Basic Operations
因爲我有點兒懶。。。所以本篇後續就基本是英文了（輸入法切換好累0.0），本教程配套有Jupyter Notebook，請在此處下載。
Python3 based PIL(python image library) tutorials from Documents
from PIL import ImageFilter
from PIL import Image
import numpy as np
import matplotlib
import matplotlib.pyplot as pltim = Image.open("test.jpg")
print('format: ', im.format)
print('size: ', im.size)
print('mode: ', im.mode)
im.show() # show the figure in photo viewerformat: JPEG
size: (625, 625)
mode: RGB
Cut A Certain Region
box = (100, 100, 400, 400)
region = im.crop(box)
# region.show()
plt.imshow(region)
plt.show()
Rotate
box = (100, 100, 400, 400)
region = im.crop(box)
region = region.transpose(Image.ROTATE_180)
im.paste(region, box)
# im.show()
plt.imshow(im)
plt.show()
Splitting and Merging Bands
im = Image.open("test.jpg")
r, g, b = im.split()
# r.show()
# g.show()
# b.show()
f, ax = plt.subplots(2, 2)
ax[0, 0].imshow(r)
ax[0, 0].set_title("R")
ax[0, 1].imshow(g)
ax[0, 1].set_title("G")
ax[1, 0].imshow(b)
ax[1, 0].set_title("B")
im = Image.merge("RGB", (g, b, r))
ax[1, 1].imshow(im)
ax[1, 1].set_title("Mixed Image")
plt.show()
Geometrical Transforms
im = Image.open("test.jpg")
out = im.resize((128,128))
f, ax = plt.subplots(2, 4)
ax[0, 0].imshow(out)
out = im.rotate(45) # degrees counter-clockwise, black background if empty
ax[0, 1].imshow(out)
out = im.transpose(Image.FLIP_LEFT_RIGHT)
ax[0, 2].imshow(out)
out = im.transpose(Image.FLIP_TOP_BOTTOM)
ax[0, 3].imshow(out)
out = im.transpose(Image.ROTATE_90)
ax[1, 0].imshow(out)
out = im.transpose(Image.ROTATE_180)
ax[1, 1].imshow(out)
out = im.transpose(Image.ROTATE_270)
ax[1, 2].imshow(out)
plt.show()
Colour Transform
"""
The official explanation of convert:
| convert(self, mode=None, matrix=None, dither=None, palette=0, colors=256)
| Returns a converted copy of this image. For the "P" mode, this
| method translates pixels through the palette. If mode is
| omitted, a mode is chosen so that all information in the image
| and the palette can be represented without a palette.
|
| The current version supports all possible conversions between
| "L", "RGB" and "CMYK." The **matrix** argument only supports "L"
| and "RGB".
|
| When translating a color image to black and white (mode "L"),
| the library uses the ITU-R 601-2 luma transform::
|
| L = R * 299/1000 + G * 587/1000 + B * 114/1000
|
| The default method of converting a greyscale ("L") or "RGB"
| image into a bilevel (mode "1") image uses Floyd-Steinberg
| dither to approximate the original image luminosity levels. If
| dither is NONE, all non-zero values are set to 255 (white). To
| use other thresholds, use the :py:meth:`~PIL.Image.Image.point`
| method.
|
| :param mode: The requested mode. See: :ref:`concept-modes`.
| :param matrix: An optional conversion matrix. If given, this
| should be 4- or 12-tuple containing floating point values.
| :param dither: Dithering method, used when converting from
| mode "RGB" to "P" or from "RGB" or "L" to "1".
| Available methods are NONE or FLOYDSTEINBERG (default).
| :param palette: Palette to use when converting from mode "RGB"
| to "P". Available palettes are WEB or ADAPTIVE.
| :param colors: Number of colors to use for the ADAPTIVE palette.
| Defaults to 256.
| :rtype: :py:class:`~PIL.Image.Image`
| :returns: An :py:class:`~PIL.Image.Image` object.
"""
im = Image.open("test.jpg")
gray_im = im.convert("L")
plt.imshow(gray_im)
plt.show()
Image Filter
from PIL import ImageFilter
im = Image.open("test.jpg")
plt.imshow(im)
plt.title('Original Figure')
plt.show()
im = Image.open("test.jpg")
out = im.filter(ImageFilter.BLUR)
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.imshow(im)
ax1.set_title('Original Figure')
ax2.imshow(out)
ax2.set_title('Image Filter BLUR')
plt.imshow(out)
plt.show()
im = Image.open("test.jpg")
out = im.filter(ImageFilter.CONTOUR)
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.imshow(im)
ax1.set_title('Original Figure')
ax2.imshow(out)
ax2.set_title('Image Filter CONTOUR')
plt.imshow(out)
plt.show()
im = Image.open("test.jpg")
out = im.filter(ImageFilter.DETAIL)
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.imshow(im)
ax1.set_title('Original Figure')
ax2.imshow(out)
ax2.set_title('Image Filter DETAIL')
plt.imshow(out)
plt.show()
im = Image.open("test.jpg")
out = im.filter(ImageFilter.EDGE_ENHANCE)
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.imshow(im)
ax1.set_title('Original Figure')
ax2.imshow(out)
ax2.set_title('Image Filter EDGE_ENHANCE')
plt.imshow(out)
plt.show()
im = Image.open("test.jpg")
out = im.filter(ImageFilter.EDGE_ENHANCE_MORE)
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.imshow(im)
ax1.set_title('Original Figure')
ax2.imshow(out)
ax2.set_title('Image Filter EDGE_ENHANCE_MORE')
plt.imshow(out)
plt.show()
im = Image.open("test.jpg")
out = im.filter(ImageFilter.EMBOSS)
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.imshow(im)
ax1.set_title('Original Figure')
ax2.imshow(out)
ax2.set_title('Image Filter EMBOSS')
plt.imshow(out)
plt.show()
im = Image.open("test.jpg")
out = im.filter(ImageFilter.FIND_EDGES)
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.imshow(im)
ax1.set_title('Original Figure')
ax2.imshow(out)
ax2.set_title('Image Filter FIND_EDGES')
plt.imshow(out)
plt.show()
im = Image.open("test.jpg")
out = im.filter(ImageFilter.SMOOTH)
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.imshow(im)
ax1.set_title('Original Figure')
ax2.imshow(out)
ax2.set_title('Image Filter SMOOTH')
plt.imshow(out)
plt.show()
im = Image.open("test.jpg")
out = im.filter(ImageFilter.SMOOTH_MORE)
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.imshow(im)
ax1.set_title('Original Figure')
ax2.imshow(out)
ax2.set_title('Image Filter SMOOTH_MORE')
plt.imshow(out)
plt.show()
im = Image.open("test.jpg")
out = im.filter(ImageFilter.SHARPEN)
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.imshow(im)
ax1.set_title('Original Figure')
ax2.imshow(out)
ax2.set_title('Image Filter SHARPEN')
plt.imshow(out)
plt.show()
Point Transform
im = Image.open("test.jpg")
out = im.point(lambda i: i*1.2)
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.imshow(im)
ax1.set_title('Original Figure')
ax2.imshow(out)
ax2.set_title('Point 1.2')
plt.imshow(out)
plt.show()
Processing Individual Bands
im = Image.open("test.jpg")
# split the image into individual bands
source = im.split()
R, G, B = 0, 1, 2
# select regions where red is less than 100
mask = source[R].point(lambda i: i < 220 and i > 100)
plt.imshow(mask)
# process the green band
out = source[G].point(lambda i: i * 0.7)
# plt.imshow(out)
# paste the processed band back, but only where red was < 100
source[G].paste(out, None, mask)
# build a new multiband image
im = Image.merge(im.mode, source)
# plt.imshow(im)
plt.show()
im = Image.open("point.jpg")
im_point = im.crop((0, 250, 1000, 1670))
# split the image into individual bands
source = im_point.split()
R, G, B = 0, 1, 2
f, ax = plt.subplots(1, 5, sharey=True)
# select regions where red is less than 100
mask1 = source[R].point(lambda i: i > 215 and i < 250)
# plt.imshow(mask)
ax[0].imshow(mask1)
# process the green band
mask2 = source[G].point(lambda i: i > 200 and i < 204)
# plt.imshow(mask2)
ax[1].imshow(mask2)
mask3 = source[B].point(lambda i: i > 206 and i < 215)
# plt.imshow(mask3)
ax[2].imshow(mask3)
# paste the processed band back, but only where red was < 100
# source[G].paste(mask2, None, mask1)
# build a new multiband image
mask_logic = (np.array(mask1).astype(bool) & np.array(mask2).astype(bool) & np.array(mask3).astype(bool))
ax[3].imshow(mask_logic)
ax[4].imshow(im_point)
plt.show()
Convert to JPEG Format
import os, sys
from PIL import Image
for infile in sy.argv[1:]:
f, e = os.path.splitext(infile)
outfile = f + '.jpg'
if infile != outfile:
try:
Image.open(infile).save(outfile)
except IOError:
print("Cannot convert ", infile)