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In [1]:

    
%matplotlib inline
from matplotlib import pyplot as plt, cm
from PIL import Image
from PIL import ImageFilter
from PIL import ImageEnhance
from PIL import ImageChops
from PIL import ImageOps 
import numpy as np
import os
from operator import itemgetter



In [84]:

    
# 이미지 파일 경로
im1 = "./data/IMG_4139.jpg"
im2 = "./data/IMG_4164.jpg"



In [85]:

    
# 이미지 파일 로드
img1 = Image.open(im1)
img2 = Image.open(im2)



In [86]:

    
print "file name is %s" %img1.filename
print " - format is %s" %img1.format
print " - color mode is %s" %img1.mode
print " - dpi is %s" %str(img1.info['dpi'])
print " - size is %s" %str(img1.size)
print 
print 
print "file name is %s" %img2.filename
print " - format is %s" %img2.format
print " - color mode is %s" %img2.mode
print " - dpi is %s" %str(img2.info['dpi'])
print " - size is %s" %str(img2.size)









    



file name is ./data/IMG_4139.jpg
 - format is JPEG
 - color mode is RGB
 - dpi is (72, 72)
 - size is (3264, 2448)


file name is ./data/IMG_4164.jpg
 - format is JPEG
 - color mode is RGB
 - dpi is (72, 72)
 - size is (3264, 2448)



In [87]:

    
img1_np = np.asarray(img1)
img2_np = np.asarray(img2)
print "the dimension of %s is %s" %(img1.filename, str(img1_np.shape))
print "the dimension of %s is %s" %(img2.filename, str(img2_np.shape))









    



the dimension of ./data/IMG_4139.jpg is (2448, 3264, 3)
the dimension of ./data/IMG_4164.jpg is (2448, 3264, 3)



In [88]:

    
# img1 : <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3264x2448 at 0x1058E2DD0>
# img1_np : numpy array 
# 둘 다 plt.imshow로 출력됨

# fig size setting
plt.figure(figsize=(16,12))

plt.subplot(121)
plt.axis('off')
plt.imshow(img1)

plt.subplot(122)
plt.axis('off')
plt.imshow(img2_np)









    Out[88]:





<matplotlib.image.AxesImage at 0x14d744890>



In [89]:

    
# Create JPEG thumbnails
outfile = os.path.splitext(img1.filename)[0] + ".thumbnail.jpg"
size = (128, 128)
img1.thumbnail(size)
img1.save(outfile, "JPEG")



In [90]:

    
# 썸네일이 정상적으로 생성됨
!ls -al ./data/*.thumbnail.jpg









    



-rw-r--r--@ 1 dikien  staff  3455  5 13 12:52 ./data/IMG_4139.thumbnail.jpg



In [91]:

    
# Copying a subrectangle from an image
# 이미지 파일 로드
img1 = Image.open(im1)
img2 = Image.open(im2)

print "the original size of %s is %s" %(im1, str(img1.size))
print "the original size of %s is %s" %(im2, str(img2.size))

# The region is defined by a 4-tuple, where coordinates are (left, upper, right, lower).
box = (0, 0, 300, 200)
region1 = img1.crop(box)
region2 = img2.crop(box)

print "the size of %s is %s after subrectangle" %(im1, str(region2.size))
print "the size of %s is %s after subrectangle" %(im2, str(region2.size))

plt.figure(figsize=(16,12))

plt.subplot(121)
plt.axis('off')
plt.imshow(region1)

plt.subplot(122)
plt.axis('off')
plt.imshow(region2)









    



the original size of ./data/IMG_4139.jpg is (3264, 2448)
the original size of ./data/IMG_4164.jpg is (3264, 2448)
the size of ./data/IMG_4139.jpg is (300, 200) after subrectangle
the size of ./data/IMG_4164.jpg is (300, 200) after subrectangle






    Out[91]:





<matplotlib.image.AxesImage at 0x14d797ad0>



In [92]:

    
# transpose
# 이미지 파일 로드
img1 = Image.open(im1)
img2 = Image.open(im2)

box = (0, 0, 300, 200)
region = img2.crop(box)
region1 = region.transpose(Image.ROTATE_180)
region2 = region.transpose(Image.ROTATE_270)

# img2.paste(region, img1)
# im.paste(region, img1)

plt.figure(figsize=(16,12))

plt.subplot(121)
plt.axis('off')
plt.imshow(region1)

plt.subplot(122)
plt.axis('off')
plt.imshow(region2)









    Out[92]:





<matplotlib.image.AxesImage at 0x12e306990>



In [93]:

    
# Splitting and merging bands
im = Image.open(im1)

r, g, b = im.split()
im = Image.merge("RGB", (r, g, b))

plt.figure(figsize=(16,12))

plt.subplot(1,3,1)
plt.axis('off')
plt.imshow(im)

im = Image.merge("RGB", (b, r, g))
plt.subplot(1,3,2)
plt.axis('off')
plt.imshow(im)

im = Image.merge("RGB", (g, b, r))
plt.subplot(1,3,3)
plt.axis('off')
plt.imshow(im)









    Out[93]:





<matplotlib.image.AxesImage at 0x12e77c690>



In [94]:

    
# Splitting and merging bands
im = Image.open(im1)

r, g, b = im.split()
im = Image.merge("RGB", (r, g, b))

plt.figure(figsize=(16,12))

plt.subplot(3,1,1)
plt.axis('off')
plt.imshow(im)

im = Image.merge("RGB", (b, r, g))
plt.subplot(3,1,2)
plt.axis('off')
plt.imshow(im)

im = Image.merge("RGB", (g, b, r))
plt.subplot(3,1,3)
plt.axis('off')
plt.imshow(im)









    Out[94]:





<matplotlib.image.AxesImage at 0x132630c50>



In [130]:

    
# Geometrical transforms

plt.figure(figsize=(15,12))

im = Image.open(im1)
out = im.rotate(45)
plt.subplot(1,3,1)
plt.axis('off')
plt.title('ROTATE_45')
plt.imshow(out)

out = im.transpose(Image.FLIP_LEFT_RIGHT)
plt.subplot(1,3,2)
plt.axis('off')
plt.title('FLIP_LEFT_RIGHT')
plt.imshow(out)

out = im.transpose(Image.FLIP_TOP_BOTTOM)
plt.subplot(1,3,3)
plt.axis('off')
plt.title('FLIP_TOP_BOTTOM')
plt.imshow(out)









    Out[130]:





<matplotlib.image.AxesImage at 0x208d927d0>



In [129]:

    
plt.figure(figsize=(15,12))

im = Image.open(im1)
out = im.transpose(Image.ROTATE_90)
plt.subplot(1,3,1)
plt.axis('off')
plt.title('ROTATE_90')
plt.imshow(out)

out = im.transpose(Image.ROTATE_180)
plt.subplot(1,3,2)
plt.axis('off')
plt.title('ROTATE_180')
plt.imshow(out)

out = im.transpose(Image.ROTATE_270)
plt.subplot(1,3,3)
plt.axis('off')
plt.title('ROTATE_270')
plt.imshow(out)









    Out[129]:





<matplotlib.image.AxesImage at 0x204212210>



In [128]:

    
plt.figure(figsize=(15,12))

im = Image.open(im2)
out = im.convert('L') # convert image to monochrome - this works
plt.subplot(1,2,1)
plt.axis('off')
plt.title('convert image to monochrome - this works')
plt.imshow(out)

out = im.convert('1') # convert image to black and white
plt.subplot(1,2,2)
plt.axis('off')
plt.title('convert image to black and white')
plt.imshow(out)









    Out[128]:





<matplotlib.image.AxesImage at 0x1ae183e50>



In [144]:

    
plt.figure(figsize=(15,12))

im = Image.open(im2)
out = im.convert('P') # P (8-bit pixels, mapped to any other mode using a color palette)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('P')
plt.imshow(out)

out = im.convert('RGBA') # RGBA (4x8-bit pixels, true color with transparency mask)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('RGBA')
plt.imshow(out)









    Out[144]:





<matplotlib.image.AxesImage at 0x2190ed190>



In [145]:

    
plt.figure(figsize=(15,12))

im = Image.open(im2)
out = im.convert('CMYK') # CMYK (4x8-bit pixels, color separation)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('CMYK')
plt.imshow(out)

out = im.convert("YCbCr") # YCbCr (3x8-bit pixels, color video format)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('YCbCr')
plt.imshow(out)









    Out[145]:





<matplotlib.image.AxesImage at 0x23aad9050>



In [146]:

    
plt.figure(figsize=(15,12))

im = Image.open(im2)
out = im.convert('HSV') # HSV (3x8-bit pixels, Hue, Saturation, Value color space)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('HSV')
plt.imshow(out)

out = im.convert("I") # I (32-bit signed integer pixels)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('I')
plt.imshow(out)









    Out[146]:





<matplotlib.image.AxesImage at 0x243442310>



In [150]:

    
plt.figure(figsize=(15,12))

im = Image.open(im2)
out = im.convert("F") # F (32-bit floating point pixels)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('F')
plt.imshow(out)

out = ImageChops.invert(im)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('invert')
plt.imshow(out)









    Out[150]:





<matplotlib.image.AxesImage at 0x248f60bd0>



In [127]:

    
# Applying filters
plt.figure(figsize=(15,12))

im = Image.open("./data/IMG_4135.jpg")
out = im.filter(ImageFilter.BLUR)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('BLUR')
plt.imshow(out)

out = im.filter(ImageFilter.CONTOUR)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('CONTOUR')
plt.imshow(out)









    Out[127]:





<matplotlib.image.AxesImage at 0x1adf1e350>



In [126]:

    
# Applying filters
plt.figure(figsize=(15,12))

im = Image.open("./data/IMG_4135.jpg")
out = im.filter(ImageFilter.DETAIL)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('DETAIL')
plt.imshow(out)

out = im.filter(ImageFilter.EDGE_ENHANCE)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('EDGE_ENHANCE')
plt.imshow(out)









    Out[126]:





<matplotlib.image.AxesImage at 0x1aded7090>



In [131]:

    
# Applying filters
plt.figure(figsize=(15,12))

im = Image.open("./data/IMG_4135.jpg")
out = im.filter(ImageFilter.EMBOSS)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('EMBOSS')
plt.imshow(out)

out = im.filter(ImageFilter.FIND_EDGES)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('FIND_EDGES')
plt.imshow(out)









    Out[131]:





<matplotlib.image.AxesImage at 0x20f3dc410>



In [132]:

    
# Applying filters
plt.figure(figsize=(15,12))

im = Image.open("./data/IMG_4135.jpg")
out = im.filter(ImageFilter.GaussianBlur(radius=5))
plt.subplot(1,2,1)
plt.axis('off')
plt.title('GaussianBlur')
plt.imshow(out)

out = im.filter(ImageFilter.Kernel((3, 3), [1, 2, 1, 2, 4, 2, 1, 2, 1], 16))
plt.subplot(1,2,2)
plt.axis('off')
plt.title('Kernel')
plt.imshow(out)









    Out[132]:





<matplotlib.image.AxesImage at 0x210481350>



In [136]:

    
# Applying filters
plt.figure(figsize=(15,12))

im = Image.open("./data/IMG_4135.jpg")
out = im.filter(ImageFilter.MaxFilter(size=9))
plt.subplot(1,2,1)
plt.axis('off')
plt.title('MaxFilter')
plt.imshow(out)

out = im.filter(ImageFilter.MedianFilter(size=9))
plt.subplot(1,2,2)
plt.axis('off')
plt.title('MedianFilter')
plt.imshow(out)









    Out[136]:





<matplotlib.image.AxesImage at 0x211632ed0>



In [137]:

    
# Applying filters
plt.figure(figsize=(15,12))

im = Image.open("./data/IMG_4135.jpg")
out = im.filter(ImageFilter.ModeFilter(size=9))
plt.subplot(1,2,1)
plt.axis('off')
plt.title('ModeFilter')
plt.imshow(out)

out = im.filter(ImageFilter.SHARPEN)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('SHARPEN')
plt.imshow(out)









    Out[137]:





<matplotlib.image.AxesImage at 0x21b7dbc90>



In [138]:

    
# Applying filters
plt.figure(figsize=(15,12))

im = Image.open("./data/IMG_4135.jpg")
out = im.filter(ImageFilter.SMOOTH)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('SMOOTH')
plt.imshow(out)

out = im.filter(ImageFilter.UnsharpMask)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('UnsharpMask')
plt.imshow(out)









    Out[138]:





<matplotlib.image.AxesImage at 0x210914390>



In [139]:

    
# Applying filters
plt.figure(figsize=(15,12))

im = Image.open("./data/IMG_4135.jpg")
# multiply each pixel by 1.5
out = im.point(lambda i: i * 1.5)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('1.5')
plt.imshow(out)

# multiply each pixel by 2.2
out = im.point(lambda i: i * 2.2)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('2.2')
plt.imshow(out)









    Out[139]:





<matplotlib.image.AxesImage at 0x21b923210>



In [143]:

    
# Applying Contrast
plt.figure(figsize=(15,12))

im = Image.open("./data/IMG_4135.jpg")
# 30% more contrast
enh = ImageEnhance.Contrast(im)
enh.enhance(1.3)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('30% more contrast')
plt.imshow(im)

# 90% more contrast
enh = ImageEnhance.Contrast(im)
enh.enhance(1.9)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('90% more contrast')
plt.imshow(im)









    Out[143]:





<matplotlib.image.AxesImage at 0x2358cb690>



In [155]:

    
plt.figure(figsize=(15,12))

# 이미지 파일 경로
im1 = "./data/IMG_4139.jpg"
im2 = "./data/IMG_4164.jpg"

# 이미지 파일 로드
img1 = Image.open(im1)
img2 = Image.open(im2)

out1 = Image.blend(img1, img2, 9.3)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('blend')
plt.imshow(out1)

out2 = ImageChops.darker(img1, img2)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('darker')
plt.imshow(out2)









    Out[155]:





<matplotlib.image.AxesImage at 0x26111f3d0>



In [156]:

    
plt.figure(figsize=(15,12))

# 이미지 파일 경로
im1 = "./data/IMG_4139.jpg"
im2 = "./data/IMG_4164.jpg"

# 이미지 파일 로드
im1 = Image.open(im1)
im2 = Image.open(im2)

out1 = ImageChops.multiply(im1, im2)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('multiply')
plt.imshow(out1)

out2 = ImageChops.screen(im1, im2)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('screen')
plt.imshow(out2)









    Out[156]:





<matplotlib.image.AxesImage at 0x234c0aad0>



In [165]:

    
plt.figure(figsize=(15,12))

# 이미지 파일 경로
im1 = "./data/IMG_4139.jpg"
im2 = "./data/IMG_4164.jpg"

# 이미지 파일 로드
im1 = Image.open(im1)
im2 = Image.open(im2)

out1 = ImageOps.autocontrast(im1, cutoff=30)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('autocontrast cutoff=30')
plt.imshow(out1)

out2 = ImageOps.autocontrast(im1, cutoff=50)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('autocontrast cutoff=50')
plt.imshow(out2)









    Out[165]:





<matplotlib.image.AxesImage at 0x2556cdd90>



In [164]:

    
plt.figure(figsize=(15,12))

# 이미지 파일 경로
im1 = "./data/IMG_4139.jpg"
im2 = "./data/IMG_4164.jpg"

# 이미지 파일 로드
im1 = Image.open(im1)
im2 = Image.open(im2)

out1 = ImageOps.crop(im2, border=40)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('crop border=40')
plt.imshow(out1)

out2 = ImageOps.crop(im2, border=160)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('crop border=160')
plt.imshow(out2)









    Out[164]:





<matplotlib.image.AxesImage at 0x2381e2110>



In [162]:

    
plt.figure(figsize=(15,12))

# 이미지 파일 경로
im1 = "./data/IMG_4139.jpg"
im2 = "./data/IMG_4164.jpg"

# 이미지 파일 로드
im1 = Image.open(im1)
im2 = Image.open(im2)

out1 = ImageOps.solarize(im1, threshold=50)
plt.subplot(1,2,1)
plt.axis('off')
plt.title('solarize threshold=50')
plt.imshow(out1)

out2 = ImageOps.solarize(im1, threshold=100)
plt.subplot(1,2,2)
plt.axis('off')
plt.title('solarize threshold=100')
plt.imshow(out2)









    Out[162]:





<matplotlib.image.AxesImage at 0x2108bd150>



In [169]:

    
plt.figure(figsize=(15,12))

# 이미지 파일 경로
im1 = "./data/IMG_4139.jpg"
im2 = "./data/IMG_4164.jpg"

# 이미지 파일 로드
im1 = Image.open(im1)
im2 = Image.open(im2)

out1 = ImageOps.expand(im1, border=5, fill=12) 
plt.subplot(1,2,1)
plt.axis('off')
plt.title('expand border=5, fill=12')
plt.imshow(out1)

out2 = ImageOps.expand(im1, border=20, fill=50) 
plt.subplot(1,2,2)
plt.axis('off')
plt.title('expand border=20, fill=50')
plt.imshow(out2)









    Out[169]:





<matplotlib.image.AxesImage at 0x2cefdc410>



In [4]:

    
# 히스토그램 출력하기

# 이미지 파일 경로
im = "./data/IMG_4139.jpg"

# 이미지 파일 로드
im = Image.open(im)

his = im.histogram()

values = {}

for i in range(256):
  values[i] = his[i]

print "The first column is the colour’s id. The second is the number of pixels in the image of that colour."

for j,k in sorted(values.items(), key=itemgetter(1), reverse=True)[:10]:
  print j,k









    



The first column is the colour’s id. The second is the number of pixels in the image of that colour.
13 430001
231 363448
227 301249
230 225963
16 190139
20 173877
226 136136
23 126087
205 125705
234 118988



In [14]:

    
plt.figure(figsize=(15,12))


# 이미지 파일 경로
im = "./data/IMG_4139.jpg"

# 이미지 파일 로드
im = Image.open(im)

im2 = Image.new("RGB", im.size, 255)

temp = {}

for x in range(im.size[1]):
  for y in range(im.size[0]):
    pix = im.getpixel((y,x))
    temp[pix] = pix
    if pix < 25: # these are the numbers to get
      im2.putpixel((y,x),pix)

plt.subplot(111)
plt.axis('off')
plt.title('change pixels')
plt.imshow(im2)

plt.subplot(1,2,1)
plt.axis('off')
plt.title('Before')
plt.imshow(im)

plt.subplot(1,2,2)
plt.axis('off')
plt.title('After')
plt.imshow(im2)









    Out[14]:





<matplotlib.image.AxesImage at 0x13cd36810>