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line_recoganize.py
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import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
def get_radiant_winrate(dir_name,file_name,to_name):
files=os.listdir("D:\\Desktop\\"+dir_name)
files.sort(key=lambda x: int(x.split('.')[0]))
df = pd.read_csv("D:\\Desktop\\duration\\" + file_name)
for ii in range(len(files)):
src=cv2.imread("D:\\Desktop\\"+dir_name+"\\"+files[ii])
img = cv2.blur(src, (5, 5)) # 降噪
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # 色彩空间转换为hsv，分离.
# 色相（H）是色彩的基本属性，就是平常所说的颜色名称，如红色、黄色等。
# 饱和度（S）是指色彩的纯度，越高色彩越纯，低则逐渐变灰，取0-100%的数值。
# 明度（V），取0-100%。
# OpenCV中绿色的H,S,V范围是35-77,43-255,46-255
low = np.array([35, 43, 46])
high = np.array([77, 255, 255])
dst = cv2.inRange(src=hsv, lowerb=low, upperb=high) # HSV高低阈值，提取图像部分区域
# 寻找白色的像素点坐标。
# 白色像素值是255，所以np.where(dst==255)
R = 255
G = 255
B = 255
xy = np.column_stack(np.where(dst == R * 0.3 + G * 0.59 + B * 0.11))
# print(xy)
# 在原图的红色数字上用 金黄色 描点填充。
win_rate = []
for c in xy:
win_rate.append([c[1], c[0]])
win_rate = sorted(win_rate, key=lambda x: x[0])
print(win_rate)
i = 0
while win_rate[i][1] < 300 or win_rate[i][1] > 500:
i += 1
result_x = []
result_y = []
while i < len(win_rate):
if win_rate[i][0] == win_rate[i - 1][0] or win_rate[i][1] - win_rate[i - 1][1] >= 30 or win_rate[i][1] - \
win_rate[i - 1][1] <= -30:
i += 1
continue
# print(win_rate[i])
result_x.append(win_rate[i][0])
result_y.append(win_rate[i][1])
# cv2.circle(img=img, center=(int(win_rate[i][0]), int(win_rate[i][1])), radius=1, color=(0, 215, 255), thickness=1)
i += 1
# 注意颜色值是(b,g,r)，不是(r,g,b)
# 坐标:c[1]是x,c[0]是y
x = np.array(result_x)
y = np.array(result_y)
x2 = np.array(range(win_rate[0][0], x[-1]))
y2 = np.interp(x2, x, y)
duration = df['duration'][ii] / 60
x3 = x2 * (duration / x2[-1])
if y2[-1]<y2[0]:
b = 50 / (y2[0] - y2[-1])
a = 100 + y2[-1] * b
if y2[-1]>y2[0]:
b = 50/(y2[-1]-y2[0])
a = y2[-1]*b
y3 = a - y2 * b
# print(x3,y3)
# plt.scatter(x,y,color='b')
plt.plot(x3, y3, 'o', markersize=1)
plt.show()
# x3和y3是最终的坐标：
for j in range(len(x2)):
print('{:.2f},{:.2f}'.format(x3[j], y3[j]))
cv2.circle(img=img, center=(int(x2[j]), int(y2[j])), radius=1, color=(0, 215, 255), thickness=1)
dirs = "D:\\Desktop\\" + to_name
if not os.path.exists(dirs):
os.makedirs(dirs)
lst=zip(x3,y3)
data=pd.DataFrame(lst,columns=['time','win_rate'])
data.to_csv(dirs+"\\"+str(df['match_id'][ii])+".csv", index=None)
# cv2.imshow('dst', dst)
# cv2.imshow('result', img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
def get_dire_winrate(dir_name,file_name,to_name):
files = os.listdir("D:\\Desktop\\" + dir_name)
files.sort(key=lambda x: int(x.split('.')[0]))
df = pd.read_csv("D:\\Desktop\\duration\\" + file_name)
for ii in range(len(files)):
src = cv2.imread("D:\\Desktop\\" + dir_name + "\\" + files[ii])
img = cv2.blur(src, (5, 5)) # 降噪
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # 色彩空间转换为hsv，分离.
# 色相（H）是色彩的基本属性，就是平常所说的颜色名称，如红色、黄色等。
# 饱和度（S）是指色彩的纯度，越高色彩越纯，低则逐渐变灰，取0-100%的数值。
# 明度（V），取0-100%。
# OpenCV中绿色的H,S,V范围是35-77,43-255,46-255
low = np.array([35, 43, 46])
high = np.array([77, 255, 255])
dst = cv2.inRange(src=hsv, lowerb=low, upperb=high) # HSV高低阈值，提取图像部分区域
# 寻找白色的像素点坐标。
# 白色像素值是255，所以np.where(dst==255)
R = 255
G = 255
B = 255
xy = np.column_stack(np.where(dst == R * 0.3 + G * 0.59 + B * 0.11))
# print(xy)
# 在原图的红色数字上用 金黄色 描点填充。
win_rate = []
for c in xy:
win_rate.append([c[1], c[0]])
win_rate = sorted(win_rate, key=lambda x: x[0])
print(win_rate)
i = 0
while win_rate[i][1] < 300 or win_rate[i][1] > 500:
i += 1
result_x = []
result_y = []
while i < len(win_rate):
if win_rate[i][0] == win_rate[i - 1][0] or win_rate[i][1] - win_rate[i - 1][1] >= 30 or win_rate[i][1] - \
win_rate[i - 1][1] <= -30:
i += 1
continue
# print(win_rate[i])
result_x.append(win_rate[i][0])
result_y.append(win_rate[i][1])
# cv2.circle(img=img, center=(int(win_rate[i][0]), int(win_rate[i][1])), radius=1, color=(0, 215, 255), thickness=1)
i += 1
# 注意颜色值是(b,g,r)，不是(r,g,b)
# 坐标:c[1]是x,c[0]是y
x = np.array(result_x)
y = np.array(result_y)
x2 = np.array(range(win_rate[0][0], x[-1]))
y2 = np.interp(x2, x, y)
duration = df['duration'][ii] / 60
x3 = x2 * (duration / x2[-1])
if y2[-1] < y2[0]:
b = 50 / (y2[0] - y2[-1])
a = 100 + y2[-1] * b
if y2[-1] > y2[0]:
b = 50 / (y2[-1] - y2[0])
a = y2[-1] * b
y3 = a - y2 * b
# print(x3,y3)
# plt.scatter(x,y,color='b')
plt.plot(x3, y3, 'o', markersize=1)
plt.show()
# x3和y3是最终的坐标：
for j in range(len(x2)):
print('{:.2f},{:.2f}'.format(x3[j], y3[j]))
cv2.circle(img=img, center=(int(x2[j]), int(y2[j])), radius=1, color=(0, 215, 255), thickness=1)
lst=zip(x3,100-y3)
data=pd.DataFrame(lst,columns=['time','win_rate'])
dirs = "D:\\Desktop\\"+to_name
if not os.path.exists(dirs):
os.makedirs(dirs)
data.to_csv(dirs + "\\"+str(df['match_id'][ii])+".csv", index=None)
# cv2.imshow('dst', dst)
# cv2.imshow('result', img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
if __name__ == "__main__":
dir_name="pic13"
file_name="rng_radiant_duration.csv"
to_name="rng_radiant_winrate"
get_radiant_winrate(dir_name,file_name,to_name)