Python OpenVINO

projects/OpenVINO/Python/.gitignore

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+# models
+intel/
+*.onnx

projects/OpenVINO/Python/README.md

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+# ...
+
+## Экспорт YOLOv8 в OpenVINO
+
+Документация на сайте [Ultralytics](https://docs.ultralytics.com/ru/integrations/openvino/).
+
+```sh
+yolo export model=yolov8n.pt format=openvino
+```
+
+##
+
+```sh
+pip3 install  openvino-dev
+omz_downloader --name person-detection-0106
+```

projects/OpenVINO/Python/images/bus.jpg

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+../../../assets/bus.jpg

projects/OpenVINO/Python/images/girl_faces.png

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+../..//../assets/girl_faces.png

projects/OpenVINO/Python/infer.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# https://docs.openvino.ai/2023.3/openvino_sample_hello_reshape_ssd.html
+
+import logging as log
+import os
+import sys
+
+import cv2
+import numpy as np
+import openvino as ov
+
+
+def main():
+    log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
+
+    # Parsing and validation of input arguments
+    if len(sys.argv) != 4:
+        log.info(f'Usage: {sys.argv[0]} <path_to_model> <path_to_image> <device_name>')
+        return 1
+
+    model_path = sys.argv[1]
+    image_path = sys.argv[2]
+    device_name = sys.argv[3]
+
+# --------------------------- Step 1. Initialize OpenVINO Runtime Core ------------------------------------------------
+    log.info('Creating OpenVINO Runtime Core')
+    core = ov.Core()
+
+# --------------------------- Step 2. Read a model --------------------------------------------------------------------
+    log.info(f'Reading the model: {model_path}')
+    # (.xml and .bin files) or (.onnx file)
+    model = core.read_model(model_path)
+
+    if len(model.inputs) != 1:
+        log.error('Sample supports only single input topologies')
+        return -1
+
+    if len(model.outputs) != 1:
+        log.error('Sample supports only single output topologies')
+        return -1
+
+# --------------------------- Step 3. Set up input --------------------------------------------------------------------
+    # Read input image
+    image = cv2.imread(image_path)
+    # Add N dimension
+    input_tensor = np.expand_dims(image, 0)
+
+    log.info('Reshaping the model to the height and width of the input image')
+    n, h, w, c = input_tensor.shape
+    model.reshape({model.input().get_any_name(): ov.PartialShape((n, c, h, w))})
+
+# --------------------------- Step 4. Apply preprocessing -------------------------------------------------------------
+    ppp = ov.preprocess.PrePostProcessor(model)
+
+    # 1) Set input tensor information:
+    # - input() provides information about a single model input
+    # - precision of tensor is supposed to be 'u8'
+    # - layout of data is 'NHWC'
+    ppp.input().tensor() \
+        .set_element_type(ov.Type.u8) \
+        .set_layout(ov.Layout('NHWC'))  # noqa: N400
+
+    # 2) Here we suppose model has 'NCHW' layout for input
+    ppp.input().model().set_layout(ov.Layout('NCHW'))
+
+    # 3) Set output tensor information:
+    # - precision of tensor is supposed to be 'f32'
+    ppp.output().tensor().set_element_type(ov.Type.f32)
+
+    # 4) Apply preprocessing modifing the original 'model'
+    model = ppp.build()
+
+# ---------------------------Step 4. Loading model to the device-------------------------------------------------------
+    log.info('Loading the model to the plugin')
+    compiled_model = core.compile_model(model, device_name)
+
+# --------------------------- Step 6. Create infer request and do inference synchronously -----------------------------
+    log.info('Starting inference in synchronous mode')
+    results = compiled_model.infer_new_request({0: input_tensor})
+
+# ---------------------------Step 6. Process output--------------------------------------------------------------------
+    predictions = next(iter(results.values()))
+
+    # Change a shape of a numpy.ndarray with results ([1, 1, N, 7]) to get another one ([N, 7]),
+    # where N is the number of detected bounding boxes
+    detections = predictions.reshape(-1, 7)
+
+    for detection in detections:
+        confidence = detection[2]
+
+        if confidence > 0.5:
+            class_id = int(detection[1])
+
+            xmin = int(detection[3] * w)
+            ymin = int(detection[4] * h)
+            xmax = int(detection[5] * w)
+            ymax = int(detection[6] * h)
+
+            log.info(f'Found: class_id = {class_id}, confidence = {confidence:.2f}, ' f'coords = ({xmin}, {ymin}), ({xmax}, {ymax})')
+
+            # Draw a bounding box on a output image
+            cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
+
+    cv2.imwrite('out.bmp', image)
+
+    if os.path.exists('out.bmp'):
+        log.info('Image out.bmp was created!')
+    else:
+        log.error('Image out.bmp was not created. Check your permissions.')
+
+# ----------------------------------------------------------------------------------------------------------------------
+    log.info('This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n')
+    return 0
+
+
+if __name__ == '__main__':
+    sys.exit(main())

projects/OpenVINO/Python/requirements.txt

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+black==24.4.2
+certifi==2024.7.4
+charset-normalizer==3.3.2
+click==8.1.7
+defusedxml==0.7.1
+idna==3.7
+mypy-extensions==1.0.0
+networkx==3.1
+numpy==1.24.4
+opencv-python==4.10.0.84
+openvino==2024.3.0
+openvino-dev==2024.3.0
+openvino-telemetry==2024.1.0
+packaging==24.1
+pathspec==0.12.1
+pillow==10.4.0
+pkg_resources==0.0.0
+platformdirs==4.2.2
+PyYAML==6.0.2
+requests==2.32.3
+tomli==2.0.1
+typing_extensions==4.12.2
+urllib3==2.2.2