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Merge pull request #8 from PINTO0309/post-process
後処理をONNXに全部マージしたバージョンを作ってみました
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@@ -133,3 +133,7 @@ dmypy.json | |
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| # mp4 | ||
| *.mp4 | ||
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| # tensorrt | ||
| *.engine | ||
| *.profile | ||
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| { | ||
| "version": "0.2.0", | ||
| "configurations": [ | ||
| { | ||
| "name": "simple_demo", | ||
| "type": "python", | ||
| "request": "launch", | ||
| "program": "${file}", | ||
| "console": "integratedTerminal", | ||
| "justMyCode": true, | ||
| "args": [ | ||
| "--width", "640", | ||
| "--height", "480", | ||
| ] | ||
| }, | ||
| { | ||
| "name": "simple_demo_with_post", | ||
| "type": "python", | ||
| "request": "launch", | ||
| "program": "${file}", | ||
| "console": "integratedTerminal", | ||
| "justMyCode": true, | ||
| "args": [ | ||
| "--width", "640", | ||
| "--height", "480", | ||
| ] | ||
| } | ||
| ] | ||
| } |
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| @@ -0,0 +1,113 @@ | ||
| #!/usr/bin/env python | ||
| # -*- coding: utf-8 -*- | ||
| import copy | ||
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| import cv2 | ||
| import numpy as np | ||
| import onnxruntime | ||
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| class YoloxONNX(object): | ||
| def __init__( | ||
| self, | ||
| model_path='yolox_nano.onnx', | ||
| input_shape=(416, 416), | ||
| class_score_th=0.3, | ||
| with_p6=False, | ||
| providers=[ | ||
| ( | ||
| 'TensorrtExecutionProvider', { | ||
| 'trt_engine_cache_enable': True, | ||
| 'trt_engine_cache_path': '.', | ||
| 'trt_fp16_enable': True, | ||
| } | ||
| ), | ||
| 'CUDAExecutionProvider', | ||
| 'CPUExecutionProvider', | ||
| ], | ||
| ): | ||
| # 入力サイズ | ||
| self.input_shape = input_shape | ||
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| # 閾値 | ||
| self.class_score_th = class_score_th | ||
| self.with_p6 = with_p6 | ||
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| # モデル読み込み | ||
| self.onnx_session = onnxruntime.InferenceSession( | ||
| model_path, | ||
| providers=providers, | ||
| ) | ||
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| self.input_name = self.onnx_session.get_inputs()[0].name | ||
| self.output_name = self.onnx_session.get_outputs()[0].name | ||
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| def inference(self, image): | ||
| temp_image = copy.deepcopy(image) | ||
| image_height, image_width = image.shape[0], image.shape[1] | ||
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| # 前処理 | ||
| image, ratio = self._preprocess(temp_image, self.input_shape) | ||
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| # 推論実施 | ||
| results = self.onnx_session.run( | ||
| None, | ||
| {self.input_name: image[None, :, :, :]}, | ||
| ) | ||
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| # 後処理 | ||
| bboxes, scores, class_ids = self._postprocess( | ||
| results[0], | ||
| ratio, | ||
| image_width, | ||
| image_height, | ||
| ) | ||
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| return bboxes, scores, class_ids | ||
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| def _preprocess(self, image, input_size, swap=(2, 0, 1)): | ||
| if len(image.shape) == 3: | ||
| padded_image = np.ones( | ||
| (input_size[0], input_size[1], 3), dtype=np.uint8) * 114 | ||
| else: | ||
| padded_image = np.ones(input_size, dtype=np.uint8) * 114 | ||
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| ratio = min(input_size[0] / image.shape[0], | ||
| input_size[1] / image.shape[1]) | ||
| resized_image = cv2.resize( | ||
| image, | ||
| (int(image.shape[1] * ratio), int(image.shape[0] * ratio)), | ||
| interpolation=cv2.INTER_LINEAR, | ||
| ) | ||
| resized_image = resized_image.astype(np.uint8) | ||
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| padded_image[:resized_image.shape[0], :resized_image.shape[1]] = resized_image | ||
| padded_image = padded_image.transpose(swap) | ||
| padded_image = np.ascontiguousarray(padded_image, dtype=np.float32) | ||
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| return padded_image, ratio | ||
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| def _postprocess( | ||
| self, | ||
| dets: np.ndarray, | ||
| ratio, | ||
| max_width: int, | ||
| max_height: int, | ||
| ): | ||
| bbox = np.array([]) | ||
| score = np.array([]) | ||
| class_id = np.array([]) | ||
| if dets is not None and dets.shape[0] >= 1: | ||
| class_ids, scores, bboxes = dets[..., 1:2], dets[..., 2:3], dets[..., 3:] | ||
| keep_idx = np.argmax(scores, axis=0) | ||
| class_id = class_ids[keep_idx, ...] | ||
| score = scores[keep_idx, ...] | ||
| bbox = bboxes[keep_idx, ...][0] | ||
| bbox /= ratio | ||
| bbox[0] = max(0, bbox[0]) | ||
| bbox[1] = max(0, bbox[1]) | ||
| bbox[2] = min(bbox[2], max_width) | ||
| bbox[3] = min(bbox[3], max_height) | ||
| bbox = bbox[np.newaxis, :] | ||
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| return bbox.astype(np.float32), score.astype(np.float32), class_id.astype(np.int32) |
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