python 就是随便玩玩，生成gif图，生成汉字图片，超级简单

文章目录

- 主方法调用
- LetterDrawing
- WordDoingImage

上图

你也想玩的话，可以直接上码云去看码云链接

主方法调用

import analysisdata.WordDoingImage as WordDoingImage
import analysisdata.LetterDrawing as LetterDrawing

if __name__ == '__main__':
    # 输入的文本，生成的动态图，没弄英文的
    text_str = '天生我材必有用，千金散尽还复来'
	#移除中文符号
    text_str = WordDoingImage.remove_number(text_str)
    # 生成汉字图片的模版
    WordDoingImage.main_method(text_str)
    # 将汉字做成散点图合成gif
    LetterDrawing.main_method(text_str=text_str, bg_color='#9ACD32')
    #清除使用完毕的图片
    LetterDrawing.delete_word_photo(text_str=text_str)

封装了两个类，调用起来更清晰了
散点图部分，参考了下面朋友的分析，大家可以去看看
https://blog.csdn.net/cainiao_python/article/details/117137163
下面是

LetterDrawing

的类

# *_m 代表独立方法，*_p 代表运行过程的方法
import os
import numpy as np
import matplotlib.pyplot as plt
import imageio
import random
import cv2


# 跟据数据情况，转化为多个随机点
def random_point_m(text, intensity=2):
    # 多个随机点填充字母
    random.seed(420)
    x = []
    y = []

    for i in range(intensity):
        x = x + random.sample(range(0, 1000), 500)
        y = y + random.sample(range(0, 1000), 500)

    if text == ' ':
        return x, y

    # 获取图片的mask
    mask = cv2.imread(f'../photomodel/word/{text}.png', 0)
    mask = cv2.flip(mask, 0)

    # 检测点是否在mask中
    result_x = []
    result_y = []
    for i in range(len(x)):
        if (mask[y[i]][x[i]]) == 0:
            result_x.append(x[i])
            result_y.append(y[i])

    # 返回x,y
    return result_x, result_y


# 将输入的文本进行切割
def split_text_m(text, repeat=True, intensity=2):
    print('将文本转换为数据\n')
    letters = []
    for i in text.upper():
        letters.append(random_point_m(i, intensity=intensity))
    # 如果repeat为1时,重复第一个字母
    if repeat:
        letters.append(random_point_m(text[0], intensity=intensity))
    return letters


# 画图，生成git

def build_git_m(coordinates_lists, gif_name, n_frames, bg_color, marker_color, marker_size, font_color):
    print('生成图表\n')
    filenames = []
    for index in np.arange(0, len(coordinates_lists) - 1):
        # 获取当前图像及下一图像的x与y轴坐标值
        x = coordinates_lists[index][0]
        y = coordinates_lists[index][1]

        x1 = coordinates_lists[index + 1][0]
        y1 = coordinates_lists[index + 1][1]

        # 查看两点差值
        while len(x) < len(x1):
            diff = len(x1) - len(x)
            x = x + x[:diff]
            y = y + y[:diff]

        while len(x1) < len(x):
            diff = len(x) - len(x1)
            x1 = x1 + x1[:diff]
            y1 = y1 + y1[:diff]

        # 计算路径
        x_path = np.array(x1) - np.array(x)
        y_path = np.array(y1) - np.array(y)

        for i in np.arange(0, n_frames + 1):
            # 计算当前位置
            x_temp = (x + (x_path / n_frames) * i)
            y_temp = (y + (y_path / n_frames) * i)

            # 绘制图表
            fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect="equal"))
            ax.set_facecolor(bg_color)
            plt.xticks([])  # 去掉x轴
            plt.yticks([])  # 去掉y轴
            plt.axis('off')  # 去掉坐标轴

            plt.scatter(x_temp, y_temp, c=marker_color, s=marker_size)

            plt.xlim(0, 1000)
            plt.ylim(0, 1000)

            # 移除框线
            ax.spines['right'].set_visible(False)
            ax.spines['top'].set_visible(False)

            # 网格线
            ax.set_axisbelow(True)
            ax.yaxis.grid(color=font_color, linestyle='dashed', alpha=0.1)
            ax.xaxis.grid(color=font_color, linestyle='dashed', alpha=0.1)

            # 保存图片
            filename = f'../photomodel/frame_{index}_{i}.png'

            if (i == n_frames):
                for i in range(5):
                    filenames.append(filename)

            filenames.append(filename)

            # 保存
            plt.savefig(filename, dpi=96, facecolor=bg_color)
            plt.close()
    print('保存图表\n')
    # 生成GIF
    print('生成GIF\n')
    with imageio.get_writer(f'../photomodel/{gif_name}.gif', mode='I') as writer:
        for filename in filenames:
            image = imageio.v2.imread(filename)
            writer.append_data(image)
    print('保存GIF\n')
    print('删除图片\n')
    # 删除图片
    for filename in set(filenames):
        os.remove(filename)

    print('完成')
    pass


def main_method(text_str, bg_color):
    coordinates_obj = split_text_m(text_str, repeat=True, intensity=50)
    build_git_m(coordinates_obj,
                gif_name=text_str[0:5],
                n_frames=7,
                bg_color=bg_color,
                marker_color='#000000',
                marker_size=0.2,
                font_color='#000000')
    pass


def delete_word_photo(text_str):
    text_list = [text_str[i:i + 1] for i in range(0, len(text_str), 1)]
    for t in text_list:
        file_name = f'../photomodel/word/{t}.png'
        os.remove(file_name)
    pass

以下是图片生成类

WordDoingImage

，使用的词云工具，每个字生成一个图片，不用费劲的去找网络的模版图片，直接自己弄多好

# 2号词云：面朝大海，春暖花开
# B站专栏：同济子豪兄 2019-5-23

import wordcloud
import multiprocessing
import re


# 将生成的词云保存为output2-poem.png图片文件，保存到当前文件夹中

# 将汉字生成黑底的图片
def split_text_m(text_str):
    """
    拆分字符串
    通过slice语法切割字符串成单个汉字，形成一个数组
    :return:
    """
    # [word_list_analysis[i:i + num] for i in range(0, len(word_list_analysis), num)]
    return [text_str[i:i + 1] for i in range(0, len(text_str), 1)]


# 作图，根据汉字形状
def draw_image(word):
    # 构建词云对象w，设置词云图片宽、高、字体、背景颜色等参数,生成白底黑字的图片
    for w in word:
        file_name = f'../photomodel/word/{w}.png'
        w = wordcloud.WordCloud(width=1000, height=1000,
                                background_color='white',
                                font_path='../fontmodel/mashanzhengmaobikaishu.ttf',
                                color_func=lambda *args, **kwargs: (0, 0, 0)).generate(w)
        # 调用词云对象的generate方法，将文本传入
        w.to_file(file_name)


# 多进程处理，加快速度
def multi_process(text_list, num):
    pool = multiprocessing.Pool(num)
    # 将数组拆分为多块
    parts = [text_list[i:i + num] for i in range(0, len(text_list), num)]
    pool.map(draw_image, parts)
    pool.close()
    pass


# 过滤中文符号
def remove_number(text_str):
    pattern = re.compile(u'[^a-zA-Z0-9\u4e00-\u9fa5]')
    return re.sub(pattern, '', text_str)


# 主方法
def main_method(text_str):
    text_str = remove_number(text_str)
    text_list = split_text_m(text_str)
    multi_process(text_list, 4)