Upload DroneBird/toolkit/make_data_density.py

DroneBird/toolkit/make_data_density.py

@@ -0,0 +1,87 @@
+# -- coding: utf-8 --
+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
+    # 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]] = 1.0
+        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  # case: 1 point
+        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)))