哈夫曼编码压缩图片Python

哈夫曼编码是一种基于最优二叉树的编码方法，可以用于无损压缩数据。在图片压缩方面，哈夫曼编码可以用于对图片像素值进行编码，以实现对图片的压缩。

在Python中，你可以使用Pillow库来读取和处理图片，使用heapq模块来实现哈夫曼树的构建。以下是一个简单的示例代码：

from PIL import Image
import heapq

class HuffmanCoding:
    def __init__(self, image_path):
        self.image_path = image_path
        self.image = Image.open(self.image_path)

    def get_pixel_frequencies(self):
        pixel_frequencies = {}
        for pixel in self.image.getdata():
            if pixel in pixel_frequencies:
                pixel_frequencies[pixel] += 1
            else:
                pixel_frequencies[pixel] = 1
        return pixel_frequencies

    def build_huffman_tree(self, pixel_frequencies):
        heap = [[weight, [pixel, ""]] for pixel, weight in pixel_frequencies.items()]
        heapq.heapify(heap)
        while len(heap) > 1:
            low = heapq.heappop(heap)
            high = heapq.heappop(heap)
            for pair in low[1:]:
                pair = '0' + pair
            for pair in high[1:]:
                pair = '1' + pair
            heapq.heappush(heap, [low + high] + low[1:] + high[1:])
        return sorted(heapq.heappop(heap)[1:], key=lambda p: (len(p[-1]), p))

    def get_encoded_image(self, huffman_codes):
        encoded_image = ""
        for pixel in self.image.getdata():
            encoded_image += huffman_codes[pixel]
        return encoded_image

    def compress_image(self):
        pixel_frequencies = self.get_pixel_frequencies()
        huffman_codes = dict(self.build_huffman_tree(pixel_frequencies))
        encoded_image = self.get_encoded_image(huffman_codes)
        return encoded_image

image_path = "test.jpg"
huffman_coding = HuffmanCoding(image_path)
compressed_image = huffman_coding.compress_image()

以上代码使用Pillow库打开指定路径的图片，使用get_pixel_frequencies()函数获取每个像素值出现的频率，使用build_huffman_tree()函数构建哈夫曼树并返回编码表，使用get_encoded_image()函数将图片像素值转换为哈夫曼编码，最后使用compress_image()函数返回压缩后的二进制字符串。