towhee
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retinaface-face-detection
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74 lines
2.8 KiB
74 lines
2.8 KiB
3 years ago
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# Copyright 2021 Zilliz. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import NamedTuple, List
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from PIL import Image
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import torch
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from torchvision import transforms
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import sys
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import towhee
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from pathlib import Path
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import numpy
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from towhee.operator import Operator
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from towhee.utils.pil_utils import to_pil
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from timm.data import resolve_data_config
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from timm.data.transforms_factory import create_transform
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import os
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class RetinafaceFaceDetection(Operator):
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"""
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Embedding extractor using efficientnet.
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Args:
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model_name (`string`):
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Model name.
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weights_path (`string`):
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Path to local weights.
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"""
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def __init__(self, need_crop = True, framework: str = 'pytorch') -> None:
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super().__init__()
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if framework == 'pytorch':
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import importlib.util
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path = os.path.join(str(Path(__file__).parent), 'pytorch', 'model.py')
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opname = os.path.basename(str(Path(__file__))).split('.')[0]
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spec = importlib.util.spec_from_file_location(opname, path)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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self.need_crop = need_crop
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self.model = module.Model()
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def __call__(self, image: 'towhee.types.Image') -> List[NamedTuple('Outputs', [('boxes', numpy.ndarray),
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('keypoints', numpy.ndarray),
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('cropped_imgs', numpy.ndarray)])]:
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Outputs = NamedTuple('Outputs', [('boxes', numpy.ndarray), ('keypoints', numpy.ndarray), ('cropped_imgs', numpy.ndarray)])
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img = torch.FloatTensor(numpy.asarray(to_pil(image)))
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bboxes, keypoints = self.model(img)
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croppeds = []
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if self.need_crop is True:
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h, w, _ = img.shape
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for bbox in bboxes:
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x1, y1, x2, y2, _ = bbox
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x1 = max(int(x1), 0)
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y1 = max(int(y1), 0)
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x2 = min(int(x2), w)
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y2 = min(int(y2), h)
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croppeds.append(img[y1:y2, x1:x2, :].numpy())
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outputs = []
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for i in range(len(croppeds)):
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output = Outputs(bboxes[i], keypoints[i,:], croppeds[i])
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outputs.append(output)
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return outputs
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