-
Notifications
You must be signed in to change notification settings - Fork 0
/
ayuda_polinomial.py
53 lines (42 loc) · 1.39 KB
/
ayuda_polinomial.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
import scipy
from scipy import ndimage
import numpy as np
import cv2
from matplotlib import pyplot as plt
from pylab import *
#x = [ 421.25571634 ,426.25279224 ,431.24986815 ,436.24694405, 441.24401995, 446.24109586]
#y = [ 0.02931459,0.03093554, 0.03563261, 0.03440331, 0.03535223, 0.03594375]
#coeffs = numpy.polyfit(x, y, 5)
#ffit = numpy.poly1d(coeffs)
#print(coeffs)
'''
para ex2
y=[282, 260, 278, 267, 278, 263 ,290, 282]
x=[0, 260 ,315 ,416 ,606, 674, 718, 915]
y=[282, 209, 356, 254, 280, 242, 263, 281, 282]
x=[0, 71, 144, 374, 498, 575, 674, 745, 915]
y=[282, 215, 356, 198, 241, 245, 267, 268 ,338, 290, 282]
x=[0 , 56, 144, 194, 302, 324, 416, 561, 627, 718, 915]
[0, [349, 68], [209, 147], [341, 202], [209, 299], [241, 378], [352, 431], [237, 532], [278, 682], [266, 741], [327, 794]]
'''
y = [282, 349, 209, 341, 209, 241, 352, 237, 278, 266,327]
x = [0, 68, 147, 202, 299, 378, 431, 532, 682, 741, 794]
# calculate polynomial
z = np.polyfit(x, y, 5)
f = np.poly1d(z)
print (f)
# calculate new x's and y's
x_new = np.linspace(x[0], x[-1], 50)
y_new = f(x_new)
img = cv2.imread('exx2.png')
width = 915
height = 564
dim = (width, height)
resized = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)
fig, ax = plt.subplots()
aa=ax.imshow(resized)
# height, width, number of channels in image
altura = img.shape[0]
width = img.shape[1]
the_plot=plt.plot(x, y, 'o', x_new, y_new)
plt.show()