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import cv2

import numpy as np

import re

import matplotlib.pyplot as plt

import seaborn as sns

from tkinter import _flatten

def get_maxpixel(img,pixel1,pixel2):

pixel = []

for w in range(pixel1[0],pixel2[0]):

for h in range(pixel1[1],pixel2[1]):

pixel.append(img[w][h][2])

return min(pixel)

# 计算像素点对应的对热图中的位置

def cal_hot(pixel_position):

x_position = pixel_position[0]//16

y_position = pixel_position[1]//16

if x_position < 0:

x_position = 0

elif x_position > 39:

x_position = 39

if y_position < 0:

y_position = 0

elif y_position >29:

y_position = 29

return (x_position,y_position)

# 处理

def pretreatment(pixel_position):

a1 = pixel_position[0]

a2 = pixel_position[1]

if a1 < 0:

a1 = 0

elif a1 >= 640:

a1 = 639

if a2 < 0:

a2 = 0

elif a2 >= 480:

a2= 479

return (a1,a2)

def cal_velocity(pixel1,pixel2):

return np.sqrt(((pixel2[0] - pixel1[0])**2)+((pixel2[1] - pixel1[1])**2))

# 将整张图分格子 每个点投进该格子 该格子数值加一

# 读取txt文件数据

f = open('D:\\Documents\\TestProject_multi\\1\\Tracking_0.txt')

# f = open('F:\\Project\\Machinelearning\\Vedio_libo\\point_location1.txt')

# Init hot figure array

List_location = [[0]*40 for row in range(30)]

Line_length = 0

while True:

lines = f.readline() # readline

if lines:

Line_length = Line_length + 1

list_str = re.findall(r"\d+\.?\d*", lines) # 查找数字

# x = int(float(list_str[1])) # str转为整型 自己的数据

# y = int(float(list_str[2]))

x = int(float(list_str[3])) # str转为整型

y = int(float(list_str[4]))

temp_hot = cal_hot((x,y))

print(temp_hot)

List_location[temp_hot[1]][temp_hot[0]] = List_location[temp_hot[1]][temp_hot[0]]+1

# print(List_location)

else: # 读到文本最后一句跳出

break

A = np.array(List_location)/Line_length

plt.figure(1)

sns.set()

print(len(A),len(A[0]))

ax1 = sns.heatmap(A, annot=False,cmap="coolwarm")

plt.figure(2)

B = (list(_flatten(List_location)))

plt.plot(B)

plt.show()

# # calculate the Trajectory

# first_flag = 0

# img = np.ones((760,1024,3),dtype=np.uint8)*125

# f = open('F:\\Project\\Machinelearning\\Vedio_libo\\point_location.txt')

# # f = open('D:\\Documents\\TestProject\\1\\Tracking_0.txt')

# jpeg = cv2.imread('F:\\Project\\Machinelearning\\Vedio_libo\\Vedio\\image\\0.jpg')

# temp_position = []

# lost_frame = 0

# while True:

# lines = f.readline() # readline

# if lines:

# list_str = re.findall(r"\d+\.?\d*", lines) # 查找数字

# if list_str:

# x = int(float(list_str[1])) # str转为整型

# y = int(float(list_str[2]))

# if x <= 0 or y <= 0 or x > 640 or y > 480:

# print(x,y)

# else:

# location = pretreatment((x,y))

# if first_flag != 0:

# cv2.line(jpeg, (temp_position[0], temp_position[1]),\

# (location[0], location[1]), (0,255,0),3)

# temp_position.clear()

# temp_position.append(location[0])

# temp_position.append(location[1])

# else:

# first_flag = first_flag + 1

# temp_position.clear()

# temp_position.append(location[0])

# temp_position.append(location[1])

# else:

# lost_frame = lost_frame + 1

# print(lost_frame)

# else:

# break

# cv2.imwrite("trace.jpg",jpeg)

# cv2.imshow('img',jpeg)

# cv2.waitKey(0)

# 计算老鼠速度

# 计算需要保存上一步老鼠数据

temp_data = []

figure_data = []

f = open('F:\\Project\\Machinelearning\\Vedio_libo\\point_location.txt')

while True:

lines = f.readline()

if lines:

list_str = re.findall(r"\d+\.?\d*", lines)

if list_str:

x = (float(list_str[1])) # str转为整型

y = (float(list_str[2]))

if temp_data:

EuropeanDis = cal_velocity(temp_data[0],(x,y))

temp_data.clear()

temp_data.append((x,y))

figure_data.append(EuropeanDis)

print(EuropeanDis)

else:

temp_data.clear()

temp_data.append((x,y))

else:

break

plt.plot(figure_data)

plt.show()

# img = np.ones((760,1024,3),dtype=np.uint8)*125

# b = []

# flag = 0

# temp_position = [] # 里面数据实时更新

# f = open('point_location2.txt')

#

# while True:

# lines = f.readline()

# # lines.encode("utf-8")

# list_str = re.findall(r"\d+\.?\d*", lines)

#

# if not lines:

# break

# else:

# max_pixel = get_maxpixel(img,

# (round(float(list_str[1])) - 20,round(float(list_str[2])) - 20),

# (round(float(list_str[1])) + 20, round(float(list_str[2])) + 20))

#

# if flag != 0:

# # cv2.line(img,(temp_position[0],temp_position[1]),(round(float(list_str[1])),round(float(list_str[2]))),(0,255,0),3)

# # cv2.circle(img, (round(float(list_str[1])),round(float(list_str[2]))), 1, ( 0,255, 0), 8)

# # cv2.circle(img, (int(list_str[4]), int(list_str[5])), 3, (255, 0, 0), 8)

# # cv2.circle(img, (int(list_str[7]), int(list_str[8])), 3, (0, 0, 255), 8)

# # cv2.line(img,(temp_position[0],temp_position[1]),(round(float(list_str[1])),round(float(list_str[2]))),(0,255,0),3)

# # cv2.line(img,(temp_position[2],temp_position[3]),(int(list_str[4]),int(list_str[5])),(255,0,0),3)

# # cv2.line(img,(temp_position[4],temp_position[5]),(int(list_str[7]),int(list_str[8])),(0,0,255),3)

# # cv2.rectangle(img,(110,113),(200,300),(255, 0, 0), thickness=-1)

# cv2.rectangle(img,\

# (round(float(list_str[1]))-10,round(float(list_str[2]))-10),\

# (round(float(list_str[1]))+ 10,round(float(list_str[2])) + 10),\

# (0, \

# 0, \

# int(max_pixel-50)),thickness=-1)

# print(img[150, 200][0])

#

#

#

# temp_position.clear()

# temp_position.append(round(float(list_str[1])))

# temp_position.append(round(float(list_str[2])))

# # temp_position.append(int(list_str[4]))

# # temp_position.append(int(list_str[5]))

# # temp_position.append(int(list_str[7]))

# # temp_position.append(int(list_str[8]))

# else:

# flag = flag+1

# temp_position.clear()

# temp_position.append(round(float(list_str[1])))

# temp_position.append(round(float(list_str[2])))

# # temp_position.append(int(list_str[4]))

# # temp_position.append(int(list_str[5]))

# # temp_position.append(int(list_str[7]))

# # temp_position.append(int(list_str[8]))

# cv2.imwrite("trace.jpg",img)

# cv2.imshow('img',img)

# cv2.waitKey(0)