删除验证码的背景。
图像保留数字和噪音
噪音线全部为一种颜色:RGB(127,127,127)
然后使用形态学方法。
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
self.im = cv2.morphologyEx(self.im, cv2.MORPH_CLOSE, kernel)
数字的某些部分将被删除 如何使用morphologyEx()只删除RGB中的颜色(127,127,127)?
答案 0 :(得分:1)
为了消除特定范围内的颜色,您必须使用cv2.inRange()
函数。
以下是代码:
lower = np.array([126,126,126]) #-- Lower range --
upper = np.array([127,127,127]) #-- Upper range --
mask = cv2.inRange(img, lower, upper)
res = cv2.bitwise_and(img, img, mask= mask) #-- Contains pixels having the gray color--
cv2.imshow('Result',res)
这就是我为你拍摄的两张照片所得到的:
图片1:
图片2:
你从这里继续。
答案 1 :(得分:1)
这是我的解决方案。
你的答案显然比我的好。
def mop_close(self):
def morphological(operator=min):
height, width, _ = self.im.shape
# create empty image
out_im = np.zeros((height,width,3), np.uint8)
out_im.fill(255) # fill with white
for y in range(height):
for x in range(width):
try:
if self.im[y,x][0] ==127 and self.im[y,x][1] ==127 and self.im[y,x][2] ==127:
nlst = neighbours(self.im, y, x)
out_im[y, x] = operator(nlst,key = lambda x:np.mean(x))
else:
out_im[y,x] = self.im[y,x]
except Exception as e:
print(e)
return out_im
def neighbours(pix,y, x):
nlst = []
# search pixels around im[y,x] add them to nlst
for yy in range(y-1,y+1):
for xx in range(x-1,x+1):
try:
nlst.append(pix[yy, xx])
except:
pass
return np.array(nlst)
def erosion(im):
return morphological(min)
def dilation(im):
return morphological(max)
self.im = dilation(self.im)
self.im = erosion(self.im)
答案 2 :(得分:1)
color_dict_HSV = {'black': [[180, 255, 30], [0, 0, 0]],
'white': [[180, 18, 255], [0, 0, 231]],
'red1': [[180, 255, 255], [159, 50, 70]],
'red2': [[9, 255, 255], [0, 50, 70]],
'green': [[89, 255, 255], [36, 50, 70]],
'blue': [[128, 255, 255], [90, 50, 70]],
'yellow': [[35, 255, 255], [25, 50, 70]],
'purple': [[158, 255, 255], [129, 50, 70]],
'orange': [[24, 255, 255], [10, 50, 70]],
'gray': [[180, 18, 230], [0, 0, 40]]}
阿里·哈希米安
因为你们大多数人都想这样做,即在我的情况下,任务是从图像中删除蓝色,我使用以下代码,从我的图像中删除蓝色墨水图章和蓝色刻度线,以便使用 Tesseract 进行正确的 OCR。
[颜色去除]代码
import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# image path:
#path = "D://opencvImages//"
#fileName = "out.jpg"
# Reading an image in default mode:
inputImage = cv2.imread('0.jpg')
# Convert RGB to grayscale:
grayscaleImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)
# Convert the BGR image to HSV:
hsvImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2HSV)
# Create the HSV range for the blue ink:
# [128, 255, 255], [90, 50, 70]
lowerValues = np.array([90, 50, 70])
upperValues = np.array([128, 255, 255])
# Get binary mask of the blue ink:
bluepenMask = cv2.inRange(hsvImage, lowerValues, upperValues)
# Use a little bit of morphology to clean the mask:
# Set kernel (structuring element) size:
kernelSize = 3
# Set morph operation iterations:
opIterations = 1
# Get the structuring element:
morphKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernelSize, kernelSize))
# Perform closing:
bluepenMask = cv2.morphologyEx(bluepenMask, cv2.MORPH_CLOSE, morphKernel, None, None, opIterations, cv2.BORDER_REFLECT101)
# Add the white mask to the grayscale image:
colorMask = cv2.add(grayscaleImage, bluepenMask)
_, binaryImage = cv2.threshold(colorMask, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
cv2.imwrite('bwimage.jpg',binaryImage)
thresh, im_bw = cv2.threshold(binaryImage, 210, 230, cv2.THRESH_BINARY)
kernel = np.ones((1, 1), np.uint8)
imgfinal = cv2.dilate(im_bw, kernel=kernel, iterations=1)
cv2.imshow(imgfinal)
在这里你可以看到几乎所有的刻度线都被删除了,原因是因为总是有改进的空间,但这似乎是我们能得到的最好的,因为即使删除这些小标记也不是使用 Tesseract 将对 OCR 产生深远影响。