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November 8, 2017 03:04
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| import numpy as np | |
| import cv2 | |
| from itertools import * | |
| from os import listdir | |
| from os.path import isfile, join | |
| import time | |
| from operator import itemgetter | |
| def guardar_imagen(img,cont): | |
| k='byw/imagen'+str(cont)+'.png' | |
| cv2.imwrite(k,img) | |
| def cargar_imagenes(): | |
| mypath = 'test' | |
| nombres = [] | |
| 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])) | |
| k=onlyfiles[n].split('.') | |
| nombres.append(k[0]) | |
| print(len(images)) | |
| return images,nombres | |
| def clear_edges(img): | |
| return cv2.medianBlur(img,5) | |
| def imagen_to_black_and_white(img,cont): | |
| gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| minimo=min_colors(gray) | |
| im_bw=change_color(gray,minimo) | |
| guardar_imagen(im_bw,cont) | |
| return clear_edges(im_bw) | |
| def min_colors(img): | |
| return img[img>-1].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]=0 | |
| else: | |
| img[i,j]=255 | |
| 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): | |
| return np.mean(dicc) | |
| def varianza(media,dicc): | |
| return np.std(dicc) | |
| 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) | |
| analizar(var,cont) | |
| return (med,var,coef) | |
| def analizar(coeficiente,name): | |
| if(coeficiente >=6.5): | |
| print('la célula',name,'esta en telofase') | |
| initial = time.time() | |
| imagenes,names=cargar_imagenes() | |
| var= [] | |
| med = [] | |
| co=[] | |
| cont=1 | |
| for i in range(len(imagenes)): | |
| x,y,z=principal(imagenes[i],names[i]) | |
| cont=cont+1 | |
| var.append((x,names[i])) | |
| med.append((y,names[i])) | |
| co.append((z,names[i])) | |
| k= len(names) | |
| var=sorted(var, key=itemgetter(0)) | |
| med= sorted(med, key=itemgetter(0)) | |
| co= sorted(co, key=itemgetter(0)) | |
| print('ordenadas por varianza') | |
| for i in range(k): | |
| print(var[i]) | |
| print('ordenadas por media') | |
| for i in range(k): | |
| print(med[i]) | |
| print('ordenadas por coef') | |
| for i in range(k): | |
| print(co[i]) | |
| End = time.time() | |
| print('tiempo=',End-initial) | |
| print('fin') | |
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