PIL图片如何按比例裁剪
问题描述
如图片比例为 1:1 裁剪为 4:3
1.jpg
解决方案
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from PIL import Imagedef image_clip(filename, savename, width_scale, height_scale): """图像裁剪 :param filename: 原图路径 :param savename: 保存图片路径 :param width_scale: 宽的比例 :param height_scale: 高的比例 """ image = Image.open(filename) (width, height), (_width, _height) = image.size, image.size _height = width / width_scale * height_scale if _height > height: _height = height _width = width_scale * height / height_scale image.crop((0, 0, _width, _height)).save(savename) # 左上角 # image.crop((0, height - _height, _width, height)).save(savename) # 左下角 # image.crop((width - _width, 0, width, _height)).save(savename) # 右上角 # image.crop((width - _width, height - _height, width, height)).save(savename) # 右下角if __name__ == '__main__': filename = '1.jpg' savename = 'result.jpg' image_clip(filename, savename, width_scale=4, height_scale=3) # image_clip(filename, savename, width_scale=3, height_scale=4) |
效果
PIL调整图片大小
使用 PIL 在图片比例不变的情况下修改图片大小。
介绍
Image.resize
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def resize(self, size, resample=BICUBIC, box=None, reducing_gap=None): """ Returns a resized copy of this image. 返回此图像的大小调整后的副本。 :param size: The requested size in pixels, as a 2-tuple: (width, height). param size: 请求的大小(以像素为单位)，是一个二元数组：(width, height) :param resample: An optional resampling filter. This can be one of :py:attr:`PIL.Image.NEAREST`, :py:attr:`PIL.Image.BOX`, :py:attr:`PIL.Image.BILINEAR`, :py:attr:`PIL.Image.HAMMING`, :py:attr:`PIL.Image.BICUBIC` or :py:attr:`PIL.Image.LANCZOS`. Default filter is :py:attr:`PIL.Image.BICUBIC`. If the image has mode "1" or "P", it is always set to :py:attr:`PIL.Image.NEAREST`. See: :ref:`concept-filters`. param resample: 一个可选的重采样过滤器。 :param box: An optional 4-tuple of floats providing the source image region to be scaled. The values must be within (0, 0, width, height) rectangle. If omitted or None, the entire source is used. param box: 可选的4元浮点数，提供要缩放的源映像区域。 :param reducing_gap: Apply optimization by resizing the image in two steps. First, reducing the image by integer times using :py:meth:`~PIL.Image.Image.reduce`. Second, resizing using regular resampling. The last step changes size no less than by ``reducing_gap`` times. ``reducing_gap`` may be None (no first step is performed) or should be greater than 1.0. The bigger ``reducing_gap``, the closer the result to the fair resampling. The smaller ``reducing_gap``, the faster resizing. With ``reducing_gap`` greater or equal to 3.0, the result is indistinguishable from fair resampling in most cases. The default value is None (no optimization). param reducing_gap: 通过两个步骤调整图像大小来应用优化。 :returns: An :py:class:`~PIL.Image.Image` object. returns: 返回一个 PIL.Image.Image 对象 """ |
看代码吧
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from PIL import Image image = Image.open('图片路径') # 调整图片大小，并保持比例不变# 给定一个基本宽度base_width = 50 # 基本宽度与原图宽度的比例w_percent = base_width / float(image.size[0]) # 计算比例不变的条件下新图的长度h_size = int(float(image.size[1]) * float(w_percent)) # 重新设置大小# 默认情况下，PIL使用Image.NEAREST过滤器进行大小调整，从而获得良好的性能，但质量很差。image = image.resize((base_width, h_size), Image.ANTIALIAS) |
以上为个人经验，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://xercis.blog.csdn.net/article/details/122365539
