图像梯度
sobelxy = cv2.Sobel(img,cv2.CV_64F,1,1,ksize=3)# sobelx=cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)#1,0表示只在x方向求一阶导数# sobely=cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)#0,1表示只在y方向求一阶导数# mag=cv2.magnitude(sobelx,sobely) #对应每个像素的梯度矩阵#对于Sobel函数有个注意点，他的第二个参数是扩展了像素的数值范围，因为梯度是有方向的， #所以sobel函数得到的是有正有负的值，所以相当于扩大了取值。 #通常情况下我们会加上下面的函数来得到梯度的绝对值： #sobelx=cv2.convertScaleAbs(cv2.Sobel(im,cv2.CV_64F,1,0,ksize=3)) #由于magnitude计算的是sqrt(x^2+y^2),所以不用考虑正负的问题# laplacian = cv2.Laplacian(sub_im,cv2.CV_64F) #二阶导数,用于获取边缘信息
腐蚀，膨胀
#对二值化的图像进行腐蚀，膨胀，的形态学组合变换,#先进行腐蚀再进行膨胀的运算就是开运算，腐蚀可以让那些在图像外面的小点点去掉，#然后把主图像膨胀回去，实现去除图像外噪声。#先进行膨胀再进行腐蚀的运算就是闭运算，膨胀可以让那些在图像里面的小点点去掉，#然后把主图像腐蚀回去，实现去除图像内噪声。# 膨胀和腐蚀操作的核函数element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))#(9, 1)element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))#(8, 6)# 膨胀一次，让轮廓突出dilation = cv2.dilate(binary, element2, iterations = 1)# 腐蚀一次，去掉细节erosion = cv2.erode(dilation, element1, iterations = 1)# 再次膨胀，让轮廓明显一些binary = cv2.dilate(erosion, element2,iterations = 3)
灰度转换
from skimage.color import rgb2graygray_im = rgb2gray(rgb_im)# 或者使用自定义函数:def rgb2grey(rgb): if len(rgb.shape) is 3: return np.dot(rgb[...,:3], [0.299, 0.587, 0.114]) else: print 'Current image is already in grayscale.' return rgb
也可使用PIL图像处理库或者OpenCV的python接口.
python 绘图
faster r-cnn中的画框再加文字
import matplotlib.pyplot as pltfig, ax = plt.subplots(figsize=(12, 12))ax.imshow(im, aspect='equal')ax.add_patch( plt.Rectangle((bbox[0], bbox[1]), bbox[2] - bbox[0], bbox[3] - bbox[1], fill=False, edgecolor='red', linewidth=3.5) )ax.text(bbox[0], bbox[1] - 2, '{:s} {:.3f}'.format(class_name, score), bbox=dict(facecolor='blue', alpha=0.5), fontsize=14, color='white')ax.set_title(('{} detections with ' 'p({} | box) >= {:.1f}').format(class_name, class_name, thresh), fontsize=14)plt.axis('off')plt.tight_layout()plt.draw()plt.show() OpenCV、Skimage、PIL图像处理的细节差异
PIL 图像处理库
PIL(Python Imaging Library) 是Python中最基础的图像处理库
from PIL import Imageimport numpy as npimage = Image.open('test.jpg') # 图片是400x300 宽x高print type(image) # out: PIL.JpegImagePlugin.JpegImageFileprint image.size # out: (400,300)print image.mode # out: 'RGB'print image.getpixel((0,0)) # out: (143, 198, 201)# resizeimage = image.resize(200,100，Image.NEAREST)print image.size # out: (200,100) 代码解释:
注意image 是PIL.Image.Image 类的对象，它有很多属性，比如它的size是(w,h),通道是RGB.有很多方法，比如获取getpixel((x,y))某个位置的像素，得到三个通道的值，x最大可取w-1，y最大可取h-1 比如resize方法，可以实现图片的放缩，具体参数如下 resize(self, size, resample=0) method of PIL.Image.Image instance Returns a resized copy of this image. :param size: The requested size in pixels, as a 2-tuple: (width, height). 注意size是 (w,h),和原本的(w,h)保持一致 :param resample: An optional resampling filter. This can be one of :py:attr:`PIL.Image.NEAREST`, :py:attr:`PIL.Image.BOX`, :py:attr:`PIL.Image.BILINEAR`, :py:attr:`PIL.Image.HAMMING`, :py:attr:`PIL.Image.BICUBIC` or :py:attr:`PIL.Image.LANCZOS`. If omitted, or if the image has mode "1" or "P", it is set :py:attr:`PIL.Image.NEAREST`. See: :ref:`concept-filters`. 注意这几种插值方法，默认NEAREST最近邻（分割常用），分类常用BILINEAR双线性，BICUBIC立方 :returns: An :py:class:`~PIL.Image.Image` object.
转换为numpy,注意h,w维度的变化:
image = np.array(image,dtype=np.float32) # image = np.array(image)默认是uint8print image.shape # out: (100, 200, 3)# 原PIL image对象的维度的w和h换了，变成(h,w,c)了# 注意ndarray中是 行row x 列col x 维度dim 所以行数是高，列数是宽
其它使用示例:
1.将图片转换为灰度图：
from PIL import Imagepil_im=Image.open('empire.jpg') 上述代码的返回值 pil_im 是一个 PIL 图像对象。
图像的颜色转换可以使用 convert() 方法来实现。要读取一幅图像，并将其转换成灰度图像，只需要加上 convert('L')，如下所示：
pil_im=Image.open('empire.jpg').convert('L') 2.将numpy.ndarray中的图像数据存储到图片(ndarray可以是2维的灰度数据矩阵)：
im = Image.fromarray(ndarray）im.save('out.png') pillow支持的存储模式:
Modes
The mode of an image defines the type and depth of a pixel in the image. The current release supports the following standard modes: 1 (1-bit pixels, black and white, stored with one pixel per byte) L (8-bit pixels, black and white) P (8-bit pixels, mapped to any other mode using a color palette) RGB (3x8-bit pixels, true color) RGBA (4x8-bit pixels, true color with transparency mask) CMYK (4x8-bit pixels, color separation) YCbCr (3x8-bit pixels, color video format) LAB (3x8-bit pixels, the Lab color space) HSV (3x8-bit pixels, Hue, Saturation, Value color space) I (32-bit signed integer pixels) F (32-bit floating point pixels)PIL also provides limited support for a few special modes, including LA (L with alpha), RGBX (true color with padding) and RGBa (true color with premultiplied alpha). However, PIL doesn’t support user-defined modes; if you to handle band combinations that are not listed above, use a sequence of Image objects.
You can read the mode of an image through the mode attribute. This is a string containing one of the above values.
3.等比例压缩、裁剪压缩、缩略(水印)图
region = (x1,y1,x2,y2)#裁切图片cropImg = pil_im.crop(region)#保存裁切后的图片cropImg.save(r'positive/'+mat_file+'_'+str(col)+'.jpg')#压缩缩放pil_im.resize((newWidth,newHeight),Image.ANTIALIAS).save(arg['dst_img'],quality=75) ''' 除了Image.ANTIALIAS还有如下值： NEAREST: use nearest neighbour BILINEAR: linear interpolation in a 2x2 environment BICUBIC:cubic spline interpolation in a 4x4 environment ANTIALIAS:best down-sizing filter '''#水印(这里仅为图片水印)def waterMark(**args): args_key = {'ori_img':'','dst_img':'','mark_img':'','water_opt':''} arg = {} for key in args_key: if key in args: arg[key] = args[key] im = image.open(arg['ori_img']) ori_w,ori_h = im.size mark_im = image.open(arg['mark_img']) mark_w,mark_h = mark_im.size option ={'leftup':(0,0),'rightup':(ori_w-mark_w,0),'leftlow':(0,ori_h-mark_h), 'rightlow':(ori_w-mark_w,ori_h-mark_h) }im.paste(mark_im,option[arg['water_opt']],mark_im.convert('RGBA'))im.save(arg['dst_img']) Skimage
skimage即是
import skimagefrom skimage import io,transformimport numpy as npimage= io.imread('test.jpg',as_grey=False)# 第一个参数是文件名可以是网络地址，第二个参数默认为False，True时为灰度图print type(image) # out: numpy.ndarrayprint image.dtype # out: dtype('uint8')print image.shape # out: (300, 400, 3) (h,w,c)前面介绍了ndarray的特点# mode也是RGBprint image'''注意此时image里都是整数uint8,范围[0-255]array([ [ [143, 198, 201 (dim=3)],[143, 198, 201],... (w=200)], [ [143, 198, 201],[143, 198, 201],... ], ...(h=100) ], dtype=uint8)'''image= io.imread('test.jpg',as_grey=True)print image.shape # out: (300, 400)print image'''此时image范围变为[0-1]array([[ 0.73148549, 0.73148549, 0.73148549, ..., 0.73148549, 0.73148549, 0.73148549], [ 0.73148549, 0.73148549, 0.73148549, ..., 0.73148549, .....]])'''print image.dtype # out: dtype('float64')image = io.imread('test.jpg',as_grey=False) image = transform.resize(image,(100, 200),order=1) # order默认是1，双线性#resize后image范围又变成[0-1]print image.dtype # out: dtype('float64')print image.shape # out: (100, 200, 3)print image'''array([[[ 0.56078431, 0.77647059, 0.78823529], [ 0.56078431, 0.77647059, 0.78823529], [ 0.56078431, 0.77647059, 0.78823529], ..., ...]])''''''resize函数接口resize(image, output_shape, order=1, mode='constant', cval=0, clip=True, preserve_range=False)order : int, optional The order of interpolation. The order has to be in the range 0-5: - 0: Nearest-neighbor - 1: Bi-linear (default) - 2: Bi-quadratic - 3: Bi-cubic - 4: Bi-quartic - 5: Bi-quintic'''print skimage.img_as_float(image).dtype # out: float64# img_as_float可以把image转为double，即float64 OpenCV
是个很强大的图像处理库，性能也很好。
import cv2import numpy as npimage = cv2.imread('test.jpg')print type(image) # out: numpy.ndarrayprint image.dtype # out: dtype('uint8')print image.shape # out: (300, 400, 3) (h,w,c) 和skimage类似print image'''array([ [ [143, 198, 201 (dim=3)],[143, 198, 201],... (w=200)], [ [143, 198, 201],[143, 198, 201],... ], ...(h=100) ], dtype=uint8)'''image = cv2.resize(image,(100,200),interpolation=cv2.INTER_LINEAR)print image.dtype # out: dtype('uint8')print image.shape # out: (200, 100, 3) '''注意注意注意 和skimage不同 resize(src, dsize[, dst[, fx[, fy[, interpolation]]]]) 关键字参数为dst,fx,fy,interpolationdst为缩放后的图像dsize为(w,h),但是image是(h,w,c)fx,fy为图像x,y方向的缩放比例，interplolation为缩放时的插值方式，有三种插值方式：cv2.INTER_AREA:使用象素关系重采样。当图像缩小时候，该方法可以避免波纹出现。当图像放大时，类似于 CV_INTER_NN方法 cv2.INTER_CUBIC: 立方插值cv2.INTER_LINEAR: 双线形插值 cv2.INTER_NN: 最近邻插值''' 在进行图像处理时一点要注意 各个库之间的细微差异，还有要注意图像放缩时插值方法的选择，而且即使是相同的插值方法，各个库的实现也不同，结果也会有些许差异。