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基础使用

简介​

PIL(Python Imaging Library)是Python一个强大方便的图像处理库，名气也比较大。

PIL官方网站：http://www.pythonware.com/products/pil/

Pillow是PIL的一个派生分支，但如今已经发展成为比PIL本身更具活力的图像处理库。目前最新版本是3.0.0。 Pillow的Github主页：https://github.com/python-pillow/Pillow

Pillow的文档(对应版本v3.0.0)：https://pillow.readthedocs.org/en/latest/handbook/index.html Pillow的文档中文翻译(对应版本v2.4.0)：http://pillow-cn.readthedocs.org/en/latest/ Pillow是PIL的一个派生分支，但如今已经发展成为比PIL本身更具活力的图像处理库。目前最新版本是3.0.0。

Pillow的Github主页：https://github.com/python-pillow/Pillow

基础概念​

过滤器​

官方文档

image-20221008224004237

安装​

  • 3.x
pip install pillow
python -m pip install pillow
./python3 -m pip install pillow -i https://pypi.tuna.tsinghua.edu.cn/simple

pip 安装​

python -m pip 
./python.exe -m pip install pillow -i https://pypi.tuna.tsinghua.edu.cn/simple/

引入​

import PIL
# or
from PIL import Image

基础使用​

from PIL import Image

模板示例​

保存图片​

im.save(fp, format, quality=75)

矩阵 倾斜​

# 需要导入模块: from PIL import Image [as 别名]
# 或者: from PIL.Image import PERSPECTIVE [as 别名]
def draw_picture(img_src):
    fap_path = os.path.join(os.path.dirname(__file__), "fap.png")

    # ?????????? ????????
    response = requests.get(img_src)
    response.raise_for_status()
    image_bytes = io.BytesIO(response.content)

    # ?????????
    image = Image.open(image_bytes)
    fap_pic = Image.open(fap_path)

    if image.mode != 'RGBA':
        image = image.convert('RGBA')

    image_width, image_height = image.size
    fap_width, fap_height = fap_pic.size

    def find_coeffs(pa, pb):
        """ https://stackoverflow.com/questions/14177744/
            ????? ????????? ????, ??? ??????? - ???????
            """
        matrix = []
        for p1, p2 in zip(pa, pb):
            matrix.append([p1[0], p1[1], 1, 0, 0, 0, -p2[0]*p1[0], -p2[0]*p1[1]])
            matrix.append([0, 0, 0, p1[0], p1[1], 1, -p2[1]*p1[0], -p2[1]*p1[1]])
        A = numpy.matrix(matrix, dtype=numpy.float)
        B = numpy.array(pb).reshape(8)
        res = numpy.dot(numpy.linalg.inv(A.T * A) * A.T, B)
        return numpy.array(res).reshape(8)

    trans_coeff = find_coeffs(
        [(217,111),(412,115),(222,372),(403,371)],
        [(0,0), (image_width-1,0), (0,image_height-1), (image_width-1, image_height-1)])

    resp_pic = image.transform(fap_pic.size, Image.PERSPECTIVE, trans_coeff, Image.BILINEAR)

    # ??????????? gesture ? image
    resp_bytes = io.BytesIO()
    Image.alpha_composite(resp_pic, fap_pic).save(resp_bytes, format='PNG')
    resp_bytes.seek(0)

    return resp_bytes