8鄰域去噪
import cv2
def noise_remove_cv2(image_name, k):
"""
8鄰域降噪
Args:
image_name: 圖片文件命名
k: 判斷閾值
Returns:
"""
def calculate_noise_count(img_obj, w, h):
"""
計算鄰域非白色的個數
Args:
img_obj: img obj
w: width
h: height
Returns:
count (int)
"""
count = 0
width, height = img_obj.shape
for _w_ in [w - 1, w, w + 1]:
for _h_ in [h - 1, h, h + 1]:
if _w_ > width - 1:
continue
if _h_ > height - 1:
continue
if _w_ == w and _h_ == h:
continue
if img_obj[_w_, _h_] < 230: # 二值化的圖片設置為255
count += 1
return count
img = cv2.imread(image_name, 1)
# 灰度
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
w, h = gray_img.shape
for _w in range(w):
for _h in range(h):
if _w == 0 or _h == 0:
gray_img[_w, _h] = 255
continue
# 計算鄰域pixel值小於255的個數
pixel = gray_img[_w, _h]
if pixel == 255:
continue
if calculate_noise_count(gray_img, _w, _h) < k:
gray_img[_w, _h] = 255
return gray_img
if __name__ == '__main__':
image = noise_remove_cv2("./output/001.jpg", 2)
# cv2.imshow('img', image)
cv2.imwrite("./remove_noise_output/001.jpg", image)
邊緣的白點
from PIL import Image
# 去除干擾線
im = Image.open('./output2/2.jpg')
# 圖像二值化
data = im.getdata()
w, h = im.size
# black_point = 0
white_point = 0
for x in range(1, w - 1):
for y in range(1, h - 1):
mid_pixel = data[w * y + x] # 中央像素點像素值
if mid_pixel < 50: # 找出上下左右四個方向像素點像素值
top_pixel = data[w * (y - 1) + x]
left_pixel = data[w * y + (x - 1)]
down_pixel = data[w * (y + 1) + x]
right_pixel = data[w * y + (x + 1)]
# 判斷上下左右的黑色像素點總個數
if top_pixel > 240:
white_point += 1
if left_pixel > 240:
white_point += 1
if down_pixel > 240:
white_point += 1
if right_pixel > 240:
white_point += 1
if white_point < 1:
im.putpixel((x, y), 0)
# print(black_point)
white_point = 0
im.save('xxxx.jpg')
邊緣去掉
from PIL import Image
# 去除干擾線
im = Image.open('./output1/1.jpg')
# 圖像二值化
data = im.getdata()
w, h = im.size
black_point = 0
for x in range(1, w - 1):
for y in range(1, h - 1):
if x < 10 or y < 10:
im.putpixel((x - 1, y - 1), 0)
if x > w - 3 or y > h - 3:
im.putpixel((x + 1, y + 1), 0)
im.save('xxxxx.jpg')
