mindspore.dataset.vision.Grayscale
- class mindspore.dataset.vision.Grayscale(num_output_channels=1)[源代码]
将输入PIL图像转换为灰度图。
- 参数:
num_output_channels (int) - 输出灰度图的通道数,取值可为
1
或3
。当取值为3
时,返回图像各通道的像素值将相同。默认值:1
。
- 异常:
TypeError - 当 num_output_channels 的类型不为int。
ValueError - 当 num_output_channels 取值不为
1
或3
。
- 支持平台:
CPU
样例:
>>> import os >>> import numpy as np >>> from PIL import Image, ImageDraw >>> import mindspore.dataset as ds >>> import mindspore.dataset.vision as vision >>> from mindspore.dataset.transforms import Compose >>> >>> # Use the transform in dataset pipeline mode >>> class MyDataset: ... def __init__(self): ... self.data = [] ... img = Image.new("RGB", (300, 300), (255, 255, 255)) ... draw = ImageDraw.Draw(img) ... draw.ellipse(((0, 0), (100, 100)), fill=(255, 0, 0), outline=(255, 0, 0), width=5) ... img.save("./1.jpg") ... data = np.fromfile("./1.jpg", np.uint8) ... self.data.append(data) ... ... def __getitem__(self, index): ... return self.data[0] ... ... def __len__(self): ... return 5 >>> >>> my_dataset = MyDataset() >>> generator_dataset = ds.GeneratorDataset(my_dataset, column_names="image") >>> transforms_list = Compose([vision.Decode(to_pil=True), ... vision.Grayscale(3), ... vision.ToTensor()]) >>> # apply the transform to dataset through map function >>> generator_dataset = generator_dataset.map(operations=transforms_list, input_columns="image") >>> for item in generator_dataset.create_dict_iterator(num_epochs=1, output_numpy=True): ... print(item["image"].shape, item["image"].dtype) ... break (3, 300, 300) float32 >>> os.remove("./1.jpg") >>> >>> # Use the transform in eager mode >>> img = Image.new("RGB", (300, 300), (255, 255, 255)) >>> draw = ImageDraw.Draw(img) >>> draw.polygon([(50, 50), (150, 50), (100, 150)], fill=(0, 255, 0), outline=(0, 255, 0)) >>> img.save("./2.jpg") >>> data = Image.open("./2.jpg") >>> output = vision.Grayscale(3)(data) >>> print(np.array(output).shape, np.array(output).dtype) (300, 300, 3) uint8 >>> os.remove("./2.jpg")
- 教程样例: