Python PIL库处理图片常用操作，图像识别数据增强的方法

在博客AlexNet原理及tensorflow实现训练神经网络的时候，做了数据增强，对图片的处理采用的是PIL(Python Image Library), PIL是Python常用的图像处理库.

下面对PIL中常用到的操作进行整理：
1. 改变图片的大小

from PIL import Image, ImageFont, ImageDraw
 
 
def image_resize(image, save, size=(100, 100)):
    """
    :param image:  原图片 
    :param save:   保存地址
    :param size:    大小
    :return: 
    """
    image = Image.open(image)   # 读取图片
    image.convert("RGB")
    re_sized = image.resize(size, Image.BILINEAR)  # 双线性法
    re_sized.save(save)   # 保存图片
    return re_sized

2. 对图片进行旋转：

from PIL import Image, ImageFont, ImageDraw
import matplotlib.pyplot as plt
 
 
def image_rotate(image_path, save_path, angle):
    """
    对图像进行一定角度的旋转
    :param image_path:  图像路径
    :param save_path:   保存路径
    :param angle:       旋转角度
    :return:
    """
    image = Image.open(image_path)
    image_rotated = image.rotate(angle, Image.BICUBIC)
    image_rotated.save(save_path)
    return image_rotated

3. 对图片进行左右反转：

from PIL import Image, ImageFont, ImageDraw
import matplotlib.pyplot as plt
 
 
def image_flip(image_path, save_path):
    """
    图图象进行左右反转
    :param image_path:
    :param save_path:
    :return:
    """
    image = Image.open(image_path)
    image_transpose = image.transpose(Image.FLIP_LEFT_RIGHT)   # Image.FLIP_TOP_BOTTOM 上下反转
    image_transpose.save(save_path)
    return image_transpose

4. 对图像进行裁剪：

from PIL import Image, ImageFont, ImageDraw
import matplotlib.pyplot as plt
 
 
def image_crop(image_path, save_path, crop_region):
    """
    对图像进行裁剪
    :param image_path:
    :param save_path:
    :param crop_region:  裁剪的区域  crop_window=(x_min, y_min, x_max, y_max)
    :return:
    """
    image = Image.open(image_path)
    image_crop = image.crop(crop_region)  # (width_min, height_min, width_max, height_max) 图像的原点是在左上角
    image_crop.save(save_path)
    return image_crop

5. 在图片上添加文字

from PIL import Image, ImageFont, ImageDraw
import matplotlib.pyplot as plt
 
 
def image_title(image_path, save_path, font_pos, font_size, text):
    """
     对图像添加文字
    :param image_path:
    :param save_path:
    :param font_pos:   文字位置(x, y)
    :param font_size:  文字大小
    :param text:        文字内容
    :return:
    """
    image = Image.open(image_path)
    font = ImageFont.truetype(font=r"C:\Windows\Fonts\Times New Roman\times.ttf", size=font_size)
    draw = ImageDraw.Draw(image)
    draw.text(xy=font_pos, text=text, fill=(255, 0, 0), font=font)
    image.save(save_path)
    return image

除以上之外，数据增强的方法还有对图像颜色进行抖动，和对图像进行高斯噪声处理

1. 颜色抖动：

from PIL import Image, ImageFont, ImageDraw, ImageEnhance
import matplotlib.pyplot as plt
import numpy as np
 
 
def image_color(image_path, save_path):
    """
    对图像进行颜色抖动
    :param image_path:
    :param save_path:
    :return:
    """
    image = Image.open(image_path)
    print(type(image))
    random_factor = np.random.randint(low=0, high=31) / 10.0        # 随机的扰动因子
    color_image = ImageEnhance.Color(image).enhance(random_factor)    # 调整图像的饱和度
    random_factor = np.random.randint(low=10, high=21) / 10.0
    bright_image = ImageEnhance.Brightness(color_image).enhance(random_factor)   # 调整图像的亮度
    random_factor = np.random.randint(low=10, high=21) / 10.0
    contrast_image = ImageEnhance.Contrast(bright_image).enhance(random_factor)  # 调整图像的对比度
    random_factor = np.random.randint(low=0, high=31) / 10.0
    sharp_image = ImageEnhance.Sharpness(contrast_image).enhance(random_factor)  # 调整图像的锐度
    sharp_image.save(save_path)
    return sharp_image

2.高斯噪声处理：

from PIL import Image, ImageFont, ImageDraw, ImageEnhance
import matplotlib.pyplot as plt
import numpy as np
import random
 
 
def image_gauss(image_path, save_path, mean, stddev):
    """
    对图像进行颜色抖动
    :param image_path:
    :param save_path:
    :return:
    """
    def gaussNoisy(pixel_array, mean, stddev):
        """
        对图像的单个通道进行高斯噪声处理
        :param pixel_array:
        :param mean:  均值
        :param stddev:  方差
        :return:
        """
        for x in pixel_array:
            x += random.gauss(mean, stddev)
        return pixel_array
 
    # 对图像进行数据转换
    image = Image.open(image_path)
    image.convert("RGB")
    img = np.asarray(image)   # 将PIL中的数据格式转化为array
    img.flags.writeable = True    # 将数组改为读写模式
    width, height = img.shape[: 2]
    img_r = img[:, :, 0].flatten()
    img_g = img[:, :, 1].flatten()
    img_b = img[:, :, 2].flatten()
    img_r_new = gaussNoisy(pixel_array=img_r, mean=mean, stddev=stddev)
    img_g_new = gaussNoisy(pixel_array=img_g, mean=mean, stddev=stddev)
    img_b_new = gaussNoisy(pixel_array=img_b, mean=mean, stddev=stddev)
    img[:, :, 0] = img_r_new.reshape([width, height])
    img[:, :, 1] = img_g_new.reshape([width, height])
    img[:, :, 2] = img_b_new.reshape([width, height])
    image_new = Image.fromarray(np.uint8(img))    # 将array中的数据格式转化为PIL中的数据格式
    image_new.save(save_path)    
    return image_new