
colorx = np.array([[255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255], [255, 255, 255],
[0, 0, 255], [0, 255, 0], [255, 51, 255], [102, 178, 255], [51, 153, 255],[255, 153, 153],
[255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102], [51, 255, 51],
[255, 102, 255], [153, 204, 255], [255, 0, 0], [255, 255, 255]], dtype=np.uint8)
color = ((0, 0, 255), (255, 128, 0))
# img = cv2.imread("./shopcart/cart_tempt/edgeline.png")
boxes = track.boxes
cornpoints = track.cornpoints
trajlens = [int(t) for t in track.trajlens]
trajdist = [int(t) for t in track.trajdist]
if len(track.trajmin):
trajstd = np.std(track.trajmin)
else:
trajstd = 0
trajlen_min, trajlen_max, trajdist_min, trajdist_max, trajlen_rate, trajdist_rate = track.TrajFeat
for i in range(boxes.shape[0]):
cv2.circle(img, (int(cornpoints[i, 0]), int(cornpoints[i, 1])), 6, (255, 255, 255), 2)
cv2.circle(img, (int(cornpoints[i, 2]), int(cornpoints[i, 3])), 6, (255, 0, 255), 2)
cv2.circle(img, (int(cornpoints[i, 4]), int(cornpoints[i, 5])), 6, (0, 255, 0), 2)
cv2.circle(img, (int(cornpoints[i, 6]), int(cornpoints[i, 7])), 6, (64, 128, 255), 2)
cv2.circle(img, (int(cornpoints[i, 8]), int(cornpoints[i, 9])), 6, (255, 128, 64), 2)
label_0 = f"ID: {track.tid}, Class: {track.cls}"
label_1 = f"trajlens: {trajlens}, trajlen_min: {int(trajlen_min)}"
label_2 = f"trajdist: {trajdist}: trajdist_max: {int(trajdist_max)}"
label_3 = "trajlen_min/trajlen_max: {:.2f}/{:.2f} = {:.2f}".format(trajlen_min, trajlen_max, trajlen_rate)
label_4 = "trajdist_min/mwh : {:.2f}/{:.2f} = {:.2f}".format(trajdist_min, track.mwh, trajdist_rate)
label_5 = "std(trajmin) : {:.2f}".format(trajstd)
label_6 = "PCA(variance_ratio) : "
label_7 = "Rect W&H&Ratio : "
label_8 = ""
# label_8 = "IOU of incart/maxbox/minbox: {:.2f}, {:.2f}, {:.2f}".format(
# track.feature_ious[0], track.feature_ious[3], track.feature_ious[4])
'''=============== 最小轨迹长度索引 ===================='''
trajlens = [int(t) for t in track.trajrects_wh]
if track.isCornpoint:
idx = 0
else:
idx = trajlens.index(min(trajlens))
'''=============== PCA ===================='''
if trajlens[idx] > 12:
X = cornpoints[:, 2*idx:2*(idx+1)]
pca = PCA()
pca.fit(X)
label_6 = "PCA(variance_ratio): {:.2f}".format(pca.explained_variance_ratio_[0])
# if sum(np.isnan(pca.explained_variance_ratio_)) == 0:
for i, (comp, var) in enumerate(zip(pca.components_, pca.explained_variance_ratio_)):
pt1 = (pca.mean_ - comp*var*200).astype(np.int64)
pt2 = (pca.mean_ + comp*var*200).astype(np.int64)
cv2.line(img, pt1, pt2, color=color[i], thickness=2)
'''=============== RECT ===================='''
rect = track.trajrects[idx]
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(img, [box], 0, (0, 255, 0), 2)
label_7 = "Rect W&H&Ratio: {}, {}, {:.2f}".format(int(rect[1][0]), int(rect[1][1]), min(rect[1])/(max(rect[1])+0.001))
'''=============== 显示文字 ===================='''
# label = [label_0, label_1, label_2, label_3, label_4, label_5, label_6, label_7, label_8]
# w, h = cv2.getTextSize('abc', 0, fontScale=2, thickness=1)[0]
# for i in range(len(label)):
# cv2.putText(img, label[i], (20, int((i+1)*1.1*h)), 0, 1,
# [int(x) for x in colorx[i]], 2, lineType=cv2.LINE_AA)
# pth = save_dir.joinpath(f"{file}_{track.tid}.png")
# cv2.imwrite(pth, img)
'''撰写专利需要，生成黑白图像'''
# imgbt = cv2.bitwise_not(img)
# for i in range(box.shape[0]):
# cv2.circle(imgbt, (int(cornpoints[i, 0]), int(cornpoints[i, 1])), 14, (0, 0, 0), 2)
# cv2.drawMarker(imgbt, (int(cornpoints[i, 2]), int(cornpoints[i, 3])), color= (0, 0, 0), markerType=3, markerSize = 30, thickness=2)
# cv2.drawMarker(imgbt, (int(cornpoints[i, 4]), int(cornpoints[i, 5])), color= (0, 0, 0), markerType=4, markerSize = 30, thickness=2)
# cv2.drawMarker(imgbt, (int(cornpoints[i, 6]), int(cornpoints[i, 7])), color= (0, 0, 0), markerType=5, markerSize = 30, thickness=2)
# cv2.drawMarker(imgbt, (int(cornpoints[i, 8]), int(cornpoints[i, 9])), color= (0, 0, 0), markerType=6, markerSize = 30, thickness=2)
# cv2.imwrite(pth + f"/zhuanli/{file}_{track.tid}.png", imgbt)
return img
def drawTrack(tracks, img):
# img = cv2.imread("./shopcart/cart_tempt/edgeline.png")
annotator = TrackAnnotator(img, line_width=2)
for track in tracks:
if isinstance(track, np.ndarray):
annotator.plotting_track(track)
else:
annotator.plotting_track(track.boxes)
img = annotator.result()
# pth = save_dir.joinpath(f"{filename}")
# cv2.imwrite(pth, img)
return img
if __name__ == "__main__":
y = np.array([5.0, 20, 40, 41, 42, 55, 56])