图像处理#
让我们使用 Pillow 和 LLM 构建一个简单的图像处理 API！
支持的操作类型仅限于已创建的函数列表。
函数列表是使用 chat gpt 生成的（包括类型注释和文档字符串）。
Kork 还不知道如何使用 kwargs，因此接受 kwargs 的文档字符串已修改，并解释了要使用的默认值。
让我们安装 Pillow
!pip install Pillow > /dev/null
[notice] A new release of pip is available: 23.1 -> 23.1.2
[notice] To update, run: python3.10 -m pip install --upgrade pip
from PIL import Image, ImageOps, ImageFilter
让我们手动创建一些函数
def resize(img: Image.Image, width: int, height: int) -> Image:
"""Use to resize an image to the given width and height"""
return img.resize((width, height))
def upscale(img: Image.Image, scale: float) -> Image.Image:
"""Upscale the image by the given scale"""
height = int(img.height * scale)
width = int(img.width * scale)
return img.resize((width, height))
def downscale(img: Image.Image, scale: float) -> Image.Image:
"""Downscale the image by the given scale"""
return upscale(img, 1 / scale)
def apply_sepia_filter(img: Image.Image) -> Image:
"""Apply a sepia filter."""
# Convert the image to grayscale
grayscale_image = img.convert("L")
# Apply sepia filter
sepia_image = ImageOps.colorize(grayscale_image, "#704214", "#C0C090")
return sepia_image
下面是由 chat gpt（主要）创建的函数列表，这样我们就不必手动编写大量样板代码。
显示代码单元格源
def apply_blur(img: Image.Image, radius: int = 2) -> Image.Image:
"""Apply a blur filter to the image. Use default radius of 2, if not specified.
Args:
img (Image.Image): The input image.
radius (int): The blur radius. Default is 2.
Returns:
Image.Image: The filtered image.
"""
return img.filter(ImageFilter.GaussianBlur(radius=radius))
def apply_contour(img: Image.Image) -> Image.Image:
"""Apply a contour filter to the image.
Args:
img (Image.Image): The input image.
Returns:
Image.Image: The filtered image.
"""
return img.filter(ImageFilter.CONTOUR())
def apply_detail(img: Image.Image) -> Image.Image:
"""Apply a detail filter to the image.
Args:
img (Image.Image): The input image.
detail (int): The level of detail enhancement. Default is 2.
Returns:
Image.Image: The filtered image.
"""
return img.filter(ImageFilter.DETAIL())
def apply_edge_enhance(img: Image.Image) -> Image.Image:
"""Apply an edge enhance filter to the image.
Args:
img (Image.Image): The input image.
Returns:
Image.Image: The filtered image.
"""
return img.filter(ImageFilter.EDGE_ENHANCE())
def apply_edge_enhance_more(img: Image.Image) -> Image.Image:
"""Apply a stronger edge enhance filter to the image.
Args:
img (Image.Image): The input image.
Returns:
Image.Image: The filtered image.
"""
return img.filter(ImageFilter.EDGE_ENHANCE_MORE())
def apply_emboss(img: Image.Image) -> Image.Image:
"""Apply an emboss filter to the image.
Args:
img (Image.Image): The input image.
Returns:
Image.Image: The filtered image.
"""
return img.filter(ImageFilter.EMBOSS())
def apply_find_edges(img: Image.Image) -> Image.Image:
"""Apply a find edges filter to the image.
Args:
img (Image.Image): The input image.
Returns:
Image.Image: The filtered image.
"""
return img.filter(ImageFilter.FIND_EDGES())
def apply_sharpen(img: Image.Image) -> Image.Image:
"""Apply a sharpen filter to the image.
Args:
img (Image.Image): The input image.
Returns:
Image.Image: The filtered image.
"""
return img.filter(ImageFilter.SHARPEN)
def apply_smooth(img: Image.Image) -> Image.Image:
"""Apply a smooth filter to the image.
Args:
img (Image.Image): The input image.
Returns:
Image.Image: The filtered image.
"""
return img.filter(ImageFilter.SMOOTH())
def apply_smooth_more(img: Image.Image) -> Image.Image:
"""Apply a stronger smooth filter to the image.
Args:
img (Image.Image): The input image.
Returns:
Image.Image: The filtered image.
"""
return img.filter(ImageFilter.SMOOTH_MORE())
from typing import List, Any, Optional
import langchain
from langchain.llms import OpenAI
from kork.parser import parse
from kork import InterpreterResult, Environment, run_interpreter, CodeChain
llm = OpenAI(temperature=0)
examples = [
("resize the image to (20, 10)", "var result = resize(img, 20, 10)"),
("apply sepia filter to the image", "var result = apply_sepia_filter(img)"),
]
examples_in_ast = [(query, parse(code)) for query, code in examples]
让我们从维基百科获取一张测试图像。
from io import BytesIO
import requests
def get_image(url: str) -> Image.Image:
"""Get an image from the given URL."""
headers = {
"User-Agent": "User-Agent: KorkDemo/0.0 (https://github.com/eyurtsev/kork/tree/main/kork;)"
}
response = requests.get(url, headers=headers)
# Create a PIL image from the downloaded image data
img = Image.open(BytesIO(response.content))
return img
url = "https://upload.wikimedia.org/wikipedia/commons/thumb/6/68/Orange_tabby_cat_sitting_on_fallen_leaves-Hisashi-01A.jpg/1024px-Orange_tabby_cat_sitting_on_fallen_leaves-Hisashi-01A.jpg"
img = get_image(url)
resize(img, 100, 120)
funcs = [
apply_blur,
apply_contour,
apply_detail,
apply_edge_enhance,
apply_edge_enhance_more,
apply_emboss,
apply_find_edges,
apply_sharpen,
apply_smooth,
apply_smooth_more,
resize,
apply_sepia_filter,
get_image,
downscale,
upscale,
]
chain = CodeChain.from_defaults(
llm=llm,
examples=examples_in_ast,
interpreter=run_interpreter,
context=funcs,
)
from kork.display import as_html_dict, display_html_results
queries = [
"resize the image to 100,100 and smooth it a lot",
"resize the image to 100,100 and sharpen",
"resize the image to 100, 100 and blur and apply sepia",
"resize the image to 100, 80, and then downscale it by another factor of 2x.",
"resize the image to 100, 90, and detect edges",
"resize the image to 100, 80, detect edges and finally apply sepia.",
]
code_results = []
for query in queries:
code_results.append(chain(inputs={"query": query, "variables": {"img": img}}))
html_results = [
as_html_dict(code_result, result_key="result") for code_result in code_results
]
display_html_results(html_results, columns=["query", "code", "result"])
| 查询 | 代码 | 结果 | |
|---|---|---|---|
| 0 | 将图像大小调整为 100,100 并平滑 很多 |
var result = apply_smooth_more( |
 |
| 1 | 将图像大小调整为 100,100 并锐化 | var result = apply_sharpen( |
 |
| 2 | 将图像大小调整为 100, 100 并模糊 并应用棕褐色 |
var result = apply_sepia_filter( |
 |
| 3 | 将图像大小调整为 100, 80，然后 将其再次缩小 2 倍。 |
var result = downscale( |
 |
| 4 | 将图像大小调整为 100, 90，并检测 边缘 |
var result = apply_find_edges( |
 |
| 5 | 将图像大小调整为 100, 80，检测 边缘并最终应用棕褐色。 |
var result = apply_sepia_filter( |
 |