def ReadOneImage(filename):
datain = []
im = Image.open(filename)
for y in range(im.size[1]):
for x in range(im.size[0]):
datain.append(im.getpixel((x,y)))
return datain
def best_image_function(name, images_all, images_name):
highest = 0
max_width = 0
max_img = ""
Ratios = process.extract(name, images_all)
print(Ratios)
if Ratios != []:
highest = process.extractOne(name, images_all)
#print highest
if highest[1] < 50:
Ratios_name = process.extract(name, images_name)
#print Ratios_name
if Ratios_name != []:
highest = process.extractOne(name, images_name)
if highest[1] > 50:
return validate_url(images_all[images_name.index(highest[0])])
else:
for img in images_all:
#print img
real_url = validate_url(img)
try:
file = StringIO.StringIO(
urllib.urlopen(real_url).read())
im = image.open(file)
width, height = im.size
if width > max_width:
max_width = width
max_img = real_url
print(width)
print(height)
except IOError:
pass
return max_img
else:
return validate_url(highest[0])
def imageResize(img, username):
'''
Image resize to 58 * 58;
:param img:
:return:
'''
try:
standard_size = (58, 58)
file_type = '.jpg'
user_pic_list = [username, file_type]
log.debug(str(user_pic_list))
user_pic_str = ''.join(user_pic_list)
log.debug('37')
log.debug('user_pic_path: %s' % str(settings.STATICFILES_DIRS))
user_pic_path = os.path.join(settings.STATICFILES_DIRS[0], 'img', user_pic_str)
im = Image.open(img)
im_new = im.resize(standard_size, Image.ANTIALIAS)
im_new.save(user_pic_path, 'JPEG', quality=100)
log.info('The user pic resize successed')
try:
all_static = settings.STATIC_ROOT
all_static_url = os.path.join(all_static, 'img')
except AttributeError, e:
all_static_url = ''
log.info('The config settings.py no STATIC_ROOT attribute')
if all_static_url and os.path.exists(all_static_url):
shutil.copy(user_pic_path, all_static_url)
log.info('The user picture copy to all_static_url')
else:
log.debug('The all_static_url is None or do not exist')
def faceCrop(imagePattern, boxScale=1):
# Select one of the haarcascade files:
# haarcascade_frontalface_alt.xml <-- Best one?
# haarcascade_frontalface_alt2.xml
# haarcascade_frontalface_alt_tree.xml
# haarcascade_frontalface_default.xml
# haarcascade_profileface.xml
faceCascade = cv2.Load(
r'C:\Users\LeNoVo T430\PycharmProjects\MlLib\haarcascades\haarcascade_frontalface_alt.xml'
)
imgList = glob.glob(imagePattern)
if len(imgList) <= 0:
print('No Images Found')
return
for img in imgList:
pil_im = image.open(img)
cv_im = pil2cvGrey(pil_im)
faces = DetectFace(cv_im, faceCascade)
if faces:
n = 1
for face in faces:
croppedImage = imgCrop(pil_im, face[0], boxScale=boxScale)
fname, ext = os.path.splitext(img)
croppedImage.save(fname + '_crop' + str(n) + ext)
n += 1
else:
print('No faces found:', img)
def resize_by_width(source_image, destination_image, w_divide_h):
"""按照宽度进行所需比例缩放"""
im = image.open(source_image)
(x, y) = im.size
x_s = x
y_s = x / w_divide_h
out = im.resize((x_s, y_s), image.ANTIALIAS)
out.save(destination_image)
def resize_by_height(source_image, destination_image, w_divide_h):
"""按照高度进行所需比例缩放"""
im = image.open(source_image)
(x, y) = im.size
x_s = y * w_divide_h
y_s = y
out = im.resize((x_s, y_s), image.ANTIALIAS)
out.save(destination_image)
def __getitem__(self, index):
img = image.open(self.img_path[index]).convert('RGB')
if self.transforms is not None:
img = self.transforms(img)
# 设置最长的字符长度为6个
lbl = np.array(self.img_label[index], dtype=np.int)
# 使用10填充剩余的位置
lbl = list(lbl) + (6 - len(lbl)) * [10]
return img, torch.from_numpy(np.array(lbl[:6]))
def setWallPaper(imagePath):
"""
Given a path to an image, convert it to bmp and set it as wallpaper
"""
bmpImage = image.open(imagePath)
newPath = StoreFolder + '\\mywallpaper.bmp'
bmpImage.save(newPath, "BMP")
setWallpaperFromBMP(newPath)
def ReadImages():
datain = []
for infile in glob.glob("*.pgm"):
#filename, ext = os.path.splitext(infile)
#print filename
im = Image.open(infile)
pixels = []
for y in range(im.size[1]):
for x in range(im.size[0]):
pixels.append(im.getpixel((x,y)))
datain.append(pixels)
return datain
def save_picture(form_file, folder_name):
random_hex = random.token_hex(8)
_, f_ext = os.path.splitext(form_file.filename)
picture_fn = random_hex + f_ext
picture_path = os.path.join(current_app.root_path, 'static', folder_name,
picture_fn)
output_size = (125, 125)
i = image.open(form_file)
i.thumbnail(output_size)
i.save(picture_path)
return picture_fn
def read_2d_data(f):
if f.endswith('.dat'): return read_dat(f)
if f.endswith('.txt'): return loadtxt(f, dtype=float32)
# if f.endswith('.dcm'): return read_dicom(f)
if f.endswith('.mat'): return read_matlab(f)
if f.endswith('.mtx'): return read_mtx(f)
if f.endswith('.npz') or f.endswith('.npy'): return read_numpy(f)
if any([f.endswith(e) for e in ['.png', '.PNG', '.jpg', '.JPG', '.jpeg', '.JPEG', '.bmp', '.BMP', '.PGM', '.tif', '.TIF']]):
img = Image.open(f)
tmp = numpy.asarray(img)
return tmp
if f.endswith('.nrrd'): return read_nrrd(f)
assert False, "unknown file type: " + f
def resize_by_size(source_image, destination_image, size):
"""按照生成图片文件大小进行处理(单位KB)"""
size *= 1024
im = image.open(source_image)
size_tmp = os.path.getsize(source_image)
q = 100
while size_tmp > size and q > 0:
out = im.resize(im.size, image.ANTIALIAS)
out.save(destination_image, quality=q)
size_tmp = os.path.getsize(destination_image)
q -= 5
if q == 100:
shutil.copy(source_image, destination_image)
def main():
try:
#Relative Path
img = image.open("a.jpg")
#Angle given
img = img.rotate(180)
#Saved in the same relative location
img.save("rotated_picture.jpg")
detect_web(img)
except IOError:
pass
def test(imageFilePath):
pil_im = image.open(imageFilePath)
cv_im = pil2cvGrey(pil_im)
# Select one of the haarcascade files:
# haarcascade_frontalface_alt.xml <-- Best one?
# haarcascade_frontalface_alt2.xml
# haarcascade_frontalface_alt_tree.xml
# haarcascade_frontalface_default.xml
# haarcascade_profileface.xml
faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
face_im = DetectFace(cv_im, faceCascade, returnImage=True)
img = cv2pil(face_im)
img.show()
img.save('test.png')
def createImg():
x = 0
y = 0
imgs = os.listdir("img")
random.shuffle(imgs)
newImg = image.new("RGBA", (640, 640))
width = int(math.sqrt(640 * 640 / len(imgs)))
numLine = int(640 / width)
for i in imgs:
img = image.open("img/" + i)
img = img.resize((width, width), image.ANTIALIAS)
newImg.paste(img, (x * width, y * width))
x += 1
if x >= numLine:
x = 0
y += 1
newImg.save("all.png")
def cut_by_ratio(source_image, destination_image, width, height):
"""按照图片长宽比进行分割"""
im = image.open(source_image)
width = float(width)
height = float(height)
(x, y) = im.size
if width > height:
region = (0, int((y - (yi * (height / width))) / 2), x,
int((y + (y * (height / width))) / 2))
elif width < height:
region = (int((x - (x * (width / height))) / 2), 0,
int((x + (x * (width / height))) / 2), y)
else:
region = (0, 0, x, y)
#裁切图片
crop_img = im.crop(region)
#保存裁切后的图片
crop_img.save(destination_image)
def post(self, template_variables={}):
template_variables = {}
if (not "avatar" in self.request.files):
template_variables["errors"] = {}
template_variables["errors"]["invalid_avatar"] = [u"请先选择要上传的头像"]
self.get(template_variables)
return
user_info = self.current_user
user_id = user_info["uid"]
avatar_name = "%s" % uuid.uuid5(uuid.NAMESPACE_DNS, str(user_id))
avatar_raw = self.request.files["avatar"][0]["body"]
avatar_buffer = StringIO.StringIO(avatar_raw)
avatar = image.open(avatar_buffer)
# crop avatar if it's not square
avatar_w, avatar_h = avatar.size
avatar_border = avatar_w if avatar_w < avatar_h else avatar_h
avatar_crop_region = (0, 0, avatar_border, avatar_border)
avatar = avatar.crop(avatar_crop_region)
avatar_96x96 = avatar.resize((96, 96), image.ANTIALIAS)
avatar_48x48 = avatar.resize((48, 48), image.ANTIALIAS)
avatar_32x32 = avatar.resize((32, 32), image.ANTIALIAS)
avatar_96x96.save(
"/srv/www/3n1b.com/static/avatar/b_%s.png" % avatar_name, "PNG")
avatar_48x48.save(
"/srv/www/3n1b.com/static/avatar/m_%s.png" % avatar_name, "PNG")
avatar_32x32.save(
"/srv/www/3n1b.com/static/avatar/s_%s.png" % avatar_name, "PNG")
result = self.user_model.set_user_avatar_by_uid(
user_id, "%s.png" % avatar_name)
template_variables["success_message"] = [u"用户头像更新成功"]
# update `updated`
updated = self.user_model.set_user_base_info_by_uid(
user_id, {"updated": time.strftime('%Y-%m-%d %H:%M:%S')})
self.get(template_variables)
def rescale(data, width, height, force=True):
"""Rescale the given image, optionally cropping it to make sure the result image has the specified width and height."""
import image as pil
from cStringIO import StringIO
max_width = width
max_height = height
input_file = StringIO(data)
img = pil.open(input_file)
if not force:
img.thumbnail((max_width, max_height), pil.ANTIALIAS)
else:
img = ImageOps.fit(img, (max_width, max_height), method=pil.ANTIALIAS)
tmp = StringIO()
img.save(tmp, "PNG")
tmp.seek(0)
output_data = tmp.getvalue()
input_file.close()
tmp.close()
return output_data
def rescale(data, width, height, force=True): """Rescale the given image, optionally cropping it to make sure the result image has the specified width and height.""" import image as pil from cStringIO import StringIO max_width = width max_height = height input_file = StringIO(data) img = pil.open(input_file) if not force: img.thumbnail((max_width, max_height), pil.ANTIALIAS) else: img = ImageOps.fit(img, (max_width, max_height,), method=pil.ANTIALIAS) tmp = StringIO() img.save(tmp, 'PNG') tmp.seek(0) output_data = tmp.getvalue() input_file.close() tmp.close() return output_data
# coding=utf-8
# /************************************************************************************
# ***
# *** File Author: Dell, 2018年 08月 19日 星期日 20:38:18 CST
# ***
# ************************************************************************************/
import sys
import image
import torch
def dehaze_filter(device, img, r=3):
model = image.DehazeFilter(r)
model.to(device)
t = image.to_tensor(img)
t = model(t)
return image.from_tensor(t)
if __name__ == '__main__':
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
img = image.open(sys.argv[1])
img = dehaze_filter(device, img, 5)
img.show()
def load_image(infilename):
img = image.open(infilename)
img.load()
data = np.asarray(img, dtype="float")
return data
import image
im = image.open("earthEyes.py")
# _*_ coding:utf-8 _*_
# 给定图片位置添加数字
import image
im = image.open("../img/mm.jpg")
print im
#!/usr/bin/python
from sys import argv
import zbar
import image as Image
print(argv)
if len(argv) < 2: exit(1)
# create a reader
scanner = zbar.ImageScanner()
# configure the reader
scanner.parse_config('enable')
# obtain image data
pil = Image.open(argv[1]).convert('L')
width, height = pil.size
raw = pil.tostring()
# wrap image data
image = zbar.Image(width, height, 'Y800', raw)
# scan the image for barcodes
scanner.scan(image)
# extract results
for symbol in image:
# do something useful with results
print 'decoded', symbol.type, 'symbol', '"%s"' % symbol.data
# clean up
from flask import Flask, render_template, request
import requests
from bs4 import BeautifulSoup
import datetime
import selenium
from selenium import webdriver
import image
#https://www.weather.go.kr/mini/marine/wavemodel_c.jsp?prefix=kim_cww3_%5BAREA%5D_wdpr_&area=jeju&tm=2020.04.28.09&ftm=s000&newTm=2020.04.28.09&x=4&y=10
a = input("날짜")
url = "https://www.weather.go.kr/mini/marine/wavemodel_c.jsp?prefix=kim_cww3_%5BAREA%5D_wdpr_&area=jeju&tm=" + a
driver = webdriver.Chrome()
driver.implicitly_wait(3)
driver.get(url)
soup = BeautifulSoup(driver.page_source, "html.parser")
for i in soup.select("#chart_image"):
src = i.find("img")['src']
image = image.open(src)
image.show()
def save_in_image(self, string, image_file, out_file):
if out_file[-4:] != ".png":
show_message("The output file should be a png-image")
image1 = image.open(image_file)
image2 = stepic.encode(image1, string)
image2.save(out_file)
import image
def color_separator(im):
if im.getpalette():
im = im.convert('RGB')
colors = im.getcolors()
width, height = im.size
colors_dict = dict((val[1],image.new('RGB', (width, height), (0,0,0)))
for val in colors)
pix = im.load()
for i in xrange(width):
for j in xrange(height):
colors_dict[pix[i,j]].putpixel((i,j), pix[i,j])
return colors_dict
im = image.open("colorwheel.tiff")
colors_dict = color_separator(im)
#show the images:
colors_dict.popitem()[1].show()
colors_dict.popitem()[1].show()
parser.add_argument('--input',
type=str,
default="hazeimgs/*.jpg",
help="input image")
parser.add_argument('--output',
type=str,
default="output",
help="output directory")
args = parser.parse_args()
# Create directory to store results
if not os.path.exists(args.output):
os.makedirs(args.output)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = dehaze_filter(3, device)
image_filenames = glob.glob(args.input)
for index, filename in enumerate(image_filenames):
print("Dehazing {} ... ".format(filename))
img = image.open(filename)
input_tensor = image.to_tensor(img)
output_tensor = model(input_tensor)
oimg = image.grid_image([input_tensor, output_tensor], nrow=2)
oimg.save(args.output + "/dehaze_" + os.path.basename(filename))
string = '%dpx %dpx 0px 1px rgb%s,\n'
for y in range(0, im.size[1], 1):
for x in range(0, im.size[0], 1):
if im.size[1] - y <= 1 and im.size[0] - x <= 1:
string = '%dpx %dpx 0px 1px rgb%s;\n'
color = im.getpixel((x, y))
css += string % (x, y, color)
return css
def gethtml(css):
"""docstring for gethtml"""
html = """
<div style="
%s"></div>
""" % css
return html
if __name__ == '__main__':
filename = sys.argv[1]
#outfile = sys.argv[2]
im = image.open(filename)
ratio = 0.5
size = (int(ratio * im.size[0]), int(ratio * im.size[1]))
im.thumbnail(size)
html = gethtml(getcss(im))
print html
# with open(outfile, 'wb') as f:
# f.write(html)__author__ = 'royxu'
img = plt.imread('G:\\个人资料\\图库\\桌面\\112.jpg')
img = plt.imread('G:\\个人资料\\图库\\桌面\\1111.png')
imshow(img)
img
get_ipython().magic('matplotlib inline')
import pandas as pd
import image
get_ipython().magic('matplotlib inline')
import pandas as pd
import image
imshow(img)
img.show()
img = image.open('G:\\个人资料\\图库\\桌面\\1111.png')
img = plt.imread('G:\\个人资料\\图库\\桌面\\1111.png')
plt.imshow(img)
plt(randn(1000).cumsum())
get_ipython().magic('matplotlib inline')
import pandas as pd
import image
from numpy.random import randn
import numpy as np
import os
import matplotlib.pyplot as plt
img = plt.imread('G:\\个人资料\\图库\\桌面\\1111.png')
plt.imshow(img)
plt(randn(1000).cumsum())
a = plt(randn(1000).cumsum())
def extract_from_image(self, image_file, out):
file = open(out, 'w')
file.write(stepic.decode(image.open(image_file)))
now we need to merge all file
"""
pathsave = []
print('A')
try:
#search all image in temp path. file name ends with uuid_set value
# new_folder_name = (os.path.basename(filepdf)).split('.PDF')[0]
# print(new_folder_name)
# new_folder_path = "/home/hjiang/superlist/pdftoimage/%s" % new_folder_name
# print(new_folder_path)
# os.makedirs(new_folder_path)
list_im = glob.glob(new_folder_path + "/%s*.jpeg" % uuid_set)
print('B')
list_im.sort() #sort the file before joining it
print('C')
imgs = [Img.open(i) for i in list_im]
print('D')
#now lets Combine several images vertically with Python
min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
print('E')
imgs_comb = np.vstack(
(np.asarray(i.resize(min_shape)) for i in imgs))
print('F')
# for horizontally change the vstack to hstack
imgs_comb = Img.fromarray(imgs_comb)
print('G')
pathsave = new_folder_path + "MyPdf%s.jpeg" % uuid_set
print('H')
#now save the image
imgs_comb.save(pathsave)
print('I')
"""
Created on Tue Oct 2 13:23:48 2018
@author: Tayro
"""
import numpy as np
import matplotlib.pyplot as mplot
import image
img = Image.open("mgb.jpg")
img=np.float64(img)
img1=image.open("jag.jpg")
img1=np.gloat64(img1)
avg_img=img+img1
avg_img=avg_img/2.0
for row in range(0, len(avg_img)):
for col in range(0, len(avg_img[row])):
if(avg_img[row][col] < [100, 100, 100]).any()
avg_img=np.clip(avg_img, 0, 255)
avg_img=np.uint8(avg_img)
mplot.imshow(avg_img)
def fixed_size(source_image, destination_image, width, height):
"""按照固定尺寸处理图片"""
im = image.open(source_image)
out = im.resize((width, height), image.ANTIALIAS)
out.save(destination_image)
def main(conf):
img = image.open(conf.file)
segments = image.segment(img, int(conf.cluster), it=5)
image.save('./output/' + str(time.time()), segments)
