Created
November 7, 2017 01:35
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| import numpy as np | |
| import cv2 | |
| from itertools import * | |
| from os import listdir | |
| from os.path import isfile, join | |
| def guardar_imagen(img,cont): | |
| k='byw/'+str(cont)+'.png' | |
| cv2.imwrite(k,img) | |
| def cargar_imagenes(): | |
| mypath = 'test2' | |
| onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))] | |
| images = np.empty(len(onlyfiles), dtype=object) | |
| for n in range(0, len(onlyfiles)): | |
| images[n] = cv2.imread(join(mypath, onlyfiles[n])) | |
| print(len(images)) | |
| return images | |
| def imagen_to_black_and_white(img,cont): | |
| gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| #cv2.imshow('gray',gray) | |
| #cv2.waitKey(0) | |
| minimo=min_colors(gray) | |
| im_bw=change_color(gray,minimo) | |
| #cv2.imshow('byw', im_bw) | |
| #cv2.waitKey(0) | |
| guardar_imagen(im_bw,cont) | |
| return im_bw | |
| def min_colors(img): | |
| filas, colum = img.shape | |
| min=255 | |
| for i in range(filas): | |
| for j in range(colum): | |
| if(min>img[i,j]): | |
| min=img[i,j] | |
| #print('min=',min) | |
| return min | |
| def change_color(img,minimo): | |
| filas, colum = img.shape | |
| for i in range(filas): | |
| for j in range(colum): | |
| if (img[i, j]<minimo+25): | |
| img[i, j]=255 | |
| else: | |
| img[i,j]=0 | |
| return img | |
| def dist (x,y): | |
| x1,y1=x | |
| x2,y2=y | |
| return np.sqrt(pow((x1-x2), 2) + pow((y1-y2), 2)) | |
| def media(dicc): | |
| tam = len(dicc) | |
| total=0 | |
| for i in range(tam): | |
| total=total+dicc[i] | |
| total=total/tam | |
| return total | |
| def varianza(media,dicc): | |
| tam=len(dicc) | |
| total=0 | |
| for i in range(tam): | |
| total=total+pow((media-dicc[i]),2) | |
| total=total/tam | |
| return total | |
| def coeficiente_varianza(media,varianza): | |
| return (varianza/media) | |
| def crear_Diccionario(image,num): | |
| filas, colum, channels=image.shape | |
| diccionario = {} | |
| img=imagen_to_black_and_white(image,num) | |
| elemi1=0 | |
| puntos=[] | |
| for i in product(range(0,filas),range(elemi1,colum)): | |
| if(img[i]==0): | |
| puntos.append(i) | |
| limite=len(puntos) | |
| lista=[] | |
| for i in range(0,limite,1): | |
| for j in range(i+1,limite,1): | |
| lista.append(dist(puntos[i],puntos[j])) | |
| return lista | |
| def principal(image,cont): | |
| lista=crear_Diccionario(image,cont) | |
| med=media(lista) | |
| var=varianza(med,lista) | |
| coef=coeficiente_varianza(med,var) | |
| #print('media=',med) | |
| #print('varianza=',var) | |
| #print('coef=',coef) | |
| print(cont) | |
| return (med,var,coef) | |
| imagenes=cargar_imagenes() | |
| var= [] | |
| med = [] | |
| co=[] | |
| cont=1 | |
| for i in range(len(imagenes)): | |
| #print('imagen:',cont) | |
| x,y,z=principal(imagenes[i],cont) | |
| cont=cont+1 | |
| var.append(x) | |
| med.append(y) | |
| co.append(z) | |
| for i in range(i,cont-1): | |
| print('imagen',i,':=',var[i],' ',med[i],co[i]) | |
| print('fin') | |
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