1 opencv读取数据的通道顺序
1.1 opencv读取数据相关说明
opencv默认读取的颜色通道顺序是：BGR
opencv读取的数据类型是numpy数组，是uint8的整型数据，范围为0-255
import cv2import matplotlib.pyplot as pltfrom PIL import Imageimport numpy as npimport torchfrom torchvision import datasets, models, transforms#1 opencv读取数据的通道顺序 默认读取的颜色通道是BGR 数据通道顺序是 hwcdef opencv_channel(img_path, show_mode=1): image = cv2.imread(img_path) print(f"image type: {type(image)}, image shape: {image.shape}") # image shape: (305, 500, 3) h w c # image type: <class 'numpy.ndarray'>, image shape: (305, 500, 3) print(f"image type: {image.dtype}") # image type: uint8 print(f"min value: {np.min(image)}, max value: {np.max(image)}") # min value: 0, max value: 255 if show_mode == 1: # 如果用matplotlib显示opencv读取的图片，图片会发蓝，这是因为opencv读取的颜色通道顺序是BGR，而matplotlib读取的颜色通道顺序是RGB plt.imshow(image) plt.title("opencv BGR") plt.show() elif show_mode == 2: cv2.imshow("image", image) cv2.waitKey(0) # 使用plt正确显示opencv读取的数据，需要改变颜色通道顺序 BGR2RGB # 下面三种方法都可以 把opencv读取的BGR颜色通道顺序 更改为 RGB颜色通道顺序 # 方法一： cvColor_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # 方法二： b, g, r = cv2.split(image) cvColor_image2 = cv2.merge([r, g, b]) # 方法三： cvColor_image3 = image[:, :, :: -1] if show_mode == 1: plt.imshow(cvColor_image) plt.title("BGR2RGB") plt.show() elif show_mode == 2: cv2.imshow("image", cvColor_image) cv2.waitKey(0)if __name__ == '__main__': img_path = "./bee.jpg" opencv_channel(img_path)1.2 显示opencv读取的数据
1、使用cv2.imshow()
这种显示的是正常的
2、使用plt.imshow()显示opencv读取数据
你会发现显示如下，这是因为plt默认显示的颜色通道顺序为RGB，因此我们需要把opencv读取的数据从BGR转换为RGB
1.3 把opencv读取的BGR转换RGB的三种方式
1、方法一：
cvColor_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
2、方法二：
b, g, r = cv2.split(image) cvColor_image2 = cv2.merge([r, g, b])
3、方法三：
cvColor_image3 = image[:, :, :: -1]
把BGR转换为RGB之后，再用plt.imshow()进行显示，可以发现颜色已经正常了！
2 matplotlib读取数据的通道顺序
2.1 matplotlib读取数据相关说明
plt.imread()默认读取的颜色通道顺序是：RGB
plt.imread()读取的数据类型是numpy数组，是uint8的整型数据，范围为0-255
import cv2import matplotlib.pyplot as pltfrom PIL import Imageimport numpy as npimport torchfrom torchvision import datasets, models, transforms#2 matplotlib读取数据的通道顺序 默认读取的颜色通道是RGB 数据通道顺序是 hwcdef plt_channel(img_path): image = plt.imread(img_path) print(f"image type: {type(image)}, image shape: {image.shape}") # image type: <class 'numpy.ndarray'>, image shape: (305, 500, 3) h w c plt.imshow(image) plt.title("plt image") plt.show() # 可以把numpy数据转换为pillow数据 pil_image = Image.fromarray(image) plt.imshow(pil_image) plt.title("numpy convert to pillow type") plt.show()if __name__ == '__main__': img_path = "./bee.jpg" plt_channel(img_path)显示结果如下：
2.2 把numpy数组类型转换为pillow类型
也可以把plt读取的numpy数组类型转化为pillow类型：
pil_image = Image.fromarray(image)
3 pillow读取数据的通道顺序
3.1 pillow读取数据相关说明
pillow默认读取的颜色通道顺序是：RGB
pillow有自己的数据结构的，但是可以转换成numpy数组，转换后的数组为unit8，0-255
import cv2import matplotlib.pyplot as pltfrom PIL import Imageimport numpy as npimport torchfrom torchvision import datasets, models, transforms#3 pillow读取数据的通道顺序def pillow_channel(img_path, show_mode=1): image = Image.open(img_path) print(f"image mode: {image.mode}") # image mode: RGB print(f"image type: {type(image)}, image shape: {image.size}") # image type: <class 'PIL.JpegImagePlugin.JpegImageFile'>, image shape: (500, 305) w, h if show_mode == 1: plt.imshow(image) plt.title("pillow image") plt.show() elif show_mode == 2: image.show() # 把pillow数据转换为numpy数据 np_image = np.array(image) print(f"image type: {type(np_image)}, image shape: {np_image.shape}") # image type: <class 'numpy.ndarray'>, image shape: (305, 500, 3) h w c plt.imshow(np_image) plt.title("pillow convert to numpy type") plt.show()if __name__ == '__main__': img_path = "./bee.jpg" pillow_channel(img_path)因为，pillow默认读取图片的颜色通道也是RGB，因此用plt显示的时候是没有问题的！显示结果如下：
3.2 把pillow类型转换为numpy类型
把pillow类型的数据转换为numpy数组类型数据：
np_image = np.array(image)
4 pytorch读取数据的通道顺序
pytorch 读取数据类型为tensor
pytorch读取数据类型的通道顺序为：NCHW
import cv2import matplotlib.pyplot as pltfrom PIL import Imageimport numpy as npimport torchfrom torchvision import datasets, models, transforms#4 pytorch读取数据的通道顺序def torch_channel(imgs_dir): # 1、数据增强 train_data_transforms = transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), ]) # 2、从目录中读取数据集 # 存放数据的目录 root_data_dir = imgs_dir train_datasets = datasets.ImageFolder(root_data_dir, train_data_transforms) # 3、加载数据增强后的数据 train_dataloaders = torch.utils.data.DataLoader(train_datasets, batch_size=2, shuffle=True, num_workers=0) # 遍历读取数据 for inputs, labels in train_dataloaders: print(f"inputs shape: {inputs.shape}") print(f"labels shape: {labels.shape}") ''' # 输出结果： inputs shape: torch.Size([2, 3, 224, 224]) bs, c, h, w 即：NCHW， tensorflow读取的顺序为NHWC labels shape: torch.Size([2]) inputs shape: torch.Size([2, 3, 224, 224]) labels shape: torch.Size([2]) inputs shape: torch.Size([2, 3, 224, 224]) labels shape: torch.Size([2]) inputs shape: torch.Size([2, 3, 224, 224]) labels shape: torch.Size([2]) ''' # 可视化其中的图片，batch_size=2, 因此每个batch中有存储两张图片的数据 # 先把tensor类型转换为numpy类型 np_inputs = inputs.numpy() print(f"np_inputs type: {type(np_inputs)}, np_inputs shape: {np_inputs.shape}") # np_inputs type: <class 'numpy.ndarray'>, np_inputs shape: (2, 3, 224, 224) # 更改图片的数据的通道顺序， NCHW 改为 NHWC 0123 0231 np_change_channel = np_inputs.transpose(0, 2, 3, 1 ) print(f"np_change_channel type: {type(np_change_channel)}, np_change_channel shape: {np_change_channel.shape}") # np_change_channel type: <class 'numpy.ndarray'>, np_change_channel shape: (2, 224, 224, 3) # 显示图片，这里把每个batch中的两张图片放到一起显示 out_image = np.hstack((np_change_channel[0], np_change_channel[1])) # # 如果用opencv显示需要再在转换一下颜色空间，转换为BGR，因为torchvision内部是基于Pillow实现的，默认是RGB颜色通道 # out_image = cv2.cvtColor(out_image, cv2.COLOR_RGB2BGR) # cv2.imshow("image", out_image) # cv2.waitKey(0) plt.imshow(out_image) plt.title("pytorch tensor convert to numpy data") plt.show()if __name__ == '__main__': imgs_dir = './hymenoptera/train' torch_channel(imgs_dir)5 全部完整代码
完整代码如下：
'''比较 opencv、matplotlib、pillow 和 pytorch读取数据的通道顺序'''__Author__ = "Shliang"__Email__ = "shliang0603@gmail.com"import cv2import matplotlib.pyplot as pltfrom PIL import Imageimport numpy as npimport torchfrom torchvision import datasets, models, transforms#1 opencv读取数据的通道顺序 默认读取的颜色通道是BGR 数据通道顺序是 hwcdef opencv_channel(img_path, show_mode=1): image = cv2.imread(img_path) print(f"image type: {type(image)}, image shape: {image.shape}") # image shape: (305, 500, 3) h w c # image type: <class 'numpy.ndarray'>, image shape: (305, 500, 3) print(f"image type: {image.dtype}") # image type: uint8 print(f"min value: {np.min(image)}, max value: {np.max(image)}") # min value: 0, max value: 255 if show_mode == 1: # 如果用matplotlib显示opencv读取的图片，图片会发蓝，这是因为opencv读取的颜色通道顺序是BGR，而matplotlib读取的颜色通道顺序是RGB plt.imshow(image) plt.title("opencv BGR") plt.show() elif show_mode == 2: cv2.imshow("image", image) cv2.waitKey(0) # 使用plt正确显示opencv读取的数据，需要改变颜色通道顺序 BGR2RGB # 下面三种方法都可以 把opencv读取的BGR颜色通道顺序 更改为 RGB颜色通道顺序 # 方法一： cvColor_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # 方法二： b, g, r = cv2.split(image) cvColor_image2 = cv2.merge([r, g, b]) # 方法三： cvColor_image3 = image[:, :, :: -1] if show_mode == 1: plt.imshow(cvColor_image) plt.title("BGR2RGB") plt.show() elif show_mode == 2: cv2.imshow("image", cvColor_image) cv2.waitKey(0)#2 matplotlib读取数据的通道顺序 默认读取的颜色通道是RGB 数据通道顺序是 hwcdef plt_channel(img_path): image = plt.imread(img_path) print(f"image type: {type(image)}, image shape: {image.shape}") # image type: <class 'numpy.ndarray'>, image shape: (305, 500, 3) h w c plt.imshow(image) plt.title("plt image") plt.show() # 可以把numpy数据转换为pillow数据 pil_image = Image.fromarray(image) plt.imshow(pil_image) plt.title("numpy convert to pillow type") plt.show()#3 pillow读取数据的通道顺序def pillow_channel(img_path, show_mode=1): image = Image.open(img_path) print(f"image mode: {image.mode}") # image mode: RGB print(f"image type: {type(image)}, image shape: {image.size}") # image type: <class 'PIL.JpegImagePlugin.JpegImageFile'>, image shape: (500, 305) w, h if show_mode == 1: plt.imshow(image) plt.title("pillow image") plt.show() elif show_mode == 2: image.show() # 把pillow数据转换为numpy数据 np_image = np.array(image) print(f"image type: {type(np_image)}, image shape: {np_image.shape}") # image type: <class 'numpy.ndarray'>, image shape: (305, 500, 3) h w c plt.imshow(np_image) plt.title("pillow convert to numpy type") plt.show()#4 pytorch读取数据的通道顺序def torch_channel(imgs_dir): # 1、数据增强 train_data_transforms = transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), ]) # 2、从目录中读取数据集 # 存放数据的目录 root_data_dir = imgs_dir train_datasets = datasets.ImageFolder(root_data_dir, train_data_transforms) # 3、加载数据增强后的数据 train_dataloaders = torch.utils.data.DataLoader(train_datasets, batch_size=2, shuffle=True, num_workers=0) # 遍历读取数据 for inputs, labels in train_dataloaders: print(f"inputs shape: {inputs.shape}") print(f"labels shape: {labels.shape}") ''' # 输出结果： inputs shape: torch.Size([2, 3, 224, 224]) bs, c, h, w 即：NCHW， tensorflow读取的顺序为NHWC labels shape: torch.Size([2]) inputs shape: torch.Size([2, 3, 224, 224]) labels shape: torch.Size([2]) inputs shape: torch.Size([2, 3, 224, 224]) labels shape: torch.Size([2]) inputs shape: torch.Size([2, 3, 224, 224]) labels shape: torch.Size([2]) ''' # 可视化其中的图片，batch_size=2, 因此每个batch中有存储两张图片的数据 # 先把tensor类型转换为numpy类型 np_inputs = inputs.numpy() print(f"np_inputs type: {type(np_inputs)}, np_inputs shape: {np_inputs.shape}") # np_inputs type: <class 'numpy.ndarray'>, np_inputs shape: (2, 3, 224, 224) # 更改图片的数据的通道顺序， NCHW 改为 NHWC 0123 0231 np_change_channel = np_inputs.transpose(0, 2, 3, 1 ) print(f"np_change_channel type: {type(np_change_channel)}, np_change_channel shape: {np_change_channel.shape}") # np_change_channel type: <class 'numpy.ndarray'>, np_change_channel shape: (2, 224, 224, 3) # 显示图片，这里把每个batch中的两张图片放到一起显示 out_image = np.hstack((np_change_channel[0], np_change_channel[1])) # # 如果用opencv显示需要再在转换一下颜色空间，转换为BGR，因为torchvision内部是基于Pillow实现的，默认是RGB颜色通道 # out_image = cv2.cvtColor(out_image, cv2.COLOR_RGB2BGR) # cv2.imshow("image", out_image) # cv2.waitKey(0) plt.imshow(out_image) plt.title("pytorch tensor convert to numpy data") plt.show()if __name__ == '__main__': img_path = "./bee.jpg" opencv_channel(img_path) plt_channel(img_path) pillow_channel(img_path) imgs_dir = './hymenoptera/train' torch_channel(imgs_dir)参考：https://www.cnblogs.com/ranjiewen/p/10278234.html 参考：https://blog.csdn.net/cxx654/article/details/104237214 # 还有imagei和scipy 参考：https://blog.csdn.net/qq_36941368/article/details/82998296 参考：https://blog.csdn.net/oLingFengYu/article/details/88033668 # 不同框架通道顺序
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