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343 lines (292 loc) · 9.36 KB
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# -*- coding: utf-8 -*-
"""
Created on Fri Apr 2 00:44:09 2022
@author: PC
"""
from matplotlib import pyplot as plt
import numpy as np
import torch
import kornia as K
from kornia import morphology as morph
splited = 100
#%% Funciones Morfológicas
def Erosion(img_rgb):
device = "cuda:0"
img_rgb = img_rgb.to(device)
img_rgb :torch.Tensor = torch.split(img_rgb,splited)
img_rgb = list(img_rgb)
kernel = torch.tensor([[0, 1, 0],[1, 1, 1],[0, 1, 0]]).to(device)
contador=0
for img in img_rgb:
# img = img.float()/255.
torch.cuda.empty_cache()
img = morph.erosion(img, kernel)
# img = img.float()*255.
# img = torch.clamp(img,min=0.0,max=255.0)
img_rgb[contador]=img
contador+=1
del img
torch.cuda.empty_cache()
img_rgb: torch.Tensor = torch.cat(img_rgb,dim=0)
del kernel
torch.cuda.empty_cache()
img_rgb = img_rgb.to("cpu")
torch.cuda.empty_cache()
return img_rgb
def Dilation(img_rgb):
device = "cuda:0"
img_rgb = img_rgb.to(device)
img_rgb :torch.Tensor = torch.split(img_rgb,splited)
img_rgb = list(img_rgb)
kernel = torch.tensor([[0, 1, 0],[1, 1, 1],[0, 1, 0]]).to(device)
contador=0
for img in img_rgb:
# img = img.float()/255
torch.cuda.empty_cache()
img = morph.dilation(img, kernel)
# img = img.float()*255
# img = torch.clamp(img,min=0.0,max=255.0)
img_rgb[contador]=img
del img
torch.cuda.empty_cache()
contador+=1
img_rgb: torch.Tensor = torch.cat(img_rgb,dim=0)
del kernel
img_rgb = img_rgb.to("cpu")
torch.cuda.empty_cache()
return img_rgb
def Closing(img_rgb):
device = "cuda:0"
img_rgb = img_rgb.to(device)
img_rgb :torch.Tensor = torch.split(img_rgb,splited)
img_rgb = list(img_rgb)
kernel = torch.tensor([[0, 1, 0],[1, 1, 1],[0, 1, 0]]).to(device)
contador=0
for img in img_rgb:
# img = img.float()/255
torch.cuda.empty_cache()
img = morph.closing(img, kernel)
# img = img.float()*255
# img = torch.clamp(img,min=0.0,max=255.0)
img_rgb[contador]=img
del img
torch.cuda.empty_cache()
contador+=1
img_rgb: torch.Tensor = torch.cat(img_rgb,dim=0)
del kernel
img_rgb = img_rgb.to("cpu")
torch.cuda.empty_cache()
return img_rgb
def Opening(img_rgb):
device = "cuda:0"
img_rgb = img_rgb.to(device)
img_rgb :torch.Tensor = torch.split(img_rgb,splited)
img_rgb = list(img_rgb)
kernel = torch.tensor([[0, 1, 0],[1, 1, 1],[0, 1, 0]]).to(device)
contador=0
for img in img_rgb:
# img = img.float()/255
torch.cuda.empty_cache()
img = morph.opening(img, kernel)
# img = img.float()*255
# img = torch.clamp(img,min=0.0,max=255.0)
img_rgb[contador]=img
del img
torch.cuda.empty_cache()
contador+=1
img_rgb: torch.Tensor = torch.cat(img_rgb,dim=0)
del kernel
img_rgb = img_rgb.to("cpu")
torch.cuda.empty_cache()
return img_rgb
#%%Detección de bordes
def Sobel(img_rgb):
device = "cuda:0"
img_rgb = img_rgb.to(device)
img_rgb :torch.Tensor = torch.split(img_rgb,splited)
img_rgb = list(img_rgb)
contador=0
for img in img_rgb:
# img = img.float()/255
torch.cuda.empty_cache()
img = K.filters.sobel(img)
# img = img.float()*255
img = torch.clamp(img,min=0.0,max=1.0)
img_rgb[contador]=img
del img
torch.cuda.empty_cache()
contador+=1
img_rgb: torch.Tensor = torch.cat(img_rgb,dim=0)
img_rgb = img_rgb.to("cpu")
torch.cuda.empty_cache()
return img_rgb
def LaPlacian(img_rgb):
"""LaPlacian"""
device = "cuda:0"
img_rgb = img_rgb.to(device)
img_rgb :torch.Tensor = torch.split(img_rgb,splited)
img_rgb = list(img_rgb)
contador=0
for img in img_rgb:
# img = img.float()/255
torch.cuda.empty_cache()
img = K.filters.laplacian(img, kernel_size=5)
# img = img.float()*255
img = torch.clamp(img,min=0.0,max=1.0)
img_rgb[contador]=img
del img
torch.cuda.empty_cache()
contador+=1
img_rgb: torch.Tensor = torch.cat(img_rgb,dim=0)
img_rgb = img_rgb.to("cpu")
torch.cuda.empty_cache()
return img_rgb
def Gradient(img_rgb):
device = "cuda:0"
img_rgb = img_rgb.to(device)
img_rgb :torch.Tensor = torch.split(img_rgb,splited)
img_rgb = list(img_rgb)
kernel = torch.tensor([[0, 1, 0],[1, 1, 1],[0, 1, 0]]).to(device)
contador=0
for img in img_rgb:
# img = img.float()/255
torch.cuda.empty_cache()
img = morph.gradient(img, kernel)
# img = img.float()*255
img = torch.clamp(img,min=0.0,max=1.0)
img_rgb[contador]=img
del img
torch.cuda.empty_cache()
contador+=1
img_rgb: torch.Tensor = torch.cat(img_rgb,dim=0)
del kernel
img_rgb = img_rgb.to("cpu")
torch.cuda.empty_cache()
return img_rgb
#%%Funciones aritméticas
def suma_imgs2(img1, img2):
device = "cuda:0"
img1 = img1.to(device)
# img1 = img1.float()/255
img2 = img2.to(device)
# img2 = img2.float()/255
suma = img1+img2
# suma = suma.float()*255
suma = torch.clamp(suma, min=0.0,max=1.0)
img1 = img1.to("cpu")
img2 = img2.to("cpu")
suma = suma.to("cpu")
return suma
def suma_imgs3(img1, img2, img3):
device = "cuda:0"
img1 = img1.to(device)
# img1 = img1.float()/255
img2 = img2.to(device)
# img2 = img2.float()/255
img3 = img3.to(device)
# img3 = img3.float()/255
suma = img1+img2+img3
# suma = suma.float()*255
suma = torch.clamp(suma, min=0.0,max=1.0)
img1 = img1.to("cpu")
img2 = img2.to("cpu")
img3 = img3.to("cpu")
suma = suma.to("cpu")
return suma
def resta_imgs(img1, img2):
device = "cuda:0"
img1 = img1.to(device)
# img1 = img1.float()/255
img2 = img2.to(device)
# img2 = img2.float()/255
resta = img1-img2
# resta = resta.float()*255
# resta = torch.clamp(resta, min=0.0,max=255.0)
img1 = img1.to("cpu")
img2 = img2.to("cpu")
resta = resta.to("cpu")
return resta
def sqrt(img):
device = "cuda:0"
img = img.to(device)
# img = img.float()/255
sq = torch.sqrt(img)
# sq = sq.float()*255
# sq = torch.clamp(sq, min=0.0, max=255.0)
img = img.to("cpu")
sq = sq.to("cpu")
return sq
#%%Funciones de filtrado y transformaciones de intensidad
def Gaussian_blur_2d(img_rgb):
"""Gaussian blur 2D = GB_2D
Size kernel should be odd int positive (3,5,7,9,11,13,15,17) are the possible numbers to operate the function
The seccond parameter is the standard deviation of the kernel. Must be a float number
"""
device = "cuda:0"
img_rgb = img_rgb.to(device)
img_rgb :torch.Tensor = torch.split(img_rgb,splited)
img_rgb = list(img_rgb)
kernel_size=7
contador=0
for img in img_rgb:
# img = img.float()/255
torch.cuda.empty_cache()
img = K.filters.gaussian_blur2d(img, (kernel_size,kernel_size), (10.0, 10.0))
# img = img.float()*255
# img = torch.clamp(img,min=0.0,max=255.0)
img_rgb[contador]=img
del img
torch.cuda.empty_cache()
contador+=1
img_rgb: torch.Tensor = torch.cat(img_rgb,dim=0)
img_rgb = img_rgb.to("cpu")
torch.cuda.empty_cache()
# print(img_rgb.max(), img_rgb.min())
return img_rgb
def En_adbright(img_rgb):
"""Enhance adjust brightness
factor = must be float between [-0.5,0.5]
"""
device = "cuda:0"
img_rgb = img_rgb.to(device)
img_rgb :torch.Tensor = torch.split(img_rgb,splited)
img_rgb = list(img_rgb)
contador=0
factor=0.2
for img in img_rgb:
# img = img.float()/255
torch.cuda.empty_cache()
img = K.enhance.adjust_brightness(img, factor)
# img = img.float()*255
# img = torch.clamp(img,min=0.0,max=255.0)
img_rgb[contador]=img
del img
torch.cuda.empty_cache()
contador+=1
img_rgb: torch.Tensor = torch.cat(img_rgb,dim=0)
img_rgb = img_rgb.to("cpu")
torch.cuda.empty_cache()
# print(img_rgb.max(), img_rgb.min())
return img_rgb
def En_equal(img_rgb):
# print('before',img_rgb.max(), img_rgb.min())
"""Enhance adjust brightness """
device = "cuda:0"
img_rgb = img_rgb.to(device)
img_rgb :torch.Tensor = torch.split(img_rgb,splited)
img_rgb = list(img_rgb)
contador=0
for img in img_rgb:
#print(img.shape)
# img = (img.float()/255)
img = K.enhance.equalize(img)
# img = img.float()*255
img = torch.clamp(img,min=0.0,max=1.0)
img_rgb[contador]=img
del img
torch.cuda.empty_cache()
contador+=1
img_rgb: torch.Tensor = torch.cat(img_rgb,dim=0)
img_rgb = img_rgb.to("cpu")
torch.cuda.empty_cache()
return img_rgb