CNN RGB 3차원 합성곱 연산

내용출처 : 이수안컴퓨터연구소 Youtube

Setting

import cv2
import numpy as np 
import matplotlib.pyplot as plt
import urllib
import requests
from io import BytesIO

1. util functions

• 자주쓰는 기능 함수로 정의

def url_to_image(url, gray=False):
  resp = urllib.request.urlopen(url)
  image = np.asarray(bytearray(resp.read()), dtype="uint8")

  if gray == True:
    image = cv2.imdecode(image, cv2.IMREAD_GRAYSCALE)
  else:
    image = cv2.imdecode(image, cv2.IMREAD_COLOR)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

  return image

def filtered_image(image, filter, output_size):
  filtered_img = np.zeros((output_size, output_size))
  filter_size = filter.shape[0]

  for i in range(output_size):
    for j in range(output_size):
      multiply_values = image[i:(i + filter_size), j:(j+filter_size)] * filter
      sum_value = np.sum(multiply_values)

      if (sum_value > 255):
        sum_value = 255

      filtered_img[i, j] = sum_value

  return filtered_img

2. 이미지 확인

• 예시이므로 정사각형 사이즈로 진행

img_url = "https://upload.wikimedia.org/wikipedia/ko/thumb/2/24/Lenna.png/440px-Lenna.png"

image = url_to_image(img_url, gray=True)
print("image.shape:", image.shape)

plt.imshow(image, cmap='gray')
plt.show()

image.shape: (440, 440)

S

3. 필터연산 적용

vertical_filter = np.array([[1., 2., 1.], 
                            [0., 0., 0.],
                            [-1., -2., -1.]])

horizontal_filter = np.array([[1., 2., -1.], 
                              [2. ,0., -2.],
                              [-1.,0. ,-1.]])

output_size = int((image.shape[0] - 3) / 1 + 1)
print("output size:", output_size)

vertical_filtered = filtered_image(image, vertical_filter, output_size)
horizontal_filtered = filtered_image(image, horizontal_filter, output_size)

plt.figure(figsize=(10,10))
plt.subplot(1, 2, 1)
plt.title("Vertical")
plt.imshow(vertical_filtered, cmap='gray')

plt.subplot(1, 2, 2)
plt.title("Horizontal")
plt.imshow(vertical_filtered, cmap='gray')
plt.show()

output size: 438

이미지 필터를 적용한 최종 결과

sobel_img = np.sqrt(np.square(horizontal_filtered) + np.square(vertical_filtered)) 

plt.imshow(sobel_img, cmap='gray')

<matplotlib.image.AxesImage at 0x7fd891f3f850>

4. 3차원 데이터의 합성곱 연산

• 이미지는 3차원으로 구성
  • 가로, 세로 채널 수
  • 채널 : RGB (색상값 정도에 따라 color 결정)

img_url = "https://upload.wikimedia.org/wikipedia/ko/thumb/2/24/Lenna.png/440px-Lenna.png"

image = url_to_image(img_url)
print("image.shape:", image.shape)

plt.imshow(image)
plt.show()

image.shape: (440, 440, 3)

image_copy = image.copy()
image_copy[:,:,1] = 0  # 2차원(G) 그린채널 모두 0
image_copy[:,:,2] = 0  # 3차원(B) 블루채널 모두 0
image_red = image_copy # 레드만 남은 상황

print(image_red)

[[[226   0   0]
  [225   0   0]
  [222   0   0]
  ...
  [233   0   0]
  [224   0   0]
  [202   0   0]]

 [[226   0   0]
  [225   0   0]
  [222   0   0]
  ...
  [233   0   0]
  [224   0   0]
  [202   0   0]]

 [[226   0   0]
  [225   0   0]
  [222   0   0]
  ...
  [232   0   0]
  [223   0   0]
  [201   0   0]]

 ...

 [[ 84   0   0]
  [ 86   0   0]
  [ 93   0   0]
  ...
  [174   0   0]
  [169   0   0]
  [172   0   0]]

 [[ 82   0   0]
  [ 86   0   0]
  [ 95   0   0]
  ...
  [177   0   0]
  [178   0   0]
  [183   0   0]]

 [[ 81   0   0]
  [ 86   0   0]
  [ 96   0   0]
  ...
  [178   0   0]
  [181   0   0]
  [185   0   0]]]

image_copy = image.copy()
image_copy[:,:,0] = 0  # 1차원(R) 레드채널 모두 0
image_copy[:,:,2] = 0  # 3차원(B) 블루채널 모두 0
image_green = image_copy # 그린만 남은 상황

image_copy = image.copy()
image_copy[:,:,0] = 0  # 1차원(R) 레드채널 모두 0
image_copy[:,:,1] = 0  # 2차원(B) 블루채널 모두 0
image_blue = image_copy # 블루만 남은 상황

fig = plt.figure(figsize=(12,8))

title_list = ['R', 'G', 'B', 
              'R - grayscale', 'G - grayscale', 'B - grayscale']
image_list = [image_red, image_green, image_blue,
              image_red[:,:,0], image_green[:,:,1], image_blue[:,:,2]]

for i, image in enumerate(image_list):
  ax = fig.add_subplot(2, 3, i+1)
  ax.title.set_text("{}".format(title_list[i]))

  if i >=3:
    plt.imshow(image, cmap='gray')
  else: 
    plt.imshow(image)

plt.show()