|
|
| 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, ndimage
|
| from multiprocessing import Pool
|
| from functools import partial
|
| import time
|
| import json
|
|
|
|
|
| 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
|
|
|
| tree = spatial.KDTree(pts.copy(), leafsize=leafsize)
|
|
|
| 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]] = 1.0
|
| if gt_count > 1:
|
| sigma = (distances[i][1] + distances[i][2] + distances[i][3]) * 0.1
|
| else:
|
| sigma = np.average(np.array(gt.shape)) / 2.0 / 2.0
|
| sigma = 6
|
| density += scipy.ndimage.filters.gaussian_filter(pt2d, sigma, mode="constant")
|
|
|
| return density
|
|
|
|
|
| def process(idx, img_paths):
|
| start = time.time()
|
| img_path = img_paths[idx]
|
| mat_path = (
|
| img_path.replace(".jpg", ".mat")
|
| .replace("images", "ground_truth")
|
| .replace("img", "GT_img")
|
| )
|
| mat = io.loadmat(mat_path)
|
| img = plt.imread(img_path)
|
| k = np.zeros((int(img.shape[0] / 2), int(img.shape[1] / 2)))
|
| gt = mat["locations"]
|
| 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(mat_path.replace("mat", "h5"), "w") as hf:
|
| hf["density"] = k
|
| end = time.time()
|
| print(idx, len(img_paths), img_path, str(end - start) + "s")
|
|
|
|
|
| if __name__ == "__main__":
|
|
|
| img_paths = []
|
| data_path = "."
|
| for img_path in glob.glob(os.path.join(data_path, "*/images", "*.jpg")):
|
| h5_path = (
|
| img_path.replace(".jpg", ".h5")
|
| .replace("images", "ground_truth")
|
| .replace("img", "GT_img")
|
| )
|
| if not os.path.exists(h5_path):
|
| img_paths.append(img_path)
|
|
|
| img_paths.sort()
|
| print(img_paths)
|
| print(len(img_paths))
|
|
|
| pool = Pool(10)
|
| partial = partial(process, img_paths=img_paths)
|
| _ = pool.map(partial, range(len(img_paths)))
|
|
|
