鬼灭之刃剧场版 无限列车篇-完整版小鴨線上看~【2020】

鬼灭之刃 剧场版 无限列车篇完整版本(2020-HD)
【鬼灭之刃 剧场版 无限列车篇】完整版小鴨線上看 

鬼灭之刃 剧场版 无限列车篇-線上看小鴨 '(電影-2020*HD!BluRay)' 在线流高清-TW"
《Kimetsu no Yaiba: Mugen Ressha-Hen》（韓語：Kimetsu no Yaiba: Mugen Ressha-Hen，香港译《上流寄生族》，台湾译《Kimetsu no Yaiba: Mugen Ressha-Hen》）是2020年韓國導演奉俊昊執導的劇情長片，宋康昊、李善均、赵汝珍、崔宇植和朴素淡所主演。並在第72屆坎城影展的官方競賽單元中獲得金棕榈奖，該片成為第一部獲得该獎的韓國電影。

改編自日本漫畫家吾峠呼世晴的作品，延續動畫第一季劇情。劇情描述炭治郎等人完成「蝴蝶屋」的訓練，下一個目的地是開往黑暗的「無限列車」。炭治郎與「炎柱‧煉獄杏壽郎」會合，新的任務即將開始！

************ஜ۩۞۩ஜ*************

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导演: 外崎春雄
编剧: Ufotable / 吾峠呼世晴
主演: 花江夏树 / 鬼头明里 / 下野纮 / 松冈祯丞 / 日野聪 / 更多...
类型: 动画
制片国家/地区: 日本
语言: 日语
上映日期: 2020-10-16(日本)
片长: 117分钟
又名: Kimetsu no Yaiba: Mugen Ressha-Hen
IMDb链接: tt11032374

鬼灭之刃 剧场版 无限列车篇的剧情简介 · · · · · ·
　　该片基于吾峠呼世所著漫画《鬼灭之刃》创作而成，是2019年播出的TV动画的续篇，讲述灶门炭治郎和炼狱杏寿郎与下弦之壹魇梦作战的故事。

Dikutip dari Risingballers, tiga klub Premier League tengah memantau situasi kontrak Elkan Baggott, yakni Manchester United, West Ham dan Everton.
Kontrak Baggott di Ipswich menyisakan 6 bulan lagi, alias akan habis pada 30 Juni 2021.
.
_
Wahh seandainya beneran nih.. bagusnya kemana nih si Elkan gengss ?

As you might know, sketching or creating a cartoon doesn’t always need to be done manually. Nowadays, many apps can turn your photos into cartoons. But what if I tell you, that you can create your own effect with few lines of code?
There is a library called OpenCV which provides a common infrastructure for computer vision applications and has optimized machine learning algorithms. It can be used to recognize objects, detect, and produce high-resolution images.
In this tutorial, I will show you how to give a cartoon-effect to an image in Python by utilizing OpenCV. I use Google Colab to write and run the code. You can access the full code in Google Colab here
To create a cartoon effect, we need to pay attention to two things; edge and color palette. Those are what make the differences between a photo and a cartoon. To adjust that two main components, there are four main steps that we will go through:
Load image
Create edge mask
Reduce the color palette
Combine edge mask with the colored image
Before jumping to the main steps, don’t forget to import the required libraries in your notebook, especially cv2 and NumPy.
import cv2
import numpy as np
# required if you use Google Colab
from google.colab.patches import cv2_imshow
from google.colab import files
1. Load Image
The first main step is loading the image. Define the read_file function, which includes the cv2_imshow to load our selected image in Google Colab.

Call the created function to load the image.
uploaded = files.upload()
filename = next(iter(uploaded))
img = read_file(filename)
I choose the image below to be transformed into a cartoon.
Image for post
Image by Kate Winegeart in Unsplash
2. Create Edge Mask
Commonly, a cartoon effect emphasizes the thickness of the edge in an image. We can detect the edge in an image by using the cv2.adaptiveThreshold() function.
Overall, we can define the egde_mask function as:

In that function, we transform the image into grayscale. Then, we reduce the noise of the blurred grayscale image by using cv2.medianBlur. The larger blur value means fewer black noises appear in the image. And then, apply adaptiveThreshold function, and define the line size of the edge. A larger line size means the thicker edges that will be emphasized in the image.
After defining the function, call it and see the result.
line_size = 7
blur_value = 7
edges = edge_mask(img, line_size, blur_value)
cv2_imshow(edges)
Image for post
Edge Mask Detection
3. Reduce the Color Palette
The main difference between a photo and a drawing in terms of color is the number of distinct colors in each of them. A drawing has fewer colors than a photo. Therefore, we use color quantization to reduce the number of colors in the photo.
Color Quantization
To do color quantization, we apply the K-Means clustering algorithm which is provided by the OpenCV library. To make it easier in the next steps, we can define the color_quantization function as below.

We can adjust the k value to determine the number of colors that we want to apply to the image.
total_color = 9
img = color_quantization(img, total_color)
In this case, I use 9 as the k value for the image. The result is shown below.
Image for post
After Color Quantization
Bilateral Filter
After doing color quantization, we can reduce the noise in the image by using a bilateral filter. It would give a bit blurred and sharpness-reducing effect to the image.
blurred = cv2.bilateralFilter(img, d=7, sigmaColor=200,sigmaSpace=200)
There are three parameters that you can adjust based on your preferences:
d — Diameter of each pixel neighborhood
sigmaColor — A larger value of the parameter means larger areas of semi-equal color.
sigmaSpace –A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough.
Image for post
Result of Bilateral Filter
4. Combine Edge Mask with the Colored Image
The final step is combining the edge mask that we created earlier, with the color-processed image. To do so, use the cv2.bitwise_and function.
cartoon = cv2.bitwise_and(blurred, blurred, mask=edges)
And there here is! We can see the “cartoon-version” of the original photo below.
Image for post
Final Result
Now you can start playing around with the codes to create your own version of the cartoon effect. Besides adjusting the value in parameters that we used above, you can also add another function from OpenCV to give special effects to your photos. There’s still a lot of things in the library that we can explore. Happy trying!
References:
https://www.programcreek.com/python/example/89394/cv2.kmeans
http://datahacker.rs/002-opencv-projects-how-to-cartoonize-an-image-with-opencv-in-python/
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Tazki Anida
Playing around with data
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