Python图片批量转素描图 利用Python将图片批量转化成素描图的过程记录
guihunkun 人气:0想了解利用Python将图片批量转化成素描图的过程记录的相关内容吗，guihunkun在本文为您仔细讲解Python图片批量转素描图的相关知识和一些Code实例，欢迎阅读和指正，我们先划重点：python把图片转化为素描画,把图片转化成素描,python把图片转化成素描图，下面大家一起来学习吧。
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)
结果
总结
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