Hello 大家好，我是小张，好久不见~

本期推文与计算机视觉相关，用不到 20 行Python代码将一张图片由自然风转化为手绘风，期间未对图片进行任何预处理、后处理；代码中只借助了两个常见库，核心计算由 Numpy 负责 ，Pillow 负责图片读写

在正文开始之前，先看一下最初效果，下面是单张图片转换前后对比

图一

图片

图二

图片

图三

图片

为了增加趣味性，后面将这段代码应用到一个视频中，加上一个背景音乐，新鲜的 “手绘风视频” 出炉
Python 手绘风视频制作！

“手绘风”实现步骤

讲解之前，需要了解手绘图像的三个主要特点：

• 图片需为灰度图，是单通道的；

• 边缘部分线条较重涂抹为黑色，相同或相近像素值转换后趋于白色；

• 在光源效果的加持下，灰度变化可模拟人类视觉的远近效果

读取图片，转化为数组

因为后面要用到像素计算，为了方便，事先将读取后的图片转化为数组

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="" cid="n24" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">a = np.asarray(Image.open("Annie1.jpg").convert('L')).astype('float')</pre>

计算 x,y,z 轴梯度值，并归一化

刚才提到手绘照片的一个特点，就是 手绘照片对边缘区域更加侧重，定位图片边缘部分，最有效方式就是计算梯度，用灰度变化来模拟图片远近效果，depth 表示预设深度，z 轴默认梯度为 1

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="" cid="n27" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">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.</pre>

对梯度值完成归一化操作

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="" cid="n29" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">A = np.sqrt(grad_x ** 2 + grad_y ** 2 + 1.)
uni_x = grad_x / A
uni_y = grad_y / A
uni_z = 1. / A</pre>

加入光源效果

手绘风图片除了计算梯度值之外，还需要考虑光源影响；根据光源入射的角度不同最有对x,y,z 各轴上的梯度值有不同程度的影响，添加一个模拟光源，放置在斜上方，与 x , y 分别形成两个夹角

图片

并且这两个夹角是通过实验得到是已知的，然后根据正弦余弦函数计算出最终新的像素值

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="" cid="n34" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">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,8 255
b = b.clip(0, 255)# 对像素值低于0，高于255部分做截断处理</pre>

导出图片，并保存

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="" cid="n36" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">im.save("Annie_shouhui.jpg")</pre>

以下是该步骤涉及到的的全部代码

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="" cid="n38" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">from PIL import Image
import numpy as np

a = np.asarray(Image.open("Annie1.jpg").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.)
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("Annie_shouhui.jpg")</pre>

制作手绘风视频

图片转化后的效果虽然也不错，但图片毕竟是静态的，人作为视觉动物，如果能做成动态的那再好不过了，知道上面的方法之后，只需对视频再加上一个拆帧合并操作，就能制作一个手绘风 视频效果

you-get 下载视频

这里我用 you-get 命令在 B 站上找了一个视频，下载了下来，

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="" cid="n43" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">you-get --format=dash-flv -o ./ https://www.bilibili.com/video/BV1tT4y1j7a9?from=search&8014393453748720686</pre>

图片

下载完之后，用 OpenCV2 对视频进行切帧操作，切帧同时对图片进行转化，写出到本地视频文件中

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="" cid="n46" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"> vc = cv2.VideoCapture(video_path)
c = 0
if vc.isOpened():
rval,frame = vc.read()
height,width = frame.shape[0],frame.shape[1]
print(height, width)
else:
rval = False
height,width = 960,1200

jpg_list = [os.path.join('Pic_Directory/',i) for i in os.listdir('Pic_Directory') if i.endswith('.jpg')]

fps = 24 # 视频帧率
video_path1 = './text.mp4'
video_writer = cv2.VideoWriter(video_path1,cv2.VideoWriter_fourcc(*'mp4v'),fps,(width,height))

while rval:
rval,frame = vc.read()# 读取视频帧
img = coonvert_jpg(Image.fromarray(frame))
frame_converted = np.array(img)

转化为三通道

image = np.expand_dims(frame_converted,axis = 2)
result_arr = np.concatenate((image,image,image),axis = -1)

video_writer.write(result_arr)
print('Sucessfully Conveted---------{}'.format(c))
c = c + 1
if c >= 3000:
break
video_writer.release()</pre>

在图片序列提取时，需要注意一点，因为转化后的图片是单通道的，直接借助 OpenCV 生成视频序列是无法播放的，需增加一个步骤单通道转化为三通道！

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="" cid="n48" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;"> # 转化为三通道
image = np.expand_dims(frame_converted,axis = 2)
result_arr = np.concatenate((image,image,image),axis = -1)</pre>

想让生成的视频更有感觉的话可以添加一个背影音乐，借助剪辑软件、Python 都可，这里建议最好用剪辑软件，原因是 Python 自定义增加音频效果并不理想，添加音乐时需要有实时反馈， 而 Python 暂时无法满足此要求

数据源码获取

文中涉及到的源码将数据获取方式，关注微信公号：小张Python，后台回复关键字：210322 即可！

小结

本文主要介绍了如何用 Python将一张图片转化为手绘风格，代码量很少但涉及知识领域与数学、物理相关，所以不容易理解，本篇文章目的只是为了向大家介绍图片手绘风转换有这么一种方法，当然如果有感兴趣的小伙伴可以深究一下

好了以上就是本篇文章的全部内容了，最后感谢大家的阅读，我们下期见~