def getMsg(self):
self.datas = []
# 1. =======compute the average MAE of methods=========
print("------1. Compute the average MAE of Methods------")
aveMAE, gt2rs_mae = compute_ave_MAE_of_methods(self.gt_name_list, self.rs_dir_lists)
for i in range(0, len(self.rs_dirs)):
print(
'>>%s: num_rs/num_gt-> %d/%d, aveMAE-> %.4f' % (
self.rs_dirs[i], gt2rs_mae[i], len(self.gt_name_list), aveMAE[i]))
data_MAE = str(aveMAE[i])
self.datas.append(data_MAE)
# 2. =======compute the Precision, Recall and F-measure of methods=========
from measures import compute_PRE_REC_FM_of_methods, plot_save_pr_curves, plot_save_fm_curves
print("------2. Compute the Precision, Recall and F-measure of Methods------")
PRE, REC, FM, gt2rs_fm = compute_PRE_REC_FM_of_methods(self.gt_name_list, self.rs_dir_lists, beta=0.3)
for i in range(0, FM.shape[0]):
print(">>", self.rs_dirs[i], ":", "num_rs/num_gt-> %d/%d," % (int(gt2rs_fm[i][0]), len(self.gt_name_list)),
"maxF->%.4f, " % (np.max(FM, 1)[i]), "meanF->%.4f, " % (np.mean(FM, 1)[i]),
"PRE->%.4f, " % (np.mean(PRE, 1)[i]), "REC->%.4f, " % (np.mean(REC, 1)[i]))
data_maxF = np.max(FM, 1)[i]
data_meanF = np.mean(FM, 1)[i]
data_PRE = np.mean(PRE, 1)[i]
data_REC = np.mean(REC, 1)[i]
self.datas.append(data_maxF)
self.datas.append(data_meanF)
self.datas.append(data_PRE)
self.datas.append(data_REC)
print('\n')
print(self.datas)
# 3. =======Plot and save precision-recall curves=========
print("------ 3. Plot and save precision-recall curves------")
plot_save_pr_curves(PRE, # numpy array (num_rs_dir,255), num_rs_dir curves will be drawn
REC, # numpy array (num_rs_dir,255)
method_names=self.rs_dirs,
# method names, shape (num_rs_dir), will be included in the figure legend
lineSylClr=self.lineSylClr, # curve styles, shape (num_rs_dir)
linewidth=self.linewidth, # curve width, shape (num_rs_dir)
xrange=(0.5, 1.0), # the showing range of x-axis
yrange=(0.5, 1.0), # the showing range of y-axis
dataset_name=self.data_name, # dataset name will be drawn on the bottom center position
save_dir=self.data_dir, # figure save directory
save_fmt='png') # format of the to-be-saved figure
print('\n')
# 4. =======Plot and save F-measure curves=========
print("------ 4. Plot and save F-measure curves------")
plot_save_fm_curves(FM, # numpy array (num_rs_dir,255), num_rs_dir curves will be drawn
mybins=np.arange(0, 256),
method_names=self.rs_dirs,
# method names, shape (num_rs_dir), will be included in the figure legend
lineSylClr=self.lineSylClr, # curve styles, shape (num_rs_dir)
linewidth=self.linewidth, # curve width, shape (num_rs_dir)
xrange=(0.0, 1.0), # the showing range of x-axis
yrange=(0.0, 1.0), # the showing range of y-axis
dataset_name=self.data_name, # dataset name will be drawn on the bottom center position
save_dir=self.data_dir, # figure save directory
save_fmt='png') # format of the to-be-saved figure
print(self.datas)
return self.datas
linewidth = [2,1] # line width, same size with rs_dirs
# >>>>>>> Above have to be manually configured <<<<<<< #
print(data_dir+gt_dir+'/'+'*.png')
gt_name_list = glob.glob(data_dir+gt_dir+'/'+'*.png') # get the ground truth file name list
## get directory list of predicted maps
rs_dir_lists = []
for i in range(len(rs_dirs)):
rs_dir_lists.append(data_dir+rs_dirs[i]+'/')
print('\n')
## 1. =======compute the average MAE of methods=========
print("------1. Compute the average MAE of Methods------")
aveMAE, gt2rs_mae = compute_ave_MAE_of_methods(gt_name_list,rs_dir_lists)
print('\n')
for i in range(0,len(rs_dirs)):
print('>>%s: num_rs/num_gt-> %d/%d, aveMAE-> %.3f'%(rs_dirs[i], gt2rs_mae[i], len(gt_name_list), aveMAE[i]))
## 2. =======compute the Precision, Recall and F-measure of methods=========
from measures import compute_PRE_REC_FM_of_methods,plot_save_pr_curves,plot_save_fm_curves
print('\n')
print("------2. Compute the Precision, Recall and F-measure of Methods------")
PRE, REC, FM, gt2rs_fm = compute_PRE_REC_FM_of_methods(gt_name_list,rs_dir_lists,beta=0.3)
for i in range(0,FM.shape[0]):
print(">>", rs_dirs[i],":", "num_rs/num_gt-> %d/%d,"%(int(gt2rs_fm[i][0]),len(gt_name_list)), "maxF->%.3f, "%(np.max(FM,1)[i]), "meanF->%.3f, "%(np.mean(FM,1)[i]))
print('\n')