2种手绘风格效果比较，你更喜欢哪一种呢？

2种手绘风格比较，你更喜欢哪一种呢？

先看视频效果：

2种手绘效果与赵盼儿舞蹈剪辑之比较

下边看2种不同手绘风格的代码:

导入工具包

import cv2
import numpy as np
#导入python绘图matplotlib
import matplotlib.pyplot as plt
#使用ipython的魔法方法，将绘制出的图像直接嵌入在notebook单元格中
%matplotlib inline

#定义可视化图像函数
def look_img(img):
    '''opencv读入图像格式为BGR，matplotlib可视化格式为RGB，因此需将BGR转RGB'''
    img_RGB = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    plt.imshow(img_RGB)
    plt.show()

手绘效果1

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
depth = 10. # (0-100)
im=cv2.imread('zhaopaner.jpg',0)    #加载灰度图
look_img(im)

a = np.asarray(im).astype('float')
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.
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
A = np.sqrt(grad_x**2 + grad_y**2 + 1.)
uni_x = grad_x/A
uni_y = grad_y/A
uni_z = 1./A
a2 = 255*(dx*uni_x + dy*uni_y + dz*uni_z) #光源归一化
a2 = a2.clip(0,255)
im2=np.asarray(a2).astype('uint8')
look_img(im2)
cv2.imwrite('zhaopaner_1.jpg',im2)

​

True

手绘效果2

img = cv2.imread('zhaopaner.jpg')
height=img.shape[0]   #可自行修改尺寸，此处和原尺寸相同
width=img.shape[1]    #可自行修改尺寸，此处和原尺寸相同
dim = (width,height)
resized = cv2.resize(img,dim,interpolation = cv2.INTER_AREA)  

kernel_sharpening = np.array([[-1,-1,-1], 
                              [-1, 9,-1],
                              [-1,-1,-1]])
sharpened = cv2.filter2D(resized,-1,kernel_sharpening)  


gray = cv2.cvtColor(sharpened , cv2.COLOR_BGR2GRAY)   
object_detection = cv2.cvtColor(sharpened, cv2.COLOR_BGR2HSV ) 


inv = 255-gray
gauss = cv2.GaussianBlur(inv,ksize=(15,15),sigmaX=0,sigmaY=0) 

pencil = cv2.divide(gray,255-gauss,scale=256)

look_img(resized)
look_img(sharpened)
look_img(gray)
look_img(pencil)
cv2.imwrite('zhaopaner_2.jpg',pencil)

​

True

图片拼接

import cv2
from PIL import Image

im1 = cv2.imread('zhaopaner.jpg')
im2 = cv2.imread('zhaopaner_1.jpg')
im3 = cv2.imread('zhaopaner_2.jpg')
img0 = cv2.cvtColor(np.hstack((im1, im2,im3)), cv2.COLOR_BGR2RGB)
im=Image.fromarray(img0)
display(im)