def create_photomosaic(save_photo_dir, background_photo):
# 读取背景图片
bg_photo = pm.imread(background_photo)
# 读取好友头像图片，定义图片库
pool = pm.make_pool(os.path.join(save_photo_dir, '*.jpg'))
# 制作 50*50 的拼图马赛克
image = pm.basic_mosaic(bg_photo, pool, (50, 50))
# 保存结果
pm.imsave('马赛克好友头像图片.jpg', image)
def pool():
tempdirname = tempfile.mkdtemp()
pm.rainbow_of_squares(tempdirname, range_params=(0, 256, 30))
pool = pm.make_pool(os.path.join(tempdirname, '*.png'))
def delete_dm():
shutil.rmtree(tempdirname)
return pool
def pool():
tempdirname = tempfile.mkdtemp()
pm.rainbow_of_squares(tempdirname, range_params=(0, 256, 30))
pool = pm.make_pool(os.path.join(tempdirname, "*.png"))
def delete_dm():
shutil.rmtree(tempdirname)
return pool
def mosaic(filename, dirname):
from skimage.io import imread
image = imread(filename)
import photomosaic as pm
# Analyze the collection (the "pool") of images.
pool = pm.make_pool(dirname + '/*.jpg')
print(image.shape)
mos = pm.basic_mosaic(image, pool, (30, 30), depth=1)
from skimage.io import imsave
imsave(filename[:-4] + '_mosaic' + filename[-4:], mos)
def basic_mosaic(filename):
from skimage.io import imread
image = imread(filename)
import photomosaic as pm
# Generate a collection of solid-color square images.
pm.rainbow_of_squares('pool/')
# Analyze the collection (the "pool") of images.
pool = pm.make_pool('pool/*.png')
mos = pm.basic_mosaic(image, pool, (30, 30), depth=1)
from skimage.io import imsave
imsave(filename[:-4] + '_basic_mosaic' + filename[-4:], mos)
def dog_mosaic(directory):
#key image
#d_image = mpimg.imread(directory)
d_image = img_as_float(img)
#pool of images for mosaic tiles
d_pool = pm.make_pool('Data/folders/train/dog/*.jpg')
#mosaic of 250x250 tiles
d_mos = pm.basic_mosaic(d_image, d_pool, (100, 100), depth=1)
#return mosaic
return st.image(d_mos)
def cat_mosaic(directory):
#key image
c_image = mpimg.imread(directory)
c_image = img_as_float(c_image)
#pool of images for mosaic tiles
c_pool = pm.make_pool('../Data/folders/train/cat/*.jpg')
#mosaic of 250x250 tiles
c_mos = pm.basic_mosaic(c_image, c_pool, (100, 100), depth=1)
#return mosaic
return st.image(c_mos)
def Montage():
"""
将所有的王者荣耀皮肤合并在一张图片上
:return:
"""
img = cv2.imread('./1.jpg') #所需要拼图的原图
print(img.shape)
res = cv2.resize(img, (1100, 1200)) #修改原图的大小并保存,
cv2.imwrite('./1.jpg', res)
image = pm.imread('./1.jpg')
pool = pm.make_pool('./王者荣耀/*.jpg', sample_size=10000) #读取文件夹下的所有图片
mosaic = pm.basic_mosaic(image, pool, (100, 100))
pm.imsave('2s.jpg', mosaic) #保存拼图图片
def gen_pic():
target = '28387.jpg'
image = imread(target)
dims_list = [(150, 150,), ]
# Analyze the collection (the "pool") of images.
# pool = pm.make_pool('guinnesscaps/*.jpg')
# Generate a collection of solid-color square images.
pm.rainbow_of_squares('pool/', range_params=(0, 256, 128))
# Analyze the collection (the "pool") of images.
pool = pm.make_pool('pool/*.png')
for dims in dims_list:
mos = pm.basic_mosaic(image, pool, dims, depth=1)
imsave('mosaic_{}.png'.format(target), mos)
def mosaic_batch(test_dirname, dirname):
from skimage.io import imread
from skimage.io import imsave
import photomosaic as pm
# Analyze the collection (the "pool") of images.
pool = pm.make_pool(dirname + '/*.jpg')
filenames = os.listdir(test_dirname)
for filename in filenames:
if 'mosaic' not in filename and filename.endswith('.jpg'):
filename = os.path.join(test_dirname, filename)
image = imread(filename)
mos = pm.basic_mosaic(
image,
pool, (int(image.shape[0] / 50), int(image.shape[1] / 50)),
depth=1)
imsave(filename[:-4] + '_mosaic' + filename[-4:], mos)
def detail_mosaic(filename, dirname):
from skimage.io import imread
image = imread(filename)
# Size the image to be evenly divisible by the tiles.
from skimage import img_as_float
image = img_as_float(image)
# Use perceptually uniform colorspace for all analysis.
import photomosaic as pm
converted_img = pm.perceptual(image)
pool = pm.make_pool(dirname + '/*.jpg')
# Adapt the color palette of the image to resemble the palette of the pool.
adapted_img = pm.adapt_to_pool(converted_img, pool)
scaled_img = pm.rescale_commensurate(adapted_img,
grid_dims=(30, 30),
depth=1)
tiles = pm.partition(scaled_img, grid_dims=(30, 30), depth=1)
annotated_img = pm.draw_tile_layout(pm.rgb(scaled_img), tiles)
from skimage.io import imsave
imsave(filename[:-4] + '_detail_mosaic' + filename[-4:], annotated_img)
def create_mosaic(input_img, images_dataset_path, grid_dims):
# Load a sample image
image = cv2.imread(img_path)
image = img_as_float(image) #ensure image is float ranging from 0 to 1
# Analyze the collection (the "pool") of images.
pool = pm.make_pool(images_dataset_path)
# Use perceptually uniform colorspace for all analysis.
converted_img = pm.perceptual(image)
# Adapt the color palette of the image to resemble the palette of the pool.
#adapted_img = pm.adapt_to_pool(converted_img, pool)
adapted_img = converted_img
#scale = 1
#scaled_img = Image.new('RGB', (adapted_img.shape[0] * scale, adapted_img.shape[1] * scale))
#scaled_img = Image.new('RGB', (5040, 5040))
scaled_img = pm.rescale_commensurate(adapted_img,
grid_dims=grid_dims,
depth=0)
tiles = pm.partition(scaled_img, grid_dims=grid_dims, depth=0)
# Reshape the 3D array (height, width, color_channels) into
# a 2D array (num_pixels, color_channels) and average over the pixels.
tile_colors = [
np.mean(scaled_img[tile].reshape(-1, 3), 0) for tile in tiles
]
# Match a pool image to each tile.
match = pm.simple_matcher(pool)
matches = [match(tc) for tc in tile_colors]
matches_list = [x[0] for x in matches]
# Perform neural network object detection to see what classes are on which images
detect_images(matches_list)
# Concatenate list of matches images to a single mosaic image
concatenateImages(matches_list, grid_dims)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/1/2 14:00
# @Author : LiangJiangHao
# @Software: PyCharm
import photomosaic as pm
from PIL import Image
imgPath = 'F:\\1.jpg'
# im = Image.open(imgPath).convert('RGB')
# # 图片的宽度和高度
# w, h = im.size
# print(w,h)
image = pm.imread(imgPath)
# print(image)
pool = pm.make_pool('F:\ACimg\*.jpg')
print(type(pool))
mosaic = pm.basic_mosaic(image, pool, (10, 10))
pm.imsave('save.jpg', mosaic)
from skimage.io import * #read and write images in various formats
import sys # for runtime environment
import photomosaic as pm
from skimage import data #standard test images (image processing)
image = data.chelsea()# get chelsea.png #change the img as needed
imsave('image.png',image)
mosaicSize=(int (sys.argv[1]),int(sys.argv[2]))
pm.rainbow_of_squares('square/') #Generate 5832 small solid-color tiles for experimentation and testing into folder 'square/'
squarePool=pm.make_pool('square/*.png')
mosaic=pm.basic_mosaic(image,squarePool,mosaicSize)
outputFile= sys.argv[3] if len(sys.argv)==4 else 'mosaic.png'
imsave(outputFile,mosaic)
import os
import photomosaic.flickr
import photomosaic as pm
if not os.path.isfile(os.path.expanduser('~/pools/cats/pool.json')):
FLICKR_API_KEY = os.environ['FLICKR_API_KEY']
pm.set_options(flickr_api_key=FLICKR_API_KEY)
photomosaic.flickr.from_search('cats', '~/pools/cats/')
pool = pm.make_pool('~/pools/cats/*.jpg')
pm.export_pool(
pool, '~/pools/cats/pool.json') # save color analysis for future reuse
savepath = foldername
if not os.path.exists(savepath):
os.makedirs(savepath)
img.save(saveimgname, "JPEG")
trainImgNum += 1
# In[7]:
def main():
filename = 'cifar-100-python/train'
foldername = "train"
saveImage(filename, foldername)
if __name__ == "__main__":
main()
# In[1]:
import cv2
img = cv2.imread("hh.jpg")
print(img.shape)
res = cv2.resize(img, (2400, 1800))
cv2.imwrite("hh_1.jpg", res)
# In[10]:
import photomosaic as pm
image = pm.imread("hh_1.jpg")
pool = pm.make_pool('train/*.jpg', sample_size=5000)
mosaic = pm.basic_mosaic(image, pool, (480, 360))
pm.imsave('montage2.jpg', mosaic)
import os import photomosaic as pm POOL_DIR = '/tmp/photomosaic-docs-pool/' pm.rainbow_of_squares(POOL_DIR) pool = pm.make_pool(os.path.join(POOL_DIR, '*.png')) pm.export_pool(pool, os.path.join(POOL_DIR, 'pool.json'))
import os
import photomosaic.flickr
import photomosaic as pm
if not os.path.isfile(os.path.expanduser("~/pools/cats/pool.json")):
FLICKR_API_KEY = os.environ["FLICKR_API_KEY"]
pm.set_options(flickr_api_key=FLICKR_API_KEY)
photomosaic.flickr.from_search("cats", "~/pools/cats/")
pool = pm.make_pool("~/pools/cats/*.jpg")
pm.export_pool(pool, "~/pools/cats/pool.json") # save color analysis for future reuse