学会这些 Python 美图技巧，就等着女朋友夸你吧！| 原力计划

pip install opencv-python
pip install pillowpython -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simplepip install -i https://mirror.baidu.com/pypi/simple paddlehubpip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ myqr

import cv2level = 22    # 降噪等级img = cv2.imread('girl.jpg')    # 读取原图img = cv2.bilateralFilter(img, level, level*2, level/2)    # 美颜cv2.imwrite('result.jpg', img)

from PIL import Image
import numpy as npfrom wordcloud import WordCloud, ImageColorGenerator

# 读取背景图片mask = np.array(Image.open('rose.png'))

# 定义词云对象wc = WordCloud(# 设置词云背景为白色    background_color='white',    # 设置词云最大的字体    max_font_size=30,    # 设置词云轮廓    mask=mask,    # 字体路径，如果需要生成中文词云，需要设置该属性，设置的字体需要支持中文    #font_path='msyh.ttc')# 读取文本text = open('article.txt', 'r', encoding='utf-8').read()# 生成词云wc.generate(text)# 给词云上色wc = wc.recolor(color_func=ImageColorGenerator(mask))# 保存词云wc.to_file('result.png')

import cv2
import paddlehub as hub# 加载模型库stylepro_artistic = hub.Module(name="stylepro_artistic")# 进行风格迁移im = stylepro_artistic.style_transfer(    images=[{          # 原图            'content': cv2.imread("origin.jpg"),            # 风格图            'styles': [cv2.imread("style.jpg")]        }],    # 透明度    alpha = 0.1)# 从返回的数据中获取图片的ndarray对象im = im[0]['data']# 保存结果图片cv2.imwrite('result.jpg', im)

import os
import cv2import numpy as np

def getDominant(im):    """获取主色调"""    b = int(round(np.mean(im[:, :, 0])))    g = int(round(np.mean(im[:, :, 1])))    r = int(round(np.mean(im[:, :, 2])))    return (b, g, r)



def getColors(path):    """获取图片列表的色调表"""    colors = []

    filelist = [path + i for i in os.listdir(path)]    for file in filelist:        im = cv2.imdecode(np.fromfile(file, dtype=np.uint8), -1)        dominant = getDominant(im)        colors.append(dominant)    return colors



def fitColor(color1, color2):    """返回两个颜色之间的差异大小"""    b = color1[0] - color2[0]    g = color1[1] - color2[1]    r = color1[2] - color2[2]    return abs(b) + abs(g) + abs(r)



def generate(im_path, imgs_path, box_size, multiple=1):    """生成图片"""

    # 读取图片列表    img_list = [imgs_path + i for i in os.listdir(imgs_path)]

    # 读取图片    im = cv2.imread(im_path)    im = cv2.resize(im, (im.shape[1]*multiple, im.shape[0]*multiple))

    # 获取图片宽高    width, height = im.shape[1], im.shape[0]

    # 遍历图片像素    for i in range(height // box_size+1):        for j in range(width // box_size+1):

            # 图块起点坐标            start_x, start_y = j * box_size, i * box_size

            # 初始化图片块的宽高            box_w, box_h = box_size, box_size

            box_im = im[start_y:, start_x:]            if i == height // box_size:                box_h = box_im.shape[0]            if j == width // box_size:                box_w = box_im.shape[1]

            if box_h == 0 or box_w == 0:                continue

            # 获取主色调            dominant = getDominant(im[start_y:start_y+box_h, start_x:start_x+box_w])

            img_loc = 0            # 差异，同主色调最大差异为255*3            dif = 255 * 3

            # 遍历色调表，查找差异最小的图片            for index in range(colors.__len__()):                if fitColor(dominant, colors[index]) < dif:                    dif = fitColor(dominant, colors[index])                    img_loc = index

            # 读取差异最小的图片            box_im = cv2.imdecode(np.fromfile(img_list[img_loc], dtype=np.uint8), -1)

            # 转换成合适的大小            box_im = cv2.resize(box_im, (box_w, box_h))

            # 铺垫色块            im[start_y:start_y+box_h, start_x:start_x+box_w] = box_im

            j += box_w        i += box_h

    return im



if __name__ == '__main__':

    # 获取色调列表    colors = getColors('表情包/')    result_im = generate('main.jpg', '表情包/', 50, multiple=5)    cv2.imwrite('C:/Users/zaxwz/Desktop/result.jpg', result_im)

from PIL import Image
import paddlehub as hub# 加载模型humanseg = hub.Module(name='deeplabv3p_xception65_humanseg')# 抠图results = humanseg.segmentation(data={'image':['xscn.jpeg']})# 读取背景图片bg = Image.open('bg.jpg')# 读取原图im = Image.open('humanseg_output/xscn.png').convert('RGBA')im.thumbnail((bg.size[1], bg.size[1]))# 分离通道r, g, b, a = im.split()# 将抠好的图片粘贴到背景上bg.paste(im, (bg.size[0]-bg.size[1], 0), mask=a)bg.save('xscn.jpg')

from PIL import Image# 读取图片im = Image.open('xscn.jpeg')# 宽高各除 3，获取裁剪后的单张图片大小width = im.size[0]//3height = im.size[1]//3# 裁剪图片的左上角坐标start_x = 0start_y = 0# 用于给图片命名im_name = 1# 循环裁剪图片for i in range(3):    for j in range(3):        # 裁剪图片并保存        crop = im.crop((start_x, start_y, start_x+width, start_y+height))        crop.save(str(im_name) + '.jpg')        # 将左上角坐标的 x 轴向右移动        start_x += width        im_name += 1    # 当第一行裁剪完后 x 继续从 0 开始裁剪    start_x = 0    # 裁剪第二行    start_y += height

from MyQR import myqr
myqr.run(    words='http://www.baidu.com',  # 包含信息    picture='lbxx.jpg',      # 背景图片    colorized=True,      # 是否有颜色，如果为False则为黑白    save_name='code.png'  # 输出文件名