文章目錄

• • 第一種方法
  • 第二種方法

第一種方法

# -*- coding: utf-8 -*- """ @File : plot.py @Time : 2020/2/24 8:55 @Author : Dontla @Email : sxana@qq.com @Software: PyCharm """import matplotlib.pyplot as pltimport numpy as np# 如發(fā)現(xiàn)格式不對(duì)可用記事本或notepad批量替換 keyword = {'11:30.0': (50000, 13.96), '12:16.0': (54500, 13.20), '13:15.0': (47500, 12.48),'14:22.0': (55450, 12.44), '14:35.0': (55430, 13.72), '17:03.0': (13990, 11.00),'17:38.0': (9058, 11.60), '17:57.0': (5044, 12.46), '18:20.0': (1300, 13.80),'18:25.0': (900, 13.90), '18:28.0': (700, 13.96), '18:31.0': (570, 13.90),'18:33.0': (500, 13.94), '18:34.0': (450, 13.9), '18:35.0': (440, 13.88),'18:36.0': (360, 13.60), '18:37.0': (300, 13.8), '18:39.0': (250, 13.4),'18:40.0': (200, 13.34),'18:42.0': (150, 13.10), '18:44.0': (100, 11.80), '18:44.2': (90, 11.34),'18.44.4': (80, 11.38), '18:44.8': (70, 9.50), '18:45.0': (60, 9.20),'18:46.0': (50, 11.9), '18:46.3': (40, 10.8), '18:46.6': (30, 9.20),'18:49.0': (20, 9.70), '18:49.6': (15, 6.90), '18:50.3': (13, 4.70),'18:50.9': (12, 3.80), '18:51.5': (11, 2.60), '18:52.2': (10, 1.70),'18:52.9': (9, 1.00), '18:53.6': (8, 0.2), '18:54.3': (7, 0.06),'18:55.0': (6, 0.02)}data = []for key in keyword:data.append(keyword[key])data = np.array(data) # print(data) # [[5.000e+04 1.396e+01] # [5.450e+04 1.320e+01] # [4.750e+04 1.248e+01] # [5.545e+04 1.244e+01] # [5.543e+04 1.372e+01] # [1.399e+04 1.100e+01] # [9.058e+03 1.160e+01] # [5.044e+03 1.246e+01] # [1.300e+03 1.380e+01] # [9.000e+02 1.390e+01] # [7.000e+02 1.396e+01] # [2.000e+02 1.334e+01] # [1.500e+02 1.310e+01] # [1.000e+02 1.180e+01] # [9.000e+01 1.134e+01] # [8.000e+01 1.138e+01] # [7.000e+01 9.500e+00] # [6.000e+01 9.200e+00] # [5.000e+01 1.190e+01] # [4.000e+01 1.080e+01] # [3.000e+01 9.200e+00] # [2.000e+01 9.700e+00] # [1.500e+01 6.900e+00] # [1.300e+01 4.700e+00] # [1.200e+01 3.800e+00] # [1.100e+01 2.600e+00] # [1.000e+01 1.700e+00] # [9.000e+00 1.000e+00] # [8.000e+00 2.000e-01] # [7.000e+00 6.000e-02] # [6.000e+00 2.000e-02]]x = data[:, 0] # print(x) # [5.000e+04 5.450e+04 4.750e+04 5.545e+04 5.543e+04 1.399e+04 9.058e+03 # 5.044e+03 1.300e+03 9.000e+02 7.000e+02 2.000e+02 1.500e+02 1.000e+02 # 9.000e+01 8.000e+01 7.000e+01 6.000e+01 5.000e+01 4.000e+01 3.000e+01 # 2.000e+01 1.500e+01 1.300e+01 1.200e+01 1.100e+01 1.000e+01 9.000e+00 # 8.000e+00 7.000e+00 6.000e+00] y = data[:, 1] # print(y) # [13.96 13.2 12.48 12.44 13.72 11. 11.6 12.46 13.8 13.9 13.96 13.34 # 13.1 11.8 11.34 11.38 9.5 9.2 11.9 10.8 9.2 9.7 6.9 4.7 # 3.8 2.6 1.7 1. 0.2 0.06 0.02]ind = np.lexsort((x,)) # print(ind) # [30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 # 6 5 2 0 1 4 3]data_sort = [(x[i], y[i]) for i in ind]# print(data_sort) # [(6.0, 0.02), (7.0, 0.06), (8.0, 0.2), (9.0, 1.0), (10.0, 1.7), (11.0, 2.6), (12.0, 3.8), (13.0, 4.7), (15.0, 6.9), (20.0, 9.7), (30.0, 9.2), (40.0, 10.8), (50.0, 11.9), (60.0, 9.2), (70.0, 9.5), (80.0, 11.38), (90.0, 11.34), (100.0, 11.8), (150.0, 13.1), (200.0, 13.34), (700.0, 13.96), (900.0, 13.9), (1300.0, 13.8), (5044.0, 12.46), (9058.0, 11.6), (13990.0, 11.0), (47500.0, 12.48), (50000.0, 13.96), (54500.0, 13.2), (55430.0, 13.72), (55450.0, 12.44)]x_sort, y_sort = np.array(data_sort)[:, 0], np.array(data_sort)[:, 1]# 用3次多項(xiàng)式擬合 可以改為5 次多項(xiàng)式。。。。 返回三次多項(xiàng)式系數(shù) z1 = np.polyfit(x_sort, y_sort, 3) p1 = np.poly1d(z1)# 在屏幕上打印擬合多項(xiàng)式 print(p1) # 3 2 # 2.534e-13 x - 2.506e-08 x + 0.000714 x + 7.821# 設(shè)置繪制間隔 x_lin = np.arange(0, 60000, 5)yvals = p1(x_lin) # 也可以使用yvals=np.polyval(z1,x)plot1 = plt.plot(x_sort, y_sort, '*', label='original values') plot2 = plt.plot(x_lin, yvals, 'r', label='polyfit values')# 限制繪制上下限 plt.ylim(0, 16)plt.xlabel('Illumination/lm')plt.ylabel('Detect num/pcs')plt.legend(loc=4) # 指定legend的位置,讀者可以自己help它的用法plt.title('polyfitting')plt.show()plt.savefig('p1.png')

結(jié)果：

3 2 2.458e-13 x - 2.317e-08 x + 0.0006102 x + 9.119 D:\Yolov3_Tensorflow\python\python.exe C:/Users/HuaWei/Desktop/繪制不同光照條件下識(shí)別率曲線(xiàn)圖/plot.py10 9 8 7 6 -3.045e-40 x + 7.957e-35 x - 8.616e-30 x + 4.993e-25 x - 1.672e-20 x5 4 3 2+ 3.273e-16 x - 3.641e-12 x + 2.149e-08 x - 5.822e-05 x + 0.05093 x + 4.692

第二種方法

暫未找到

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