Image processing library (Opencv, Matplotlib, PIL) and the conversion between the three

1. Opencv

The basic image type of opencv can be converted to and from numpy arrays, so it can be directly called to torch.from_numpy(img)convert the image intotensor

• Read:img=cv2.imread(path)
  After OpenCV reads the image, it returns a representative image numpy.ndarrayin the format of (H,W,C)channel order BGR, value range [0,255], dtype=uint8.

import cv2
def read_img_cv(path):
	img_cv=cv2.imread(path)
	return img_cv

• show:cv2.imshow(name,img)

import cv2
def show_img_cv(img_cv):
	cv2.imshow("Image", img_cv)
	cv2.waitKey(0)  # 暂停显示图片，数字0代表按键后 0 ms执行

• keep:cv2.imwrite(path, img)

import cv2
def save_img_cv(img_cv,path):
	cv2.imwrite(path, img_cv)  # 保存图片

2. Matplotlib

matplotlib is an image drawing library developed by python based on matlab drawing. When plotting with matplotlib, it is possible to read tesnorand numpydatatype.

• read:img=mpimg.imread(path)

If it is a grayscale image: return an array of (H, W) shape
; if it is an RGB image, return an array of (H, W, 3) shape, and the image channel order is; RGB
if it is an RGBA image, return an array of (HW, 4) shape , the picture channel order isRGBA

Additionally, PNG images are returned as an array of floats (0-1) dtype=float32, and all other formats dtype=uint8are returned as an array of int, with a bit depth determined by the specific image.

import matplotlib.image as mpimg
def read_img_mat(path):
	img_mat=mpimg.imread(path)
	return img_mat

• show:plt.imshow(img) plt.show()

1. show color map

import matplotlib.pyplot as plt
# 如果在jupyter notebook中显示，需要添加如下一行代码
%matplotlib inline

def show_img_mat(img_mat):
	plt.imshow(img_mat)
	plt.axis('off')
	plt.show()

2. Display the grayscale image
   matplotlib displays the image. By default, the image is displayed in three channels. We need to add parameters in plt.imshow() gray.

def show_img_gray(img_gray):
	plt.imshow(img_gray,cmap='gray')
	plt.axis('off')
	plt.show()

3. display Imagetype image

def show_img_pil(img_pil):
	plt.imshow(img_pil)
	plt.axis('off')
	plt.show()

• keep:plt.imsave(name,img)

def save_img_pil(img_pil,name):
	plt.imsave(name,img_pil)

3. PIL

PIL is python's basic library for image processing.
The mode of the image is as follows, for example 1: binary image, Lgrayscale image, P: 8-bit color image, RGB: 24-bit color image (8 bits per channel) such as jpgan image, RGBA: more alpha channel (opacity) than RGB, for example pngImages
can use img.convert(mode)the transform mode.

• Read: img=Image.open(path)
  reads a PIL.xxxImageFIietype.

import PIL
from PIL import Image
def read_img_pil(path):
	img_pil=Image.open(path) # PIL Image 类型
	return img_pil

• show:image.show()

def show_img_pil(img_pil):
	img_pil.show()

• keep:image.save(path)

def save_img_pil(img_pil,path):
	img_pil.save(path)

4. The difference and mutual conversion among the three

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The difference between the three

• The data type of Opencv is Numpy数组, the channel order isBGR
• Matplotlib's data type is Numpy数组and the channel order isRGB
• The data type of PIL is PIL.Image类and the channel order isRGB

---

Three image processing libraries are converted to each other

• OpencvMatplotlibconversion between

# cv->mat
def cv2mat(img_cv):
	img_mat=cv2.cvtColor(img_cv,cv2.COLOR_BGR2RGB) # 将颜色通道从BGR改变成RGB
	# 另一种等价写法
	# img_mat=img_cv[:,:,::-1]
	return img_mat

def mat2cv(img_mat): # 将颜色通道从RGB改变成BGR
	img_cv=img_mat[:,:,::-1]
	return img_cv

• MatplotlibPILThe mutual conversion between img-
  np.asarry(img) >array
  Image.fromarray(array)array->img

# mat->PIL
#方法1：三通道的转换
def mat2PIL_RGB(img_mat):
	img_pil=Image.fromarray(img_mat.astype('uint8'))
	# unit8 是无符号的8位整形,用astype [0,255]截断处理
	# 另外一种写法
	# img_pil= Image.fromarray(np.unit8(img_mat))
	return img_pil 

# 方法2： 四通道的转换
def mat2PIL_RGBA(img_mat):
	img_pil=Image.fromarray(img_mat.astype('uint8')).convert('RGB')
	return img_pil

# 方法三：使用torchvision的库函数
from torchvision import transforms
def mat2PIL_trans(img_mat):
	trans=transformers.ToPILImage()
	img_pil=trans(img_mat)
	return img_pil
	
'''PIL->mat'''

def PIL2mat(img_pil):
	img_mat=np.array(img_pil) # 深拷贝
	# 如果是jpg格式，通道顺序是RGB, (H,W,3)
	# 如果是png格式，通道顺序是RGBA, (H,W,4)
	# 返回的类型均是`numpy.ndarray`, `dtype=unit8`, 取值返回[0,255]
	# 或者也可以采用浅拷贝
	# img_mat=np.asarray(img_pil)
	return img_mat

'''区间变换'''
# [0,255]->[0,1] 
def PIL2mat_norm(img_pil):
	img_mat=np.asarray(img_pil)/255.0
	return img_mat
# [0,1]->[0,255]
def mat_255(img_mat):
	img_mat=(np.maximum(img_mat, 0) / img_mat.max()) * 255.0 
	img_mat=np.unit8(img_mat)

• OpencvPILconversion between

# cv->PIL
#方法1：三通道的转换
def cv2PIL_RGB(img_cv):
	img_rgb = img_cv[:,:,::-1] # OpenCV 的通道顺序为 BGR, 转换成RGB
	# nparray 
	img_pil= Image.fromarray(np.uint8(img_rgb))
	return img_pil 

# 方法2： 四通道的转换
def cv2PIL_RGBA(img_cv):
	img_rgb = img_cv[:,:,::-1]
	img_pil=Image.fromarray(img_rgb.astype('uint8')).convert('RGB')
	return img_pil

# 方法三：使用torchvision的库函数
from torchvision import transforms
def cv2PIL_trans(img_cv):
	img_rgb = img_cv[:,:,::-1]
	trans=transformers.ToPILImage()
	img_pil=trans(img_rgb)
	return img_pil
	
# PIL->cv
def PIL2cv(img_pil):
	img_ary=np.array(img_pil) # 深拷贝，通道顺序是 RGB, (H,W,C)
	# 或者也可以采用深拷贝
	# img_ary=np.asarray(img_pil)
	img_cv=img_ary[:,:,-1]
	return img_cv

---

Mutual conversion between the three formats and Tensor

• Convert numpy format to Tensor

import torch
def nparray2tensor(npary):
	ts=torch.from_numpy(npary)
	# 如果需要修改成浮点类型
	# ts=torch.from_numpy(npary).float()
	return ts

• Converting PIL and numpy formats into Tensor
  can use the function in torchvision to convert the or : size , range transforms.ToTensor()
  in PIL into : size , rangeImagenumpy.ndarray(dtype=unit8)(H,W,C) [0,255]torch.FloatTensor(C,H,W)[0.0,1.0]

from torchvision import transforms
# img_pil: Image
trans=transforms.ToTensor() 
tens=trans(img_pil) # (C,H,W) [0.0,1,0]
# tens_hwc=tens.transpose((1,2,0))

5. Relevant transformation library in Torchvision

5.1 ToPILImage([mode])

CLASS
torchvision.transforms.ToPILImage(mode=None)

• Function

  Convert tensor or ndarray to PIL image - this will not scale the values. This conversion does not support torchscript.

  Converts the shape of C x H x Wto or torch.*Tensorthe shape of H x W x Cwhile preserving the range of values.numpy ndarrayPIL图像

• parameter

  • mode(PIL.Image mode)The color space and pixel depth of the input data (optional). When mode is None (default), the following assumptions are made about the input data:
    • When the input is 4 channels, the mode is assumed to be RGBA.
    • If the input is 3-channel, RGB mode is assumed.
    • When the input is 2 channels, it is assumed to be LA mode.
    • If the input has 1 channel, the mode is determined by the data type (i.e. int, float, short).

5.2 ToTensor

CLASS
torchvision.transforms.ToTensor

• Function:

  Convert a PIL image or ndarray to tensor, scaled accordingly. This conversion does not support torchscript.

  Convert PIL Imageor in- [0,255]interval numpy.ndarray (H x W x C)to in [0.0,1.0]-interval torch.FloatTensor (C x H x W). where PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1); if numpy.Ndarray dtype = np.uint8
  in other cases, the tensor is returned without scaling.

5.3 ARROWTensor

CLASS
torchvision.transforms.PILToTensor

• Function

  Convert a PIL image to a tensor of the same type - this will not scale the values. This conversion does not support torchscript.

  Tensor to PIL Image (H x W x C)convert to shape .(C x H x W)