bleee

import numpy as np
import os
import math
import pandas as pd
import cv2
import csv
from numpy import ndarray
from Lista02 import FuncoesML as fun
import matplotlib.pyplot as plt

#lendo a imagem
circles = []
circles = fun.loadFiles('C:/Users/Auricelia/Desktop/DataSetsML/New_shapes_dataset/circles/*.jpg',circles)

ellipsis = []
ellipsis = fun.loadFiles('C:/Users/Auricelia/Desktop/DataSetsML/New_shapes_dataset/ellipses/*.jpg', ellipsis)

hexagons = []
hexagons = fun.loadFiles('C:/Users/Auricelia/Desktop/DataSetsML/New_shapes_dataset/hexagons/*.jpg', hexagons)

lines = []
lines = fun.loadFiles('C:/Users/Auricelia/Desktop/DataSetsML/New_shapes_dataset/lines/*.jpg', lines)

rectangles = []
rectangles = fun.loadFiles('C:/Users/Auricelia/Desktop/DataSetsML/New_shapes_dataset/rectangles/*.jpg', rectangles)

rhombuses = []
rhombuses = fun.loadFiles('C:/Users/Auricelia/Desktop/DataSetsML/New_shapes_dataset/rhombuses/*.jpg', rhombuses)

squares = []
squares = fun.loadFiles('C:/Users/Auricelia/Desktop/DataSetsML/New_shapes_dataset/squares/*.jpg', squares)

trapezia = []
trapezia = fun.loadFiles('C:/Users/Auricelia/Desktop/DataSetsML/New_shapes_dataset/trapezia/*.jpg', trapezia)

triangles = []
triangles = fun.loadFiles('C:/Users/Auricelia/Desktop/DataSetsML/New_shapes_dataset/triangles/*.jpg', triangles)
print('terminou load images')

#circles = fun.resizeImages(circles,16,16)
#convertendo para níveis de cinza

# circleeee = circles[0]
# circles = fun.grayConversion(circles)
#
# #aplicando o filtro gaussiano
# circles = fun.blurConversion(circles,5, 0)
#
#
# #convertendo para binária
# circles = fun.binaryConversion(circles, 255, 31)
#
# #invertendo as cores
# circles = fun.invertConversion(circles)

# extraindo momentos da imagem
'''
im2, countours, hierachy = cv2.findContours(circles[0], cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(circleeee, countours, -1, (0,255,0), 3)
cv2.imshow('ble',circleeee)
cv2.waitKey()


peri = cv2.arcLength(countours[0], True)  # perimetro

aproxx = cv2.approxPolyDP(countours[0], 0.04 * peri, True)  # vertices


print('Vertices list', aproxx)
print('shape', aproxx.shape)
print('dtype',aproxx.dtype)
print('type',type(aproxx))

x1 = ndarray.item(aproxx,(0,0,0))         # recupera o primeiro elemento do ndarray
y1 = ndarray.item(aproxx,(0,0,1))
x2 = ndarray.item(aproxx,(1,0,0))
y2 = ndarray.item(aproxx,(1,0,1))

bla = np.reshape(aproxx, (aproxx.shape[0], (aproxx.shape[1]*aproxx.shape[2])))

print('approxPolyDp resultado', aproxx)
print('angle',fun.angleBetweenPoints(x1,y1,x2,y2))
def angle3pt(a, b, c):
    """Counterclockwise angle in degrees by turning from a to c around b
        Returns a float between 0.0 and 360.0"""
    ang = math.degrees(
        math.atan2(c[1] - b[1], c[0] - b[0]) - math.atan2(a[1] - b[1], a[0] - b[0]))
    return ang + 360 if ang < 0 else ang


if(len(bla) >= 3):

    for x in range(len(bla)):

        if x == 0:
            print("CASO INICIAL")
            bla3 = angle3pt(bla[x],bla[x+1],bla[len(bla)-1])
            print(bla3)
            #print("v0= ", bla[x][0] , ",", bla[x][1], " v1= ", bla[x+1][0],",", bla[x+1][1], " v2= ", bla[len(bla)-1][0], "," , bla[len(bla)-1][1])

        if x == len(bla)-1:
            print("CASO FINAL")
            bla4 = angle3pt(bla[x], bla[0], bla[x-1])
            print(bla4)
            print("v0= ", bla[x][0], ",", bla[x][1], " v1= ", bla[0][0], ",", bla[0][1], " v2= ", bla[x-1][0], "," , bla[x-1][1])

        if x > 0 and x < len(bla)-1 :
            print("RESTO DOS CASOS")
            bla5 = angle3pt(bla[x],bla[x+1],bla[x-1])
            print(bla5)
            bla5 = 0
            print("v0= ", bla[x][0], ",", bla[x][1], " v1= ", bla[x+1][0], ",", bla[x+1][1], " v2= ", bla[x-1][0], ",", bla[x - 1][1])


cv2.imshow("a",circleeee)
cv2.waitKey()
print()
print(bla[1][0])


print('a = ',aproxx[len(aproxx) - 1][0],' ',aproxx[len(aproxx)-1][1])
print('b = ',)
print('angle',angle3pt((aproxx[len(aproxx) - 1][0],aproxx[len(aproxx)-1][1]),(aproxx[0][0],aproxx[0][1]),(aproxx[1][0],aproxx[1][1])))



food = {'pizza': 324, 'sandwich': 78, 'hot dog': 90}
food = list(dict.values(food))
print(food)

ble = fun.extratorCaracteristicas(circles)
ble = pd.DataFrame(ble)
print(ble)
ble = fun.normalizar(ble)
ble = ble.fillna(0)
print(ble)

vetor = [1,2,3,4,5,6,7,8,9,10]
vetor2 = fun.selecionaImagens(vetor,3)
print('vetor',vetor)

print('vetor 2')
print(vetor2)

with open('C:/Users/Auricelia/Desktop/DataSetsML/Circles2.txt', 'w') as f:
    for item in circles2:
        f.write("%s\n" % item)

for r in circles2:
    y = str(x)
    print('passou do str x')
    cv2.imwrite('C:/Users/Auricelia/Desktop/DataSetsML/Circles2/' + '%s' % y + '.jpg', r, cv2.IMWRITE_JPEG_QUALITY)
    x += 1

    x = 0
for i in circles2:
    y = str(x)
    cv2.imwrite('C:/Users/Auricelia/Desktop/DataSetsML/Circles2/cir' + y + '.jpg',i)
    x += 1
'''
# circles2 = fun.selecionaImagens(circles,5)
# fun.save_images(circles2,'C:/Users/Auricelia/Desktop/DataSetsML/Circles2/')

# selecionando aleatoriamente 72 imagens de cada classe

circles_selec, circles_naoselec = fun.seleciona_imagens(circles,72)
squares_selec, squares_naoselec = fun.seleciona_imagens(squares,72)
triangles_selec, triangles_naoselec = fun.seleciona_imagens(triangles,72)
ellipsis_selec, ellipsis_naoselec = fun.seleciona_imagens(ellipsis,72)
trapezia_selec, trapezia_naoselec = fun.seleciona_imagens(trapezia,72)
rectangles_selec, rectangles_naoselec = fun.seleciona_imagens(rectangles,72)
rhombuses_selec, rhombuses_naoselec = fun.seleciona_imagens(rhombuses,72)
lines_selec, lines_naoselec = fun.seleciona_imagens(lines,72)
hexagons_selec, hexagons_naoselec = fun.seleciona_imagens(hexagons,72)

#Salvando em pastas diferentes as imagens para seleção de características e as de teste
fun.save_images(circles_selec,'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomCircles/')
fun.save_images(circles_naoselec,'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Circles/')
fun.save_images(squares_selec,'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomSquares/')
fun.save_images(squares_naoselec,'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Squares/')
fun.save_images(triangles_selec,'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomTriangles/')
fun.save_images(triangles_naoselec,'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Triangles/')
fun.save_images(ellipsis_selec, 'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomEllipses/')
fun.save_images(ellipsis_naoselec,'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Ellipses/')
fun.save_images(trapezia_selec,'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomTrapezia/')
fun.save_images(trapezia_naoselec,'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Trapezia/')
fun.save_images(rectangles_selec,'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomRectangles/')
fun.save_images(rectangles_naoselec, 'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Rectangles/')
fun.save_images(rhombuses_selec,'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomRhombuses/')
fun.save_images(rhombuses_naoselec, 'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Rhombuses/')
fun.save_images(lines_selec, 'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomLines/')
fun.save_images(lines_naoselec, 'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Lines/')
fun.save_images(hexagons_selec, 'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomHexagons/')
fun.save_images(hexagons_naoselec, 'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Hexagons/')