[CV] 이미지 기본세팅 3 (블러, 선명도, 대비)

1. 블러처리 (이미지블러, 가우시안블러)

1. 이미지블러 cv2.blur() 함수활용

import cv2
import matplotlib.pyplot as plt

image_path = "./data/cat.png"
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

image_blurry = cv2.blur(image, (5,5)) # cv2.blur() 를 활용함
# 33 55 77   # 블러강도를 나타냄  

plt.imshow(image)
plt.show()

plt.imshow(image_blurry)
plt.show()

 

 

2. 가우시안블러 cv2.Gaussianblur() 함수활용 

이걸 더 많이씀

import cv2
import matplotlib.pyplot as plt
import numpy as np

image_path = "./data/cat.png"
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

image_g = cv2.GaussianBlur(image, (9,9), 0)   # cv2.GaussianBlur() 를 사용함  
# (9,9): 주변 커널수-> 세기조절이 됨
# 0 : 표준편차

plt.imshow(image)
plt.show()

plt.imshow(image_g)
plt.show()

 

 

 

 

2. 이미지 선명하게하기

import cv2
import matplotlib.pyplot as plt
import numpy as np

image_path = "./data/cat.png"
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

kernel_ = np.array([[0,-1,0],
                    [-1,5,-1],
                    [0,-1,0]
                   ])

image_sharp = cv2.filter2D(image, -1, kernel_)

plt.imshow(image)
plt.show()

plt.imshow(image_sharp)
plt.show()

 

3. 이미지 대비높이기

1 . 그레이스케일 일때  : 히스토그램 평활화 cv2.equalizeHist()에 BGR2Gray 활용 : 

import cv2
import matplotlib.pyplot as plt
import numpy as np

image_path = "./data/cat.png"
image = cv2.imread(image_path)
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 단일 이미지에서만 적용가능!
image_enhanced = cv2.equalizeHist(image_gray)

plt.imshow(image_gray, 'gray')
plt.show()

plt.imshow(image_enhanced, 'gray')
plt.show()

 

2. 컬러일때 : RGB에 YUV를 추가함

import cv2
import matplotlib.pyplot as plt
import numpy as np

image_path = "./data/cat.png"
image = cv2.imread(image_path)

# RGB 타입으로 변경 
image_org = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# YUV 타입으로 변경 
image_yuv = cv2.cvtColor(image, cv2.COLOR_BGR2YUV)

# 히스토그램 평활화 적용 
image_yuv[:,:,0] = cv2.equalizeHist(image_yuv[:,:,0])

# YUV > RGB 
image_rgb = cv2.cvtColor(image_yuv , cv2.COLOR_YUV2RGB)


plt.imshow(image_yuv)
plt.show()

plt.imshow(image_org)
plt.show()

plt.imshow(image_rgb)
plt.show()

4. 이진화(흑백)

 

import cv2
import matplotlib.pyplot as plt
import numpy as np

image_path = "./data/cat.png"
image = cv2.imread(image_path)

image_gray_01 = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 이미지 이진화 
max_output_value = 255 
neighborhood_size = 99
subtract_from_mean = 10
image_binarized = cv2.adaptiveThreshold(
                                image_gray_01,
                                max_output_value,
                                cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                cv2.THRESH_BINARY, # cv2.THRESH_BINARY_INV(반전)
                                neighborhood_size,
                                subtract_from_mean
)

plt.imshow(image_binarized, 'gray')
plt.show()