import matplotlib.pyplot as plt
input_txt = 'New_Text_Document_pro.txt'
f = open(input_txt)
x = []
y = []
for line in f:
line = line.strip('\n')
line = line.split(' ')
x.append('%03d' % int(line[0].split(':')[1]))
y.append(float(line[2].split('=')[1].replace('%', '')))
f.close
plt.plot(x, y, marker='o', label='lost plot')
plt.xticks(x[0:len(x):10], x[0:len(x):10], rotation=45)
plt.margins(0)
plt.xlabel("train step")
plt.ylabel("Accuracy")
plt.title("matplotlip plot")
plt.tick_params(axis="both")
plt.savefig("Epoch_Accuracy.jpg")
plt.show()
以下是整理封装好的折线图画法：
import matplotlib.pyplot as plt
def x_y_data(txt_path):
f = open(txt_path)
content = f.readlines()
x = []
y_loss = []
y_acc = []
for i in range(0, 10):
line = content[i].strip('\n')
line = content[i].split(' ')
x.append('%03d' % int(line[0].split(':')[1]))
y_loss.append(float(line[1].split(':')[1]))
y_acc.append(float(line[2].split('=')[1].replace('%', '')))
f.close
return x, y_loss, y_acc
class make_picture(object):
def __init__(self, tips):
self.tips = tips
def loss_pic(self, x, y_loss):
plt.plot(x, y_loss, marker='o', label='lost plot')
plt.xticks(x[0:len(x):10], x[0:len(x):10], rotation=45)
plt.margins(0)
plt.xlabel("Epoch")
plt.ylabel("avg_loss")
plt.title("matplotlip plot")
plt.tick_params(axis="both")
plt.savefig("Epoch_Loss.jpg")
plt.show()
print(self.tips)
def acc_pic(self, x, y_acc):
plt.plot(x, y_acc, marker='o', label='lost plot')
plt.xticks(x[0:len(x):10], x[0:len(x):10], rotation=45)
plt.margins(0)
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("matplotlip plot")
plt.tick_params(axis="both")
plt.savefig("Epoch_Accuracy.jpg")
plt.show()
print(self.tips)
def acc_pic2(self, x, y_acc):
x_label = range(0, 201, 10)
y_label = range(0, 101, 10)
override = {
'fontsize': '13',
'verticalalignment': 'center',
'horizontalalignment': 'center',}
plt.plot(x, y_acc)
plt.xticks(x_label, rotation=45)
plt.xlim(0, 210)
plt.yticks(y_label)
plt.xlabel("Epoch", override)
plt.ylabel("Accuracy(%)")
plt.tick_params(axis="both")
plt.savefig("Epoch_Accuracy.jpg")
plt.show()
print(self.tips)
if __name__ == '__main__':
txt_path = 'New_Text_Document_pro.txt'
tips = 'The picture has done!'
x, y_loss, y_acc = x_y_data(txt_path)
PIC = make_picture(tips)
PIC.loss_pic(x, y_loss)
PIC.acc_pic(x, y_acc)
以下是整理封装好的曲线图画法：
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import spline
import json, time
def test():
a = [2,3,5,6,7]
for i in a:
num = np.sum(list(map(lambda x: x <= i, a)))
for i in np.arange(0, 0.51, 0.05):
num = '%.2f' % i
b = np.array([1,2,3,4,5])
print('b lenth: ', len(b), b[0])
def percentage(txt_path, json_path):
f = open(txt_path)
lines = f.readlines()
distanse_list = []
for line in lines:
distanse_list.append(float(line.split(',')[0]))
distanse_array = np.array(distanse_list)
per_result = {}
for i in np.arange(0, 0.51, 0.05):
x_index = float('%.2f' % i)
num = np.sum(list(map(lambda x: x <= x_index, distanse_list)))
per = num/len(distanse_list)
per_result[x_index] = per
json_str = json.dumps(per_result)
with open(json_path, 'w') as json_file:
json_file.write(json_str)
def curve_pic(json_path, save_path):
f = open(json_path)
x_list = []
per_list = []
per_result = json.load(f)
for k_x, v_per in per_result.items():
x_list.append(k_x)
per_list.append(v_per)
x_data = np.array(list(map(float, x_list)))
y_data = np.array(list(map(float, per_list))) * 100
y_ticks = np.arange(0, 101, 10)
xnew = np.linspace(x_data.min(),x_data.max(),300)
power_smooth = spline(x_data,y_data,xnew)
x_new_data = []
x_data = list(x_data)
for i in range(len(x_data)):
if i == 0:
x_new_data.append(0)
else:
x_new_data.append(x_data[i])
plt.plot(xnew,power_smooth)
plt.xticks(x_new_data[0:], x_new_data[0:])
plt.yticks(y_ticks[0:], y_ticks[0:])
plt.xlim(0, 0.5)
plt.ylim(0, 100)
plt.title('PCK total')
plt.xlabel('Normalized distance')
plt.ylabel('Detection rate(%)')
plt.savefig(save_path)
plt.show()
def per_range(txt_path):
f = open(txt_path)
lines = f.readlines()
distanse_list = []
for line in lines:
distanse_list.append(float(line.split(',')[0]))
num_1 = 0
num_2 = 0
num_3 = 0
num_4 = 0
num_5 = 0
for i in distanse_list:
if i <= 0.1:
num_1 += 1
if i <= 0.2:
num_2 += 1
if i <= 0.3:
num_3 += 1
if i <= 0.4:
num_4 += 1
if i <= 0.5:
num_5 += 1
total = len(distanse_list)
per_1 = num_1 / total
per_2 = num_2 / total
per_3 = num_3 / total
per_4 = num_4 / total
per_5 = num_5 / total
print('%.5f' % per_1)
print('%.5f' % per_2)
print('%.5f' % per_3)
print('%.5f' % per_4)
print('%.5f' % per_5)
print(per_1+per_2+per_3+per_4+per_5)
def per_15_range(txt_path):
f = open(txt_path)
lines = f.readlines()
dict_1 = {}
dict_2 = {}
dict_3 = {}
dict_4 = {}
dict_5 = {}
dict_15 = {}
for index in range(0, 16):
num = 0
num_1 = 0
num_2 = 0
num_3 = 0
num_4 = 0
num_5 = 0
for line in lines:
rate = float(line.split(',')[0])
id = int(line.split(',')[1])
if id == index:
num += 1
if rate <= 0.1:
num_1 += 1
if rate <= 0.2:
num_2 += 1
if rate <= 0.3:
num_3 += 1
if rate <= 0.4:
num_4 += 1
if rate <= 0.5:
num_5 += 1
dict_15[index] = num
dict_1[index] = num_1
dict_2[index] = num_2
dict_3[index] = num_3
dict_4[index] = num_4
dict_5[index] = num_5
per_1_dict = {}
per_2_dict = {}
per_3_dict = {}
per_4_dict = {}
per_5_dict = {}
for k_index, v_number in dict_15.items():
per_1_dict[k_index] = dict_1[k_index] / v_number
per_2_dict[k_index] = dict_2[k_index] / v_number
per_3_dict[k_index] = dict_3[k_index] / v_number
per_4_dict[k_index] = dict_4[k_index] / v_number
per_5_dict[k_index] = dict_5[k_index] / v_number
print('per_0.1: ', per_1_dict)
print('per_0.2: ', per_2_dict)
print('per_0.3: ', per_3_dict)
print('per_0.4: ', per_4_dict)
print('per_0.5: ', per_5_dict)
def json_data(json_path):
f = open(json_path)
json_file = json.load(f)
print(type(json_file))
print(len(json_file.keys()))
print(len(json_file.values()))
print(json_file.keys())
if __name__ == '__main__':
txt_path = '/home/jianghusanren/Pictures/FX/4/pckh.txt'
pic_save_path = '/home/jianghusanren/Pictures/FX/4/PCK_total.jpg'
json_path = '/home/jianghusanren/Pictures/FX/4/pckh_dis_per_2.json'
statt = time.time()
curve_pic(json_path, pic_save_path)
end = time.time()
