如何在ipy画布中绘制像素

Question

我不知道如何在中绘制像素。我正在绘制矩形，而不是像素，这使得绘制非常慢。

使用以下方法绘制矩形：

canvas.fill_rect

在ipy画布中显示图像的代码：

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt

from PIL import Image

import ipycanvas
from ipycanvas import Canvas

import requests
from io import BytesIO

url = r"https://wallpapercave.com/dwp1x/wp1816238.jpg"
response = requests.get(url)
img = Image.open(BytesIO(response.content))

array = img.tobytes()

canvas = Canvas(width=img.width, height=img.height)
with ipycanvas.hold_canvas():
    for i in range(int(len(array)/3)):
        r = array[i * 3 + 0] # red
        g = array[i * 3 + 1] # green
        b = array[i * 3 + 2] # blue
        canvas.fill_style = f"#{r:02x}{g:02x}{b:02x}" # setting color
        canvas.fill_rect(i%img.width, int(i/img.width), 1, 1) # drawing rectangle
canvas

输出：

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我正在绘制一个像素的图像，因为我想在图像中应用过滤器。

如何在ipycanvas中绘制像素

Answer 1

不确定这是否有用，但考虑到您正在讨论的是过滤，我假设您指的是像卷积这样的东西。Numpy和Scipy帮助了很多人，并提供了各种方法来应用这些方法，并能很好地处理枕头上的图像。

例如：

import requests
from io import BytesIO
from PIL import Image

import numpy as np
from scipy import signal

image_req = requests.get("https://wallpapercave.com/dwp1x/wp1816238.jpg")
image_req.raise_for_status()

image = Image.open(BytesIO(image_req.content))

# create gaussian glur of a given standard deviation
sd = 3
filt = np.outer(*2*[signal.windows.gaussian(int(sd*5)|1, sd)])
filt /= filt.sum()

# interpret image as 3d array
arr = np.array(image)

# apply it to each channel independently, this loop runs in ~0.1 seconds
for chan in range(3):
    arr[:,:,chan] = signal.oaconvolve(arr[:,:,chan], filt, mode='same')

# array back into image for display in notebook
Image.fromarray(arr)

这样产生的图像如下：

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