File size: 2,094 Bytes
fa173f9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | import h5py
import scipy
import scipy.io as io
import numpy as np
import os
import glob
from matplotlib import pyplot as plt
from scipy import spatial
from multiprocessing import Pool
from functools import partial
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(list(zip(np.nonzero(gt)[1], np.nonzero(gt)[0])))
leafsize = 2048
# build kdtree
tree = spatial.KDTree(pts.copy(), leafsize=leafsize)
# query kdtree
distances, locations = tree.query(pts, k=4)
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')
return density
def process(idx, img_paths):
img_path = img_paths[idx]
mat = io.loadmat(img_path.replace('.jpg','.mat').replace('images','ground_truth').replace('img','GT_img'))
img = plt.imread(img_path)
k = np.zeros((int(img.shape[0]/2),int(img.shape[1]/2)))
gt = mat["image_info"][0,0][0,0][0]
for i in range(0,len(gt)):
if int(gt[i][1]/2)<img.shape[0]/2 and int(gt[i][0]/2)<img.shape[1]/2:
k[int(gt[i][1]/2),int(gt[i][0]/2)]=1
k = gaussian_filter_density(k)
with h5py.File(img_path.replace('.jpg','.h5').replace('images','ground_truth'), 'w') as hf:
hf['density'] = k
print(idx,len(img_paths))
part_train = os.path.join('train_data','images')
part_test = os.path.join('val_data','images')
img_paths = []
for img_path in glob.glob(os.path.join(part_train, '*.jpg')):
img_paths.append(img_path)
for img_path in glob.glob(os.path.join(part_test, '*.jpg')):
img_paths.append(img_path)
img_paths.sort()
pool = Pool(10)
partial = partial(process, img_paths=img_paths)
_ = pool.map(partial, range(len(img_paths))) |