Python实现图片批量转素描图的两种算法与代码详解

向北走

发布时间：2026-10-14 | 2人浏览过

原创

本文详细介绍利用Python将普通图片批量转换为素描效果的两种核心算法。Method 1基于图像灰度梯度与光照模型计算明暗，Method 2结合灰度反转与高斯模糊进行混合运算。文章提供了完整的Python代码示例，涵盖单张处理与文件夹批量处理逻辑，帮助开发者快速实现图片素描化功能。

前言

将正常图片转化为素描风格，本质上是对图像像素矩阵进行数学变换与处理。目前市面上多数修图App已内置此功能，其核心算法代码量通常不超过30行。以下通过Python示例展示如何读取单张图片并生成素描效果。若需处理大量图片，只需在循环中遍历指定文件夹内的图像文件即可执行批量转换。完整可运行代码已上传至GitHub，供读者下载参考。

程序

Method 1


def plot_sketch(origin_picture, out_picture) :
    a = np.asarray(Image.open(origin_picture).convert('L')).astype('float')
    depth = 10.  # (0-100)
    grad = np.gradient(a)  # 取图像灰度的梯度值
    grad_x, grad_y = grad  # 分别取横纵图像梯度值
    grad_x = grad_x * depth / 100.
    grad_y = grad_y * depth / 100.
    A = np.sqrt(grad_x ** 2 + grad_y ** 2 + 1.0)
    uni_x = grad_x / A
    uni_y = grad_y / A
    uni_z = 1. / A

    vec_el = np.pi / 2.2  # 光源的俯视角度，弧度值
    vec_az = np.pi / 4.  # 光源的方位角度，弧度值
    dx = np.cos(vec_el) * np.cos(vec_az)  # 光源对x 轴的影响
    dy = np.cos(vec_el) * np.sin(vec_az)  # 光源对y 轴的影响
    dz = np.sin(vec_el)  # 光源对z 轴的影响

    b = 255 * (dx * uni_x + dy * uni_y + dz * uni_z)  # 光源归一化
    b = b.clip(0, 255)

    im = Image.fromarray(b.astype('uint8'))  # 重构图像
    im.save(out_picture)
    print("转换成功，请查看 ： ", out_picture)

Method 2


def plot_sketch2(origin_picture, out_picture, alpha=1.0):
    img = Image.open(origin_picture)
    blur = 20
    img1 = img.convert('L')  # 图片转换成灰色
    img2 = img1.copy()
    img2 = ImageOps.invert(img2)
    for i in range(blur):  # 模糊度
        img2 = img2.filter(ImageFilter.BLUR)
    width, height = img1.size
    for x in range(width):
        for y in range(height):
            a = img1.getpixel((x, y))
            b = img2.getpixel((x, y))
            img1.putpixel((x, y), min(int(a*255/(256-b*alpha)), 255))
    img1.save(out_picture)

完整代码


from PIL import Image, ImageFilter, ImageOps
import numpy as np
import os


def plot_sketch(origin_picture, out_picture) :
    a = np.asarray(Image.open(origin_picture).convert('L')).astype('float')
    depth = 10.  # (0-100)
    grad = np.gradient(a)  # 取图像灰度的梯度值
    grad_x, grad_y = grad  # 分别取横纵图像梯度值
    grad_x = grad_x * depth / 100.
    grad_y = grad_y * depth / 100.
    A = np.sqrt(grad_x ** 2 + grad_y ** 2 + 1.0)
    uni_x = grad_x / A
    uni_y = grad_y / A
    uni_z = 1. / A

    vec_el = np.pi / 2.2  # 光源的俯视角度，弧度值
    vec_az = np.pi / 4.  # 光源的方位角度，弧度值
    dx = np.cos(vec_el) * np.cos(vec_az)  # 光源对x 轴的影响
    dy = np.cos(vec_el) * np.sin(vec_az)  # 光源对y 轴的影响
    dz = np.sin(vec_el)  # 光源对z 轴的影响

    b = 255 * (dx * uni_x + dy * uni_y + dz * uni_z)  # 光源归一化
    b = b.clip(0, 255)

    im = Image.fromarray(b.astype('uint8'))  # 重构图像
    im.save(out_picture)
    print("转换成功，请查看 ： ", out_picture)


def plot_sketch2(origin_picture, out_picture, alpha=1.0):
    img = Image.open(origin_picture)
    blur = 20
    img1 = img.convert('L')  # 图片转换成灰色
    img2 = img1.copy()
    img2 = ImageOps.invert(img2)
    for i in range(blur):  # 模糊度
        img2 = img2.filter(ImageFilter.BLUR)
    width, height = img1.size
    for x in range(width):
        for y in range(height):
            a = img1.getpixel((x, y))
            b = img2.getpixel((x, y))
            img1.putpixel((x, y), min(int(a*255/(256-b*alpha)), 255))
    img1.save(out_picture)


if __name__ == '__main__':
    origin_picture = "pictures/5.jpg"
    out_picture = "sketchs/sketch.jpg"
    plot_sketch(origin_picture, out_picture)

    origin_path = "./pictures"
    out_path = "./sketchs"
    dirs = os.listdir(origin_path)
    for file in dirs:
        origin_picture = origin_path + "/" + file
        out_picture = out_path + "/" + "sketch_of_" + file
        plot_sketch2(origin_picture, out_picture)