Spaces:
Running
Running
File size: 7,413 Bytes
94f372a |
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 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 |
import hydra
import torch
import numpy as np
import pandas as pd
import statistics
from os.path import join, dirname
import matplotlib.pyplot as plt
class QuadTree(object):
def __init__(self, data, id="", depth=3, do_split=5000):
self.id = id
self.data = data
coord = data[["latitude", "longitude"]].to_numpy()
# if mins is None:
mins = coord.min(0)
# if maxs is None:
maxs = coord.max(0)
self.mins = np.asarray(mins)
self.maxs = np.asarray(maxs)
self.sizes = self.maxs - self.mins
self.children = []
# sort by latitude
sorted_data_lat = sorted(coord, key=lambda point: point[0])
# get the median lat
median_lat = statistics.median(point[0] for point in sorted_data_lat)
# Divide the cell into two half-cells based on the median lat
data_left = [point for point in sorted_data_lat if point[0] <= median_lat]
data_right = [point for point in sorted_data_lat if point[0] > median_lat]
# Sort the data points by long in each half-cell
sorted_data_left_lon = sorted(data_left, key=lambda point: point[1])
sorted_data_right_lon = sorted(data_right, key=lambda point: point[1])
# Calculate the median ylong coordinate in each half-cell
median_lon_left = statistics.median(point[1] for point in sorted_data_left_lon)
median_lon_right = statistics.median(
point[1] for point in sorted_data_right_lon
)
if (depth > 0) and (len(self.data) >= do_split):
# split the data into four quadrants
data_q1 = data[
(data["latitude"] < median_lat) & (data["longitude"] < median_lon_left)
]
data_q2 = data[
(data["latitude"] < median_lat) & (data["longitude"] >= median_lon_left)
]
data_q3 = data[
(data["latitude"] >= median_lat)
& (data["longitude"] < median_lon_right)
]
data_q4 = data[
(data["latitude"] >= median_lat)
& (data["longitude"] >= median_lon_right)
]
# recursively build a quad tree on each quadrant which has data
if data_q1.shape[0] > 0:
self.children.append(
QuadTree(
data_q1,
id + "0",
depth - 1,
do_split=do_split,
)
)
if data_q2.shape[0] > 0:
self.children.append(
QuadTree(
data_q2,
id + "1",
depth - 1,
do_split=do_split,
)
)
if data_q3.shape[0] > 0:
self.children.append(
QuadTree(
data_q3,
id + "2",
depth - 1,
do_split=do_split,
)
)
if data_q4.shape[0] > 0:
self.children.append(
QuadTree(
data_q4,
id + "3",
depth - 1,
do_split=do_split,
)
)
def unwrap(self):
if len(self.children) == 0:
return {self.id: [self.mins, self.maxs, self.data.copy()]}
else:
d = dict()
for child in self.children:
d.update(child.unwrap())
return d
def extract(qt, name_new_column):
cluster = qt.unwrap()
boundaries, data = {}, []
for i, (id, vs) in zip(np.arange(len(cluster)), cluster.items()):
(min_lat, min_lon), (max_lat, max_lon), points = vs
points[name_new_column] = int(i)
data.append(points)
boundaries[i] = (
float(min_lat),
float(min_lon),
float(max_lat),
float(max_lon),
points["latitude"].mean(),
points["longitude"].mean(),
)
data = pd.concat(data)
return boundaries, data
def vizu(name_new_column, df_train, boundaries, do_split):
plt.hist(df_train[name_new_column], bins=len(boundaries))
plt.xlabel("Cluster ID")
plt.ylabel("Number of images")
plt.title("Cluster distribution")
plt.yscale("log")
plt.ylim(10, do_split)
plt.savefig(f"{name_new_column}_distrib.png")
plt.clf()
plt.scatter(
df_train["longitude"].to_numpy(),
df_train["latitude"].to_numpy(),
c=np.random.permutation(len(boundaries))[df_train[name_new_column].to_numpy()],
cmap="tab20",
s=0.1,
alpha=0.5,
)
plt.xlabel("Longitude")
plt.ylabel("Latitude")
plt.title("Quadtree map")
plt.savefig(f"{name_new_column}_map.png")
@hydra.main(
config_path="../configs/scripts",
config_name="enrich-metadata-quadtree",
version_base=None,
)
def main(cfg):
data_path = join(cfg.data_dir, "osv5m")
name_new_column = f"adaptive_quadtree_{cfg.depth}_{cfg.do_split}"
# Create clusters from train images
train_fp = join(data_path, f"train.csv")
df_train = pd.read_csv(train_fp)
qt = QuadTree(df_train, depth=cfg.depth, do_split=cfg.do_split)
boundaries, df_train = extract(qt, name_new_column)
vizu(name_new_column, df_train, boundaries, cfg.do_split)
# Save clusters
boundaries = pd.DataFrame.from_dict(
boundaries,
orient="index",
columns=["min_lat", "min_lon", "max_lat", "max_lon", "mean_lat", "mean_lon"],
)
boundaries.to_csv(f"{name_new_column}.csv", index_label="cluster_id")
# Assign test images to clusters
test_fp = join(data_path, f"test.csv")
df_test = pd.read_csv(test_fp)
above_lat = np.expand_dims(df_test["latitude"].to_numpy(), -1) > np.expand_dims(
boundaries["min_lat"].to_numpy(), 0
)
below_lat = np.expand_dims(df_test["latitude"].to_numpy(), -1) < np.expand_dims(
boundaries["max_lat"].to_numpy(), 0
)
above_lon = np.expand_dims(df_test["longitude"].to_numpy(), -1) > np.expand_dims(
boundaries["min_lon"].to_numpy(), 0
)
below_lon = np.expand_dims(df_test["longitude"].to_numpy(), -1) < np.expand_dims(
boundaries["max_lon"].to_numpy(), 0
)
mask = np.logical_and(
np.logical_and(above_lat, below_lat), np.logical_and(above_lon, below_lon)
)
df_test[name_new_column] = np.argmax(mask, axis=1)
# save index_to_gps_quadtree file
lat = torch.tensor(boundaries["mean_lat"])
lon = torch.tensor(boundaries["mean_lon"])
coord = torch.stack([lat / 90, lon / 180], dim=-1)
torch.save(
coord,
join(
data_path, f"index_to_gps_adaptive_quadtree_{cfg.depth}_{cfg.do_split}.pt"
),
)
# Overwrite test.csv and train.csv
if cfg.overwrite_csv:
df_train.to_csv(train_fp, index=False)
df_test.to_csv(test_fp, index=False)
if __name__ == "__main__":
main()
|