OpenVINO Model Server的服务化部署——step4（实现天空替换）

原创

mb7cd348aefc0e3 2022-12-24 09:07:49 ©著作权

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前期已经基于OpenVINO搭建成功了天空识别模型，并且能够得到着色的结果图片，下一步就是继续来实现“天空替换”

一、天空替换重构

在OpenVINO着色结果基础上，重新编写c++和python版本的天空替换代码。

// 天空之子算法研究
      // 2019年11月30日
      
#
      include 
      "pch.h"
      
#
      include 
      "cv_helper.h"
      using 
      namespace cv;
      using 
      namespace std;
      int main()
      
{
      
    Mat matSrc 
      = imread(
      "E:\\未来项目\\天空替换(天空分割)\\测试图片\\测试图片\\sky14.jpg");
      
    Mat matCloud 
      = imread(
      "E:\\未来项目\\你的名字滤镜\\算法实验\\算法实验\\sky1.png");
      
    Mat matMask 
      = imread(
      "E:\\未来项目\\天空替换(天空分割)\\skyInBlue.png",
      0);
      
    matMask 
      = (matMask 
      == 
      124);
      //统一按照matSrc的大小进行缩放
      
    resize(matCloud, matCloud, matSrc.size());
      
    resize(matMask, matMask, matSrc.size());
      
    Point center 
      = Point (matMask.cols 
      / 
      2, matMask.rows 
      / 
      2);
      //Mat normal_clone;
      //cartoonifyImage(matCloud, matCloud);
      
    Mat normal_clone;
      
    seamlessClone(matCloud, matSrc, matMask, center, normal_clone, MIXED_CLONE);
      
    cv
      :
      :waitKey();
      
}

import cv2

import numpy as np

matSrc =cv2.imread( 'E:/template/sky14.jpg')

matCloud = cv2.imread( 'E:/template/cloud3.jpg')

matMask = cv2.imread( 'E:/template/skyInBlue.png', 0)

rows,cols=matMask.shape

for i in range(rows):

for j in range(cols):

if (matMask[i,j]== 124):

matMask[i,j]= 255

else:

matMask[i,j]= 0

height,width=matSrc.shape[: 2]

matCloud=cv2.resize(matCloud,(width,height),interpolation=cv2.INTER_CUBIC)

matMask=cv2.resize(matMask,(width,height),interpolation=cv2.INTER_CUBIC)

center = (width // 2, height // 2)

# Seamlessly clone src into dst and put the results in output

normal_clone = cv2.seamlessClone(matCloud, matSrc, matMask, center, cv2.NORMAL_CLONE)

mixed_clone = cv2.seamlessClone(matCloud, matSrc, matMask, center, cv2.MIXED_CLONE)

cv2.imshow( 'normal_clone',normal_clone)

cv2.imshow( 'mixed_clone',mixed_clone)

cv2.waitKey( 0)

原图：

OpenVINO Model Server的服务化部署——step4（实现天空替换）_算法研究

替换图：

OpenVINO Model Server的服务化部署——step4（实现天空替换）_算法研究_02

这个已经实现了天空替换的结果，但是颜色还需要调亮一点。

二、 相关的代码融合

view，主要是融入代码：

def process_detail(request,param1):
   
   

        options = [(
    'grpc.max_receive_message_length', 
    100 * 
    1024 * 
    1024),(
    'grpc.max_send_message_length', 
    100 * 
    1024 * 
    1024)]
   
   

        channel = grpc.insecure_channel(
    "{}:{}".format(
    'localhost',
    9000),options = options)
   
   

        stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
   
   

        batch_size = 
    1
   
   

        
    #
    TODO
     filepath
   
   

        output_str=
    'filepath'
   
   

        imgfile = os.path.join(
    '/root/mysites/goApp/images',param1)
   
   

        print(imgfile)
   
   

        img = load_image(imgfile)
   
   

        imgs = np.zeros((
    0,
    3,
    1024,
    2048), np.dtype(
    '<f'))
   
   

        imgs = np.append(imgs, img, axis=
    0)
   
   

        request = predict_pb2.PredictRequest()
   
   

        request.model_spec.name = 
    "semantic-segmentation-adas"
   
   

        print(
    "\nRequest shape", img.shape)
   
   

        img = imgs[
    0:
    1]
   
   

        request.inputs[
    "data"].CopyFrom(make_tensor_proto(img, shape=(img.shape)))  
   
   

        result = stub.Predict(request, 
    10.0)    
    # result includes a dictionary with all model outputs print(img.shape) 
   
   

        output = make_ndarray(result.outputs[
    "4455.1"])
   
   

        
    for y 
    in range(
    0,img.shape[
    0]):  
    # iterate over responses from all images in the batch
   
   

            img_out = output[y,:,:,:]
   
   

            print(
    "image in batch item",y, 
    ", output shape",img_out.shape)
   
   

            img_out = img_out.transpose(
    1,
    2,
    0)
   
   

            print(
    "saving result to",os.path.join(
    '/root/mysites/goApp/results',param1+
    '.result.jpg'))
   
   

            out_h, out_w,_ = img_out.shape
   
   

            print(out_h)
   
   

            print(out_w)
   
   

            
    for batch, data 
    in enumerate(output):
   
   

                classes_map = np.zeros(shape=(out_h, out_w, 
    3), dtype=np.int)
   
   

                
    for i 
    in range(out_h):
   
   

                    
    for j 
    in range(out_w):
   
   

                        
    if len(data[:, i, j]) == 
    1:
   
   

                            pixel_class = int(data[:, i, j])
   
   

                        
    else:
   
   

                            pixel_class = np.argmax(data[:, i, j])
   
   

                        classes_map[i, j, :] = classes_color_map[min(pixel_class, 
    20)]        
   
   

                classes_map = np.uint8(classes_map)
   
   

                matMask = cv2.cvtColor(classes_map,cv2.COLOR_BGR2GRAY)
   
   

                matMask = np.uint8(matMask)
   
   

                matCloud = cv2.imread(
    '/root/mysites/goApp/images/cloud3.jpg')
   
   

                rows,cols=matMask.shape
   
   

                
    for i 
    in range(rows):
   
   

                    
    for j 
    in range(cols):
   
   

                        
    if (matMask[i,j]==
    134):
   
   

                            matMask[i,j]=
    255
   
   

                        
    else:
   
   

                            matMask[i,j]=
    0
   
   

                matsrc = cv2.imread(imgfile)
   
   

                matsrc = cv2.resize(matsrc,(out_w,out_h),interpolation=cv2.INTER_CUBIC)
   
   

                matCloud=cv2.resize(matCloud,(out_w,out_h),interpolation=cv2.INTER_CUBIC)
   
   

                matMask=cv2.resize(matMask,(out_w,out_h),interpolation=cv2.INTER_CUBIC)
   
   

                center = (out_w // 
    2, out_h // 
    2)
   
   

                normal_clone = cv2.seamlessClone(matCloud,matsrc, matMask, center, cv2.NORMAL_CLONE)
   
   

                output_str = os.path.join(
    '/root/mysites/goApp/results',param1+
    '.result.jpg')
   
   

                cv2.imwrite(output_str,normal_clone)
   
   

        
    return HttpResponse(output_str)