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这篇文章主要讲解了“python PIL Image图像处理基本操作有哪些”，文中的讲解内容简单清晰，易于学习与理解，下面请大家跟着小编的思路慢慢深入，一起来研究和学习“python PIL Image图像处理基本操作有哪些”吧！
from PIL import Image
img = Image.open('01.jpg')
imgGrey = img.convert('L')
img.show()
imgGrey.show()
img.save('img_copy.jpg')
imgGrey.save('img_gray.jpg')from PIL import Image
import numpy as np
img = Image.open('01.jpg')
width, height = img.size
channel_mode = img.mode
mean_value = np.mean(img)
print(width)
print(height)
print(channel_mode)
print(mean_value)from PIL import Image
width = 200
height = 100
img_white = Image.new('RGB', (width,height), (255,255,255))
img_black = Image.new('RGB', (width,height), (0,0,0))
img_L = Image.new('L', (width, height), (255))
img_white.show()
img_black.show()
img_L.show()from PIL import Image
img = Image.open('01.jpg')
width, height = img.size
# 获取指定坐标位置像素值
pixel_value = img.getpixel((width/2, height/2))
print(pixel_value)
# 或者使用load方法
pim = img.load()
pixel_value1 = pim[width/2, height/2]
print(pixel_value1)
# 设置指定坐标位置像素的值
pim[width/2, height/2] = (0, 0, 0)
# 或使用putpixel方法
img.putpixel((w//2, h//2), (255,255,255))
# 设置指定区域像素的值
for w in range(int(width/2) - 40, int(width/2) + 40):
for h in range(int(height/2) - 20, int(height/2) + 20):
pim[w, h] = (255, 0, 0)
# img.putpixel((w, h), (255,255,255))
img.show()from PIL import Image
img = Image.open('01.jpg')
# 通道分离
R, G, B = img.split()
R.show)
G.show()
B.show()
# 通道合并
img_RGB = Image.merge('RGB', (R, G, B))
img_BGR = Image.merge('RGB', (B, G, R))
img_RGB.show()
img_BGR.show()from PIL import Image, ImageDraw, ImageFont
img = Image.open('01.jpg')
# 创建Draw对象:
draw = ImageDraw.Draw(img)
# 字体颜色
fillColor = (255, 0, 0)
text = 'print text on PIL Image'
position = (200,100)
draw.text(position, text, fill=fillColor)
img.show()from PIL import Image
img = Image.open('01.jpg')
width, height = img.size
img_NEARESET = img.resize((width//2, height//2)) # 缩放默认模式是NEARESET(最近邻插值)
img_BILINEAR = img.resize((width//2, height//2), Image.BILINEAR) # BILINEAR 2x2区域的双线性插值
img_BICUBIC = img.resize((width//2, height//2), Image.BICUBIC) # BICUBIC 4x4区域的双三次插值
img_ANTIALIAS = img.resize((width//2, height//2), Image.ANTIALIAS) # ANTIALIAS 高质量下采样滤波from PIL import Image
img = Image.open('01.jpg').convert('L')
width, height = img.size
pim = img.load()
for w in range(width):
for h in range(height):
if pim[w, h] > 100:
img.putpixel((w, h), 255)
# pim[w, h] = 255
else:
img.putpixel((w, h), 0)
# pim[w, h] = 0
img.show()from PIL import Image
img = Image.open('01.jpg').convert('L')
width, height = img.size
threshold = 125
for w in range(width):
for h in range(height):
if img.getpixel((w, h)) > threshold:
img.putpixel((w, h), 255)
else:
img.putpixel((w, h), 0)
img.save('binary.jpg')from PIL import Image
img = Image.open('01.jpg')
width, height = img.size
# 前两个坐标点是左上角坐标
# 后两个坐标点是右下角坐标
# width在前， height在后
box = (100, 100, 550, 350)
region = img.crop(box)
region.save('crop.jpg')# 边界扩展
from PIL import Image
img = Image.open('test.png')
width, height = img.size
channel_mode = img.mode
img_makeBorder_full = Image.new(channel_mode, (2*width, height))
img_makeBorder_part = Image.new(channel_mode, (width+200, height))
# 图像水平扩展整个图像
img_makeBorder_full.paste(img, (0, 0, width, height))
img_makeBorder_full.paste(img, (width, 0, 2*width, height))
# 前两个坐标点是左上角坐标
# 后两个坐标点是右下角坐标
# width在前， height在后
box = (width-200, 0, width, height)
region = img.crop(box)
# 图像水平右侧扩展一个ROI
img_makeBorder_part.paste(img, (0, 0, width, height))
img_makeBorder_part.paste(region, (width, 0, width+200, height))
img_makeBorder_part.show()
img_makeBorder_full.show()from PIL import Image
import numpy as np
img = Image.open('01.jpg')
array = np.array(img) # PIL.Image 转 numpy
img1 = Image.fromarray(array) # numpy转 PIL.Image
img1 = Image.fromarray(array.astype('uint8'))
img1.save('from_array.jpg')感谢各位的阅读，以上就是“python PIL Image图像处理基本操作有哪些”的内容了，经过本文的学习后，相信大家对python PIL Image图像处理基本操作有哪些这一问题有了更深刻的体会，具体使用情况还需要大家实践验证。这里是亿速云，小编将为大家推送更多相关知识点的文章，欢迎关注！
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