通常，corwding counter任务中数据集只有图像和对应的标注文件，标注文件中为每个人的坐标（一个人对应一个坐标）。如果需要采用density map进行训练的话，需要生成对应的density map。
本代码用于将mat文件生成对应的density map；最终用.h5文件保存；
import h5py import scipy.io as io import PIL.Image as Image import numpy as np import os import glob from matplotlib import pyplot as plt from scipy.ndimage.filters import gaussian_filter from matplotlib import cm as CM from image import * # root is the path to ShanghaiTech dataset root='' part_B_train = os.path.join(root,'justPoint','images') # part_B_test = os.path.join(root,'justPoint','images') path_sets = [part_B_train] img_paths = [] for path in path_sets: for img_path in glob.glob(os.path.join(path, '*.jpg')): img_paths.append(img_path) for img_path in img_paths: print(img_path) mat = io.loadmat(img_path.replace('.jpg','.mat').replace('images','ground_truth').replace('IMG_','GT_IMG_')) img= plt.imread(img_path) k = np.zeros((img.shape[0],img.shape[1])) gt = mat["image_info"][0,0][0,0][0] for i in range(0,len(gt)): if int(gt[i][1])<img.shape[0] and int(gt[i][0])<img.shape[1]: k[int(gt[i][1]),int(gt[i][0])]=1 k = gaussian_filter(k,15) with h5py.File(img_path.replace('.jpg','.h5').replace('images','ground_truth'), 'w') as hf: hf['density'] = k
