删除多余代码

Python/Segmentation/Unet.py

-import numpy as np
-import cv2
-import migraphx
-
-def Preprocessing(pil_img, newW, newH):
-    assert newW > 0 and newH > 0, 'Scale is too small' 
-    img_nd = cv2.cvtColor(pil_img, cv2.COLOR_BGR2RGB)    # BGR转换为RGB
-    img_nd = cv2.resize(img_nd, (newW, newH))            # 将图像尺寸修改为256x256
-    
-    if len(img_nd.shape) == 2:
-        img_nd = np.expand_dims(img_nd, axis=2)
-
-    img_trans = img_nd.transpose((2, 0, 1))              # HWC转换为CHW
-    img_trans = np.expand_dims(img_trans, 0)             # CHW扩展为NCHW
-    img_trans = np.ascontiguousarray(img_trans)          # 保证内存连续存储
-    img_trans = img_trans.astype(np.float32)             # 转换成浮点型数据
-    if img_trans.max() > 1:                             
-        img = img_trans / 255.0                          # 保证数据处于0-1之间的浮点数
-
-    return img
-
-def Sigmoid(x):
-  return 1 / (1 + np.exp(-x))
-
-if __name__ == '__main__':
-    
-    # 加载模型
-    model = migraphx.parse_onnx("../../Resource/Models/unet_13_256.onnx")
-    inputName = model.get_parameter_names()
-    inputShape = model.get_parameter_shapes()
-    print("inputName:{0} \ninputShape:{1}".format(inputName, inputShape))
-
-    # 编译模型
-    model.compile(migraphx.get_target("gpu"), device_id=0)   # device_id: 设置GPU设备，默认为0号设备
-
-    # 图像预处理
-    img = cv2.imread("../../Resource/Images/car1.jpeg")
-    input_img = Preprocessing(img, 256, 256)
-
-    # 模型推理
-    mask = model.run({'inputs':input_img})      
-    output_mask = np.array(mask[0])[0]                        # 获取推理结果，shape为(1,256,256)
-    probs = Sigmoid(output_mask)                              # 计算sigmoid值
-
-     # 0/1像素值，当大于0.996时，值为255，小于等于0.996时，值为0
-    output_mask[probs > 0.996] = 255
-    output_mask[probs <= 0.996] = 0
-
-    output = output_mask.astype(np.uint8)[0]                  # 将浮点型转换为uint8整型，shape为（256，256）
-    cv2.imwrite("output.jpg", output)                         # 保存图像分割结果