#基于wolf2006的evaluation方法

import glob

import os

import cv2

import numpy as np

import Polygon as plg

import matplotlib.pyplot as plt

from collections import Counter

import csv

def get_intersection(pD, pG):

return pInt.area()

def get_union(pD,pG):

return pInt.area()

#predict_txt_list=glob.glob('original_USGS_txt_04/USGS-15-CA-brawley-e1957-s1957-p1961.txt')

#predict_txt_list=glob.glob('original_USGS_txt_04/USGS-15-CA-capesanmartin-e1921-s1917.txt')

#predict_txt_list=glob.glob('D:/textDetect_evalSet/east_map_res_maplevel/*.txt')

#predict_txt_list=glob.glob('D:/textDetect_evalSet/psenet_map_res_maplevel/*.txt')

predict_txt_list=glob.glob('D:/textDetect_evalSet/synthText_model1.0_wholemap_OS/*.txt')

#predict_txt_list=glob.glob('D:/textDetect_evalSet/results_txt/synthtext_wce_w1_modelv4_vgg_textprob_centerline/usgs/txt/*.txt')

image_folder_path='E:/Spatial Computing & Informatics Laboratory/CutTextArea/dataset/original_size_OS_USGS_jpg/'

#image_folder_path='E:/Spatial Computing & Informatics Laboratory/CutTextArea/dataset/weinman19-maps/'

GT_folder_path='../original_size_OS_USGS/'

more_GT_folder_path='../original_more_OS_USGS_txt_GT/'

#GT_folder_path='weinman19_GT_txt/'

output_path='../testOutput/'

#output_path='weinman_eval/'

#用csv文件保存结果

csv_dir='../csvOutput/'

csvfile = open(csv_dir+'synthText_model1.0_wholemap_OS_tr0.5_tp0.5_k1.csv','w',newline='')

writeCSV=csv.writer(csvfile)

writeCSV.writerow(['mapName','recall','precision','f1'])

for txt_path in predict_txt_list:

# 解析predict的txt

base_name = os.path.basename(txt_path)

if base_name=='USGS-15-CA-hesperia-e1902-s1898-rp1912.txt':

continue

print('base_name:', base_name)

image_path = image_folder_path + base_name[0:len(base_name) - 4] + '.jpg'

#image_path = image_folder_path + base_name[0:len(base_name) - 4] + '.tiff'

image=cv2.imread(image_path)

image_2 = image.copy()

#image_1=image.copy()

with open(txt_path, 'r') as f:

data = f.readlines()

predict_polyList = []

for line in data:

polyStr = line.split(',')

poly = []

polyRange=0

#GoogleVision和weinman的output最后会有文字的结果

for i in range(0, len(polyStr)):

#for i in range(0, 7):

if i % 2 == 0:

poly.append([float(polyStr[i]), float(polyStr[i + 1])])

predict_polyList.append(poly)

threshold = 0

#去除过小的box

predict_polyList=list(filter(lambda x:plg.Polygon(x).area()>threshold, predict_polyList))

print('prediction all poly: ', len(predict_polyList))

for i in range(0, len(predict_polyList)):

polyPoints = np.array([predict_polyList[i]], dtype=np.int32)

cv2.polylines(image, polyPoints, True, (0, 0, 255), 1)

#cv2.imshow('prediction result',image_1)

#cv2.waitKey()

# 解析p==GT的txt

GT_txt_path=GT_folder_path+base_name

GT_polyList=[]

with open(GT_txt_path, 'r') as f:

GT_data = f.readlines()

for line in GT_data:

polyStr = line.split(',')

#处理###，正常情况下不需要!!!!

#polyStr=polyStr[:-1]

poly = []

for i in range(0, len(polyStr)):

if i % 2 == 0:

poly.append([float(polyStr[i]), float(polyStr[i + 1])])

GT_polyList.append(poly)

#去除area<threshold的部分

GT_polyList = list(filter(lambda x: plg.Polygon(x).area() > threshold, GT_polyList))

print('GT all poly: ', len(GT_polyList))

for i in range(0, len(GT_polyList)):

polyPoints = np.array([GT_polyList[i]], dtype=np.int32)

cv2.polylines(image, polyPoints, True, (255, 0, 0), 1)

# 解析Do Not Care的GT txt

more_txt_path = more_GT_folder_path + base_name

if not os.path.exists(more_txt_path):

continue

more_GT_polyList = []

with open(more_txt_path, 'r') as f:

more_GT_data = f.readlines()

for line in more_GT_data:

polyStr = line.split(',')

poly = []

for i in range(0, len(polyStr)):

if i % 2 == 0:

poly.append([float(polyStr[i]), float(polyStr[i + 1])])

more_GT_polyList.append(poly)

#去除面积为0的部分

more_GT_polyList = list(filter(lambda x: plg.Polygon(x).area() > threshold, more_GT_polyList))

print('more GT all poly: ', len(more_GT_polyList))

for i in range(0, len(more_GT_polyList)):

polyPoints = np.array([more_GT_polyList[i]], dtype=np.int32)

cv2.polylines(image, polyPoints, True, (0, 255, 0), 1)

#cv2.imwrite(output_path+'GT_predict_'+base_name[0:len(base_name) - 4] + '.jpg',image)

#算出GT box的合并

all_GT_polyList=GT_polyList+more_GT_polyList

#cv2.waitKey()

metrics_recall=[[0 for _ in range(0,len(predict_polyList))] for _ in range(0,len(all_GT_polyList))]

metrics_precision=[[0 for _ in range(0,len(predict_polyList))] for _ in range(0,len(all_GT_polyList))]

for i in range(0,len(all_GT_polyList)):

for j in range(0,len(predict_polyList)):

poly1=plg.Polygon(all_GT_polyList[i])

poly2=plg.Polygon(predict_polyList[j])

#print('GT area:', poly1.area())

#print('Predict area:', poly2.area())

inter_area=get_intersection(poly1,poly2)

#print('inter_area:',inter_area)

if poly1.area==0 or poly2.area==0 or inter_area==0:

metrics_precision[i][j]=0

metrics_recall[i][j]=0

else:

metrics_precision[i][j]=inter_area / poly2.area()

metrics_recall[i][j]=inter_area / poly1.area()

#print('area precision:',metrics_precision[i][j])

#print('area recall:', metrics_recall[i][j])

'''

#按照icdar2017的思路去计算每张地图的recall和precision

flag_GT_icdar = [0 for _ in range(0, len(GT_polyList))]

flag_predict_icdar = [0 for _ in range(0, len(predict_polyList))]

#recall

#有多少GT box被正确预测

for i in range(0,len(GT_polyList)):

for j in range(0,len(predict_polyList)):

poly1=plg.Polygon(GT_polyList[i])

poly2=plg.Polygon(predict_polyList[j])

if get_intersection(poly1,poly2)/get_union(poly1,poly2)>=0.5:

flag_GT_icdar[i]=1

flag_predict_icdar[j]=1

recall_2017=Counter(flag_GT_icdar)[1]/len(GT_polyList)

#precision

precision_2017=Counter(flag_predict_icdar)[1]/len(predict_polyList)

print('recall_icdar2017:%.3f'%recall_2017)

print('precision_icdar2017:%.3f'%precision_2017)

'''

#wolf2006, 计算polygonlevel的recall和precision

#记录那些box没有被正确预测

flag_GT=[0 for _ in range(0,len(GT_polyList))]

flag_predict=[0 for _ in range(0,len(predict_polyList))]

#可变的阈值

#tr = 0.5

#tp = 0.4

#对于one-to-many情况下的惩罚系数

k=1

recallmx=[]

precisionmx=[]

for tr in [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]:

recalllist=[]

precisionlist=[]

for tp in [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]:

#把标记数组重置为0

flag_GT = [0 for _ in range(0, len(GT_polyList))]

flag_predict = [0 for _ in range(0, len(predict_polyList))]

# 计算recall

# 计算GT中有多少box被正确预测了

#GT_count = 0

for i in range(0, len(all_GT_polyList)):

# 计算在候选的predict_box中，多少个可以被认为与当前的GT box ovelap

overlap_predict_boxlist = []

for j in range(0, len(predict_polyList)):

if metrics_precision[i][j] >= tp:

overlap_predict_boxlist.append(j)

if len(overlap_predict_boxlist) == 0:

continue

elif len(overlap_predict_boxlist) == 1:

# one-to-one

if metrics_recall[i][overlap_predict_boxlist[0]] >= tr:

#GT_count = GT_count + 1

if i<len(flag_GT):

flag_GT[i]=1

flag_predict[overlap_predict_boxlist[0]]=1

else:

# one-to-many

rs = 0

for num in overlap_predict_boxlist:

rs = rs + metrics_recall[i][num]

if rs >= tr:

#GT_count = GT_count + k

if i<len(flag_GT):

flag_GT[i]=1

#predict boxes也被正确预测了

for num in overlap_predict_boxlist:

flag_predict[num]=1

# 把GT box中area为0的去掉

GT_all = 0

for i in range(0, len(GT_polyList)):

GTpoly = plg.Polygon(GT_polyList[i])

if GTpoly.area() > 0:

GT_all += 1

# 计算precision

#predict_count = 0

for j in range(0, len(predict_polyList)):

# 与当前的predict box overlap的GT box有哪些

overlap_GT_boxlist = []

for i in range(0, len(all_GT_polyList)):

if metrics_recall[i][j] >= tr:

overlap_GT_boxlist.append(i)

if len(overlap_GT_boxlist) == 0:

continue

elif len(overlap_GT_boxlist) == 1:

# one-to-one

if metrics_precision[overlap_GT_boxlist[0]][j] >= tp:

#predict_count += 1

flag_predict[j]=1

if overlap_GT_boxlist[0]<len(flag_GT):

flag_GT[overlap_GT_boxlist[0]]=1

else:

# one-to-many

ps = 0

for num in overlap_GT_boxlist:

ps = ps + metrics_precision[num][j]

if ps >= tp:

#predict_count = predict_count + k

flag_predict[j]=1

for num in overlap_GT_boxlist:

if num<len(flag_GT):

flag_GT[num]=1

# 把predict box中area为0的去掉

predict_all = 0

for i in range(0, len(predict_polyList)):

predictpoly = plg.Polygon(predict_polyList[i])

if predictpoly.area() > 0:

predict_all += 1

#计算最终结果

overall_recall = Counter(flag_GT)[1] / GT_all

overall_precision = Counter(flag_predict)[1] / predict_all

overall_recall=round(overall_recall,3)

overall_precision=round(overall_precision,3)

recalllist.append(overall_recall)

precisionlist.append(overall_precision)

if tr==0.5 and tp==0.5:

if overall_recall + overall_precision!=0:

f1_score = round(2 * (overall_recall * overall_precision / (overall_recall + overall_precision)), 3)

else:

f1_score=0.0

print('tr:',tr,' tp:',tp,' k:',k,' recall:',overall_recall,' precision:',overall_precision, ' f1:',f1_score)

writeCSV.writerow([base_name,overall_recall,overall_precision,f1_score])

#标记未正确识别的box

if tr==0.5 and tp==0.5:

for i in range(0, len(predict_polyList)):

if flag_predict[i]==0:

polyPoints = np.array([predict_polyList[i]], dtype=np.int32)

cv2.polylines(image_2, polyPoints, True, (0, 0, 255), 1)

for i in range(0, len(GT_polyList)):

if flag_GT[i]==0:

polyPoints = np.array([GT_polyList[i]], dtype=np.int32)

cv2.polylines(image_2, polyPoints, True, (255, 0, 0), 1)

#cv2.imwrite(output_path+'Fail_GT_predict_' + base_name[0:len(base_name) - 4] + '.jpg', image_2)

recallmx.append(recalllist)

precisionmx.append(precisionlist)

col_labels = ['tp=0.1', '0.2', '0.3','0.4','0.5','0.6','0.7','0.8','0.9']

row_labels = ['tr=0.1', '0.2', '0.3','0.4','0.5','0.6','0.7','0.8','0.9']

table_vals_1 = recallmx

table_vals_2 = precisionmx

# 第一行第一列图形

#ax1 = plt.subplot(1, 2, 1)

# 第一行第二列图形

#ax2 = plt.subplot(1, 2, 2)

plt.figure(1)

my_table_1 = plt.table(cellText=table_vals_1, colWidths=[0.111] * 10, rowLabels=row_labels, colLabels=col_labels,loc='best')

#plt.sca(ax1)

plt.axis('off')

plt.title('recall')

plt.plot()

plt.show()

plt.figure(2)

my_table_2 = plt.table(cellText=table_vals_2, colWidths=[0.111] * 10, rowLabels=row_labels, colLabels=col_labels,loc='best')

#plt.sca(ax2)

plt.axis('off')

plt.title('precision')

plt.plot()

plt.show()

csvfile.close()