检测颜色并从图像中删除该颜色

Question

我的图像背景为浅紫色，字符为深蓝色。我的目标是从图像中识别文本。所以我试图从背景中去除浅紫色，以便我的图像没有噪音，但我找不到该图像的确切颜色代码，因为它在任何地方都有些不同，所以我无法掩盖图片。这是我的代码

import numpy as np
from PIL import Image

im = Image.open('capture.png')

im = im.convert('RGBA')
data = np.array(im)

rgb = data[:,:,:3]
color = [27, 49, 89]   # Original value to be mask
black = [0,0,0, 255]
white = [255,255,255,255]
mask = np.all(rgb == color, axis = -1)
data[mask] = black
data[np.logical_not(mask)] = white

new_im = Image.fromarray(data)
new_im.save('new_file.png')

所以我想如果我可以去除所有特定颜色范围内的颜色，比如 [R:0-20, G:0-20, B:80-100] 也许这会起作用。有人可以告诉我我该怎么做。

任何其他解决此问题的建议也将不胜感激。

Answer 1

由于文本和背景似乎有一个可区分的阴影，颜色阈值应该在这里起作用。想法是将图像转换为 HSV 格式，然后使用上下阈值生成二进制分段掩码，然后按位提取文本。这是使用 Python OpenCV 的实现

使用这个上下阈值，我们得到这个掩码

lower = np.array([0, 120, 0])
upper = np.array([179, 255, 255])

然后我们用原始图像按位和

最后，我们阈值得到一个二进制图像，前景文本为黑色，背景为白色

import numpy as np
import cv2

# Color threshold
image = cv2.imread('1.png')
original = image.copy()
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([0, 120, 0])
upper = np.array([179, 255, 255])
mask = cv2.inRange(hsv, lower, upper)
result = cv2.bitwise_and(original,original,mask=mask)
result[mask==0] = (255,255,255)

# Make text black and foreground white
result = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
result = cv2.threshold(result, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)[1]

cv2.imshow('mask', mask)
cv2.imshow('result', result)
cv2.waitKey()

您可以使用此 HSV 颜色阈值脚本来确定下限和上限阈值

import cv2
import sys
import numpy as np

def nothing(x):
    pass

# Load in image
image = cv2.imread('1.png')

# Create a window
cv2.namedWindow('image')

# create trackbars for color change
cv2.createTrackbar('HMin','image',0,179,nothing) # Hue is from 0-179 for Opencv
cv2.createTrackbar('SMin','image',0,255,nothing)
cv2.createTrackbar('VMin','image',0,255,nothing)
cv2.createTrackbar('HMax','image',0,179,nothing)
cv2.createTrackbar('SMax','image',0,255,nothing)
cv2.createTrackbar('VMax','image',0,255,nothing)

# Set default value for MAX HSV trackbars.
cv2.setTrackbarPos('HMax', 'image', 179)
cv2.setTrackbarPos('SMax', 'image', 255)
cv2.setTrackbarPos('VMax', 'image', 255)

# Initialize to check if HSV min/max value changes
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0

output = image
wait_time = 33

while(1):

    # get current positions of all trackbars
    hMin = cv2.getTrackbarPos('HMin','image')
    sMin = cv2.getTrackbarPos('SMin','image')
    vMin = cv2.getTrackbarPos('VMin','image')

    hMax = cv2.getTrackbarPos('HMax','image')
    sMax = cv2.getTrackbarPos('SMax','image')
    vMax = cv2.getTrackbarPos('VMax','image')

    # Set minimum and max HSV values to display
    lower = np.array([hMin, sMin, vMin])
    upper = np.array([hMax, sMax, vMax])

    # Create HSV Image and threshold into a range.
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lower, upper)
    output = cv2.bitwise_and(image,image, mask= mask)

    # Print if there is a change in HSV value
    if( (phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
        print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
        phMin = hMin
        psMin = sMin
        pvMin = vMin
        phMax = hMax
        psMax = sMax
        pvMax = vMax

    # Display output image
    cv2.imshow('image',output)

    # Wait longer to prevent freeze for videos.
    if cv2.waitKey(wait_time) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()