如何使用python将文本布局颜色更改为白色并将文本更改为黑色？

Question

我有这张图片。

我想将所有彩色标题设为白色，将其中的文本设为黑色。

我尝试在下面使图像全黑。

img_grey = cv2.imread('img.jpg', cv2.IMREAD_GRAYSCALE)
thresh = 170
img_binary = cv2.threshold(img_grey, thresh, 250, cv2.THRESH_BINARY)[1]
cv2.imwrite('bw_img.jpg',img_binary) 

现在这些标题是黑色的，其中的文本是白色的。但我想让文本变黑，标题布局变白。那么，谁能帮我解决这个问题？

Answer 1

您可以将图像转换为 HSV，应用阈值来查找彩色区域，并将 cv2.threshold 和 cv2.THRESH_BINARY_INV 的结果仅复制到彩色区域。

建议解决方案的主要阶段：

• 从 BGR 转换为 HSV 颜色空间，并获得饱和颜色通道。
  所有黑色和白色都为零，彩色像素高于零。
• 对饱和通道应用阈值。
• 在二值化饱和度通道上查找轮廓。
• 在黑色背景上将轮廓绘制为白色（255 个值）以形成蒙版。
• 应用形态闭合来闭合一些黑色间隙。
• 仅从img_binary_inv 获取掩码内的区域（您的代码使用cv2.THRESH_BINARY_INV 的结果）。
• 仅将蒙版的img_binary_inv 复制到img_grey，以蒙版为白色的像素为单位。

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完整的代码示例：

import numpy as np
import cv2

img_bgr = cv2.imread('img.jpg')  # Read image as BGR

img_grey = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)  # Convert from BGR to grayscale.

thresh = 170
img_binary_inv = cv2.threshold(img_grey, thresh, 255, cv2.THRESH_BINARY_INV)[1]  # Apply threshold and invert black/white

# Convert from BGR to HSV color space.
hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)

# Get the saturation color channel - all black and white are zero, and colored pixels are above zero.
s = hsv[:, :, 1]

thresh = 100
s_binary = cv2.threshold(s, thresh, 255, cv2.THRESH_BINARY)[1]  # Apply threshold to the saturation channel.

# Find contours on s_binary
cnts = cv2.findContours(s_binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]  # Use index [-2] to be compatible to OpenCV 3 and 4

# Draw the contours as white (255 values) on black background.
mask = np.zeros_like(s_binary)
cv2.drawContours(mask, cnts, -1, 255, -1)

# Apply morphological closing for closing some black gaps.
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, np.ones((3, 3)))

# Get only the area inside the mask from img_binary_inv
masked_binary_inv = cv2.bitwise_or(img_binary_inv, img_binary_inv, mask=mask)

# Copy masked_binary_inv to img_grey only in pixels that mask is white.
cv2.copyTo(masked_binary_inv, mask, img_grey)

cv2.imwrite('img_grey.png', img_grey)  # Save result (as PNG and not JPEG for better quality).

# Show images
cv2.imshow('img_bgr', img_bgr)
cv2.imshow('s_binary', s_binary)
cv2.imshow('mask', mask)
cv2.imshow('masked_binary_inv', masked_binary_inv)
cv2.imshow('img_grey', img_grey)
cv2.waitKey()
cv2.destroyAllWindows()

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结果（img_grey）：

由于输入图像的质量相对较低，结果看起来不太好。

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中间结果：

s_binary:

mask:

masked_binary_inv: