python跑ncnn(验证模型是否转换成功)

为了转ncnn模型是否成功，用python验证一下先

pip install ncnn

分割模型的验证代码

import ncnn
import cv2
import numpy as np# 创建ncnn的网络对象
net = ncnn.Net()# 加载ONNX模型
net.load_param('E:\\Android_Projects\\ncnn-android-deeplabv3plus-main\\app\\src\\main\\assets\\sim.param')
net.load_model('E:\\Android_Projects\\ncnn-android-deeplabv3plus-main\\app\\src\\main\\assets\\sim.bin')# 加载图像
image = cv2.imread(r'E:\cpp\ncnn-portrait-segmentation\data\1.jpg')# 调整图像尺寸为模型输入尺寸
input_size = (800, 800)
resized_image = cv2.resize(image, input_size)# 减去均值
mean_vals = (0.37802792*255.0,0.32611448*255.0,0.29480308*255.0)
norm_vals = (1 / 0.348492 / 255.0, 1 / 0.3070657 / 255.0, 1 / 0.28770673 / 255.0)
input_blob = ncnn.Mat.from_pixels(resized_image, ncnn.Mat.PixelType.PIXEL_BGR2RGB, 800, 800)
# 运行网络
input_blob.substract_mean_normalize(mean_vals, norm_vals)
ex = net.create_extractor()
# net_input = ncnn.Extractor(net)
ex.input("input", input_blob)
output_blob = ncnn.Mat()
ex.extract("output", output_blob)# 获取分类结果
# output_data = output_blob.to_numpy()# output_blob = output_blob.reshape(2,800 , 800)
output_blob = np.array(output_blob)
mask = output_blob[0]>0.8
print(800*800,';;;;;',np.sum(mask))img0 = np.array(image*mask[:,:,None],dtype=np.uint8)cv2.imshow('hh',img0)
cv2.waitKey(0)img1 =  np.array(image*~mask[:,:,None],dtype=np.uint8)cv2.imshow('hh1',img1)
cv2.waitKey(0)print(1)