利用Python将图片批量转化成素描图的过程记录

2024-04-02 19:04:59 390人浏览八月长安

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摘要

目录前言程序Method 1Method 2完整代码结果总结前言正常图片转化成素描图片无非对图片像素的处理，矩阵变化而已。目前很多拍照修图App都有这一功能，核心代码不

前言

正常图片转化成素描图片无非对图片像素的处理，矩阵变化而已。目前很多拍照修图App都有这一功能，核心代码不超30行。如下利用 python 实现读取一张图片并将其转化成素描图片。至于批处理也简单，循环读取文件夹里的图片处理即可。具体代码可以去我的 GitHub 下载。

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)