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import cv2
import random
import numpy as np
from PIL import Image
#Author: Alican Akca
class pixL:
def __init__(self,numOfSquaresW = None, numOfSquaresH= None, size = [True, (512,512)],square = 6,ImgH = None,ImgW = None,images = [],background_image = None):
self.images = images
self.size = size
self.ImgH = ImgH
self.ImgW = ImgW
self.square = square
self.numOfSquaresW = numOfSquaresW
self.numOfSquaresH = numOfSquaresH
def preprocess(self):
for image in self.images:
size = (image.shape[0] - (image.shape[0] % 4), image.shape[1] - (image.shape[1] % 4))
image = cv2.resize(image, size)
return self.images
def toThePixL(self,images, pixel_size):
self.images = []
self.square = pixel_size
for image in images:
image = Image.fromarray(image)
image = image.convert("RGB")
self.ImgW, self.ImgH = image.size
self.images.append(pixL.epicAlgorithm(self, image))
return pixL.preprocess(self)
def numOfSquaresFunc(self):
self.numOfSquaresW = round((self.ImgW / self.square) + 1)
self.numOfSquaresH = round((self.ImgH / self.square) + 1)
def optimizer(RGB):
R_ = RGB[2]
G_ = RGB[1]
B_ = RGB[0]
if R_ < 50 and G_ < 50 and B_ < 50:
return (R_, G_, B_)
else:
sign = lambda x, y: random.choice([x,y])
R_ = RGB[2] + sign(+1,-1)*random.randint(1,10)
G_ = RGB[1] + sign(+1,-1)*random.randint(1,10)
B_ = RGB[0] + sign(+1,-1)*random.randint(1,10)
R_ = 0 if R_ < 0 else (255 if R_ > 255 else R_)
G_ = 0 if G_ < 0 else (255 if G_ > 255 else G_)
B_ = 0 if B_ < 0 else (255 if B_ > 255 else B_)
return (R_, G_, B_)
def epicAlgorithm(self, image):
pixValues = []
pixL.numOfSquaresFunc(self)
for j in range(1,self.numOfSquaresH):
for i in range(1,self.numOfSquaresW):
pixValues.append((image.getpixel((
i * self.square - self.square//2,
j * self.square - self.square//2)),
(i * self.square - self.square//2,
j * self.square - self.square//2)))
background = 255 * np.ones(shape=[self.ImgH - self.square,
self.ImgW - self.square*2, 3],
dtype=np.uint8)
for pen in range(len(pixValues)):
cv2.rectangle(background,
pt1=(pixValues[pen][1][0] - self.square, pixValues[pen][1][1] - self.square), #0, 0 -> 0, 0
pt2=(pixValues[pen][1][0], pixValues[pen][1][1]), #6, 6 -> 3, 3
color=(pixL.optimizer(pixValues[pen][0])),
thickness=-1)
cv2.rectangle(background,
pt1=(pixValues[pen][1][0], pixValues[pen][1][1] - self.square), #0, 0 -> 3, 0
pt2=(pixValues[pen][1][0] + self.square, pixValues[pen][1][1]), #6, 6 -> 6, 3
color=(pixL.optimizer(pixValues[pen][0])),
thickness=-1)
cv2.rectangle(background,
pt1=(pixValues[pen][1][0] - self.square, pixValues[pen][1][1]), #0, 0 -> 0, 3
pt2=(pixValues[pen][1][0], pixValues[pen][1][1] + self.square), #6, 6 -> 3, 6
color=(pixL.optimizer(pixValues[pen][0])),
thickness=-1)
cv2.rectangle(background,
pt1=(pixValues[pen][1][0], pixValues[pen][1][1]), #0, 0 -> 3, 3
pt2=(pixValues[pen][1][0] + self.square, pixValues[pen][1][1] + self.square), #6, 6 -> 6, 6
color=(pixL.optimizer(pixValues[pen][0])),
thickness=-1)
background = np.array(background).astype(np.uint8)
background = cv2.resize(background, (self.ImgW,self.ImgH), interpolation = cv2.INTER_AREA)
return background
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