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tools.py
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tools.py
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import cv2
import psutil
import os
import cmath
import numpy as np
class Tools:
def __init__(self):
self.palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
# 预测框解码,返回框中点和宽高
def bbox_rel(self, *xyxy):
"""" Calculates the relative bounding box from absolute pixel values. """
bbox_left = min([xyxy[0].item(), xyxy[2].item()])
bbox_top = min([xyxy[1].item(), xyxy[3].item()])
bbox_w = abs(xyxy[0].item() - xyxy[2].item())
bbox_h = abs(xyxy[1].item() - xyxy[3].item())
x_c = (bbox_left + bbox_w / 2)
y_c = (bbox_top + bbox_h / 2)
w = bbox_w
h = bbox_h
return x_c, y_c, w, h
def compute_color_for_labels(self, label):
"""
Simple function that adds fixed color depending on the class
"""
color = [int((p * (label ** 2 - label + 1)) % 255) for p in self.palette]
return tuple(color)
def draw_boxes(self, img, bbox, clses, confs, names, identities=None, offset=(0, 0)):
for i, box in enumerate(bbox):
x1, y1, x2, y2 = [int(i) for i in box]
x1 += offset[0]
x2 += offset[0]
y1 += offset[1]
y2 += offset[1]
# box text and bar
id = int(identities[i]) if identities is not None else 0
color = self.compute_color_for_labels(id)
label = '{}{:d}'.format("", id)
cls = clses[i]
conf = confs[i]
class_str = f'{names[int(cls)]}'
text = label + ':' + class_str + ':' + str(conf)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0]
cv2.rectangle(img, (x1, y1), (x2, y2), color, 3)
cv2.rectangle(
img, (x1, y1), (x1 + t_size[0] + 3, y1 + t_size[1] + 4), color, -1)
cv2.putText(img, text, (x1, y1 +
t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 2, [255, 255, 255], 2)
return img
def draw_boxes_kalman(self, img, bbox, identities=None):
for i, box in enumerate(bbox):
x1, y1, x2, y2 = [int(i) for i in box]
# box text and bar
id = int(identities[i]) if identities is not None else 0
color = self.compute_color_for_labels(id)
label = '{}{:d}'.format("", id)
text = label
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0]
cv2.rectangle(img, (x1, y1), (x2, y2), color, 3)
cv2.rectangle(
img, (x1, y1), (x1 + t_size[0] + 3, y1 + t_size[1] + 4), color, -1)
cv2.putText(img, text, (x1, y1 +
t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 2, [255, 255, 255], 2)
return img
# KS: 联动关闭
def CheckPID(self, pid):
if (pid != 0):
if not psutil.pid_exists(pid):
os.kill(os.getpid(), 0);
"""
KS:控制kalman和yolo刷新的次数
"""
class Counter:
def __init__(self, maxAge):
self.kalmanAge = maxAge
self.yoloAge = 1
self.maxAge = maxAge
self.status = "yolo"
self.maxTimers=20
self.minTimers=1
def Update(self):
if (self.status == "yolo"):
self.yoloAge -= 1
if (self.yoloAge > 0):
return self.status
else:
self.yoloAge = 1 # KS: reset
if (self.status == "yolo"): # KS: 切换状态
self.status = "kalman"
else:
self.status = "yolo"
return self.status
elif (self.status == "kalman"):
self.kalmanAge -= 1
if (self.kalmanAge > 0):
return self.status
else:
self.kalmanAge = self.maxAge # KS: reset
if (self.status == "yolo"): # KS: 切换状态
self.status = "kalman"
else:
self.status = "yolo"
return self.status
"""
KS:动态调整检测频率
"""
def AdaptedTimes(self, boxesNumbers):
tempTimes = cmath.sqrt(self.maxTimers/boxesNumbers)
if(tempTimes>self.maxTimers):
tempTimes=self.maxTimers
elif(tempTimes<self.minTimers):
tempTimes=self.minTimers
return tempTimes
class MeanSpeed:
def __init__(self, time):
self.oldTime = time
def Count(self, currentTime, box, x1, y1, x2, y2):
midX = (x2 + x1) / 2
midY = (y2 + y1) / 2
mid = [midX, midY]
diffTime = currentTime - self.oldTime
diff_x=mid[0] - box.oldMid[0]
diff_y=mid[1] - box.oldMid[1]
meanX = diff_x / diffTime
meanY = diff_y / diffTime
#KS: 更新数据
box.oldMid = mid
print("{0}meanSpeed:X:{1},Y:{2}".format(box.id, meanX, meanY))
return meanX,meanY
class IOU:
def Iou(self,box1, box2, wh=False):
if wh == False:
xmin1, ymin1, xmax1, ymax1 = box1
xmin2, ymin2, xmax2, ymax2 = box2
else:
xmin1, ymin1 = int(box1[0] - box1[2] / 2.0), int(box1[1] - box1[3] / 2.0)
xmax1, ymax1 = int(box1[0] + box1[2] / 2.0), int(box1[1] + box1[3] / 2.0)
xmin2, ymin2 = int(box2[0] - box2[2] / 2.0), int(box2[1] - box2[3] / 2.0)
xmax2, ymax2 = int(box2[0] + box2[2] / 2.0), int(box2[1] + box2[3] / 2.0)
# 获取矩形框交集对应的左上角和右下角的坐标(intersection)
xx1 = np.max([xmin1, xmin2])
yy1 = np.max([ymin1, ymin2])
xx2 = np.min([xmax1, xmax2])
yy2 = np.min([ymax1, ymax2])
# 计算两个矩形框面积
area1 = (xmax1 - xmin1) * (ymax1 - ymin1)
area2 = (xmax2 - xmin2) * (ymax2 - ymin2)
inter_area = (np.max([0, xx2 - xx1])) * (np.max([0, yy2 - yy1])) # 计算交集面积
iou = inter_area / (area1 + area2 - inter_area + 1e-6) # 计算交并比
return iou