Python小项目—照片马赛克

项目内容

该项目用Python创建照片马赛克，将目标图像划分成较小图像的网络，并用适当的图像替换网络中的每一小块，创建原始图像的照片的马赛克

项目知识点

用Python图像库（PIL）创建图像
计算图像的平均RGB值
剪切图像
通过粘贴另一张图像来替代原图像的一部分
利用平均距离测量来比较RGB值

工作原理

照片马赛克的实质：将一张图像分割成长方形的网络，每个长方形由另一张匹配“目标”的图像替代。
创建照片马赛克，从目标图像的块状低分配率版本开始（图像的分配率决定马赛克的维度MXN）。

读入一些小块图像，它们将取代原始图像中的小块
读入目标图像，将它们分隔成M*N的小块网络
对于每个小块，从输入的小块图像中找到最佳匹配
将选择的输入图像安排在M*N的网络中，创建最终的照片马赛克

1.分割目标图像

x 轴表示网格的列，y 轴表示网格的行。
下标为 (i,j) 的小块，左上角坐标为 (iw,ij) ，右下角坐标为 ((i+1)∗w,(j+1)∗h)
w 和 h 分别是小块的宽度和高度

2.平均颜色值

图像中的每个像素颜色由红、绿、蓝值表示。若一幅图像共有N个像素，平均RGB（三元组）：

计算平均RGB目的：匹配图像小块和目标图像

3.匹配图像

匹配图像：对于目标图像的每个小块，需要在用户指定的输入文件夹的一些图像中，找到一副匹配图像
确定两图像是否匹配：最接近的匹配就是最接近平均的RGB值的图像，故需计算一个像素中的RGB值之间的距离
（三维图像两点之间的距离：）

项目code

读取小块图像
#os.listdir()方法将imageDir目录中的文件放入一个列表
#os.path()和os.path.join（）获取图像的完整文件名

def getImages(imageDir) :"""given a directory of images, return a list of Images"""files = os.listdir(imageDir)      #os.listdir()方法将imageDir目录中的文件放入一个列表images = []for file in files :   #遍历列表中的每个文件filePath = os.path.abspath(os.path.join(imageDir, file))        #os.path()和os.path.join（）获取图像的完整文件名try :# explicit load so we don't run into resource crunchfp = open(filePath, "rb")                       #打开每个小块图像im = Image.open(fp)                     # Image.open()方法将文件句柄fp传入PIL（）images.append(im)# force loading image data from fileim.load()#加载# close the filefp.close()                                    #关闭文件句柄并释放系统资源except :# skipprint("Invalid image: %s" % (filePath,))return images

计算输入图像的平均颜色值

def getAverageRGB(image) :"""Given PIL Image, return average value of color as (r, g, b)"""# get image as numpy arrayim = np.array(image)            #用nunmpy将每个Image对象转换为数据数组，返回的numpy数组形为（w,h,d）对应（R,G,B）# get shapew, h, d = im.shape                    #保存shape元组，然后计算平均值，reshape（）：将数组形式变为形状(w * h, d）# get averagereturn tuple(np.average(im.reshape(w * h, d), axis=0))                   #average（）:计算RGB平均值

将目标图像分割成网络

def splitImage(image, size) :"""Given Image and dims (rows, cols) returns an m*n list of Images"""W, H = image.size[0], image.size[1]  #得到目标图像维度m, n = size  #目标图像尺寸w, h = int(W / n), int(H / m) #目标图像每一小块的尺寸# image listimgs = []# generate list of dimensionsfor j in range(m) : #迭代遍历网络的维度for i in range(n) :# append cropped imageimgs.append(image.crop((i * w, j * h, (i + 1) * w, (j + 1) * h)))#image.crop():分割并将每一小块保存为单独的图像return imgs

寻找小块的最佳匹配

#img.paste(images[index], (col * width, row * height))：第一个参数要粘贴的对象，第二个参数左上角的坐标


def getBestMatchIndex(input_avg, avgs) :# input_avg：最匹配平均RGB值；avgs：小块图像平均值的RGB列表"""return index of best Image match based on RGB value distance"""# input image averageavg = input_avg# get the closest RGB value to input, based on x/y/z distanceindex = 0min_index = 0#最接近匹配下标初始化为0min_dist = float("inf")#最小距离初始化为无穷大for val in avgs :#遍历平均值列表中的值，计算最小距离并保存dist = ((val[0] - avg[0]) * (val[0] - avg[0]) +(val[1] - avg[1]) * (val[1] - avg[1]) +(val[2] - avg[2]) * (val[2] - avg[2]))if dist < min_dist :min_dist = distmin_index = indexindex += 1return min_index

创建图像网络

#img.paste(images[index], (col * width, row * height))：第一个参数要粘贴的对象，第二个参数左上角的坐标

def createImageGrid(images, dims) :"""Given a list of images and a grid size (m, n), createa grid of images."""m, n = dims                 #取得图像尺寸大小，并用assert检车提供给的图像的数量是否符合网络的大小# sanity checkassert m * n == len(images)                   # assert（）:检查代码中的假定# get max height and width of images# ie, not assuming they are all equalwidth = max([img.size[0] for img in images])  #计算小块图像的最大宽度和高度，若果输出的图像能完全填充小块，显示背景色height = max([img.size[1] for img in images])#否则默认为黑色# create output imagegrid_img = Image.new('RGB', (n * width, m * height)) #创建一个空的Image,大小符合网络中的所有图像# paste imagesfor index in range(len(images)) :#遍历选定的图像（小块在图像网络 中的下表有N*rol+col给出）row = int(index / n)#行col = index - n * row#列grid_img.paste(images[index], (col * width, row * height))#img.paste():粘贴到相应的网络中return grid_img

创建照片马赛克
#createPhotomosaic(target_image, input_images, grid_size,
reuse_images=True)
参数：目标图像、输入图像列表、生成照片马赛克的大、表明图像是否可以复用的标志

def createPhotomosaic(target_image, input_images, grid_size,reuse_images=True) :"""Creates photomosaic given target and input images."""print('splitting input image...')# split target imagetarget_images = splitImage(target_image, grid_size)#将目标图像分隔成一个网络print('finding image matches...')# for each target image, pick one from inputoutput_images = []#对每个小块，寻找匹配的图像# for user feedbackcount = 0batch_size = int(len(target_images) / 10)# batch_size设置为小块总数的1/10# calculate input image averagesavgs = []for img in input_images :#为文件夹中的每个图像计算平均RGB值，并保存在 avgs 列表中avgs.append(getAverageRGB(img))for img in target_images :#迭代目标图像网络中的平均RGB值，并将值保存在avg# target sub-image averageavg = getAverageRGB(img)# find match index #在输入图像的平均值列表中寻找最佳匹配match_index = getBestMatchIndex(avg, avgs)#返回的是个下标output_images.append(input_images[match_index])#取Images对象并保存在列表# user feedback 向用户更新信息if count > 0 and batch_size > 10 and count % batch_size is 0 :print('processed %d of %d...' % (count, len(target_images)))count += 1# remove selected image from input if flag set 判断图像是否可以复用if not reuse_images :input_images.remove(match)print('creating mosaic...')# draw mosaic to image 创建最终的照片马赛克mosaic_image = createImageGrid(output_images, grid_size)# return mosaicreturn mosaic_image

添加命令行选项
#此段代码包含3个必要的命令行参数：目标图像的名称、输入文件夹的名称、网络的尺寸//4：可选的文件名

 # parse argumentsparser = argparse.ArgumentParser(description='Creates a photomosaic from input images')# add argumentsparser.add_argument('--target-image', dest='target_image', required=True)parser.add_argument('--input-folder', dest='input_folder', required=True)parser.add_argument('--grid-size', nargs=2, dest='grid_size', required=True)parser.add_argument('--output-file', dest='outfile', required=False)

控制照片的马赛克大小
#若基于目标图像中匹配的小块，盲目将图像粘在一起，得到巨大的照片马赛克，比目标图像大得多，故需要控制照片的马赛克大小

 print('resizing images...')# for given grid size, compute max dims w,h of tiles#根据指定的网络大小，计算目标图像的维度dims = (int(target_image.size[0] / grid_size[1]),int(target_image.size[1] / grid_size[0]))print("max tile dims: %s" % (dims,))# resizefor img in input_images :img.thumbnail(dims)            #thumbnail()调整图像

完整代码

"""
photomosaic.py
Creates a photomosaic given a target image and a folder of input images
Author: Mahesh Venkitachalam
"""import sys, os, random, argparse
from PIL import Image
import imghdr
import numpy as npdef getAverageRGBOld(image):"""Given PIL Image, return average value of color as (r, g, b)"""# no. of pixels in imagenpixels = image.size[0]*image.size[1]# get colors as [(cnt1, (r1, g1, b1)), ...]cols = image.getcolors(npixels)# get [(c1*r1, c1*g1, c1*g2),...]sumRGB = [(x[0]*x[1][0], x[0]*x[1][1], x[0]*x[1][2]) for x in cols] # calculate (sum(ci*ri)/np, sum(ci*gi)/np, sum(ci*bi)/np)# the zip gives us [(c1*r1, c2*r2, ..), (c1*g1, c1*g2,...)...]avg = tuple([int(sum(x)/npixels) for x in zip(*sumRGB)])return avgdef getAverageRGB(image):"""Given PIL Image, return average value of color as (r, g, b)"""# get image as numpy arrayim = np.array(image)# get shapew,h,d = im.shape# get averagereturn tuple(np.average(im.reshape(w*h, d), axis=0))def splitImage(image, size):"""Given Image and dims (rows, cols) returns an m*n list of Images """W, H = image.size[0], image.size[1]m, n = sizew, h = int(W/n), int(H/m)# image listimgs = []# generate list of dimensionsfor j in range(m):for i in range(n):# append cropped imageimgs.append(image.crop((i*w, j*h, (i+1)*w, (j+1)*h)))return imgsdef getImages(imageDir):"""given a directory of images, return a list of Images"""files = os.listdir(imageDir)images = []for file in files:filePath = os.path.abspath(os.path.join(imageDir, file))try:# explicit load so we don't run into resource crunchfp = open(filePath, "rb")im = Image.open(fp)images.append(im)# force loading image data from fileim.load() # close the filefp.close() except:# skipprint("Invalid image: %s" % (filePath,))return imagesdef getImageFilenames(imageDir):"""given a directory of images, return a list of Image file names"""files = os.listdir(imageDir)filenames = []for file in files:filePath = os.path.abspath(os.path.join(imageDir, file))try:imgType = imghdr.what(filePath) if imgType:filenames.append(filePath)except:# skipprint("Invalid image: %s" % (filePath,))return filenamesdef getBestMatchIndex(input_avg, avgs):"""return index of best Image match based on RGB value distance"""# input image averageavg = input_avg# get the closest RGB value to input, based on x/y/z distanceindex = 0min_index = 0min_dist = float("inf")for val in avgs:dist = ((val[0] - avg[0])*(val[0] - avg[0]) +(val[1] - avg[1])*(val[1] - avg[1]) +(val[2] - avg[2])*(val[2] - avg[2]))if dist < min_dist:min_dist = distmin_index = indexindex += 1return min_indexdef createImageGrid(images, dims):"""Given a list of images and a grid size (m, n), create a grid of images. """m, n = dims# sanity checkassert m*n == len(images)# get max height and width of images# ie, not assuming they are all equalwidth = max([img.size[0] for img in images])height = max([img.size[1] for img in images])# create output imagegrid_img = Image.new('RGB', (n*width, m*height))# paste imagesfor index in range(len(images)):row = int(index/n)col = index - n*rowgrid_img.paste(images[index], (col*width, row*height))return grid_imgdef createPhotomosaic(target_image, input_images, grid_size,reuse_images=True):"""Creates photomosaic given target and input images."""print('splitting input image...')# split target image target_images = splitImage(target_image, grid_size)print('finding image matches...')# for each target image, pick one from inputoutput_images = []# for user feedbackcount = 0batch_size = int(len(target_images)/10)# calculate input image averagesavgs = []for img in input_images:avgs.append(getAverageRGB(img))for img in target_images:# target sub-image averageavg = getAverageRGB(img)# find match indexmatch_index = getBestMatchIndex(avg, avgs)output_images.append(input_images[match_index])# user feedbackif count > 0 and batch_size > 10 and count % batch_size is 0:print('processed %d of %d...' %(count, len(target_images)))count += 1# remove selected image from input if flag setif not reuse_images:input_images.remove(match)print('creating mosaic...')# draw mosaic to imagemosaic_image = createImageGrid(output_images, grid_size)# return mosaicreturn mosaic_image# Gather our code in a main() function
def main():# Command line args are in sys.argv[1], sys.argv[2] ..# sys.argv[0] is the script name itself and can be ignored# parse argumentsparser = argparse.ArgumentParser(description='Creates a photomosaic from input images')# add argumentsparser.add_argument('--target-image', dest='target_image', required=True)parser.add_argument('--input-folder', dest='input_folder', required=True)parser.add_argument('--grid-size', nargs=2, dest='grid_size', required=True)parser.add_argument('--output-file', dest='outfile', required=False)args = parser.parse_args()###### INPUTS ####### target imagetarget_image = Image.open(args.target_image)# input imagesprint('reading input folder...')input_images = getImages(args.input_folder)# check if any valid input images found  if input_images == []:print('No input images found in %s. Exiting.' % (args.input_folder, ))exit()# shuffle list - to get a more varied output?random.shuffle(input_images)# size of gridgrid_size = (int(args.grid_size[0]), int(args.grid_size[1]))# outputoutput_filename = 'mosaic.png'if args.outfile:output_filename = args.outfile# re-use any image in inputreuse_images = True# resize the input to fit original image size?resize_input = True##### END INPUTS #####print('starting photomosaic creation...')# if images can't be reused, ensure m*n <= num_of_images if not reuse_images:if grid_size[0]*grid_size[1] > len(input_images):print('grid size less than number of images')exit()# resizing inputif resize_input:print('resizing images...')# for given grid size, compute max dims w,h of tilesdims = (int(target_image.size[0]/grid_size[1]), int(target_image.size[1]/grid_size[0])) print("max tile dims: %s" % (dims,))# resizefor img in input_images:img.thumbnail(dims)# create photomosaicmosaic_image = createPhotomosaic(target_image, input_images, grid_size,reuse_images)# write out mosaicmosaic_image.save(output_filename, 'PNG')print("saved output to %s" % (output_filename,))print('done.')# Standard boilerplate to call the main() function to begin
# the program.
if __name__ == '__main__':

实验素材

所需素材

本实验包括一个 .py 文件和若干图片组成
所有的图片都放置在 test-data 文件夹下
test-data/a.jpg是目标图像
test-data/set1/ 文件夹下存放的是小块图像

实验效果

运行照片马赛克生成程序

$ python photomosaic.py --target-image test-data/a.jpg --input-folder test-data/set1/ --grid-size 128 128

效果图

原图：

效果图

参考资料

《Python极客项目编程》
github项目代码
实验楼Python创建照片马赛克
NeroChang

附加A——python两种编程方式：

交互式编程——写一行python语句马上运行一行并显示效果
脚本式编程——现在相关的文本文件中，写好相关的python代码，一口气执行

附加B——课后练习