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Proseacimiento.py
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Proseacimiento.py
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"""Necesito de aca poder obtener aquellas imagenes que sirvan
- Contorno
- Im Gris
- Im Binaria ---> Mascara
"""
import cv2
import numpy as np
def Aplicar_Mascara(img , mask):
res = cv2.bitwise_and(img,img,mask = mask)
cv2.imshow('Enmascaramiento',res)
return res
def Apila_horizontal(imagenes):
np_horizontal = np.hstack(imagenes)
# cv2.imshow(nombre,np_horizontal)
return np_horizontal
def Mostrar_con_bordes(nombre,imagenes):
bordes =list()
mascaras = list()
for im in imagenes :
canny_temp = cv2.Canny(im,1,400)
canny_1 = cv2.cvtColor(canny_temp, cv2.COLOR_GRAY2BGR)
bordes.append(canny_1)
_ ,mascara_rgb = Get_Mascara(im)
mascaras.append(mascara_rgb)
# Contornear(im)
np_vert = np.vstack((Apila_horizontal(imagenes), Apila_horizontal(bordes),Apila_horizontal(mascaras)))
cv2.imshow(nombre, np_vert)
def Get_Mascara(img):
kernel = np.ones((5,5), np.uint8)
gris_1 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
canny_temp = cv2.Canny(gris_1,1,150)
img_contours, _ = cv2.findContours(gris_1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# thresh = cv2.adaptiveThreshold(gris_1,256,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,101,0)
# cv2.drawContours(gris_1, img_contours, -1, (0, 0, 0))
binaria = cv2.adaptiveThreshold(gris_1,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,401,0)
#retval, binaria = cv2.threshold(gris_1,0, 255 ,0)
binaria = cv2.morphologyEx(binaria, cv2.MORPH_CLOSE, kernel)
rgb = cv2.cvtColor(binaria, cv2.COLOR_GRAY2BGR)
return binaria, rgb
def Contornear(img):
kernel = np.ones((5,5), np.uint8)
gris_1 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
canny_temp = cv2.Canny(gris_1,1,200)
thresh = cv2.adaptiveThreshold(canny_temp,256,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,101,0)
img_contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, img_contours, -1, (0, 255, 0))
return img
def Get_Contorno(img):
kernel_close = np.ones((9,9), np.uint8)
kernel_erosion = np.ones((3,3), np.uint8)
contorno = None
gris = cv2.cvtColor( img, cv2.COLOR_BGR2GRAY)
# blur = cv2.GaussianBlur(gris,(5,5),0)
# t, th = cv2.threshold(gris, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
th = cv2.adaptiveThreshold(gris, 255 , cv2.ADAPTIVE_THRESH_GAUSSIAN_C ,cv2.THRESH_BINARY_INV, 555 ,2)
# ret, th = cv2.threshold(gris, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
mascara = cv2.morphologyEx(th, cv2.MORPH_CLOSE, kernel_close)
img_erosion = cv2.erode(mascara, kernel_erosion, iterations=1)
contours, _ = cv2.findContours(img_erosion, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
area = cv2.contourArea(c)
if (img.shape[0] * img.shape[1])*0.1 < area and area < (img.shape[0] * img.shape[1])*0.75:
contorno = c
# cv2.drawContours(img, [c], 0, (0, 255, 0), 2, cv2.LINE_AA)
# cv2.imshow('imaima',img)
return img_erosion, contorno
def Filtrados(imagen):
"""
Recibe un IMAGEN\n
DEVUELVE un MASCARA Y CONTONRNO
"""
# ---- PREPROCESAMIENTO Y FILTRADO ----------
img = imagen.copy()
# img_erosion = cv2.erode(img, kernel, iterations=1)
# img_dilation = cv2.dilate(img_erosion, kernel, iterations=1)
kernel_close = np.ones((9,9), np.uint8)
kernel_erosion = np.ones((3,3), np.uint8)
contorno = None
# PRUEBA 1
# edge_1 = cv2.edgePreservingFilter( img, flags = 2, sigma_s= 50 , sigma_r = 0.30 )
# style_0 = cv2.stylization(edge_1 , sigma_s = 60, sigma_r = 0.6 )
edge = cv2.edgePreservingFilter( img, flags = 2, sigma_s= 50 , sigma_r = 0.50 )
style = cv2.stylization(edge , sigma_s = 50, sigma_r = 0.4 )
gris = cv2.cvtColor( style, cv2.COLOR_BGR2GRAY)
# blur = cv2.GaussianBlur(gris,(5,5),0)
# t, th = cv2.threshold(gris, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
th = cv2.adaptiveThreshold(gris, 255 , cv2.ADAPTIVE_THRESH_GAUSSIAN_C ,cv2.THRESH_BINARY_INV, 555 ,2)
# ret, th = cv2.threshold(gris, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
mascara = cv2.morphologyEx(th, cv2.MORPH_CLOSE, kernel_close)
img_erosion = cv2.erode(mascara, kernel_erosion, iterations=1)
contours, _ = cv2.findContours(img_erosion, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
buenos_cnt =list()
for c in contours:
area = cv2.contourArea(c)
if (img.shape[0] * img.shape[1])*0.1 < area and area < (img.shape[0] * img.shape[1])*0.75:
buenos_cnt.append(c)
# cv2.drawContours(img, [c], 0, (0, 255, 0), 2, cv2.LINE_AA)
# cv2.imshow('imaima',img)
cnt = max(buenos_cnt, key=cv2.contourArea)
# cv2.imshow('img orig', imagen)
# cv2.imshow('edge', edge)
# cv2.imshow('style', style)
# cv2.imshow('img_erosion', img_erosion)
# Aplicar_Mascara(imagen,img_erosion)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
MASCARA = img_erosion
CONTORNO = cnt
return MASCARA, CONTORNO
def PreProcesamiento(img):
img_original = img.copy()
edge = cv2.edgePreservingFilter( img_original, flags = 2, sigma_s= 50 , sigma_r = 0.50 )
style = cv2.stylization(edge , sigma_s = 50, sigma_r = 0.4 )
gris = cv2.cvtColor(style, cv2.COLOR_BGR2GRAY)
gauss = cv2.GaussianBlur(gris, (7,7), 0)
canny = cv2.Canny(gauss, 50, 150)
canny = cv2.dilate(canny , None, iterations= 2)
canny = cv2.morphologyEx(canny, cv2.MORPH_CLOSE, (15,5))
canny = cv2.erode(canny, (9,9), iterations=1)
(contornos,_) = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
buenos_contornos = list()
for c in contornos:
area = cv2.contourArea(c)
if (img_original.shape[0] * img_original.shape[1])*0.3 < area and area < (img_original.shape[0] * img_original.shape[1])*0.75:
buenos_contornos.append( c )
cnt = max(contornos, key=cv2.contourArea)
aux = img_original.copy()
cv2.drawContours(aux,[cnt],-1,(255,255,255), -1)
gris = cv2.cvtColor(aux, cv2.COLOR_BGR2GRAY)
ret, th = cv2.threshold(gris, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# th = cv2.adaptiveThreshold(gauss, 255 , cv2.ADAPTIVE_THRESH_GAUSSIAN_C ,cv2.THRESH_BINARY, 555 ,2)
kernel_close = np.ones((7,7), np.uint8)
th = cv2.morphologyEx(th, cv2.MORPH_CLOSE,kernel_close)
gauss_th = cv2.GaussianBlur(th, (15,15), 0)
canny_th = cv2.Canny(gauss_th, 50, 150)
canny_th= cv2.dilate(canny_th , None, iterations= 1)
contours, _ = cv2.findContours(canny_th, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contorno_grandes =list()
for c in contours:
area = cv2.contourArea(c)
if (img_original.shape[0] * img_original.shape[1])*0.1 < area and area < (img_original.shape[0] * img_original.shape[1])*0.75:
print('entro')
contorno_grandes.append(c)
contorno_final = np.concatenate(contorno_grandes, axis=0)
cnt_1 = max(contorno_grandes, key=cv2.contourArea)
th = cv2.erode(th, (9,9), iterations=1)
# cv2.imshow('edge', style)
# cv2.drawContours(img_original,[cnt],-1,(0,0,255), 1)
# cv2.imshow('img original',img_original)
# cv2.imshow('canny',canny_th)
# res = cv2.bitwise_and(img_original,img_original ,mask = th)
# cv2.imshow('Enmascaramiento',res)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
return th, cnt_1
if __name__ == "__main__":
imagen = cv2.imread("img/banana_5.jpg")
imagen = cv2.resize(imagen,(300,300))
mascara , contorno = Filtrados(imagen)
x, y, width, height = cv2.boundingRect(contorno)
# cv2.imshow('Im original', mascara)
roi = cv2.resize(mascara[y:y + height, x:x + width], (50, 50))
moments = cv2.HuMoments(cv2.moments(roi)).flatten()
print(moments)
cv2.imshow('Region interes', roi)
cv2.waitKey(0)
cv2.destroyAllWindows()