提高计算机视觉任务的图像质量
AI算法与图像处理
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2021-10-26 16:24
点击下方“AI算法与图像处理”，一起进步！
重磅干货，第一时间送达
二值化/阈值化 降噪 纠偏 重新缩放 形态学操作 为了尝试这些操作，我们将使用Python3语言及其两个库， Pillow 和OpenCV。
二值化
## import dependencies
import cv2
from PIL import Image
import matplotlib.pyplot as plt
## reading image
img = cv2.imread('text_document.jpg',0)
## apply binary thresholding
ret,thresh1 = cv2.threshold(img,170,255,cv2.THRESH_BINARY)
## plot original and binarised image
titles = ['Original Image', 'Binary Thresholding']
images = [img, thresh1]
for i in range(2):
plt.figure(figsize=(20,20))
plt.subplot(2,3,i+1),plt.imshow(images[i],'gray',vmin=0,vmax=255)
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
自适应阈值均值：阈值是平均值附近区域减去固定的Ç。 自适应高斯阈值：阈值是邻域值减去常数C的高斯加权总和。
## import dependencies
import cv2
from PIL import Image
import matplotlib.pyplot as plt
## reading image
img = cv2.imread('lighting_conditions.jpg', 0)
## apply adaptive thresholding
## adaptive mean thresholding
th1 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY,11,2)
## adaptive gaussian thresholding
th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,11,2)
## plot original and binarised image
titles = ['Original Image', 'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
images = [img, th1, th2]
plt.figure(figsize=(20,20))
for i in range(3):
plt.subplot(2,3,i+1),plt.imshow(images[i],'gray',vmin=0,vmax=255)
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
## import dependencies
import cv2
from PIL import Image
import matplotlib.pyplot as plt
## reading image
img = cv2.imread('lighting_conditions.jpg', 0)
## apply Otru's thresholding
ret3,th1 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
## plot original and binarised image
titles = ["Original Image", "Binary Otsu's Thresholding"]
images = [img, th1]
plt.figure(figsize=(20,20))
for i in range(2):
plt.subplot(2,3,i+1),plt.imshow(images[i],'gray',vmin=0,vmax=255)
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
降噪
## import dependencies
import cv2
from PIL import Image
import matplotlib.pyplot as plt
## reading image
img = cv2.imread('noisy_image.jpg')
## apply image denoising
dst = cv2.fastNlMeansDenoisingColored(img,None,10,10,7,21)
## plot original and denoised image
titles = ["Original Image", "Denoised Image"]
images = [img, dst]
plt.figure(figsize=(20,20))
for i in range(2):
plt.subplot(2,3,i+1),plt.imshow(images[i],'gray',vmin=0,vmax=255)
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
纠偏
重新缩放
## import dependencies
import cv2
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
## reading image
img = Image.open('noisy_image.jpg')
## apply image rescaling and making image 300x300 (downscaling)
dst = img.resize((50,50))
## plot original and downscaled image
titles = ["Original Image", "Rescaled Image"]
images = [np.asarray(img), np.asarray(dst)]
plt.figure(figsize=(20,20))
for i in range(2):
plt.subplot(2,3,i+1),plt.imshow(images[i],'gray',vmin=0,vmax=255)
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
形态学操作
## import dependencies
import cv2
from PIL import Image
import matplotlib.pyplot as plt
## reading image
img = cv2.imread('text_document.jpg', 0)
## apply erosion
kernel = np.ones((5,5),np.uint8)
erosion = cv2.erode(img,kernel,iterations = 1)
## plot original and eroded image
titles = ["Original Image", "Eroded Image"]
images = [img, erosion]
plt.figure(figsize=(20,20))
for i in range(2):
plt.subplot(2,3,i+1),plt.imshow(images[i],'gray',vmin=0,vmax=255)
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
## import dependencies
import cv2
from PIL import Image
import matplotlib.pyplot as plt
## reading image
img = cv2.imread('text_document.jpg', 0)
## apply dilation
kernel = np.ones((5,5),np.uint8)
dilation = cv2.dilate(img,kernel,iterations = 1)
## plot original and dilated image
titles = ["Original Image", "Dilated Image"]
images = [img, dilation]
plt.figure(figsize=(20,20))
for i in range(2):
plt.subplot(2,3,i+1),plt.imshow(images[i],'gray',vmin=0,vmax=255)
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
结论
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