更新 detacttracking
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lee
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detecttracking/contrast/genfeats.py
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detecttracking/contrast/genfeats.py
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# -*- coding: utf-8 -*-
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"""
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Created on Sun Nov 3 12:05:19 2024
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@author: ym
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"""
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import os
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import time
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# import torch
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import pickle
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# import json
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import numpy as np
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from PIL import Image
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from feat_extract.config import config as conf
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# from model import resnet18 as resnet18
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from feat_extract.inference import FeatsInterface #, inference_image
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IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
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# def model_init(conf, mpath=None):
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# '''======= 0. 配置特征提取模型地址 ======='''
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# if mpath is None:
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# model_path = conf.test_model
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# else:
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# model_path = mpath
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# ##============ load resnet mdoel
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# model = resnet18().to(conf.device)
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# # model = nn.DataParallel(model).to(conf.device)
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# model.load_state_dict(torch.load(model_path, map_location=conf.device))
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# model.eval()
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# print('load model {} '.format(conf.testbackbone))
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# return model
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def get_std_barcodeDict(bcdpath, savepath, bcdSet):
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'''
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inputs:
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bcdpath: 已清洗的barcode样本图像，如果barcode下有'base'文件夹，只选用该文件夹下图像
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(default = r'\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_1771')
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功能：
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生成并保存只有一个key值的字典 {barcode: [imgpath1, imgpath1, ...]}，
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savepath: 字典存储地址，文件名格式：barcode.pickle
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'''
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# savepath = r'\\192.168.1.28\share\测试_202406\contrast\std_barcodes'
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'''读取数据集中 barcode 列表'''
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stdBarcodeList = []
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for filename in os.listdir(bcdpath):
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filepath = os.path.join(bcdpath, filename)
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if not os.path.isdir(filepath) or not filename.isdigit() or len(filename)<8:
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continue
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if bcdSet is None:
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stdBarcodeList.append(filename)
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elif filename in bcdSet:
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stdBarcodeList.append(filename)
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bcdPaths = [(barcode, os.path.join(bcdpath, barcode)) for barcode in stdBarcodeList]
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'''遍历数据集，针对每一个barcode，生成并保存字典{barcode: [imgpath1, imgpath1, ...]}'''
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k = 0
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errbarcodes = []
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for barcode, bpath in bcdPaths:
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pickpath = os.path.join(savepath, f"{barcode}.pickle")
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if os.path.isfile(pickpath):
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continue
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stdBarcodeDict = {}
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stdBarcodeDict[barcode] = []
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for root, dirs, files in os.walk(bpath):
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imgpaths = []
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if "base" in dirs:
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broot = os.path.join(root, "base")
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for imgname in os.listdir(broot):
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imgpath = os.path.join(broot, imgname)
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file, ext = os.path.splitext(imgpath)
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if ext not in IMG_FORMAT:
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continue
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imgpaths.append(imgpath)
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stdBarcodeDict[barcode].extend(imgpaths)
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break
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else:
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for imgname in files:
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imgpath = os.path.join(root, imgname)
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_, ext = os.path.splitext(imgpath)
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if ext not in IMG_FORMAT: continue
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imgpaths.append(imgpath)
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stdBarcodeDict[barcode].extend(imgpaths)
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pickpath = os.path.join(savepath, f"{barcode}.pickle")
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with open(pickpath, 'wb') as f:
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pickle.dump(stdBarcodeDict, f)
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print(f"Barcode: {barcode}")
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# k += 1
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# if k == 10:
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# break
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print(f"Len of errbarcodes: {len(errbarcodes)}")
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return
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def stdfeat_infer(imgPath, featPath, bcdSet=None):
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'''
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inputs:
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imgPath: 该文件夹下的 pickle 文件格式 {barcode: [imgpath1, imgpath1, ...]}
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featPath: imgPath图像对应特征的存储地址
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功能:
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对 imgPath中图像进行特征提取，生成只有一个key值的字典，
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{barcode: features}，features.shape=(nsample, 256)，并保存至 featPath 中
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'''
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# imgPath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes"
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# featPath = r"\\192.168.1.28\share\测试_202406\contrast\std_features"
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stdBarcodeDict = {}
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stdBarcodeDict_ft16 = {}
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Encoder = FeatsInterface(conf)
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'''4处同名: (1)barcode原始图像文件夹; (2)imgPath中的 .pickle 文件名;
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(3)该pickle文件中字典的key值; (4)特征向量字典中的一个key值'''
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k = 0
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for filename in os.listdir(imgPath):
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bcd, ext = os.path.splitext(filename)
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filepath = os.path.join(imgPath, filename)
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if ext != ".pickle": continue
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if bcdSet is not None and bcd not in bcdSet:
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continue
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featpath = os.path.join(featPath, f"{bcd}.pickle")
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if os.path.isfile(featpath):
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continue
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stdbDict = {}
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t1 = time.time()
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try:
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with open(filepath, 'rb') as f:
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bpDict = pickle.load(f)
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for barcode, imgpaths in bpDict.items():
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# feature = batch_inference(imgpaths, 8) #from vit distilled model of LiChen
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# feature = inference_image(imgpaths, conf.test_transform, model, conf.device)
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imgs = []
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for d, imgpath in enumerate(imgpaths):
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img = Image.open(imgpath)
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imgs.append(img)
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feature = Encoder.inference(imgs)
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feature /= np.linalg.norm(feature, axis=1)[:, None]
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# float16
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feature_ft16 = feature.astype(np.float16)
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feature_ft16 /= np.linalg.norm(feature_ft16, axis=1)[:, None]
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# uint8, 两种策略，1) 精度损失小, 2) 计算复杂度小
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# feature_uint8, _ = ft16_to_uint8(feature_ft16)
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feature_uint8 = (feature_ft16*128).astype(np.int8)
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except Exception as e:
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print(f"Error accured at: {filename}, with Exception is: {e}")
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'''================ 保存单个barcode特征 ================'''
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##================== float32
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stdbDict["barcode"] = barcode
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stdbDict["imgpaths"] = imgpaths
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stdbDict["feats_ft32"] = feature
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stdbDict["feats_ft16"] = feature_ft16
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stdbDict["feats_uint8"] = feature_uint8
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with open(featpath, 'wb') as f:
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pickle.dump(stdbDict, f)
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stdBarcodeDict[barcode] = feature
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stdBarcodeDict_ft16[barcode] = feature_ft16
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t2 = time.time()
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print(f"Barcode: {barcode}, need time: {t2-t1:.1f} secs")
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# k += 1
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# if k == 10:
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# break
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return
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def gen_bcd_features(imgpath, bcdpath, featpath, bcdSet=None):
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''' 生成标准特征集 '''
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'''1. 提取 imgpath 中样本地址，生成字典{barcode: [imgpath1, imgpath1, ...]}
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并存储于: bcdpath, 格式为 barcode.pickle'''
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get_std_barcodeDict(imgpath, bcdpath, bcdSet)
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'''2. 特征提取，并保存至文件夹 featpath 中，也根据 bcdSet 交集执行'''
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stdfeat_infer(bcdpath, featpath, bcdSet)
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def main():
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imgpath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v1.0\比对数据\整理\zhantingBase"
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bcdpath = r"D:\exhibition\dataset\bcdpath"
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featpath = r"D:\exhibition\dataset\feats"
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gen_bcd_features(imgpath, bcdpath, featpath)
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if __name__ == '__main__':
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main()
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