使用有限的阴影将图像转换为灰度？

Question

我希望将图像转换为灰度，但希望将阴影的数量限制在4-5。这是因为我试图创造一个分层的‘剪纸’效果的图像，以便我可以使用它作为一个基础，我正在工作的一些艺术作品，我有5阴影的黑白工作。

如果您对如何使用Python实现这一目标有更好的想法，我将全神贯注。如果Python中已经存在这样的过滤器，这将是非常方便的，但我似乎找不到任何东西。非常感谢。

项目的最终结果是如下所示：图像

Answer 1

您可以使用PIL对此进行量化：

​

​

这方面：

​

​

就像这样：

#!/usr/bin/env python3

from PIL import Image

# Load image and make greyscale
im = Image.open('artistic-swirl.jpg').convert('L')

# Quantize down to 5 shades and save
qu = im.quantize(5)
qu.save('result.png')

print(f'Colours: {qu.getcolors()}')

样本输出

您可以看到产生的5种颜色(调色板指数)的列表及其出现的频率如下：

Colours: [(32047, 0), (34515, 1), (59838, 2), (70181, 3), (53419, 4)]

您也可以使用ImageMagick检查颜色如下：

magick identify -verbose result.png

样本输出

Image:
  Filename: result.png
  Format: PNG (Portable Network Graphics)
  Mime type: image/png
  Class: PseudoClass
  Geometry: 500x500+0+0
  Units: Undefined
  Colorspace: sRGB
  Type: Grayscale
  Base type: Undefined
  Endianness: Undefined
  Depth: 8-bit
  Channel depth:
    Red: 8-bit
    Green: 8-bit
    Blue: 8-bit
  Channel statistics:
    Pixels: 250000
    Red:
      min: 106  (0.415686)
      max: 166 (0.65098)
      mean: 133.412 (0.523186)
      median: 139 (0.545098)
      standard deviation: 19.4166 (0.0761436)
      kurtosis: -0.979496
      skewness: 0.129338
      entropy: 0.972589
    Green:
      min: 106  (0.415686)
      max: 166 (0.65098)
      mean: 133.412 (0.523186)
      median: 139 (0.545098)
      standard deviation: 19.4166 (0.0761436)
      kurtosis: -0.979496
      skewness: 0.129338
      entropy: 0.972589
    Blue:
      min: 106  (0.415686)
      max: 166 (0.65098)
      mean: 133.412 (0.523186)
      median: 139 (0.545098)
      standard deviation: 19.4166 (0.0761436)
      kurtosis: -0.979496
      skewness: 0.129338
      entropy: 0.972589
  Image statistics:
    Overall:
      min: 106  (0.415686)
      max: 166 (0.65098)
      mean: 133.412 (0.523186)
      median: 139 (0.545098)
      standard deviation: 19.4166 (0.0761436)
      kurtosis: -0.979485
      skewness: 0.129339
      entropy: 0.972589
  Colors: 5
  Histogram:
         53419: (106,106,106) #6A6A6A srgb(106,106,106)
         70181: (125,125,125) #7D7D7D grey49
         59838: (139,139,139) #8B8B8B srgb(139,139,139)
         34515: (153,153,153) #999999 grey60
         32047: (166,166,166) #A6A6A6 grey65
  Colormap entries: 5
  Colormap:
    0: (166,166,166,1) #A6A6A6FF grey65
    1: (153,153,153,1) #999999FF grey60
    2: (139,139,139,1) #8B8B8BFF srgba(139,139,139,1)
    3: (125,125,125,1) #7D7D7DFF grey49
    4: (106,106,106,1) #6A6A6AFF srgba(106,106,106,1)
  Rendering intent: Perceptual
  Gamma: 0.454545
  Chromaticity:
    red primary: (0.64,0.33)
    green primary: (0.3,0.6)
    blue primary: (0.15,0.06)
    white point: (0.3127,0.329)
  Matte color: grey74
  Background color: white
  Border color: srgb(223,223,223)
  Transparent color: none
  Interlace: None
  Intensity: Undefined
  Compose: Over
  Page geometry: 500x500+0+0
  Dispose: Undefined
  Iterations: 0
  Compression: Zip
  Orientation: Undefined
  Properties:
    date:create: 2022-06-30T13:25:02+00:00
    date:modify: 2022-06-30T13:25:02+00:00
    png:IHDR.bit-depth-orig: 4
    png:IHDR.bit_depth: 4
    png:IHDR.color-type-orig: 3
    png:IHDR.color_type: 3 (Indexed)
    png:IHDR.interlace_method: 0 (Not interlaced)
    png:IHDR.width,height: 500, 500
    png:PLTE.number_colors: 5
    png:sRGB: intent=0 (Perceptual Intent)
    signature: ff62d5806f38bc0228513619c9822015bc70ee8466714b0317441e89ff3b815b
  Artifacts:
    verbose: true
  Tainted: False
  Filesize: 15193B
  Number pixels: 250000
  Pixel cache type: Memory
  Pixels per second: 90.1415MP
  User time: 0.000u
  Elapsed time: 0:01.002
  Version: ImageMagick 7.1.0-33 Q16-HDRI arm 20040 https://imagemagick.org

关键词：Python，图像处理，量化，减少颜色。

Answer 2

下面是如何在Python/OpenCV中直接对5个灰度进行量化。

输入：

​

​

import cv2
import numpy as np

# arguments
num_colors = 5

# read input
img = cv2.imread("bear2.png")

# convert to gray as float in range 0 to 1
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = gray.astype(np.float32)/255

# quantize and convert back to range 0 to 255 as 8-bits
result = 255*np.floor(gray*num_colors+0.5)/num_colors
result = result.clip(0,255).astype(np.uint8)

# save result
cv2.imwrite('bear2_gray5.png', result)

cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

​

​

​