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tracking by 8 states.py
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tracking by 8 states.py
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import os
import cv2
import numpy as np
from utils import plot_one_box, cal_iou, xyxy_to_xywh, xywh_to_xyxy, updata_trace_list, draw_trace
# 单目标跟踪
# 检测器获得检测框，全程只赋予1个ID，有两个相同的东西进来时，不会丢失唯一跟踪目标
# 检测器的检测框为测量值
# 目标的状态X = [x,y,h,w,delta_x,delta_y],中心坐标，宽高，中心坐标速度
# 观测值
# 如何寻找目标的观测值
# 观测到的是N个框
# 怎么找到目标的观测值
# t时刻的框与t-1后验估计时刻IOU最大的框的那个作为观测值（存在误差，交叉情况下观测值会有误差）
# 所以需要使用先验估计值进行融合
#
# 状态初始化
initial_target_box = [729, 238, 764, 339] # 目标初始bouding box
# initial_target_box = [193 ,342 ,250 ,474]
initial_box_state = xyxy_to_xywh(initial_target_box)
initial_state = np.array([[initial_box_state[0], initial_box_state[1], initial_box_state[2], initial_box_state[3],
0, 0, 0, 0]]).T # [中心x,中心y,宽w,高h,dx,dy,dw,dh]
# 状态转移矩阵，上一时刻的状态转移到当前时刻
A = np.array([[1, 0, 0, 0, 1, 0, 0, 0],
[0, 1, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 1, 0],
[0, 0, 0, 1, 0, 0, 0, 1],
[0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1]])
# 观测值丢失时使用的状态转移矩阵
A_ = np.array([[1, 0, 0, 0, 1, 0, 0, 0],
[0, 1, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1]])
# 状态观测矩阵
H = np.eye(8)
# 过程噪声协方差矩阵Q，p(w)~N(0,Q)，噪声来自真实世界中的不确定性,
# 在跟踪任务当中，过程噪声来自于目标移动的不确定性（突然加速、减速、转弯等）
Q = np.eye(8) * 0.1
# 观测噪声协方差矩阵R，p(v)~N(0,R)
# 观测噪声来自于检测框丢失、重叠等
R = np.eye(8) * 10
# 控制输入矩阵B
B = None
# 状态估计协方差矩阵P初始化
P = np.eye(8)
if __name__ == "__main__":
video_path = "./data/testvideo1.mp4"
label_path = "./data/labels"
file_name = "testvideo1"
cap = cv2.VideoCapture(video_path)
# cv2.namedWindow("track", cv2.WINDOW_NORMAL)
SAVE_VIDEO = False
if SAVE_VIDEO:
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('kalman_output.avi', fourcc, 20,(768,576))
# ---------状态初始化----------------------------------------
frame_counter = 1
X_posterior = np.array(initial_state)
P_posterior = np.array(P)
Z = np.array(initial_state)
trace_list = [] # 用于保存目标box的轨迹
while (True):
# Capture frame-by-frame
ret, frame = cap.read()
last_box_posterior = xywh_to_xyxy(X_posterior[0:4])
plot_one_box(last_box_posterior, frame, color=(255, 255, 255), target=False)
if not ret:
break
print(frame_counter)
label_file_path = os.path.join(label_path, file_name + "_" + str(frame_counter) + ".txt")
with open(label_file_path, "r") as f:
content = f.readlines()
max_iou = 0
max_iou_matched = False
# ---------使用最大IOU来寻找观测值------------
for j, data_ in enumerate(content):
data = data_.replace('\n', "").split(" ")
xyxy = np.array(data[1:5], dtype="float")
plot_one_box(xyxy, frame)
iou = cal_iou(xyxy, xywh_to_xyxy(X_posterior[0:4]))
if iou > max_iou:
target_box = xyxy
max_iou = iou
max_iou_matched = True
if max_iou_matched == True:
# 如果找到了最大IOU BOX,则认为该框为观测值
plot_one_box(target_box, frame, target=True)
xywh = xyxy_to_xywh(target_box)
box_center = (int((target_box[0] + target_box[2]) // 2), int((target_box[1] + target_box[3]) // 2))
trace_list = updata_trace_list(box_center, trace_list, 20)
cv2.putText(frame, "Tracking", (int(target_box[0]), int(target_box[1] - 5)), cv2.FONT_HERSHEY_SIMPLEX,
0.7,
(255, 0, 0), 2)
# 计算dx,dy,dw,dh
dx = xywh[0] - X_posterior[0]
dy = xywh[1] - X_posterior[1]
dw = xywh[2] - X_posterior[2]
dh = xywh[3] - X_posterior[3]
Z[0:4] = np.array([xywh]).T
Z[4::] = np.array([dx, dy, dw, dh])
if max_iou_matched:
# -----进行先验估计-----------------
X_prior = np.dot(A, X_posterior)
box_prior = xywh_to_xyxy(X_prior[0:4])
# plot_one_box(box_prior, frame, color=(0, 0, 0), target=False)
# -----计算状态估计协方差矩阵P--------
P_prior_1 = np.dot(A, P_posterior)
P_prior = np.dot(P_prior_1, A.T) + Q
# ------计算卡尔曼增益---------------------
k1 = np.dot(P_prior, H.T)
k2 = np.dot(np.dot(H, P_prior), H.T) + R
K = np.dot(k1, np.linalg.inv(k2))
# --------------后验估计------------
X_posterior_1 = Z - np.dot(H, X_prior)
X_posterior = X_prior + np.dot(K, X_posterior_1)
box_posterior = xywh_to_xyxy(X_posterior[0:4])
# plot_one_box(box_posterior, frame, color=(255, 255, 255), target=False)
# ---------更新状态估计协方差矩阵P-----
P_posterior_1 = np.eye(8) - np.dot(K, H)
P_posterior = np.dot(P_posterior_1, P_prior)
else:
# 如果IOU匹配失败，此时失去观测值，那么直接使用上一次的最优估计作为先验估计
# 此时直接迭代，不使用卡尔曼滤波
X_posterior = np.dot(A_, X_posterior)
# X_posterior = np.dot(A_, X_posterior)
box_posterior = xywh_to_xyxy(X_posterior[0:4])
# plot_one_box(box_posterior, frame, color=(255, 255, 255), target=False)
box_center = (
(int(box_posterior[0] + box_posterior[2]) // 2), int((box_posterior[1] + box_posterior[3]) // 2))
trace_list = updata_trace_list(box_center, trace_list, 20)
cv2.putText(frame, "Lost", (box_center[0], box_center[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.7,
(255, 0, 0), 2)
draw_trace(frame, trace_list)
cv2.putText(frame, "ALL BOXES(Green)", (25, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 200, 0), 2)
cv2.putText(frame, "TRACKED BOX(Red)", (25, 75), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, "Last frame best estimation(White)", (25, 100), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
cv2.imshow('track', frame)
if SAVE_VIDEO:
out.write(frame)
frame_counter = frame_counter + 1
if cv2.waitKey(0) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
# 写完了