更新 detacttracking
This commit is contained in:
lee
361
detecttracking/tracking/utils/showtrack.py
Normal file
361
detecttracking/tracking/utils/showtrack.py
Normal file
@ -0,0 +1,361 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed Jul 5 10:01:11 2023
|
||||
|
||||
@author: ym
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import os
|
||||
import cv2
|
||||
import sys
|
||||
|
||||
from scipy.spatial.distance import cdist
|
||||
import matplotlib
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.decomposition import PCA
|
||||
# from ultralytics.utils.plotting import Annotator, colors
|
||||
|
||||
|
||||
from .annotator import TrackAnnotator
|
||||
# from .processboxes import array2list
|
||||
|
||||
# boxes Format: [x1, y1, x2, y2, track_id, score, cls, frame_index]
|
||||
pth = r"D:/DeepLearning/yolov5/tracking/"
|
||||
colors = 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)
|
||||
|
||||
def array2list(bboxes):
|
||||
'''
|
||||
将 bboxes 变换为 track 列表
|
||||
bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
|
||||
Return：
|
||||
lboxes：列表，列表中元素具有同一 track_id，x1y1x2y2 格式
|
||||
[x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
|
||||
'''
|
||||
tids = set(bboxes[:, 4])
|
||||
track_ids = bboxes[:, 4].astype(int)
|
||||
lboxes = []
|
||||
for t_id in tids:
|
||||
# print(f"The ID is: {t_id}")
|
||||
idx = np.where(track_ids == t_id)[0]
|
||||
box = bboxes[idx, :]
|
||||
|
||||
lboxes.append(box)
|
||||
|
||||
return lboxes
|
||||
|
||||
|
||||
|
||||
def draw5points(bboxes, file):
|
||||
"""
|
||||
显示中心点、4角点的轨迹，以及轨迹 features
|
||||
"""
|
||||
image = cv2.imread(pth + r"/shopcart/cart_tempt/edgeline.png")
|
||||
|
||||
imgx = image.copy()
|
||||
annotator = TrackAnnotator(imgx, line_width=2)
|
||||
|
||||
lboxes = array2list(bboxes)
|
||||
|
||||
|
||||
for k in range(len(lboxes)):
|
||||
boxes = lboxes[k]
|
||||
|
||||
cls = int(boxes[0, 6])
|
||||
tid = int(boxes[0, 4])
|
||||
|
||||
# print(tid)
|
||||
|
||||
frnum = boxes.shape[0]
|
||||
cornpoints = np.zeros((frnum, 10))
|
||||
|
||||
cornpoints[:,0] = (boxes[:, 0] + boxes[:, 2]) / 2
|
||||
cornpoints[:,1] = (boxes[:, 1] + boxes[:, 3]) / 2
|
||||
cornpoints[:,2], cornpoints[:,3] = boxes[:, 0], boxes[:, 1]
|
||||
cornpoints[:,4], cornpoints[:,5] = boxes[:, 2], boxes[:, 1]
|
||||
cornpoints[:,6], cornpoints[:,7] = boxes[:, 0], boxes[:, 3]
|
||||
cornpoints[:,8], cornpoints[:,9] = boxes[:, 2], boxes[:, 3]
|
||||
|
||||
|
||||
x1, y1, x2, y2 = cornpoints[:,2],cornpoints[:,3],cornpoints[:,8],cornpoints[:,9]
|
||||
BoundPixel = 10
|
||||
BoundThresh = 0.4
|
||||
cont1 = sum(abs(x1)<BoundPixel) / frnum > BoundThresh
|
||||
cont2 = sum(abs(y1)<BoundPixel) / frnum > BoundThresh
|
||||
cont3 = sum(abs(x2-1024)<BoundPixel) / frnum > BoundThresh
|
||||
cont4 = sum(abs(y2-1280)<BoundPixel) / frnum > BoundThresh
|
||||
isImgBorder = False
|
||||
if cont1 or cont2 or cont3 or cont4:
|
||||
isImgBorder = True
|
||||
|
||||
# =============================================================================
|
||||
# '''情况1: 在商品运动过程中，商品检测框始终左下角点和图像左下角点重合, 用中心点代替'''
|
||||
# lfcn_dist = np.linalg.norm(cornpoints[:, 6:8] - [0, 1280], axis=1)
|
||||
# idx1 = lfcn_dist<10
|
||||
# if sum(idx1)/frnum > 0.5:
|
||||
# cornpoints[:, 6:8] = cornpoints[:, 0:2]
|
||||
#
|
||||
# '''情况2: 在商品运动过程中，商品检测框始终右下角点和图像右下角点重合, 用中心点代替'''
|
||||
# rtcn_dist = np.linalg.norm(cornpoints[:, 8:10] - [1024, 1280], axis=1)
|
||||
# idx2 = rtcn_dist<10
|
||||
# if sum(idx2)/frnum > 0.5:
|
||||
# cornpoints[:, 8:10] = cornpoints[:, 0:2]
|
||||
# =============================================================================
|
||||
|
||||
|
||||
mwh = (np.mean(boxes[:, 2]) + np.mean(boxes[:, 3]))/2
|
||||
|
||||
trajectory = []
|
||||
trajlens = []
|
||||
trajdist = []
|
||||
for k in range(5):
|
||||
traj = np.linalg.norm(np.diff(cornpoints[:, 2*k:2*(k+1)], axis = 0), axis=1)
|
||||
trajlen = np.sum(traj)
|
||||
ptdist = np.max(cdist(cornpoints[:, 2*k:2*(k+1)], cornpoints[:, 2*k:2*(k+1)]))
|
||||
|
||||
trajectory.append(traj)
|
||||
trajlens.append(trajlen)
|
||||
trajdist.append(ptdist)
|
||||
|
||||
if not isImgBorder:
|
||||
idx = trajlens.index(min(trajlens))
|
||||
|
||||
trajmin = trajectory[idx]
|
||||
trajlen_min = min(trajlens)
|
||||
trajdist_min = min(trajdist)
|
||||
else:
|
||||
trajmin = trajectory[0]
|
||||
trajlen_min = trajlens[0]
|
||||
trajdist_min = trajdist[0]
|
||||
|
||||
|
||||
'''最小轨迹长度/最大轨迹长度，越小，代表运动幅度越小'''
|
||||
trajlen_rate = trajlen_min/(max(trajlens)+0.0001)
|
||||
|
||||
'''最小轨迹欧氏距离/目标框尺度均值'''
|
||||
trajdist_rate = trajdist_min/(mwh+0.0001)
|
||||
|
||||
|
||||
|
||||
|
||||
# idx = trajlens.index(min(trajlens))
|
||||
# trajmin = trajectory[idx]
|
||||
|
||||
# '''最小轨迹长度/最大轨迹长度，越小，代表运动幅度越小'''
|
||||
# trajlen_rate = min(trajlens)/(max(trajlens)+0.0001)
|
||||
|
||||
# '''最小轨迹欧氏距离，越小，代表运动幅度越小'''
|
||||
# trajdist_min = min(trajdist)
|
||||
|
||||
# '''最小轨迹欧氏距离 / 目标框尺度均值'''
|
||||
# mindist_rate = min(trajdist)/(mwh+0.0001)
|
||||
|
||||
|
||||
|
||||
|
||||
img = image.copy()
|
||||
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)
|
||||
|
||||
# if frnum>=3:
|
||||
# cntpoints = cornpoints[:, 0:2].astype(np.int64)
|
||||
# rect = cv2.minAreaRect(cntpoints)
|
||||
# box = cv2.boxPoints(rect)
|
||||
# box = np.int0(box)
|
||||
# cv2.drawContours(img, [box], 0, (255, 0, 0), 2)
|
||||
|
||||
# img1 = image.copy()
|
||||
# for i in range(boxes.shape[0]-1):
|
||||
# pt1 = cornpoints[i, :].astype(np.int64)
|
||||
# pt2 = cornpoints[i+1, :].astype(np.int64)
|
||||
# cv2.line(img1, pt1, pt2, color=(255, 255, 255), thickness=2)
|
||||
# gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
|
||||
# _, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
|
||||
# contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
|
||||
|
||||
color = ((0, 0, 255), (255, 128, 0))
|
||||
label_6 = "PCA(singular_values_) : "
|
||||
label_7 = "Rect : "
|
||||
|
||||
if frnum>=3:
|
||||
if isImgBorder:
|
||||
X = cornpoints[:, 0:2]
|
||||
else:
|
||||
X = cornpoints[:, 2*idx:2*(idx+1)]
|
||||
|
||||
pca = PCA()
|
||||
pca.fit(X)
|
||||
label_6 = "PCA(variance_ratio) : {:.2f}".format(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 = cv2.minAreaRect(X.astype(np.int64))
|
||||
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))
|
||||
|
||||
|
||||
|
||||
'''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}_{tid}.png", imgbt)
|
||||
|
||||
if len(trajmin):
|
||||
trajstd = np.std(trajmin)
|
||||
else:
|
||||
trajstd = 0
|
||||
|
||||
|
||||
|
||||
trajlens = [int(t) for t in trajlens]
|
||||
trajdist = [int(t) for t in trajdist]
|
||||
label_1 = f"trajlens: {trajlens}, trajlen_min: {int(trajlen_min)}"
|
||||
label_2 = f"trajdist: {trajdist}: trajdist_min: {int(trajdist_min)}"
|
||||
label_3 = "trajlen_min/max(trajlens): {:.2f}/{} = {:.2f}".format(trajlen_min, max(trajlens), trajlen_rate)
|
||||
label_4 = "trajdist_min/mwh : {:.2f}/{} = {:.2f}".format(trajdist_min, int(mwh), trajdist_rate)
|
||||
label_5 = "std(trajmin) : {:.2f}".format(trajstd)
|
||||
|
||||
label = [label_1, label_2, label_3, label_4, label_5, label_6, label_7]
|
||||
word = 'abc'
|
||||
w, h = cv2.getTextSize('abc', 0, fontScale=2, thickness=1)[0]
|
||||
for i in range(len(label)):
|
||||
# color = [int(x) for x in colors[i]]
|
||||
cv2.putText(img,
|
||||
label[i],
|
||||
(20, int(50+(i+1)*1.2*h)),
|
||||
0,
|
||||
1,
|
||||
[int(x) for x in colors[i]],
|
||||
2,
|
||||
lineType=cv2.LINE_AA)
|
||||
cv2.imwrite(pth + f"/result/cls11_80212/{file}_{tid}.png", img)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def drawtracks(bboxes, imgshow=None):
|
||||
|
||||
"""
|
||||
Inputs
|
||||
bboxes: 原始检测跟踪后的结果，变换为 tboxes
|
||||
image：只用于获取图像的（Width, Height）
|
||||
Outputs:
|
||||
imgshow
|
||||
"""
|
||||
if imgshow is None:
|
||||
edgeline = cv2.imread(pth + r"/shopcart/cart_tempt/edgeline.png")
|
||||
# edgeline = cv2.bitwise_not(edgeline)
|
||||
H, W = edgeline.shape[0:2]
|
||||
imgshow= np.zeros((H, W, 3), np.uint8)
|
||||
|
||||
if 'edgeline' in locals().keys():
|
||||
imgshow = cv2.add(imgshow, edgeline)
|
||||
|
||||
## ==== list，其中元素格式: [x, y, w, h, track_id, score, cls, frame_index]
|
||||
|
||||
tboxes = array2list(bboxes)
|
||||
|
||||
# imgshow = cv2.bitwise_not(imgshow)
|
||||
|
||||
annotator = TrackAnnotator(imgshow, line_width=2)
|
||||
for boxes in tboxes:
|
||||
annotator.plotting_track(boxes)
|
||||
|
||||
imgshow = annotator.result()
|
||||
|
||||
return imgshow
|
||||
|
||||
|
||||
|
||||
def writefilename():
|
||||
npydir = r"D:\DeepLearning\yolov5\runs\boxes"
|
||||
files = [f.split('.')[0] for f in os.listdir(npydir)]
|
||||
with open('data.txt', 'w') as f:
|
||||
[f.write(f"{file}:\n") for file in files]
|
||||
|
||||
print("len(files)")
|
||||
# for filename in os.listdir(npydir):
|
||||
# file, ext = os.path.splitext(filename)
|
||||
|
||||
|
||||
def main():
|
||||
npydir = r"D:\DeepLearning\yolov5\runs\boxes"
|
||||
k = 0
|
||||
fields = []
|
||||
|
||||
for filename in os.listdir(npydir):
|
||||
# filename = "加购_快速置入_12.npy"
|
||||
print(filename)
|
||||
|
||||
file, ext = os.path.splitext(filename)
|
||||
filepath = os.path.join(npydir, filename)
|
||||
|
||||
try:
|
||||
bboxes = np.load(filepath)
|
||||
|
||||
imgshow = drawtracks(bboxes, file)
|
||||
draw5points(bboxes, file)
|
||||
|
||||
cv2.imwrite(pth + f"/result/cls11_80212/{file}_show.png", imgshow)
|
||||
except Exception as e:
|
||||
# print(str(e))
|
||||
pass
|
||||
|
||||
# k += 1
|
||||
# if k == 1:
|
||||
# break
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
main()
|
||||
|
||||
# writefilename()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user