From 6514fb0e86ad17212e3caf04b62a929a91c15284 Mon Sep 17 00:00:00 2001
From: Piv <18462828+Piv200@users.noreply.github.com>
Date: Wed, 28 Jul 2021 20:15:15 +0930
Subject: [PATCH] Add sample openvino inference

---
 openvino_inference.py | 74 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 74 insertions(+)
 create mode 100644 openvino_inference.py

diff --git a/openvino_inference.py b/openvino_inference.py
new file mode 100644
index 0000000..66efa54
--- /dev/null
+++ b/openvino_inference.py
@@ -0,0 +1,74 @@
+import argparse
+import time
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+from openvino.inference_engine import IECore
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse and return command line arguments"""
+    parser = argparse.ArgumentParser(add_help=False)
+    args = parser.add_argument_group('Options')
+    # fmt: off
+    args.add_argument('-h', '--help', action='help', help='Show this help message and exit.')
+    args.add_argument('-m', '--model', required=True, type=str,
+                      help='Required. Path to an .xml or .onnx file with a trained model.')
+    args.add_argument('-i', '--input', required=True, type=str, help='Required. Path to an image file.')
+    args.add_argument('-d', '--device', default='CPU', type=str,
+                      help='Optional. Specify the target device to infer on; CPU, GPU, MYRIAD, HDDL or HETERO: '
+                           'is acceptable. The sample will look for a suitable plugin for device specified. '
+                           'Default value is CPU.')
+    # fmt: on
+    return parser.parse_args()
+
+
+def sample(model_location, image_location, device='CPU'):
+    ie = IECore()
+
+    net = ie.read_network(model=model_location)
+    input_blob = next(iter(net.input_info))
+    output_blob = next(iter(net.outputs))
+
+    b, c, h, w = net.input_info[input_blob].input_data.shape
+    image = cv2.imread(image_location)
+    input_ratio = h / w
+    target_ratio = image.shape[0] / image.shape[1]
+    crop_axis = 0 if target_ratio > input_ratio else 1
+    crop_factor = input_ratio * target_ratio / 2
+    center = [image.shape[0] / 2, image.shape[1] / 2]
+    x1 = int(center[0] - image.shape[0] * crop_factor) if crop_axis == 0 else 0
+    x2 = int(center[0] + image.shape[0] * crop_factor) if crop_axis == 0 else image.shape[0]
+    y1 = int(center[1] - image.shape[1] * crop_factor) if crop_axis == 1 else 0
+    y2 = int(center[1] + image.shape[1] * crop_factor) if crop_axis == 1 else image.shape[1]
+    # Crop to target aspect ratio
+    image = image[x1:x2, y1:y2]
+    if image.shape[:-1] != (h, w):
+        image = cv2.resize(image, (w, h))
+
+    image = image.transpose((2, 0, 1))
+    # For batching
+    image = np.expand_dims(image, axis=0)
+
+    exec_net = ie.load_network(network=net, device_name=device)
+    start = time.time()
+    res = exec_net.infer(inputs={input_blob: image})
+    print('First Inference Time Seconds: ' + str(time.time() - start))
+    start = time.time()
+    res = exec_net.infer(inputs={input_blob: image})
+    print('Second Inference Time Seconds: ' + str(time.time() - start))
+    start = time.time()
+    res = exec_net.infer(inputs={input_blob: image})
+    print('Third Inference Time Seconds: ' + str(time.time() - start))
+    res = res[output_blob]
+    depth = res[0][0]
+    fig = plt.figure()
+    ii = plt.imshow(depth, interpolation='nearest')
+    fig.colorbar(ii)
+    plt.show()
+
+
+if __name__ == '__main__':
+    parsed_args = parse_args()
+    sample(parsed_args.model, parsed_args.input, parsed_args.device)