def tweet_image(message):
font = ImageFont.truetype(font="PlainPensle_BoldItalic.ttf", size=75)
W, H = (1280, 720)
w, h = font.getsize(message)
img = Image.new('RGB', (W, H), color=(240, 226, 179))
img_w, img_h = img.size
draw = ImageDraw.Draw(img)
draw.text(((W - w) / 2, 585), message, font=font, fill='black')
img2 = Image.open(
os.path.join("C:/Users/Matthew/PycharmProjects/TwitterBot/botImages",
file), 'r')
img2 = img2.resize((500, 500))
img3 = img2.convert("RGBA")
img3.resize((500, 500))
offset = (390, 65)
img.paste(img3, offset, mask=img3)
img.save('temp.png')
os.remove(
os.path.join("C:/Users/Matthew/PycharmProjects/TwitterBot/botImages",
file))
api.update_with_media('temp.png', status=message)
def __pic2array(self, filepath):
if self.__type == 'directory':
image = Image.open(filepath)
elif self.__type == 'zip':
__relativepath = self.__relativepath(filepath)
image = Image.open(self.__archive.open(self.__archive.getinfo(__relativepath)))
else:
raise Exception("Unknown type")
if self.__grayscale:
return numpy.asarray(image.convert('L').resize((self.__width, self.__height)))
else:
raise Exception("loading image except for grayscale not supported yet...")
def scanline(array):
'''creates a scanline effect akin to those on old arcade machines and/or old screens
:param The image as an array:
:return The image:
'''
w = len(array)
h = len(array[0])
new_img = Image.new("RGBA", (w, h), "black") #makes a new blank blue image
big_pixels = [[1 for _ in range(int(h / 2))] for _ in range(int(w / 2))]
for i in range(
0, w,
2): #Iterates through each pixel's colors and modifies accordingly
for j in range(0, h, 2):
if (i != w - 1 and j != h - 1):
pixel0 = array[i][j]
pixel1 = array[i + 1][j]
pixel2 = array[i][j + 1]
pixel3 = array[i + 1][j + 1]
big_pixel_array = [pixel0, pixel1, pixel2, pixel3]
red = 0
green = 0
blue = 0
for pixel in big_pixel_array:
red = red + pixel[0]
green = green + pixel[1]
blue = blue + pixel[2]
red = int(red / 4)
green = int(green / 4)
blue = int(blue / 4)
big_pixel = (red, green, blue)
new_img.putpixel((i, j), big_pixel)
return new_img
def getImage():
'''Asks the user for the image
:return the image:
'''
img_string = imageOpen.prompt_and_get_file_name()
img = Image.open(img_string)
return img
def select_image(index):
# Disable scientific notation for clarity
np.set_printoptions(suppress=True)
# Load the model
model = tf.contrib.lite.Interpreter(model_path="model_unquant.tflite")
# Create the array of the right shape to feed into the keras model
# The 'length' or number of images you can put into the array is
# determined by the first position in the shape tuple, in this case 1.
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
# Replace this with the path to your image
image = Image.open('image' + str(index) + '.jpg')
#resize the image to a 224x224 with the same strategy as in TM2:
#resizing the image to be at least 224x224 and then cropping from the center
size = (224, 224)
image = ImageOps.fit(image, size, Image.ANTIALIAS)
#turn the image into a numpy array
image_array = np.asarray(image)
# display the resized image
image.show()
# Normalize the image
normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
# Load the image into the array
data[0] = normalized_image_array
# run the inference
prediction = model.predict(data)
print(prediction)
def is_valid_image_old(path):
img = Image.open(path)
try:
img.verify()
return img.format in ['JPEG', 'JPEG2000', 'PNG', 'GIF']
except Exception:
return False
def avhash(im):
if not isinstance(im, Image.Image):
im = Image.open(im)
im = im.resize((8, 8), Image.ANTIALIAS).convert('L')
avg = reduce(lambda x, y: x + y, im.getdata()) / 64.
return reduce(lambda x, (y, z): x | (z << y),
enumerate(map(lambda i: 0
if i < avg else 1, im.getdata())), 0)
def main():
im = Image.open('dead_parrot.jpg') # Can be many different formats.
pix = im.load()
print
im.size # Get the width and hight of the image for iterating over
print
pix[x, y] # Get the RGBA Value of the a pixel of an image
pix[x, y] = value # Set the RGBA Value of the image (tuple)
im.save('alive_parrot.png') # Save the modified pixels as .png
def calculate_image_size(image):
"""Calculates width and height of image in pixels.
Args:
image as string (path to image).
Returns:
tuple (int image width in pixels, int image height in pixels).
"""
img = Image.open(image) # Pillow Image Class Method (open).
return img.size # Pillow Image Class Attribute (size).
def save(self, *args, **kwargs):
"""
Create slug based on title
If a zip file is uploaded, extract images from it and add
them to the gallery.
Removes zip file afterwards
"""
self.slug = slugify(self.title)
super(BaseGallery, self).save(*args, **kwargs)
if self.zip_import:
zip_file = ZipFile(self.zip_import)
for name in zip_file.namelist():
data = zip_file.read(name)
try:
# backwards compatibile with PIL
from Pillow import Image
image = Image.open(BytesIO(data))
image.load()
image = Image.open(BytesIO(data))
image.verify()
except ImportError:
pass
except:
continue
name = os.path.split(name)[1]
# This is a way of getting around the broken nature of
# os.path.join on Python 2.x. See also the comment below.
if isinstance(name, bytes):
encoding = charset_detect(name)['encoding']
tempname = name.decode(encoding)
else:
tempname = name
# A gallery with a slug of "/" tries to extract files
# to / on disk
slug = self.slug if self.slug != "/" else ""
path = os.path.join(UPLOAD_DIR, slug, tempname)
saved_path = default_storage.save(path, ContentFile(data))
self.images.create(file=saved_path)
zip_file.close()
self.zip_import.delete(save=True)
def modImage(array, mod="", threshold=100):
'''takes an array of an image and modifies the image based on parameters
:param An array of integers coresponding to an image:
:param A modifier for what operation will be performed on the pixels:
:param A threshold for some operations:
:returns An image made from the modified array'''
w = len(array)
h = len(array[0])
new_img = Image.new("RGBA", (w, h), "blue") #makes a new blank blue image
for i in range(
w): #Iterates through each pixel's colors and modifies accordingly
for j in range(h):
red = array[i][j][0]
green = array[i][j][1]
blue = array[i][j][2]
#Chooses formula based on given parameter
if mod == "sepia":
'''Uses Formula for Sepia'''
new_red = int((red * 0.393) + (green * 0.796) + (blue * 0.189))
new_green = int((red * 0.349) + (green * 0.686) +
(blue * 0.161))
new_blue = int((red * 0.272) + (green * 0.534) +
(blue * 0.131))
elif mod == "greyscale":
'''Uses Luminosity Formula for Greyscale'''
new_red = int((red * 0.21) + (green * 0.72) + (blue * 0.07))
new_green = int((red * 0.21) + (green * 0.72) + (blue * 0.07))
new_blue = int((red * 0.21) + (green * 0.72) + (blue * 0.07))
elif mod == "dog":
new_rgb = dogVision(red, green, blue)
new_red = new_rgb[0]
new_green = new_rgb[1]
new_blue = new_rgb[2]
elif mod == "dalton":
new_rgb = daltonize(red, green, blue)
new_red = new_rgb[0]
new_green = new_rgb[1]
new_blue = new_rgb[2]
elif mod == "lighten":
new_red = int(red + threshold)
new_green = int(green + threshold)
new_blue = int(blue + threshold)
elif mod == "darken":
'''Decreases each color by given threshold'''
new_red = int(red - threshold)
new_green = int(green - threshold)
new_blue = int(blue - threshold)
else:
new_red = red
new_green = green
new_blue = blue
new_img.putpixel((i, j), (new_red, new_green, new_blue))
return new_img
def create_thumbnail(self, size, force_crop=False): """ Creates a thumbnail for an image at the specified size """ if not size.auto_size: try: crop = self.get_crop(size) cropped_image = utils.create_cropped_image( self.image.path, crop.crop_x, crop.crop_y, crop.crop_w, crop.crop_h ) except Crop.DoesNotExist: # auto-crop if no crop is defined cropped_image = pil.open(self.image.path) else: cropped_image = pil.open(self.image.path) self.rescale(cropped_image=cropped_image, size=size, force_crop=force_crop)
def handle_uploaded_file(f, name=None):
if name is None: name = str(f)
full_path = os.path.join(BASE_DIR, 'static', 'uploads', name)
with open(full_path, 'w+b') as destination:
for chunk in f.chunks():
destination.write(chunk)
im = Image.open(full_path)
img = im.resize((250, 250), Image.ANTIALIAS)
imgIcon = im.resize((50, 50), Image.ANTIALIAS)
img.save(full_path, 'PNG')
imgIcon.save(full_path + 'tiny', 'PNG')
return full_path
def image_data(filename: str):
""" return image data for filename """
try:
im = Image.open(filename)
except IOError:
print("failed to identify", filename)
else:
print("image format:", im.format)
print("image mode:", im.mode)
print("image size:", im.size)
if "description" in im.info:
print("image description:", im.info["description"])
def make_thumbnail(old_path, new_path, f, w, h, quality):
path = old_path + '/' + f
# convert('RGB')这个必须加上, 否则可能会报错: cannot write mode P as JPEG
icon = Image.open(path).convert('RGB')
width, height = icon.size
print width, height
# 只切高度大于宽度的图片, 宽度大于高度的图片复制到另一个目录
if width < height:
icon = icon.resize((500, int(500 * height / width)), Image.ANTIALIAS)
else:
icon = icon.resize((int(500 * width / height), 500), Image.ANTIALIAS)
icon = icon.crop((0, 0, int(w), int(h)))
print new_path + '/' + f
icon.save(new_path + '/' + f, 'jpeg', quality=quality)
def get_merge_image(self, image_mix, location_list):
im = image.open(image_mix)
im_list_upper = []
im_list_down = []
for location in location_list:
if location['y'] == -58:
im_list_upper.append(
im.crop((abs(location['x']), 58, abs(location['x']) + 10,
166)))
if location['y'] == 0:
im_list_down.append(
im.crop(
(abs(location['x']), 0, abs(location['x']) + 10, 58)))
new_im = image.new('RGB', (260, 116))
x_offset = 0
for im in im_list_upper:
new_im.paste(im, (x_offset, 0))
x_offset += im.size[0]
x_offset = 0
for im in im_list_down:
new_im.paste(im, (x_offset, 58))
x_offset += im.size[0]
return new_im
def convert_dir(in_dir, out_dir):
if not os.path.exists(out_dir):
os.system("mkdir -p " + out_dir)
for img_file in tqdm.tqdm(os.listdir(in_dir)):
img_path = os.path.join(in_dir, img_file)
r_image = Image.open(img_path)
c_image = ImageProcessor.resize_file(
r_image, 32, 32
).convert("RGBA")
out_path = os.path.join(out_dir, img_file)
c_image.save(out_path.split(".")[0] + ".png")
def create_cropped_image(path=None, x=0, y=0, width=0, height=0):
"""
Crop image given a starting (x, y) position and a width and height of the cropped area
"""
if path is None:
raise ValueError("A path must be specified")
img = Image.open(path)
img.copy()
img.load()
img = img.crop((x, y, x + width, y + height))
img.load()
return img
def is_img_uniform_size(self, img_content):
"""
判断图片格式大小是否符合要求
:param img_content: Stream2bin 2进制流文件
:return: Boole True/False
"""
try:
img = Image.open(io.BytesIO(img_content))
except:
print 'Image.open io.BytesIO failed'
return
height, width = img.size
img_flag = True
if height < 150 or width < 150:
img_flag = False
return img_flag
def is_img_uniform_size(self, img_content, heigth=150, width=150):
"""
判断图片格式大小是否符合要求
:param img_content: Stream2bin 2进制流文件
:return: Boole True/False
"""
try:
img = Image.open(io.BytesIO(img_content))
except:
print('Image.open io.BytesIO failed')
return
img_h, img_w = img.size
img_flag = True
if img_h < heigth or img_w < width:
img_flag = False
return img_flag
def _dump_image(x):
(index, p_list) = x
w_dir = os.path.join(out_dir, str(index))
if not os.path.exists(w_dir):
os.system("mkdir -p " + w_dir)
out_list = []
for r_path in p_list:
name = ("-".join(r_path.split("-")[1:])).replace("/", "$")
w_path = os.path.join(w_dir, name)
image = Image.open(r_path)
image.save(w_path)
out_list.append(w_path)
return out_list
def dd_helper(image, layer, iterations, lr):
input = Variable(preprocess(image).unsqueeze(0).cuda(), requires_grad=True)
vgg.zero_grad()
for i in range(iterations):
# print('Iteration: ', i)
out = input
for j in range(layer):
out = module_list[j + 1](out)
loss = out.norm()
loss.backward()
input.data = input.data + lr * input.grad.data
input = input.data.squeeze()
input.transpose_(0, 1)
input.transpose_(1, 2)
input = np.clip(deprocess(input), 0, 1)
im = Image.fromarray(np.uint8(input * 255))
return im
def clean(self):
""" Additional file validation for saving """
if self.image:
# Check for valid file extension
if os.path.splitext(self.image.name)[1] == '':
raise ValidationError("Please make sure images have file extensions before uploading")
# Check for valid file
try:
pil_image = pil.open(self.image)
except:
raise ValidationError("Unable to open image file")
# Check for minimum size requirement
if self.validate_image_size:
for size in self.size_set.size_set.all().order_by("-width"):
if size.width > pil_image.size[0] or size.height > pil_image.size[1]:
raise ValidationError("Uploaded image (%s x %s) is smaller than a required thumbnail size: %s" % (pil_image.size[0], pil_image.size[1], size))
return super(Image, self).clean()
def reflect(array, axis="x"):
''' reflects the image over the selected axis
:param The image array:
:param The axis to reflect the image upon:
:return The new Image:
'''
w = len(array)
h = len(array[0])
new_img = Image.new("RGBA", (w, h), "blue")
for i in range(w):
for j in range(h):
'''swaps the pixels around in desired way'''
red = array[i][j][0]
green = array[i][j][1]
blue = array[i][j][2]
if axis == "x":
new_img.putpixel((-i, j)(red, green, blue))
elif axis == "y":
new_img.putpixel((i, -j)(red, green, blue))
else:
new_img.putpixel((j, i), (red, green, blue))
return new_img
def __getitem__(self, idx):
# load the image as a PIL Image
img_dict = self.obj['images'][idx]
image = Image.open(img_dict['file_name'])
w, h = img_dict['width'], img_dict['height']
img_id = img_dict['id']
# load the bounding boxes as a list of list of boxes
# in this case, for illustrative purposes, we use
# x1, y1, x2, y2 order.
anns = [ann for ann in obj['annotations'] if ann['image_id'] == img_id]
boxes = [ann['bbox'] for ann in anns]
boxes = [[x[1], x[0], x[3], x[2]] for x in boxes]
labels = torch.tensor([ann['category_id'] for ann in anns])
masks = [ann['segmentation'] for ann in anns]
for i, obj in enumerate(masks):
for j, poly in enumerate(obj):
for k in range(0, len(poly), 2):
masks[i][j][k], masks[i][j][k + 1] = poly[k + 1], poly[k]
# create a BoxList from the boxes
boxlist = BoxList(boxes, image.size, mode="xyxy")
# add the labels to the boxlist
boxlist.add_field("labels", labels)
boxlist.add_field("masks", SegmentationMask(masks, (w, h),
mode='poly'))
if self.transforms:
image, boxlist = self.transforms(image, boxlist)
# return the image, the boxlist and the idx in your dataset
return image, boxlist, idx
def main():
username = request.form['username']
print(username)
search_filter = "(&(cn=" + username + ")"
os.environ.setdefault('DJANGO_SETTINGS_MODULE',
'panoramic_trekking_app.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?") from exc
execute_from_command_line(sys.argv)
#Open image using Image module
im = Image.open("images/cuba.jpg")
#Show actual Image
im.show()
#Show rotated Image
im = im.rotate(45)
im.show()
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#' a test module '
__author__='ZhangLei'
#类似__xxx__这样的变量是特殊变量，可以被直接引用，但是有特殊用途，比如上面的__author__，__name__就是特殊变量，hello模块定义的文档注释也可以用特殊变量__doc__访问，我们自己的变量一般不要用这种变量名
#类似_xxx和__xxx这样的函数或变量就是非公开的（private），不应该被直接引用，比如_abc，__abc等
#定义为私有的函数
def _private_1(name):
return 'Hello, %s' % name
def _private_2(name):
return "Hello,%s" % name;
def greeting(name):
if(len(name) > 3):
return _private_1(name);
else:
return _private_2(name);
print(greeting("hello"))
#模块
#安装模块
#pip install Pillow
from Pillow import Image;
im = Image.open("test.png");
print(im.format,im.size,im.mode);
im.thumbnail(200,100);
im.save("test_thumbnail.jpg","JPEG");
import numpy as np
#from PIL import Image
from Pillow import Image
from timeit import default_timer as timer
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from ModelLoader import ModelLoader
from PostProcessor import PostProcessor
from preprocess import PreProcessor
import json
model_loader = ModelLoader()
img = Image.open('/home/bilal/Downloads/foto_van_yosra1.jpg')
# print("---------------------------------")
# print("0 for tinyYolo")
# val = input("give your neural network architecture type: ")
preprocessor = PreProcessor(0, img)
img_data = preprocessor.preprocess()
# print("---------------------------------------------")
# load a simple model
session = model_loader.load_session(1)
begin = timer()
# see the input name and shape
input_name = session.get_inputs()[0].name
"""# print("input name = ", input_name)
input_shape = session.get_inputs()[0].shape
from Pillow import Image
import glob, os
for infile in glob.glob('c:/Users/guest/Desktop/cards/*.jpg'):
source = Image.open(infile)
o1 = source.crop((85, 20, 772, 1009))
o2 = source.crop((771, 20, 1458, 1009))
o_path = 'c:/Users/guest/Desktop/o_cards/'
name = os.path.basename(infile)
o1.save(o_path + name + ".card1.jpg")
o2.save(o_path + name + ".card2.jpg")
time_start = time.time()
# Data descramblng
for idx in range(rx_data_size):
rx_data_buff[idx] = rx_data_buff[idx] ^ SCRAMBLE_SEQ
# Write data to file
with open(TEMP_FILE_PATH, 'wb') as temp_file:
temp_file.write(rx_data_buff[0:rx_data_size])
# Format and vizualize data
try:
rx_data_img = imageio.imread(TEMP_FILE_PATH).astype(np.uint8)
if rx_data_img.ndim == 2:
plt.imshow(rx_data_img, cmap='gray')
else:
plt.imshow(rx_data_img)
plt.show()
plt.pause(0.001)
except:
print('RX DATA CORRUPT ...')
image = Image.open(TEMP_FILE_PATH)
image.show
#os.system('gpicview -single-window %s &' % TEMP_FILE_PATH)
time_elapsed = (time.time() - time_start)
print('time for image plot %f ' % time_elapsed)
def load_image(path):
image = Image.open(path)
plt.imshow(image)
plt.title('Image loaded successfully')
return image
def save(self, *args, **kwargs):
changed_image = self.content != self.__original_image
if not self.id and not self.pub_date:
self.pub_date = datetime.datetime.today()
crop_original = kwargs.get('crop_original', False)
super(MediaContent, self).save(*args, **kwargs)
self.mimetype = mimetypes.guess_type(self.content.path)[0]
if self.mimetype:
content_type = self.mimetype.replace('/', '_')
else:
# assume everything else is text/plain
content_type = 'text_plain'
i = self.content.name.rindex('/')
thumbnail = u'%sthumbnail_%s' % (unicode(
self.content.name[:i + 1]), unicode(self.content.name[i + 1:]))
gallery = u'%sgallery_%s' % (unicode(
self.content.name[:i + 1]), unicode(self.content.name[i + 1:]))
orig = self.content.name
if (not self.thumbnail or not self.gallery
or changed_image) and content_type.split('_')[0] == 'image':
img_path = self.content.path
if content_type == 'image_svg+xml':
try:
from nebula.mediacontent import svg_to_png
svg_to_png.convert(img_path, svg_to_png.new_name(img_path))
img_path = svg_to_png.new_name(img_path)
self.content.name = self.content.name[:
-3] + self.content.name[
-3:].replace(
'svg', 'png')
except:
pass
image = Image.open(img_path)
image = convert_to_rgb(image)
image = crop_aspect(image, ratio=1.0)
# hace el thumb
image_thumb = resize(image.copy(), size=self.get_sizes()['thumb'])
image_thumb.save(os.path.join(settings.MEDIA_ROOT, thumbnail))
self.thumbnail = thumbnail
# guarda la imagen para gallery
image_gallery = resize(image.copy(),
size=self.get_sizes()['gallery'])
image_gallery.save(os.path.join(settings.MEDIA_ROOT, gallery))
self.gallery = gallery
# guarda la imagen al tamaño máximo
if crop_original:
image_normal = resize(image.copy(),
size=self.get_sizes()['normal'])
image_normal.save(os.path.join(settings.MEDIA_ROOT, orig))
elif (not self.thumbnail or not self.gallery
or changed_image) and content_type == 'application_pdf':
# Crea una imagen de la primer pagina de un PDF
from subprocess import call
cmd = "gs -q -dQUIET -dPARANOIDSAFER -dBATCH -dNOPAUSE -dNOPROMPT \
-dMaxBitmap=500000000 -dLastPage=1 -dAlignToPixels=0 -dGridFitTT=0 \
-sDEVICE=jpeg -dTextAlphaBits=4 -dGraphicsAlphaBits=4 -r150 \
-sOutputFile=%(fileout)s %(filein)s"
filein = os.path.join(settings.MEDIA_ROOT, self.content.name)
filejpg = self.content.name[:-3] + self.content.name[-3:].replace(
'pdf', 'jpg')
fileout = os.path.join(settings.MEDIA_ROOT, filejpg)
if not os.access(filein, os.R_OK):
raise 'not access %s' % filein
files = {
'filein': filein.replace(' ', '\ '),
'fileout': fileout.replace(' ', '\ '),
}
# devuelve 0 si esta OK
if not call(cmd % files, shell=True):
i = filejpg.rindex('/')
thumbnail = u'%sthumbnail_%s' % (unicode(
filejpg[:i + 1]), unicode(filejpg[i + 1:]))
gallery = u'%sgallery_%s' % (unicode(
filejpg[:i + 1]), unicode(filejpg[i + 1:]))
image = Image.open(fileout)
image = convert_to_rgb(image)
#image = crop_aspect(image, ratio=1.0)
# hace el thumb
image_thumb = resize(image.copy(),
size=None,
max_width=self.get_sizes()['gallery'][0])
image_thumb.save(os.path.join(settings.MEDIA_ROOT, thumbnail))
self.thumbnail = thumbnail
# guarda la imagen para gallery
#image_gallery = image.copy()
image_gallery = resize(image.copy(),
size=None,
max_width=self.get_sizes()['normal'][0])
image.save(os.path.join(settings.MEDIA_ROOT, gallery))
self.gallery = gallery
# borra la original porque es un PDF
try:
os.remove(fileout)
except (OSError, ValueError):
pass
super(MediaContent, self).save(*args, **kwargs)
def image_from_array(self, image):
if self.scheme == 'color':
return Image.fromarray(image, mode="RGB")
return Image.fromarray(image, mode="L")
def save(self, *args, **kwargs):
changed_image = self.content != self.__original_image
if not self.id and not self.pub_date:
self.pub_date = datetime.datetime.today()
crop_original = kwargs.get('crop_original', False)
super(MediaContent, self).save(*args, **kwargs)
self.mimetype = mimetypes.guess_type(self.content.path)[0]
if self.mimetype:
content_type = self.mimetype.replace('/', '_')
else:
# assume everything else is text/plain
content_type = 'text_plain'
i = self.content.name.rindex('/')
thumbnail = u'%sthumbnail_%s' % (unicode(self.content.name[:i+1]), unicode(self.content.name[i+1:]))
gallery = u'%sgallery_%s' % (unicode(self.content.name[:i+1]), unicode(self.content.name[i+1:]))
orig = self.content.name
if (not self.thumbnail or not self.gallery or changed_image) and content_type.split('_')[0]=='image':
img_path = self.content.path
if content_type == 'image_svg+xml':
try:
from nebula.mediacontent import svg_to_png
svg_to_png.convert(img_path, svg_to_png.new_name(img_path))
img_path = svg_to_png.new_name(img_path)
self.content.name = self.content.name[:-3] + self.content.name[-3:].replace('svg', 'png')
except:
pass
image = Image.open(img_path)
image = convert_to_rgb(image)
image = crop_aspect(image, ratio=1.0)
# hace el thumb
image_thumb = resize(image.copy(), size=self.get_sizes()['thumb'])
image_thumb.save(os.path.join(settings.MEDIA_ROOT, thumbnail))
self.thumbnail = thumbnail
# guarda la imagen para gallery
image_gallery = resize(image.copy(), size=self.get_sizes()['gallery'])
image_gallery.save(os.path.join(settings.MEDIA_ROOT, gallery))
self.gallery = gallery
# guarda la imagen al tamaño máximo
if crop_original:
image_normal = resize(image.copy(), size=self.get_sizes()['normal'])
image_normal.save(os.path.join(settings.MEDIA_ROOT, orig))
elif (not self.thumbnail or not self.gallery or changed_image) and content_type == 'application_pdf':
# Crea una imagen de la primer pagina de un PDF
from subprocess import call
cmd = "gs -q -dQUIET -dPARANOIDSAFER -dBATCH -dNOPAUSE -dNOPROMPT \
-dMaxBitmap=500000000 -dLastPage=1 -dAlignToPixels=0 -dGridFitTT=0 \
-sDEVICE=jpeg -dTextAlphaBits=4 -dGraphicsAlphaBits=4 -r150 \
-sOutputFile=%(fileout)s %(filein)s"
filein = os.path.join(settings.MEDIA_ROOT, self.content.name)
filejpg = self.content.name[:-3] + self.content.name[-3:].replace('pdf', 'jpg')
fileout = os.path.join(settings.MEDIA_ROOT, filejpg)
if not os.access(filein, os.R_OK):
raise 'not access %s' % filein
files = {
'filein': filein.replace(' ', '\ '),
'fileout': fileout.replace(' ', '\ '),
}
# devuelve 0 si esta OK
if not call(cmd % files, shell=True):
i = filejpg.rindex('/')
thumbnail = u'%sthumbnail_%s' % (unicode(filejpg[:i+1]), unicode(filejpg[i+1:]))
gallery = u'%sgallery_%s' % (unicode(filejpg[:i+1]), unicode(filejpg[i+1:]))
image = Image.open(fileout)
image = convert_to_rgb(image)
#image = crop_aspect(image, ratio=1.0)
# hace el thumb
image_thumb = resize(image.copy(), size=None, max_width=self.get_sizes()['gallery'][0])
image_thumb.save(os.path.join(settings.MEDIA_ROOT, thumbnail))
self.thumbnail = thumbnail
# guarda la imagen para gallery
#image_gallery = image.copy()
image_gallery = resize(image.copy(), size=None, max_width=self.get_sizes()['normal'][0])
image.save(os.path.join(settings.MEDIA_ROOT, gallery))
self.gallery = gallery
# borra la original porque es un PDF
try:
os.remove(fileout)
except (OSError, ValueError):
pass
super(MediaContent, self).save(*args, **kwargs)
def showImg(self):
load = Image.open('Aaron.jpg')
render = ImageTk.PhotoImage(load)
img = Label(self, image=render)
img.image = render
img.place(x=0, y=0)
def _generate_thumbnail(self, video, thumbnail_width, thumbnail_height,
crop=True, frames=100):
histogram_list = []
framw_average = []
path, full_filename = os.path.split(self.path)
filename, extension = os.path.splitext(full_filename)
# By default temp frame data is stroed under MEDIA_ROOT/temp
path = '%s/temp/' % settings.MEDIA_ROOT
if not os.path.isdir(path):
os.mkdir(path)
hashable_value = '%s%s' % (full_filename, int(time.time()))
filehash = hashlib.md5(hashable_value).hexdigest()
frame_args = {'path': path, 'filename': filehash, 'frame': '%d'}
frame = '%(path)s%(filename)s.%(frame)s.jpg' % frame_args
# Build the ffmpeg shell command and run it via subprocess
cmd_args = {'frames': frames, 'video_path':video_path, 'output':frame}
command = 'ffmpeg -i %(video_path)s -y -vframes %(frames)d %(output)s'
command = command % cmd_args
response = subprocess.call(command, shell=True, stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
# Fail silently if ffmpeg is not installed.
if reponse != 0:
return
# Loop through the generated images, open, and generate the image histogram
for index in range(1, frames+1):
frame_name = frame % index
# If for some reason the frame does not exist, go to next frame.
if not os.path.exists(frame_name):
continue
image = Image.open(frame_name)
# Convert to RGB if necessary
if image.mode not in ('L', 'RGB'):
image = image.convert(image.histogram())
frames = len(histogram_list)
# Calculate the accumulated average
for idx in range(len(histogram_list[0])):
average = 0.0
accumulation = 0.0
for idy in range(frames):
accumulation += histogram_list[idy][idx]
average = (float(accumulation) / frames)
frame_average.append(average)
minn = -1
minRMSE = -1
# Calculate the mean squared error
for idx in range(frames):
results = 0.0
average_count = len(frame_average)
for idy, average in enumerate(frame_average):
error = average - float(histogram_list[idx][idy])
results += float((error*error)) / average_count
rmse = math.sqrt(results)
if minn == -1 or rmse < minRMSE:
minn = (idx + 1)
minRMSE = rmse
frame_path = frame % (minn)
image = Image.open(frame_path)
# Crop the image if auto_crop is enabled and dimensions are the same.
if thumbnail_width == thumbnail_height and self.auto_crop:
width, height = image.size
min_size = min(width, height)
new_width = (width - min_size) / 2
new_height = (height - min_size) / 2
params = (
new_width, new_height,
width - new_width, height - new_height
)
image2 = image.crop(params)
image2.load()
image2.thumbnail((thumbnail_width, thumbnail_height), Image.ANTIALIAS)
else:
image2 = image
image2.thumbnail((thumbnail_width, thumbnail_height), Image.ANTIALIAS)
io = cStringIO.StringIO()
image2.save(io, 'jpeg')
# Unlink the temp files.
for idx in range(frames):
frame_fiel = frame % (idx + 1)
os.unlink(frame_file)
return ContentFile(io.getvalue())
#!/usr/bin/env python #coding: utf-8 myPath = "C:\\Users\\Lenovo\\Desktop\\" fontPath = "C:\\Windows\\Fonts\\" #win10上系统的字体位置 inputFile = "1_sirwill.jpg" outputFile = "output.jpg" from Pillow import Image, ImageDraw,ImageFont #打开图片 im = Image.open(myPath + inputFile) #打开一个照片对象 draw = ImageDraw.Draw(im) #创建画布 #根据图片大小确定字体大小 fontsize = (min(im.size)/4 #注意truetype方法要整数，需要取整 #加文字 #实例 font = ImageFont.truetype(fontPath + 'ALGER.TTF',fontsize) #其余参数默认 得到字体实例 draw.text((im.size[0]-fontsize,0), '5', font = font, fill = (256, 0, 0)) #确定位置、数字、实例、填充色 #保存图片 im.save(myPath + outputFile,"jpeg")
def get_main_color(img_path):
cv2_img = cv2.imread(img_path) #画像読み込み
height = cv2_img.shape[0]
width = cv2_img.shape[1]
cv2_img = cv2.resize(cv2_img, (int(width * 0.3), int(height * 0.3)))
cv2_img = cv2.cvtColor(cv2_img, cv2.COLOR_BGR2RGB) #BGRとRGBを変換
cv2_img = cv2_img.reshape(
(cv2_img.shape[0] * cv2_img.shape[1], 3)) #データ量を3から２へ
cluster = KMeans(n_clusters=5) #クラスター数を指定する
cluster.fit(X=cv2_img) #クラスタリングを行い、cluster_centers_にRGB値を格納
cluster_centers_arr = cluster.cluster_centers_.astype(
int, copy=False) #格納されたRGB値の小数を消す
IMG_SIZE = 64 #カラーパレットの一1色分の大きさ
MARGIN = 15
width = IMG_SIZE * 5 + MARGIN * 2
height = IMG_SIZE + MARGIN * 2 # 幅と高さ64px × 横並び5画像 + 上下左右余白15pxずつの画像を作成
tiled_color_img = Image.new(mode='RGB',
size=(width, height),
color='#333333') #作成した画像をグレーにする
for i, rgb_arr in enumerate(
cluster_centers_arr): #iにインデックスの数字(5)、rgb_arrに上で取得したリストの要素(RGB値)
color_hex_str = '#%02x%02x%02x' % tuple(rgb_arr) #RGB値をHEXに変換してPILに渡す
color_hex_rev = '#%02x%02x%02x' % tuple(
255 - rgb_arr) #反対色のRGB値をHEXに変換してPILに渡す
color_img = Image.new(
mode='RGB',
size=(IMG_SIZE, IMG_SIZE), #求めた色の正方形の画像を出力
color=color_hex_str)
draw = ImageDraw.Draw(color_img) #作成した画像の上にImageDrawインスタンスを作る
draw.text((0, 0), color_hex_str) #画像にHEXをいれる
draw.text(
(21, 54),
color_hex_rev,
color_hex_rev,
)
tiled_color_img.paste(im=color_img,
box=(MARGIN + IMG_SIZE * i,
MARGIN)) #グレーの画像に各クラスタの色の画像をペーストしていく
new_image = np.array(tiled_color_img, dtype=np.uint8) #PillowからCV2に変更
if new_image.ndim == 2: # モノクロ
pass
elif new_image.shape[2] == 3: # カラー
new_image = cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR)
elif new_image.shape[2] == 4: # 透過
new_image = cv2.cvtColor(new_image, cv2.COLOR_RGBA2BGRA)
path = '/Users/tani/Pictures' #保存先指定
cv2.imshow('image', new_image) #imageというウィンドウで表示
k = cv2.waitKey(0) #何かボタンを押すまで待つ
if k == ord('e'): #Eを押した場合
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.waitKey(0) #保存せずに取り消し
elif k == ord('s'): #Sを押した場合
#名前用に乱数生成 i =str(img_path)
i = str(random.randint(1, 100)) #名前用に乱数生成
cv2.imwrite(os.path.join(path, 'colorpalette' + i + '.jpg'),
new_image) #保存
cv2.waitKey(0)
cv2.destroyAllWindows() #表示取り消し
cv2.waitKey(0)
