add test.py and readme.md

README.md

+# 下载DCU支持的cupy 
+
+
+```
+wget https://cancon.hpccube.com:65024/directlink/1/DTK-24.04.1_apps-20240611/NFS3.2_CentOS7.6/cupy_v12.0.0b3_py38_nfs3.2_DTK24.04_29Mar2024.tar.gz
+tar -zxvf cupy_v12.0.0b3_py38_nfs3.2_DTK24.04_29Mar2024.tar.gz
+```
+
+# 建立conda 环境
+
+
+
+```
+conda create -n  scikit  python=3.8
+pip install cupy_v12.0.0b3_py38_nfs3.2_DTK24.04_29Mar2024/dist-py38/cupy-12.0.0b3-cp38-cp38-linux_x86_64.whl
+pip install scikit-image -i https://pypi.tuna.tsinghua.edu.cn/simple 
+
+```
+
+# 执行测试
+
+```
+import cupy as cp
+import numpy as np
+from skimage import data
+from skimage import filters
+from skimage import img_as_float
+
+# 加载示例图像
+image = img_as_float(data.coins())
+
+# 将图像从NumPy数组转换为CuPy数组，以利用GPU
+image_gpu = cp.array(image)
+
+# 定义一个使用GPU的函数（例如，应用高斯滤波器）
+def gaussian_filter_gpu(image, sigma=1):
+    from cupyx.scipy.ndimage import gaussian_filter
+    return gaussian_filter(image, sigma)
+
+# 应用高斯滤波器
+filtered_image_gpu = gaussian_filter_gpu(image_gpu, sigma=1)
+
+# 将结果转换回NumPy数组
+filtered_image = cp.asnumpy(filtered_image_gpu)
+
+# 测试输出
+print("Original Image Shape:", image.shape)
+print("Filtered Image Shape:", filtered_image.shape)
+```
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test.py

+import cupy as cp
+import numpy as np
+from skimage import data
+from skimage import filters
+from skimage import img_as_float
+
+# 加载示例图像
+image = img_as_float(data.coins())
+
+# 将图像从NumPy数组转换为CuPy数组，以利用GPU
+image_gpu = cp.array(image)
+
+# 定义一个使用GPU的函数（例如，应用高斯滤波器）
+def gaussian_filter_gpu(image, sigma=1):
+    from cupyx.scipy.ndimage import gaussian_filter
+    return gaussian_filter(image, sigma)
+
+# 应用高斯滤波器
+filtered_image_gpu = gaussian_filter_gpu(image_gpu, sigma=1)
+
+# 将结果转换回NumPy数组
+filtered_image = cp.asnumpy(filtered_image_gpu)
+
+# 测试输出
+print("Original Image Shape:", image.shape)
+print("Filtered Image Shape:", filtered_image.shape)
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