From f0d5ac9a435476c12352c4b613dd38f3ae247c2e Mon Sep 17 00:00:00 2001
From: thebin <1974911747@qq.com>
Date: Mon, 29 May 2023 15:14:27 +0800
Subject: [PATCH] Update inference_openvino.py

---
 openvino/inference_openvino.py | 76 +++++++++++++---------------------
 1 file changed, 28 insertions(+), 48 deletions(-)

diff --git a/openvino/inference_openvino.py b/openvino/inference_openvino.py
index 969e56c..5e65ca1 100644
--- a/openvino/inference_openvino.py
+++ b/openvino/inference_openvino.py
@@ -1,64 +1,45 @@
+from matplotlib import pyplot as plt
+from openvino.runtime import Core
 from time import time
 import numpy as np
 import cv2 as cv
 import argparse
 import os
 
-import openvino.runtime as ov
 
-classes = ('normal', 'pneumonia')
+def inference_resnet18sam(model_path, image_path, visualize, device):
+    # Step 1. Load and Compile the Model
+    ie = Core()
+    model = ie.read_model(model=model_path)
+    compiled_model = ie.compile_model(model=model, device_name=device)
+    output_layer = compiled_model.output(0)
 
+    # Step 2. Load and preprocess image
+    image = cv.cvtColor(cv.imread(filename=image_path), code=cv.COLOR_BGR2GRAY)
 
-def inference_resnet18sam(model_path, img_path, visualize, mode, device):
-    print(f"Device on: {device}")
-    # Step 1. Create OpenVINORuntime Core
-    core = ov.Core()
+    # resize to ResNet-18-SAM image shape
+    input_image = cv.resize(src=image, dsize=(224, 224))
 
-    # Step 2. Compile the Model
-    compiled_model = core.compile_model(model_path, device)
+    # reshape to model input shape
+    input_image = np.expand_dims([input_image], 0)
 
-    # Step 3. Create an Inference Request
-    infer_request = compiled_model.create_infer_request()
+    # Step 3. Do inference and get result
+    classes = ('normal', 'pneumonia')
 
-    # Get input shape and element type from the model
-    input_tensor = infer_request.get_input_tensor()
-    tensor_shape = input_tensor.get_shape()
-    h, w = tensor_shape[2], tensor_shape[3]
-
-    # Step 4. Set Inputs
-    src = cv.imread(img_path, 0)
-    resized_img = cv.resize(src, (w, h))
-    img = np.array([np.array([resized_img])], dtype=np.float32)  # uint8 --> float32
-
-    input_tensor = ov.Tensor(array=img, shared_memory=True)
-    infer_request.set_input_tensor(input_tensor)
-
-    # Step 5. Start Synchronous Inference
-    mode_map_info = {0: 'synchronous', 1: 'asynchronous'}
-    if mode_map_info[mode] == 'synchronous':
-        start_time = time()
-        infer_request.infer()
-        end_time = time()
-    elif mode_map_info[mode] == 'asynchronous':
-        start_time = time()
-        infer_request.start_async()
-        infer_request.wait()
-        end_time = time()
+    start_time = time()
+    result_infer = compiled_model([input_image])[output_layer]
+    end_time = time()
     print(f"Inference time: {end_time - start_time:.6f} ms")
 
-    # Step 5. Get output and post-process
-    output = infer_request.get_output_tensor()
-    output_buffer = output.data
-    res = classes[np.argmax(output_buffer).item()]
-    print(f"The result : {res}")
+    result_index = np.argmax(result_infer)
+    out = classes[result_index]
 
     # visualization of result
     if visualize:
-        resized_src = cv.resize(src, None, fx=10, fy=10)
-        cv.putText(resized_src, "Prediction:" + str(res), (0, 20), cv.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
-        cv.imshow("Result", resized_src)
-        cv.waitKey()
-        cv.destroyAllWindows()
+        plt.text(0.05, 0.95, str(out), fontsize=18,
+                 verticalalignment='top', color='red')
+        plt.imshow(image, 'gray')
+        plt.show()
 
 
 if __name__ == "__main__":
@@ -70,8 +51,6 @@ def inference_resnet18sam(model_path, img_path, visualize, mode, device):
                         help='path of the image that will be input')
     parser.add_argument('--visualize', type=bool, required=False, default=True,
                         help='Enabling visualization. (default: True)')
-    parser.add_argument('--mode', type=int, required=False, default=0,
-                        help='Inference mode. [0 | 1] represents [synchronous| asynchronous]  (default: 0)')
     parser.add_argument('--device', type=str, required=False, default='CPU',
                         help="Inference hardware. [CPU,GPU,MYRAID]  (default: CPU)")
     args = parser.parse_args()
@@ -84,5 +63,6 @@ def inference_resnet18sam(model_path, img_path, visualize, mode, device):
         print(f'Cannot find the input image: {args.image_path}')
         exit()
 
-    # Run to inference
-    inference_resnet18sam(args.model_path, args.image_path, args.visualize, args.mode, args.device)
+    # Do inference
+    inference_resnet18sam(args.model_path, args.image_path,
+                          args.visualize, args.device)