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71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
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# Copyright (C) 2018 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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import json
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import cv2
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import os
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import subprocess
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import numpy as np
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from cvat.apps.auto_annotation.inference_engine import make_plugin, make_network
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class ModelLoader():
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def __init__(self, model, weights):
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self._model = model
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self._weights = weights
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IE_PLUGINS_PATH = os.getenv("IE_PLUGINS_PATH")
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if not IE_PLUGINS_PATH:
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raise OSError("Inference engine plugin path env not found in the system.")
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plugin = make_plugin()
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network = make_network(self._model, self._weights)
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supported_layers = plugin.get_supported_layers(network)
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not_supported_layers = [l for l in network.layers.keys() if l not in supported_layers]
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if len(not_supported_layers) != 0:
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raise Exception("Following layers are not supported by the plugin for specified device {}:\n {}".
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format(plugin.device, ", ".join(not_supported_layers)))
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iter_inputs = iter(network.inputs)
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self._input_blob_name = next(iter_inputs)
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self._output_blob_name = next(iter(network.outputs))
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self._require_image_info = False
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# NOTE: handeling for the inclusion of `image_info` in OpenVino2019
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if 'image_info' in network.inputs:
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self._require_image_info = True
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if self._input_blob_name == 'image_info':
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self._input_blob_name = next(iter_inputs)
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self._net = plugin.load(network=network, num_requests=2)
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input_type = network.inputs[self._input_blob_name]
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self._input_layout = input_type if isinstance(input_type, list) else input_type.shape
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def infer(self, image):
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_, _, h, w = self._input_layout
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in_frame = image if image.shape[:-1] == (h, w) else cv2.resize(image, (w, h))
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in_frame = in_frame.transpose((2, 0, 1)) # Change data layout from HWC to CHW
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inputs = {self._input_blob_name: in_frame}
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if self._require_image_info:
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info = np.zeros([1, 3])
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info[0, 0] = h
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info[0, 1] = w
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# frame number
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info[0, 2] = 1
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inputs['image_info'] = info
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results = self._net.infer(inputs)
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if len(results) == 1:
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return results[self._output_blob_name].copy()
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else:
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return results.copy()
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def load_labelmap(labels_path):
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with open(labels_path, "r") as f:
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return json.load(f)["label_map"]
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