|
|
|
import sys |
|
import os |
|
os.environ["STATS_PBAR"] = "1" |
|
os.environ["VRE_LOGLEVEL"] = "0" |
|
import random |
|
from pathlib import Path |
|
sys.path.append(Path.cwd().parent.__str__()) |
|
from pprint import pprint |
|
from vre.readers.multitask_dataset import MultiTaskDataset |
|
from vre.representations import Representation, ReprOut |
|
from vre.utils import MemoryData, reorder_dict, lo |
|
import numpy as np |
|
import torch as tr |
|
from media_processing_lib.collage_maker import collage_fn |
|
from media_processing_lib.image import image_add_title, image_write |
|
import matplotlib.pyplot as plt |
|
from datetime import datetime |
|
|
|
from dronescapes_representations import get_dronescapes_task_types |
|
|
|
def plot_one(data: dict[str, tr.Tensor], title: str, name_to_task: dict[str, Representation], |
|
order: list[str] | None = None) -> np.ndarray: |
|
def vre_plot_fn(rgb: tr.Tensor, x: tr.Tensor, node: Representation) -> np.ndarray: |
|
node.data = ReprOut(rgb.cpu().detach().numpy()[None], MemoryData(x.cpu().detach().numpy()[None]), [0]) |
|
return node.make_images()[0] |
|
img_data = {} |
|
keys = np.random.permutation(list(data.keys())) |
|
for k in keys: |
|
start = datetime.now() |
|
img_data[k] = vre_plot_fn(data["rgb"], data[k], name_to_task[k]) |
|
print(k, (datetime.now() - start).total_seconds()) |
|
img_data = reorder_dict(img_data, order) if order is not None else img_data |
|
titles = [title if len(title) < 40 else f"{title[0:19]}..{title[-19:]}" for title in img_data] |
|
collage = collage_fn(list(img_data.values()), titles=titles, size_px=40) |
|
collage = image_add_title(collage, title, size_px=55, top_padding=110) |
|
return collage |
|
|
|
data_path = "../../data/test_set" |
|
stats_path = "../../data/train_set/.task_statistics.npz" |
|
dronescapes_task_types = get_dronescapes_task_types(include_semantics_original=False, include_gt=True, include_ci=False) |
|
task_names = ["rgb", "semantic_mask2former_r50_mapillary_converted", "semantic_mask2former_swin_coco_converted"] |
|
reader = MultiTaskDataset(data_path, task_names=task_names, |
|
task_types=dronescapes_task_types, handle_missing_data="fill_nan", |
|
normalization="min_max", cache_task_stats=True, batch_size_stats=300, |
|
statistics=np.load(stats_path, allow_pickle=True)["arr_0"].item()) |
|
print(reader) |
|
print("== Shapes ==") |
|
pprint(reader.data_shape) |
|
|
|
print("== Random loaded item ==") |
|
rand_ix = random.randint(0, len(reader) - 1) |
|
|
|
data, name = reader[rand_ix] |
|
print(name) |
|
collage = plot_one(data, title=name, name_to_task=reader.name_to_task) |
|
print(lo(collage)) |
|
|
|
|
|
image_write(collage, out_path := f"collage_{name[0:-4]}.png") |
|
print(f"Stored at '{out_path}'") |
|
|