Face identification and recognition scalable server with multiple face directories.
https://github.com/ehp/faceserver
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106 lines
3.5 KiB
106 lines
3.5 KiB
# -*- coding: utf-8 -*- |
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""" |
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Copyright 2019 Petr Masopust, Aprar s.r.o. |
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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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http://www.apache.org/licenses/LICENSE-2.0 |
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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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Adopted code from https://github.com/rainofmine/Face_Attention_Network |
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""" |
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import numpy as np |
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import torch |
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import argparse |
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import json |
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from PIL import Image, ImageDraw |
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from identification.dataloader import Normalizer, Resizer |
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from torchvision import transforms |
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def fan_detect(model, img_data, threshold=0.9, max_detections=100, is_cuda=True): |
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input_data = {'img': img_data, 'annot': np.zeros((0, 5)), 'scale': 1} |
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transform = transforms.Compose([Resizer(), Normalizer()]) |
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transformed = transform(input_data) |
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model.eval() |
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with torch.no_grad(): |
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img_data = transformed['img'].permute(2, 0, 1).float().unsqueeze(dim=0) |
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if is_cuda: |
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img_data = img_data.cuda() |
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scores, labels, boxes = model(img_data) |
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if scores is None: |
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return np.empty((0, 0)), np.empty((0, 0)) |
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scores = scores.cpu().numpy() |
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scale = transformed['scale'] |
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boxes = boxes.cpu().numpy() / scale |
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indices = np.where(scores > threshold)[0] |
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scores = scores[indices] |
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scores_sort = np.argsort(-scores)[:max_detections] |
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image_boxes = boxes[indices[scores_sort], :] |
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return image_boxes, scores[:max_detections] |
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def img_rectangles(img, output_path, boxes=None): |
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if boxes is not None: |
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draw = ImageDraw.Draw(img) |
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for arr in boxes: |
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draw.rectangle(((arr[0], arr[1]), (arr[2], arr[3])), outline="black", width=1) |
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img.save(output_path) |
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def load_model(model_path, is_cuda=True): |
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# load possible cuda model as cpu |
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model = torch.load(model_path, map_location=lambda storage, location: storage) |
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if is_cuda: |
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model = model.cuda() |
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model.anchors.is_cuda = is_cuda |
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return model |
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def load_image(filepath): |
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img = Image.open(filepath) |
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img = img.convert(mode="RGB") |
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return img |
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def main(args=None): |
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parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.') |
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parser.add_argument('--model', help='Path to model') |
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parser.add_argument('--image', help='Path to image') |
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parser.add_argument('--rect', help='Output image with rectangles') |
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parser.add_argument('--threshold', help='Probability threshold (default 0.9)', type=float, default=0.9) |
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parser.add_argument('--force-cpu', help='Force CPU for detection (default false)', dest='force_cpu', |
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default=False, action='store_true') |
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parser = parser.parse_args(args) |
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is_cuda = torch.cuda.is_available() and not parser.force_cpu |
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model = load_model(parser.model, is_cuda=is_cuda) |
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img = load_image(parser.image) |
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boxes, scores = fan_detect(model, img, threshold=parser.threshold, is_cuda=is_cuda) |
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print(json.dumps({'boxes': boxes.tolist(), 'scores': scores})) |
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if parser.rect: |
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img = load_image(parser.image) |
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img_rectangles(img, parser.rect, boxes) |
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if __name__ == '__main__': |
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main()
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