两张人像对比是否是同一人- deep-person-reid

本文主要是介绍两张人像对比是否是同一人- deep-person-reid，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

基于的项目：https://github.com/KaiyangZhou/deep-person-reid

安装

git clone https://github.com/KaiyangZhou/deep-person-reid.gitcd deep-person-reid/pip install -r requirements.txtpython setup.py develop

权重下载

https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO

reid.py

执行：

python reid.py --image_path1 input/1.jpg --image_path2 input/2.jpg 

'''
python reid.py --image_path1 input/1.jpg --image_path2 input/2.jpg 
'''
import torchreid
import torch
from PIL import Image
from torchreid.utils import FeatureExtractor
import argparse# Initialize the feature extractor with a pretrained model
extractor = FeatureExtractor(model_name='osnet_x1_0',  # You can choose other models too, like 'resnet50', 'resnet101'model_path='osnet_x1_0_imagenet.pth',  # None to use default pretrained model# device='cuda'  # or 'cpu'device='cpu'  # or 'cpu'
)def extract_feature(image_path):# 直接传递图像路径给 FeatureExtractorfeatures = extractor([image_path])  # Extract featuresreturn features[0]  # Return the feature vectordef compare_images(image_path1, image_path2):# Extract features from both imagesfeatures1 = extract_feature(image_path1)features2 = extract_feature(image_path2)# Compute the cosine similaritysimilarity = torch.nn.functional.cosine_similarity(features1, features2, dim=0)return similarity.item()if __name__ == "__main__":parser = argparse.ArgumentParser()parser.add_argument('--image_path1', type=str, default='1.jpg')parser.add_argument('--image_path2', type=str, default='2.jpg')opt = parser.parse_args()image_path1 = opt.image_path1image_path2 = opt.image_path2similarity_score = compare_images(image_path1, image_path2)threshold = 0.7  # Adjust this threshold based on your use caseif similarity_score > threshold:print("The images are likely of the same person.")else:print("The images are likely of different people.")

结果

Model: osnet_x1_0

• params: 2,193,616
• flops: 978,878,352
  Successfully loaded pretrained weights from “osnet_x1_0_imagenet.pth”
  ** The following layers are discarded due to unmatched keys or layer size: [‘classifier.weight’, ‘classifier.bias’]
  The images are likely of different people.

Model: osnet_x1_0

• params: 2,193,616
• flops: 978,878,352
  Successfully loaded pretrained weights from “osnet_x1_0_imagenet.pth”
  ** The following layers are discarded due to unmatched keys or layer size: [‘classifier.weight’, ‘classifier.bias’]
  The images are likely of the same person.

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