make_dataset.py

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
import scipy
import cv2
import json
from matplotlib import cm as CM
from image import *
from model import ASPDNet
import torch

#matplotlib inline

#this is borrowed from https://github.com/davideverona/deep-crowd-counting_crowdnet
def gaussian_filter_density(gt):
    density = np.zeros(gt.shape, dtype=np.float32)
    gt_count = np.count_nonzero(gt)
    if gt_count == 0:
        return density

    # pts = np.array(zip(np.nonzero(gt)[1], np.nonzero(gt)[0]))
    pts = np.array(list(zip(np.nonzero(gt)[1].ravel(), np.nonzero(gt)[0].ravel())))
    leafsize = 2048
    # build kdtree
    tree = scipy.spatial.KDTree(pts.copy(), leafsize=leafsize)
    # query kdtree
    distances, locations = tree.query(pts, k=4)

    print ('generate density...')
    for i, pt in enumerate(pts):
        pt2d = np.zeros(gt.shape, dtype=np.float32)
        pt2d[pt[1],pt[0]] = 1.
        if gt_count > 1:
            sigma = (distances[i][1]+distances[i][2]+distances[i][3])*0.1
        else:
            sigma = np.average(np.array(gt.shape))//2.//2. #case: 1 point
        density += scipy.ndimage.filters.gaussian_filter(pt2d, sigma, mode='constant')
    print ('done.')
    return density

#set the root to the building dataset you download
root = '..'

##now generate the RSOC_building ground truth
building_train = os.path.join(root,'../train_data','images')
building_test = os.path.join(root,'../test_data','images')

path_sets = [building_train,building_test]

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:
    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['center'][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_density(k)
    k = gaussian_filter(k, 15)
    groundtruth = np.asarray(k)
    with h5py.File(img_path.replace('.jpg','.h5').replace('images','ground_truth'), 'w') as hf:
            hf['density'] = k