use_hoho (#1)

- print (718a478058081d9f09f584f56a0d6ab02427a57b)
- multiprocess (22f7dc43c7d6b39434e74d29321c202bcad34daf)
- pull hoho to separate package (60bf65f206ddaf497dcb3ba053b61f73b8885f89)
- add hoho (fd89e737cb8b22eca7568d513be0fcaa672ef560)
- assume hoho exists (53332d75f05661cfd779afc9d43fea6763bb9231)
- fix typo (fab49d6c2f9f5941e9f6ff5afa22cd3c52ab7708)
- return key with empty solution (f73f74e61befaa8366a1bef73ff1d364d08e1730)
- add eda notebook (4285f2cc0c0c3bb8e59cbbd1ad9cf1d43e5f9255)
- rerun eda notebook (1ba09bb1b7251ad4215e5c798803977a6f949c56)
- prepare example (a2b9af36c2691bb83ee1bd357aa14f13a773a9e7)
- cleanup (48877ec32f44d5486dec5995c55bf2e4a2fc8897)
- cleanup (a1e5421db493c5f55e15f46d339dd455dfeab5e1)

.gitattributes

@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.whl filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 packages/** filter=lfs diff=lfs merge=lfs -text
+*.ipynb filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,2 +1,3 @@
 .ipynb_checkpoints
-
+__pycache__/
+data

color_mappings.py

@@ -1,182 +0,0 @@
-gestalt_color_mapping = {
-    "unclassified": (215, 62, 138),
-    "apex": (235, 88, 48),
-    "eave_end_point": (248, 130, 228),
-    "flashing_end_point": (71, 11, 161),
-    "ridge": (214, 251, 248),
-    "rake": (13, 94, 47),
-    "eave": (54, 243, 63),
-    "post": (187, 123, 236),
-    "ground_line": (136, 206, 14),
-    "flashing": (162, 162, 32),
-    "step_flashing": (169, 255, 219),
-    "hip": (8, 89, 52),
-    "valley": (85, 27, 65),
-    "roof": (215, 232, 179),
-    "door": (110, 52, 23),
-    "garage": (50, 233, 171),
-    "window": (230, 249, 40),
-    "shutter": (122, 4, 233),
-    "fascia": (95, 230, 240),
-    "soffit": (2, 102, 197),
-    "horizontal_siding": (131, 88, 59),
-    "vertical_siding": (110, 187, 198),
-    "brick": (171, 252, 7),
-    "concrete": (32, 47, 246),
-    "other_wall": (112, 61, 240),
-    "trim": (151, 206, 58),
-    "unknown": (127, 127, 127),
-}
-
-ade20k_color_mapping = {
-    'wall': (120, 120, 120),
-    'building;edifice': (180, 120, 120),
-    'sky': (6, 230, 230),
-    'floor;flooring': (80, 50, 50),
-    'tree': (4, 200, 3),
-    'ceiling': (120, 120, 80),
-    'road;route': (140, 140, 140),
-    'bed': (204, 5, 255),
-    'windowpane;window': (230, 230, 230),
-    'grass': (4, 250, 7),
-    'cabinet': (224, 5, 255),
-    'sidewalk;pavement': (235, 255, 7),
-    'person;individual;someone;somebody;mortal;soul': (150, 5, 61),
-    'earth;ground': (120, 120, 70),
-    'door;double;door': (8, 255, 51),
-    'table': (255, 6, 82),
-    'mountain;mount': (143, 255, 140),
-    'plant;flora;plant;life': (204, 255, 4),
-    'curtain;drape;drapery;mantle;pall': (255, 51, 7),
-    'chair': (204, 70, 3),
-    'car;auto;automobile;machine;motorcar': (0, 102, 200),
-    'water': (61, 230, 250),
-    'painting;picture': (255, 6, 51),
-    'sofa;couch;lounge': (11, 102, 255),
-    'shelf': (255, 7, 71),
-    'house': (255, 9, 224),
-    'sea': (9, 7, 230),
-    'mirror': (220, 220, 220),
-    'rug;carpet;carpeting': (255, 9, 92),
-    'field': (112, 9, 255),
-    'armchair': (8, 255, 214),
-    'seat': (7, 255, 224),
-    'fence;fencing': (255, 184, 6),
-    'desk': (10, 255, 71),
-    'rock;stone': (255, 41, 10),
-    'wardrobe;closet;press': (7, 255, 255),
-    'lamp': (224, 255, 8),
-    'bathtub;bathing;tub;bath;tub': (102, 8, 255),
-    'railing;rail': (255, 61, 6),
-    'cushion': (255, 194, 7),
-    'base;pedestal;stand': (255, 122, 8),
-    'box': (0, 255, 20),
-    'column;pillar': (255, 8, 41),
-    'signboard;sign': (255, 5, 153),
-    'chest;of;drawers;chest;bureau;dresser': (6, 51, 255),
-    'counter': (235, 12, 255),
-    'sand': (160, 150, 20),
-    'sink': (0, 163, 255),
-    'skyscraper': (140, 140, 140),
-    'fireplace;hearth;open;fireplace': (250, 10, 15),
-    'refrigerator;icebox': (20, 255, 0),
-    'grandstand;covered;stand': (31, 255, 0),
-    'path': (255, 31, 0),
-    'stairs;steps': (255, 224, 0),
-    'runway': (153, 255, 0),
-    'case;display;case;showcase;vitrine': (0, 0, 255),
-    'pool;table;billiard;table;snooker;table': (255, 71, 0),
-    'pillow': (0, 235, 255),
-    'screen;door;screen': (0, 173, 255),
-    'stairway;staircase': (31, 0, 255),
-    'river': (11, 200, 200),
-    'bridge;span': (255 ,82, 0),
-    'bookcase': (0, 255, 245),
-    'blind;screen': (0, 61, 255),
-    'coffee;table;cocktail;table': (0, 255, 112),
-    'toilet;can;commode;crapper;pot;potty;stool;throne': (0, 255, 133),
-    'flower': (255, 0, 0),
-    'book': (255, 163, 0),
-    'hill': (255, 102, 0),
-    'bench': (194, 255, 0),
-    'countertop': (0, 143, 255),
-    'stove;kitchen;stove;range;kitchen;range;cooking;stove': (51, 255, 0),
-    'palm;palm;tree': (0, 82, 255),
-    'kitchen;island': (0, 255, 41),
-    'computer;computing;machine;computing;device;data;processor;electronic;computer;information;processing;system': (0, 255, 173),
-    'swivel;chair': (10, 0, 255),
-    'boat': (173, 255, 0),
-    'bar': (0, 255, 153),
-    'arcade;machine': (255, 92, 0),
-    'hovel;hut;hutch;shack;shanty': (255, 0, 255),
-    'bus;autobus;coach;charabanc;double-decker;jitney;motorbus;motorcoach;omnibus;passenger;vehicle': (255, 0, 245),
-    'towel': (255, 0, 102),
-    'light;light;source': (255, 173, 0),
-    'truck;motortruck': (255, 0, 20),
-    'tower': (255, 184, 184),
-    'chandelier;pendant;pendent': (0, 31, 255),
-    'awning;sunshade;sunblind': (0, 255, 61),
-    'streetlight;street;lamp': (0, 71, 255),
-    'booth;cubicle;stall;kiosk': (255, 0, 204),
-    'television;television;receiver;television;set;tv;tv;set;idiot;box;boob;tube;telly;goggle;box': (0, 255, 194),
-    'airplane;aeroplane;plane': (0, 255, 82),
-    'dirt;track': (0, 10, 255),
-    'apparel;wearing;apparel;dress;clothes': (0, 112, 255),
-    'pole': (51, 0, 255),
-    'land;ground;soil': (0, 194, 255),
-    'bannister;banister;balustrade;balusters;handrail': (0, 122, 255),
-    'escalator;moving;staircase;moving;stairway': (0, 255, 163),
-    'ottoman;pouf;pouffe;puff;hassock': (255, 153, 0),
-    'bottle': (0, 255, 10),
-    'buffet;counter;sideboard': (255, 112, 0),
-    'poster;posting;placard;notice;bill;card': (143, 255, 0),
-    'stage': (82, 0, 255),
-    'van': (163, 255, 0),
-    'ship': (255, 235, 0),
-    'fountain': (8, 184, 170),
-    'conveyer;belt;conveyor;belt;conveyer;conveyor;transporter': (133, 0, 255),
-    'canopy': (0, 255, 92),
-    'washer;automatic;washer;washing;machine': (184, 0, 255),
-    'plaything;toy': (255, 0, 31),
-    'swimming;pool;swimming;bath;natatorium': (0, 184, 255),
-    'stool': (0, 214, 255),
-    'barrel;cask': (255, 0, 112),
-    'basket;handbasket': (92, 255, 0),
-    'waterfall;falls': (0, 224, 255),
-    'tent;collapsible;shelter': (112, 224, 255),
-    'bag': (70, 184, 160),
-    'minibike;motorbike': (163, 0, 255),
-    'cradle': (153, 0, 255),
-    'oven': (71, 255, 0),
-    'ball': (255, 0, 163),
-    'food;solid;food': (255, 204, 0),
-    'step;stair': (255, 0, 143),
-    'tank;storage;tank': (0, 255, 235),
-    'trade;name;brand;name;brand;marque': (133, 255, 0),
-    'microwave;microwave;oven': (255, 0, 235),
-    'pot;flowerpot': (245, 0, 255),
-    'animal;animate;being;beast;brute;creature;fauna': (255, 0, 122),
-    'bicycle;bike;wheel;cycle': (255, 245, 0),
-    'lake': (10, 190, 212),
-    'dishwasher;dish;washer;dishwashing;machine': (214, 255, 0),
-    'screen;silver;screen;projection;screen': (0, 204, 255),
-    'blanket;cover': (20, 0, 255),
-    'sculpture': (255, 255, 0),
-    'hood;exhaust;hood': (0, 153, 255),
-    'sconce': (0, 41, 255),
-    'vase': (0, 255, 204),
-    'traffic;light;traffic;signal;stoplight': (41, 0, 255),
-    'tray': (41, 255, 0),
-    'ashcan;trash;can;garbage;can;wastebin;ash;bin;ash-bin;ashbin;dustbin;trash;barrel;trash;bin': (173, 0, 255),
-    'fan': (0, 245, 255),
-    'pier;wharf;wharfage;dock': (71, 0, 255),
-    'crt;screen': (122, 0, 255),
-    'plate': (0, 255, 184),
-    'monitor;monitoring;device': (0, 92, 255),
-    'bulletin;board;notice;board': (184, 255, 0),
-    'shower': (0, 133, 255),
-    'radiator': (255, 214, 0),
-    'glass;drinking;glass': (25, 194, 194),
-    'clock': (102, 255, 0),
-    'flag': (92, 0, 255),
-}

handcrafted_solution.py

@@ -1,15 +1,16 @@
 # Description: This file contains the handcrafted solution for the task of wireframe reconstruction 
 
 import io
-from read_write_colmap import read_cameras_binary, read_images_binary, read_points3D_binary
 from PIL import Image as PImage
 import numpy as np
-from color_mappings import gestalt_color_mapping, ade20k_color_mapping
 from collections import defaultdict
 import cv2
 from typing import Tuple, List
 from scipy.spatial.distance import cdist
 
+from hoho.read_write_colmap import read_cameras_binary, read_images_binary, read_points3D_binary
+from hoho.color_mappings import gestalt_color_mapping, ade20k_color_mapping
+
 
 def empty_solution():
     '''Return a minimal valid solution, i.e. 2 vertices and 1 edge.'''
@@ -234,9 +235,9 @@ def predict(entry, visualize=False) -> Tuple[np.ndarray, List[int]]:
     all_3d_vertices_clean, connections_3d_clean  = prune_not_connected(all_3d_vertices, connections_3d)
     if (len(all_3d_vertices_clean) < 2) or len(connections_3d_clean) < 1:
         print (f'Not enough vertices or connections in the 3D vertices')
-        return empty_solution()
+        return (good_entry['__key__'], *empty_solution())
     if visualize:
-        from viz3d import plot_estimate_and_gt
+        from hoho.viz3d import plot_estimate_and_gt
         plot_estimate_and_gt(all_3d_vertices_clean, connections_3d_clean, good_entry['wf_vertices'],
                             good_entry['wf_edges'])
-    return  all_3d_vertices_clean, connections_3d_clean, [0 for i in range(len(connections_3d_clean))]
+    return good_entry['__key__'], all_3d_vertices_clean, connections_3d_clean, [0 for i in range(len(connections_3d_clean))]

hoho.py

@@ -1,261 +0,0 @@
-import os 
-import json
-import shutil
-from pathlib import Path
-from typing import Dict
-
-from PIL import ImageFile
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-
-LOCAL_DATADIR = None
-
-def setup(local_dir='./data/usm-training-data/data'):
-    
-    # If we are in the test environment, we need to link the data directory to the correct location
-    tmp_datadir = Path('/tmp/data/data')
-    local_test_datadir = Path('./data/usm-test-data-x/data')
-    local_val_datadir = Path(local_dir)
-    
-    os.system('pwd')
-    os.system('ls -lahtr .')
-    
-    if tmp_datadir.exists() and not local_test_datadir.exists():
-        global LOCAL_DATADIR
-        LOCAL_DATADIR = local_test_datadir
-        # shutil.move(datadir, './usm-test-data-x/data')
-        print(f"Linking {tmp_datadir} to {LOCAL_DATADIR} (we are in the test environment)")
-        LOCAL_DATADIR.parent.mkdir(parents=True, exist_ok=True)
-        LOCAL_DATADIR.symlink_to(tmp_datadir)
-    else:
-        LOCAL_DATADIR = local_val_datadir
-        print(f"Using {LOCAL_DATADIR} as the data directory (we are running locally)")
-        
-    # os.system("ls -lahtr")
-    
-    assert LOCAL_DATADIR.exists(), f"Data directory {LOCAL_DATADIR} does not exist"
-    return LOCAL_DATADIR
-    
-    
-    
-    
-import importlib
-from pathlib import Path
-import subprocess
-
-def download_package(package_name, path_to_save='packages'):
-    """
-    Downloads a package using pip and saves it to a specified directory.
-
-    Parameters:
-    package_name (str): The name of the package to download.
-    path_to_save (str): The path to the directory where the package will be saved.
-    """
-    try:
-        # pip download webdataset -d packages/webdataset --platform manylinux1_x86_64 --python-version 38 --only-binary=:all:
-        subprocess.check_call([subprocess.sys.executable, "-m", "pip", "download", package_name, 
-                               "-d", str(Path(path_to_save)/package_name),  # Download the package to the specified directory
-                               "--platform", "manylinux1_x86_64",  # Specify the platform
-                               "--python-version", "38",  # Specify the Python version
-                               "--only-binary=:all:"])  # Download only binary packages
-        print(f'Package "{package_name}" downloaded successfully')
-    except subprocess.CalledProcessError as e:
-        print(f'Failed to downloaded package "{package_name}". Error: {e}')
-              
-              
-def install_package_from_local_file(package_name, folder='packages'):
-    """
-    Installs a package from a local .whl file or a directory containing .whl files using pip.
-
-    Parameters:
-    path_to_file_or_directory (str): The path to the .whl file or the directory containing .whl files.
-    """
-    try:
-        pth = str(Path(folder) / package_name)
-        subprocess.check_call([subprocess.sys.executable, "-m", "pip", "install", 
-                               "--no-index",  # Do not use package index
-                               "--find-links", pth,  # Look for packages in the specified directory or at the file
-                               package_name])  # Specify the package to install
-        print(f"Package installed successfully from {pth}")
-    except subprocess.CalledProcessError as e:
-        print(f"Failed to install package from {pth}. Error: {e}")
-        
-        
-def importt(module_name, as_name=None):
-    """
-    Imports a module and returns it.
-
-    Parameters:
-    module_name (str): The name of the module to import.
-    as_name (str): The name to use for the imported module. If None, the original module name will be used.
-
-    Returns:
-    The imported module.
-    """
-    for _ in range(2):
-        try:
-            if as_name is None:
-                print(f'imported {module_name}')
-                return importlib.import_module(module_name)
-            else:
-                print(f'imported {module_name} as {as_name}')
-                return importlib.import_module(module_name, as_name)
-        except ModuleNotFoundError as e:
-            install_package_from_local_file(module_name)
-            print(f"Failed to import module {module_name}. Error: {e}")
-            
-    
-def prepare_submission():
-    # Download packages from requirements.txt 
-    if Path('requirements.txt').exists():
-        print('downloading packages from requirements.txt')
-        Path('packages').mkdir(exist_ok=True)
-        with open('requirements.txt') as f:
-            packages = f.readlines()
-            for p in packages:
-                download_package(p.strip())
-                
-                
-    print('all packages downloaded. Don\'t foget to include the packages in the submission by adding them with git lfs.')
-        
-
-def Rt_to_eye_target(im, K, R, t):
-    height = im.height
-    focal_length = K[0,0]
-    fov = 2.0 * np.arctan2((0.5 * height), focal_length) / (np.pi / 180.0)
-
-    x_axis, y_axis, z_axis = R
-
-    eye = -(R.T @ t).squeeze()
-    z_axis = z_axis.squeeze()
-    target = eye + z_axis
-    up = -y_axis
-    
-    return eye, target, up, fov        
-
-
-########## general utilities ##########
-import contextlib
-import tempfile 
-from pathlib import Path
-
-@contextlib.contextmanager
-def working_directory(path):
-    """Changes working directory and returns to previous on exit."""
-    prev_cwd = Path.cwd()
-    os.chdir(path)
-    try:
-        yield
-    finally:
-        os.chdir(prev_cwd)
-        
-@contextlib.contextmanager
-def temp_working_directory():
-    with tempfile.TemporaryDirectory(dir='.') as D:
-        with working_directory(D):
-            yield
-
-
-############# Dataset ############# 
-def proc(row, split='train'):
-    # column_names_train = ['ade20k', 'depthcm', 'gestalt', 'colmap', 'KRt', 'mesh', 'wireframe']
-    # column_names_test = ['ade20k', 'depthcm', 'gestalt', 'colmap', 'KRt', 'wireframe']
-    # cols = column_names_train if split == 'train' else column_names_test
-    out = {}
-    for k, v in row.items():
-        colname = k.split('.')[0]
-        if colname in {'ade20k', 'depthcm', 'gestalt'}:
-            if colname in out:
-                out[colname].append(v)
-            else:
-                out[colname] = [v]
-        elif colname in {'wireframe', 'mesh'}:
-            # out.update({a: b.tolist() for a,b in v.items()})
-            out.update({a: b for a,b in v.items()})
-        elif colname in 'kr':
-            out[colname.upper()] = v
-        else:
-            out[colname] = v
-            
-    return Sample(out)
-
-
-class Sample(Dict):
-    def __repr__(self):
-        return str({k: v.shape if hasattr(v, 'shape') else [type(v[0])] if isinstance(v, list) else type(v) for k,v in self.items()})
-
-    
-        
-def get_params():
-    exmaple_param_dict = {
-        "competition_id": "usm3d/S23DR",
-        "competition_type": "script",
-        "metric": "custom",
-        "token": "hf_**********************************",
-        "team_id": "local-test-team_id",
-        "submission_id": "local-test-submission_id",
-        "submission_id_col": "__key__",
-        "submission_cols": [
-            "__key__",
-            "wf_edges",
-            "wf_vertices",
-            "edge_semantics"
-        ],
-        "submission_rows": 180,
-        "output_path": ".",
-        "submission_repo": "<THE HF MODEL ID of THIS REPO",
-        "time_limit": 7200,
-        "dataset": "usm3d/usm-test-data-x",
-        "submission_filenames": [
-            "submission.parquet"
-        ]
-    }
-    
-    param_path = Path('params.json')
-    
-    if not param_path.exists():
-        print('params.json not found (this means we probably aren\'t in the test env). Using example params.')
-        params = exmaple_param_dict
-    else:
-        print('found params.json (this means we are probably in the test env). Using params from file.')
-        with param_path.open() as f:
-            params = json.load(f)
-    print(params)
-    return params
-
-
-
-import webdataset as wds 
-import numpy as np
-
-def get_dataset(decode='pil', proc=proc, split='train', dataset_type='webdataset'):
-    if LOCAL_DATADIR is None:
-        raise ValueError('LOCAL_DATADIR is not set. Please run setup() first.')
-        
-    local_dir = Path(LOCAL_DATADIR)
-    if split != 'all':
-        local_dir = local_dir / split
-    
-    paths = [str(p) for p in local_dir.rglob('*.tar.gz')]
-    
-    dataset = wds.WebDataset(paths)
-    if decode is not None:
-        dataset = dataset.decode(decode)
-    else:
-        dataset = dataset.decode()
-    
-    dataset = dataset.map(proc)
-    
-    if dataset_type == 'webdataset':
-        return dataset
-    
-    if dataset_type == 'hf':
-        import datasets
-        from datasets import Features, Value, Sequence, Image, Array2D
-       
-        if split == 'train':
-            return datasets.IterableDataset.from_generator(lambda: dataset.iterator())
-        elif split == 'val':
-            return datasets.IterableDataset.from_generator(lambda: dataset.iterator())
-
-    
-    

packages/webdataset/numpy-1.21.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl → notebooks/EDA.ipynb

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:dde972a1e11bb7b702ed0e447953e7617723760f420decb97305e66fb4afc54f
-size 14092363
+oid sha256:6a847cfb0c6458dc4edfc707f30c6156345dbbc486ad651abd8bfad4f0ee659a
+size 14355368

packages/webdataset/PyYAML-6.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:277a0ef2981ca40581a47093e9e2d13b3f1fbbeffae064c1d21bfceba2030287
-size 701221

packages/webdataset/braceexpand-0.1.7-py2.py3-none-any.whl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:91332d53de7828103dcae5773fb43bc34950b0c8160e35e0f44c4427a3b85014
-size 5923

packages/webdataset/webdataset-0.2.86-py3-none-any.whl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:843a2b57c6356ebba25e811adf38a476da8e176f1b192f8bd5c8270daf1a6989
-size 70378

read_write_colmap.py

@@ -1,489 +0,0 @@
-# Modified to read from bytes-like object by Dmytro Mishkin.
-# The original license is below:
-# Copyright (c) 2018, ETH Zurich and UNC Chapel Hill.
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-#     * Redistributions of source code must retain the above copyright
-#       notice, this list of conditions and the following disclaimer.
-#
-#     * Redistributions in binary form must reproduce the above copyright
-#       notice, this list of conditions and the following disclaimer in the
-#       documentation and/or other materials provided with the distribution.
-#
-#     * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
-#       its contributors may be used to endorse or promote products derived
-#       from this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
-# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-#
-# Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)
-
-import os
-import collections
-import numpy as np
-import struct
-import argparse
-
-
-CameraModel = collections.namedtuple(
-    "CameraModel", ["model_id", "model_name", "num_params"])
-Camera = collections.namedtuple(
-    "Camera", ["id", "model", "width", "height", "params"])
-BaseImage = collections.namedtuple(
-    "Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"])
-Point3D = collections.namedtuple(
-    "Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"])
-
-
-class Image(BaseImage):
-    def qvec2rotmat(self):
-        return qvec2rotmat(self.qvec)
-
-
-CAMERA_MODELS = {
-    CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3),
-    CameraModel(model_id=1, model_name="PINHOLE", num_params=4),
-    CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4),
-    CameraModel(model_id=3, model_name="RADIAL", num_params=5),
-    CameraModel(model_id=4, model_name="OPENCV", num_params=8),
-    CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8),
-    CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12),
-    CameraModel(model_id=7, model_name="FOV", num_params=5),
-    CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4),
-    CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5),
-    CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12)
-}
-CAMERA_MODEL_IDS = dict([(camera_model.model_id, camera_model)
-                         for camera_model in CAMERA_MODELS])
-CAMERA_MODEL_NAMES = dict([(camera_model.model_name, camera_model)
-                           for camera_model in CAMERA_MODELS])
-
-
-def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"):
-    """Read and unpack the next bytes from a binary file.
-    :param fid:
-    :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc.
-    :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
-    :param endian_character: Any of {@, =, <, >, !}
-    :return: Tuple of read and unpacked values.
-    """
-    data = fid.read(num_bytes)
-    return struct.unpack(endian_character + format_char_sequence, data)
-
-
-def write_next_bytes(fid, data, format_char_sequence, endian_character="<"):
-    """pack and write to a binary file.
-    :param fid:
-    :param data: data to send, if multiple elements are sent at the same time,
-    they should be encapsuled either in a list or a tuple
-    :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
-    should be the same length as the data list or tuple
-    :param endian_character: Any of {@, =, <, >, !}
-    """
-    if isinstance(data, (list, tuple)):
-        bytes = struct.pack(endian_character + format_char_sequence, *data)
-    else:
-        bytes = struct.pack(endian_character + format_char_sequence, data)
-    fid.write(bytes)
-
-
-def read_cameras_text(path):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::WriteCamerasText(const std::string& path)
-        void Reconstruction::ReadCamerasText(const std::string& path)
-    """
-    cameras = {}
-    with open(path, "r") as fid:
-        while True:
-            line = fid.readline()
-            if not line:
-                break
-            line = line.strip()
-            if len(line) > 0 and line[0] != "#":
-                elems = line.split()
-                camera_id = int(elems[0])
-                model = elems[1]
-                width = int(elems[2])
-                height = int(elems[3])
-                params = np.array(tuple(map(float, elems[4:])))
-                cameras[camera_id] = Camera(id=camera_id, model=model,
-                                            width=width, height=height,
-                                            params=params)
-    return cameras
-
-
-def read_cameras_binary(path_to_model_file=None, fid=None):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::WriteCamerasBinary(const std::string& path)
-        void Reconstruction::ReadCamerasBinary(const std::string& path)
-    """
-    cameras = {}
-    if fid is None:
-        fid = open(path_to_model_file, "rb")
-    num_cameras = read_next_bytes(fid, 8, "Q")[0]
-    for _ in range(num_cameras):
-        camera_properties = read_next_bytes(
-            fid, num_bytes=24, format_char_sequence="iiQQ")
-        camera_id = camera_properties[0]
-        model_id = camera_properties[1]
-        model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name
-        width = camera_properties[2]
-        height = camera_properties[3]
-        num_params = CAMERA_MODEL_IDS[model_id].num_params
-        params = read_next_bytes(fid, num_bytes=8*num_params,
-                                    format_char_sequence="d"*num_params)
-        cameras[camera_id] = Camera(id=camera_id,
-                                    model=model_name,
-                                    width=width,
-                                    height=height,
-                                    params=np.array(params))
-    assert len(cameras) == num_cameras
-    if path_to_model_file is not None:
-        fid.close()
-    return cameras
-
-
-def write_cameras_text(cameras, path):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::WriteCamerasText(const std::string& path)
-        void Reconstruction::ReadCamerasText(const std::string& path)
-    """
-    HEADER = "# Camera list with one line of data per camera:\n" + \
-             "#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]\n" + \
-             "# Number of cameras: {}\n".format(len(cameras))
-    with open(path, "w") as fid:
-        fid.write(HEADER)
-        for _, cam in cameras.items():
-            to_write = [cam.id, cam.model, cam.width, cam.height, *cam.params]
-            line = " ".join([str(elem) for elem in to_write])
-            fid.write(line + "\n")
-
-
-def write_cameras_binary(cameras, path_to_model_file):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::WriteCamerasBinary(const std::string& path)
-        void Reconstruction::ReadCamerasBinary(const std::string& path)
-    """
-    with open(path_to_model_file, "wb") as fid:
-        write_next_bytes(fid, len(cameras), "Q")
-        for _, cam in cameras.items():
-            model_id = CAMERA_MODEL_NAMES[cam.model].model_id
-            camera_properties = [cam.id,
-                                 model_id,
-                                 cam.width,
-                                 cam.height]
-            write_next_bytes(fid, camera_properties, "iiQQ")
-            for p in cam.params:
-                write_next_bytes(fid, float(p), "d")
-    return cameras
-
-
-def read_images_text(path):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadImagesText(const std::string& path)
-        void Reconstruction::WriteImagesText(const std::string& path)
-    """
-    images = {}
-    with open(path, "r") as fid:
-        while True:
-            line = fid.readline()
-            if not line:
-                break
-            line = line.strip()
-            if len(line) > 0 and line[0] != "#":
-                elems = line.split()
-                image_id = int(elems[0])
-                qvec = np.array(tuple(map(float, elems[1:5])))
-                tvec = np.array(tuple(map(float, elems[5:8])))
-                camera_id = int(elems[8])
-                image_name = elems[9]
-                elems = fid.readline().split()
-                xys = np.column_stack([tuple(map(float, elems[0::3])),
-                                       tuple(map(float, elems[1::3]))])
-                point3D_ids = np.array(tuple(map(int, elems[2::3])))
-                images[image_id] = Image(
-                    id=image_id, qvec=qvec, tvec=tvec,
-                    camera_id=camera_id, name=image_name,
-                    xys=xys, point3D_ids=point3D_ids)
-    return images
-
-
-def read_images_binary(path_to_model_file=None, fid=None):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadImagesBinary(const std::string& path)
-        void Reconstruction::WriteImagesBinary(const std::string& path)
-    """
-    images = {}
-    if fid is None:
-        fid = open(path_to_model_file, "rb")
-    num_reg_images = read_next_bytes(fid, 8, "Q")[0]
-    for _ in range(num_reg_images):
-        binary_image_properties = read_next_bytes(
-            fid, num_bytes=64, format_char_sequence="idddddddi")
-        image_id = binary_image_properties[0]
-        qvec = np.array(binary_image_properties[1:5])
-        tvec = np.array(binary_image_properties[5:8])
-        camera_id = binary_image_properties[8]
-        image_name = ""
-        current_char = read_next_bytes(fid, 1, "c")[0]
-        while current_char != b"\x00":   # look for the ASCII 0 entry
-            image_name += current_char.decode("utf-8")
-            current_char = read_next_bytes(fid, 1, "c")[0]
-        num_points2D = read_next_bytes(fid, num_bytes=8,
-                                        format_char_sequence="Q")[0]
-        x_y_id_s = read_next_bytes(fid, num_bytes=24*num_points2D,
-                                    format_char_sequence="ddq"*num_points2D)
-        xys = np.column_stack([tuple(map(float, x_y_id_s[0::3])),
-                                tuple(map(float, x_y_id_s[1::3]))])
-        point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3])))
-        images[image_id] = Image(
-            id=image_id, qvec=qvec, tvec=tvec,
-            camera_id=camera_id, name=image_name,
-            xys=xys, point3D_ids=point3D_ids)
-    if path_to_model_file is not None:
-        fid.close()
-    return images
-
-
-def write_images_text(images, path):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadImagesText(const std::string& path)
-        void Reconstruction::WriteImagesText(const std::string& path)
-    """
-    if len(images) == 0:
-        mean_observations = 0
-    else:
-        mean_observations = sum((len(img.point3D_ids) for _, img in images.items()))/len(images)
-    HEADER = "# Image list with two lines of data per image:\n" + \
-             "#   IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME\n" + \
-             "#   POINTS2D[] as (X, Y, POINT3D_ID)\n" + \
-             "# Number of images: {}, mean observations per image: {}\n".format(len(images), mean_observations)
-
-    with open(path, "w") as fid:
-        fid.write(HEADER)
-        for _, img in images.items():
-            image_header = [img.id, *img.qvec, *img.tvec, img.camera_id, img.name]
-            first_line = " ".join(map(str, image_header))
-            fid.write(first_line + "\n")
-
-            points_strings = []
-            for xy, point3D_id in zip(img.xys, img.point3D_ids):
-                points_strings.append(" ".join(map(str, [*xy, point3D_id])))
-            fid.write(" ".join(points_strings) + "\n")
-
-
-def write_images_binary(images, path_to_model_file):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadImagesBinary(const std::string& path)
-        void Reconstruction::WriteImagesBinary(const std::string& path)
-    """
-    with open(path_to_model_file, "wb") as fid:
-        write_next_bytes(fid, len(images), "Q")
-        for _, img in images.items():
-            write_next_bytes(fid, img.id, "i")
-            write_next_bytes(fid, img.qvec.tolist(), "dddd")
-            write_next_bytes(fid, img.tvec.tolist(), "ddd")
-            write_next_bytes(fid, img.camera_id, "i")
-            for char in img.name:
-                write_next_bytes(fid, char.encode("utf-8"), "c")
-            write_next_bytes(fid, b"\x00", "c")
-            write_next_bytes(fid, len(img.point3D_ids), "Q")
-            for xy, p3d_id in zip(img.xys, img.point3D_ids):
-                write_next_bytes(fid, [*xy, p3d_id], "ddq")
-
-
-def read_points3D_text(path):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadPoints3DText(const std::string& path)
-        void Reconstruction::WritePoints3DText(const std::string& path)
-    """
-    points3D = {}
-    with open(path, "r") as fid:
-        while True:
-            line = fid.readline()
-            if not line:
-                break
-            line = line.strip()
-            if len(line) > 0 and line[0] != "#":
-                elems = line.split()
-                point3D_id = int(elems[0])
-                xyz = np.array(tuple(map(float, elems[1:4])))
-                rgb = np.array(tuple(map(int, elems[4:7])))
-                error = float(elems[7])
-                image_ids = np.array(tuple(map(int, elems[8::2])))
-                point2D_idxs = np.array(tuple(map(int, elems[9::2])))
-                points3D[point3D_id] = Point3D(id=point3D_id, xyz=xyz, rgb=rgb,
-                                               error=error, image_ids=image_ids,
-                                               point2D_idxs=point2D_idxs)
-    return points3D
-
-
-def read_points3D_binary(path_to_model_file=None, fid=None):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadPoints3DBinary(const std::string& path)
-        void Reconstruction::WritePoints3DBinary(const std::string& path)
-    """
-    points3D = {}
-    if fid is None:
-        fid = open(path_to_model_file, "rb")
-    num_points = read_next_bytes(fid, 8, "Q")[0]
-    for _ in range(num_points):
-        binary_point_line_properties = read_next_bytes(
-            fid, num_bytes=43, format_char_sequence="QdddBBBd")
-        point3D_id = binary_point_line_properties[0]
-        xyz = np.array(binary_point_line_properties[1:4])
-        rgb = np.array(binary_point_line_properties[4:7])
-        error = np.array(binary_point_line_properties[7])
-        track_length = read_next_bytes(
-            fid, num_bytes=8, format_char_sequence="Q")[0]
-        track_elems = read_next_bytes(
-            fid, num_bytes=8*track_length,
-            format_char_sequence="ii"*track_length)
-        image_ids = np.array(tuple(map(int, track_elems[0::2])))
-        point2D_idxs = np.array(tuple(map(int, track_elems[1::2])))
-        points3D[point3D_id] = Point3D(
-            id=point3D_id, xyz=xyz, rgb=rgb,
-            error=error, image_ids=image_ids,
-            point2D_idxs=point2D_idxs)
-    if path_to_model_file is not None:
-        fid.close()
-    return points3D
-
-
-def write_points3D_text(points3D, path):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadPoints3DText(const std::string& path)
-        void Reconstruction::WritePoints3DText(const std::string& path)
-    """
-    if len(points3D) == 0:
-        mean_track_length = 0
-    else:
-        mean_track_length = sum((len(pt.image_ids) for _, pt in points3D.items()))/len(points3D)
-    HEADER = "# 3D point list with one line of data per point:\n" + \
-             "#   POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\n" + \
-             "# Number of points: {}, mean track length: {}\n".format(len(points3D), mean_track_length)
-
-    with open(path, "w") as fid:
-        fid.write(HEADER)
-        for _, pt in points3D.items():
-            point_header = [pt.id, *pt.xyz, *pt.rgb, pt.error]
-            fid.write(" ".join(map(str, point_header)) + " ")
-            track_strings = []
-            for image_id, point2D in zip(pt.image_ids, pt.point2D_idxs):
-                track_strings.append(" ".join(map(str, [image_id, point2D])))
-            fid.write(" ".join(track_strings) + "\n")
-
-
-def write_points3D_binary(points3D, path_to_model_file):
-    """
-    see: src/base/reconstruction.cc
-        void Reconstruction::ReadPoints3DBinary(const std::string& path)
-        void Reconstruction::WritePoints3DBinary(const std::string& path)
-    """
-    with open(path_to_model_file, "wb") as fid:
-        write_next_bytes(fid, len(points3D), "Q")
-        for _, pt in points3D.items():
-            write_next_bytes(fid, pt.id, "Q")
-            write_next_bytes(fid, pt.xyz.tolist(), "ddd")
-            write_next_bytes(fid, pt.rgb.tolist(), "BBB")
-            write_next_bytes(fid, pt.error, "d")
-            track_length = pt.image_ids.shape[0]
-            write_next_bytes(fid, track_length, "Q")
-            for image_id, point2D_id in zip(pt.image_ids, pt.point2D_idxs):
-                write_next_bytes(fid, [image_id, point2D_id], "ii")
-
-
-def detect_model_format(path, ext):
-    if os.path.isfile(os.path.join(path, "cameras"  + ext)) and \
-       os.path.isfile(os.path.join(path, "images"   + ext)) and \
-       os.path.isfile(os.path.join(path, "points3D" + ext)):
-        print("Detected model format: '" + ext + "'")
-        return True
-
-    return False
-
-
-def read_model(path, ext=""):
-    # try to detect the extension automatically
-    if ext == "":
-        if detect_model_format(path, ".bin"):
-            ext = ".bin"
-        elif detect_model_format(path, ".txt"):
-            ext = ".txt"
-        else:
-            print("Provide model format: '.bin' or '.txt'")
-            return
-
-    if ext == ".txt":
-        cameras = read_cameras_text(os.path.join(path, "cameras" + ext))
-        images = read_images_text(os.path.join(path, "images" + ext))
-        points3D = read_points3D_text(os.path.join(path, "points3D") + ext)
-    else:
-        cameras = read_cameras_binary(os.path.join(path, "cameras" + ext))
-        images = read_images_binary(os.path.join(path, "images" + ext))
-        points3D = read_points3D_binary(os.path.join(path, "points3D") + ext)
-    return cameras, images, points3D
-
-
-def write_model(cameras, images, points3D, path, ext=".bin"):
-    if ext == ".txt":
-        write_cameras_text(cameras, os.path.join(path, "cameras" + ext))
-        write_images_text(images, os.path.join(path, "images" + ext))
-        write_points3D_text(points3D, os.path.join(path, "points3D") + ext)
-    else:
-        write_cameras_binary(cameras, os.path.join(path, "cameras" + ext))
-        write_images_binary(images, os.path.join(path, "images" + ext))
-        write_points3D_binary(points3D, os.path.join(path, "points3D") + ext)
-    return cameras, images, points3D
-
-
-def qvec2rotmat(qvec):
-    return np.array([
-        [1 - 2 * qvec[2]**2 - 2 * qvec[3]**2,
-         2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
-         2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]],
-        [2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
-         1 - 2 * qvec[1]**2 - 2 * qvec[3]**2,
-         2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]],
-        [2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
-         2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
-         1 - 2 * qvec[1]**2 - 2 * qvec[2]**2]])
-
-
-def rotmat2qvec(R):
-    Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat
-    K = np.array([
-        [Rxx - Ryy - Rzz, 0, 0, 0],
-        [Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0],
-        [Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0],
-        [Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz]]) / 3.0
-    eigvals, eigvecs = np.linalg.eigh(K)
-    qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)]
-    if qvec[0] < 0:
-        qvec *= -1
-    return qvec
-

script.py

@@ -10,34 +10,55 @@
 ### You can use any additional files and subdirectories to organize your code.
 
 '''---compulsory---'''
-import hoho; hoho.setup() # YOU MUST CALL hoho.setup() BEFORE ANYTHING ELSE
-import subprocess
-import importlib
-from pathlib import Path
-import subprocess
-
-
-### The function below is useful for installing additional python wheels.        
-def install_package_from_local_file(package_name, folder='packages'):
-    """
-    Installs a package from a local .whl file or a directory containing .whl files using pip.
+# import subprocess
+# from pathlib import Path         
+# def install_package_from_local_file(package_name, folder='packages'):
+#     """
+#     Installs a package from a local .whl file or a directory containing .whl files using pip.
+
+#     Parameters:
+#     path_to_file_or_directory (str): The path to the .whl file or the directory containing .whl files.
+#     """
+#     try:
+#         pth = str(Path(folder) / package_name)
+#         subprocess.check_call([subprocess.sys.executable, "-m", "pip", "install", 
+#                                "--no-index",  # Do not use package index
+#                                "--find-links", pth,  # Look for packages in the specified directory or at the file
+#                                package_name])  # Specify the package to install
+#         print(f"Package installed successfully from {pth}")
+#     except subprocess.CalledProcessError as e:
+#         print(f"Failed to install package from {pth}. Error: {e}")
+        
+# install_package_from_local_file('hoho')
 
-    Parameters:
-    path_to_file_or_directory (str): The path to the .whl file or the directory containing .whl files.
-    """
-    try:
-        pth = str(Path(folder) / package_name)
-        subprocess.check_call([subprocess.sys.executable, "-m", "pip", "install", 
-                               "--no-index",  # Do not use package index
-                               "--find-links", pth,  # Look for packages in the specified directory or at the file
-                               package_name])  # Specify the package to install
-        print(f"Package installed successfully from {pth}")
-    except subprocess.CalledProcessError as e:
-        print(f"Failed to install package from {pth}. Error: {e}")
+import hoho; hoho.setup() # YOU MUST CALL hoho.setup() BEFORE ANYTHING ELSE
+# import subprocess
+# import importlib
+# from pathlib import Path
+# import subprocess
+
+
+# ### The function below is useful for installing additional python wheels.        
+# def install_package_from_local_file(package_name, folder='packages'):
+#     """
+#     Installs a package from a local .whl file or a directory containing .whl files using pip.
+
+#     Parameters:
+#     path_to_file_or_directory (str): The path to the .whl file or the directory containing .whl files.
+#     """
+#     try:
+#         pth = str(Path(folder) / package_name)
+#         subprocess.check_call([subprocess.sys.executable, "-m", "pip", "install", 
+#                                "--no-index",  # Do not use package index
+#                                "--find-links", pth,  # Look for packages in the specified directory or at the file
+#                                package_name])  # Specify the package to install
+#         print(f"Package installed successfully from {pth}")
+#     except subprocess.CalledProcessError as e:
+#         print(f"Failed to install package from {pth}. Error: {e}")
         
 
 # pip download webdataset -d packages/webdataset --platform manylinux1_x86_64 --python-version 38 --only-binary=:all:
-install_package_from_local_file('webdataset')
+# install_package_from_local_file('webdataset')
 # install_package_from_local_file('tqdm')
 
 ### Here you can import any library or module you want.
@@ -52,34 +73,11 @@ from transformers import AutoTokenizer
 import os
 import time
 import io
-from read_write_colmap import read_cameras_binary, read_images_binary, read_points3D_binary
 from PIL import Image as PImage
 import numpy as np
 
-    
-def proc(row, split='train'):
-    out = {}
-    for k, v in row.items():
-        colname = k.split('.')[0]
-        if colname in {'ade20k', 'depthcm', 'gestalt'}:
-            if colname in out:
-                out[colname].append(v)
-            else:
-                out[colname] = [v]
-        elif colname in {'wireframe', 'mesh'}:
-            # out.update({a: b.tolist() for a,b in v.items()})
-            out.update({a: b for a,b in v.items()})
-        elif colname in 'kr':
-            out[colname.upper()] = v
-        else:
-            out[colname] = v
-            
-    return Sample(out)
-
-
-class Sample(Dict):
-    def __repr__(self):
-        return str({k: v.shape if hasattr(v, 'shape') else [type(v[0])] if isinstance(v, list) else type(v) for k,v in self.items()})
+from hoho.read_write_colmap import read_cameras_binary, read_images_binary, read_points3D_binary
+from hoho import proc, Sample
 
 def convert_entry_to_human_readable(entry):
     out = {}
@@ -102,44 +100,47 @@ def convert_entry_to_human_readable(entry):
 
 '''---end of compulsory---'''
 
-def download_package(package_name, path_to_save='packages'):
+### The part below is used to define and test your solution.
+
+from pathlib import Path
+def save_submission(submission, path):
     """
-    Downloads a package using pip and saves it to a specified directory.
+    Saves the submission to a specified path.
 
     Parameters:
-    package_name (str): The name of the package to download.
-    path_to_save (str): The path to the directory where the package will be saved.
+    submission (List[Dict[]]): The submission to save.
+    path (str): The path to save the submission to.
     """
-    try:
-        # pip download webdataset -d packages/webdataset --platform manylinux1_x86_64 --python-version 38 --only-binary=:all:
-        subprocess.check_call([subprocess.sys.executable, "-m", "pip", "download", package_name, 
-                               "-d", str(Path(path_to_save)/package_name),  # Download the package to the specified directory
-                               "--platform", "manylinux1_x86_64",  # Specify the platform
-                               "--python-version", "38",  # Specify the Python version
-                               "--only-binary=:all:"])  # Download only binary packages
-        print(f'Package "{package_name}" downloaded successfully')
-    except subprocess.CalledProcessError as e:
-        print(f'Failed to downloaded package "{package_name}". Error: {e}')
-
-
-### The part below is used to define and test your solution.
+    sub = pd.DataFrame(submission, columns=["__key__", "wf_vertices", "wf_edges", "edge_semantics"])
+    sub.to_parquet(path)
+    print(f"Submission saved to {path}")
 
 if __name__ == "__main__":
     from handcrafted_solution import predict
     print ("------------ Loading dataset------------ ")
     params = hoho.get_params()
     dataset = hoho.get_dataset(decode=None, split='all', dataset_type='webdataset')
+
     print('------------ Now you can do your solution ---------------')
     solution = []
-    for i, sample in enumerate(tqdm(dataset)):
-        pred_vertices, pred_edges, semantics = predict(sample, visualize=False)
-        solution.append({
-                        '__key__': sample['__key__'], 
-                        'wf_vertices': pred_vertices.tolist(),
-                        'wf_edges': pred_edges,
-                        'edge_semantics': semantics,
-                    })
+    from concurrent.futures import ProcessPoolExecutor
+    with ProcessPoolExecutor(max_workers=8) as pool:
+        results = []
+        for i, sample in enumerate(tqdm(dataset)):
+            results.append(pool.submit(predict, sample, visualize=False))
+        
+        for i, result in enumerate(tqdm(results)):
+            key, pred_vertices, pred_edges, semantics = result.result()
+            solution.append({
+                            '__key__': key,
+                            'wf_vertices': pred_vertices.tolist(),
+                            'wf_edges': pred_edges,
+                            'edge_semantics': semantics,
+                        })
+            if i % 100 == 0:
+                # incrementally save the results in case we run out of time
+                print(f"Processed {i} samples")
+                # save_submission(solution, Path(params['output_path']) / "submission.parquet")
     print('------------ Saving results ---------------')
-    sub = pd.DataFrame(solution, columns=["__key__", "wf_vertices", "wf_edges", "edge_semantics"])
-    sub.to_parquet(Path(params['output_path']) / "submission.parquet")
+    save_submission(solution, Path(params['output_path']) / "submission.parquet")
     print("------------ Done ------------ ")

viz3d.py

@@ -1,302 +0,0 @@
-
-"""
-Copyright [2022] [Paul-Edouard Sarlin and Philipp Lindenberger]
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-
-3D visualization based on plotly.
-Works for a small number of points and cameras, might be slow otherwise.
-
-1) Initialize a figure with `init_figure`
-2) Add 3D points, camera frustums, or both as a pycolmap.Reconstruction
-
-Written by Paul-Edouard Sarlin and Philipp Lindenberger.
-"""
-# Slightly modified by Dmytro Mishkin
-
-from typing import Optional
-import numpy as np
-import pycolmap
-import plotly.graph_objects as go
-
-
-### Some helper functions for geometry
-def qvec2rotmat(qvec):
-    return np.array([
-        [1 - 2 * qvec[2]**2 - 2 * qvec[3]**2,
-         2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
-         2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]],
-        [2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
-         1 - 2 * qvec[1]**2 - 2 * qvec[3]**2,
-         2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]],
-        [2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
-         2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
-         1 - 2 * qvec[1]**2 - 2 * qvec[2]**2]])
-
-
-def to_homogeneous(points):
-    pad = np.ones((points.shape[:-1]+(1,)), dtype=points.dtype)
-    return np.concatenate([points, pad], axis=-1)
-
-def t_to_proj_center(qvec, tvec):
-    Rr = qvec2rotmat(qvec)
-    tt = (-Rr.T) @ tvec
-    return tt
-
-def calib(params):
-    out = np.eye(3)
-    if len(params) == 3:
-        out[0,0] = params[0]
-        out[1,1] = params[0]
-        out[0,2] = params[1]
-        out[1,2] = params[2]
-    else:
-        out[0,0] = params[0]
-        out[1,1] = params[1]
-        out[0,2] = params[2]
-        out[1,2] = params[3]
-    return out
-
-
-### Plotting functions
-
-def init_figure(height: int = 800) -> go.Figure:
-    """Initialize a 3D figure."""
-    fig = go.Figure()
-    axes = dict(
-        visible=False,
-        showbackground=False,
-        showgrid=False,
-        showline=False,
-        showticklabels=True,
-        autorange=True,
-    )
-    fig.update_layout(
-        template="plotly_dark",
-        height=height,
-        scene_camera=dict(
-            eye=dict(x=0., y=-.1, z=-2),
-            up=dict(x=0, y=-1., z=0),
-            projection=dict(type="orthographic")),
-        scene=dict(
-            xaxis=axes,
-            yaxis=axes,
-            zaxis=axes,
-            aspectmode='data',
-            dragmode='orbit',
-        ),
-        margin=dict(l=0, r=0, b=0, t=0, pad=0),
-        legend=dict(
-            orientation="h",
-            yanchor="top",
-            y=0.99,
-            xanchor="left",
-            x=0.1
-        ),
-    )
-    return fig
-
-
-def plot_lines_3d(
-        fig: go.Figure,
-        pts: np.ndarray,
-        color: str = 'rgba(255, 255, 255, 1)',
-        ps: int = 2,
-        colorscale: Optional[str] = None,
-        name: Optional[str] = None):
-    """Plot a set of 3D points."""
-    x = pts[..., 0]
-    y = pts[..., 1]
-    z = pts[..., 2]
-    traces = [go.Scatter3d(x=x1, y=y1, z=z1,
-                        mode='lines',
-                        line=dict(color=color, width=2)) for x1, y1, z1 in zip(x,y,z)]
-    for t in traces:
-        fig.add_trace(t)
-    fig.update_traces(showlegend=False)
-
-
-def plot_points(
-        fig: go.Figure,
-        pts: np.ndarray,
-        color: str = 'rgba(255, 0, 0, 1)',
-        ps: int = 2,
-        colorscale: Optional[str] = None,
-        name: Optional[str] = None):
-    """Plot a set of 3D points."""
-    x, y, z = pts.T
-    tr = go.Scatter3d(
-        x=x, y=y, z=z, mode='markers', name=name, legendgroup=name,
-        marker=dict(
-            size=ps, color=color, line_width=0.0, colorscale=colorscale))
-    fig.add_trace(tr)
-
-def plot_camera(
-        fig: go.Figure,
-        R: np.ndarray,
-        t: np.ndarray,
-        K: np.ndarray,
-        color: str = 'rgb(0, 0, 255)',
-        name: Optional[str] = None,
-        legendgroup: Optional[str] = None,
-        size: float = 1.0):
-    """Plot a camera frustum from pose and intrinsic matrix."""
-    W, H = K[0, 2]*2, K[1, 2]*2
-    corners = np.array([[0, 0], [W, 0], [W, H], [0, H], [0, 0]])
-    if size is not None:
-        image_extent = max(size * W / 1024.0, size * H / 1024.0)
-        world_extent = max(W, H) / (K[0, 0] + K[1, 1]) / 0.5
-        scale = 0.5 * image_extent / world_extent
-    else:
-        scale = 1.0
-    corners = to_homogeneous(corners) @ np.linalg.inv(K).T
-    corners = (corners / 2 * scale) @ R.T + t
-
-    x, y, z = corners.T
-    rect = go.Scatter3d(
-        x=x, y=y, z=z, line=dict(color=color), legendgroup=legendgroup,
-        name=name, marker=dict(size=0.0001), showlegend=False)
-    fig.add_trace(rect)
-
-    x, y, z = np.concatenate(([t], corners)).T
-    i = [0, 0, 0, 0]
-    j = [1, 2, 3, 4]
-    k = [2, 3, 4, 1]
-
-    pyramid = go.Mesh3d(
-        x=x, y=y, z=z, color=color, i=i, j=j, k=k,
-        legendgroup=legendgroup, name=name, showlegend=False)
-    fig.add_trace(pyramid)
-    triangles = np.vstack((i, j, k)).T
-    vertices = np.concatenate(([t], corners))
-    tri_points = np.array([
-        vertices[i] for i in triangles.reshape(-1)
-    ])
-
-    x, y, z = tri_points.T
-    pyramid = go.Scatter3d(
-        x=x, y=y, z=z, mode='lines', legendgroup=legendgroup,
-        name=name, line=dict(color=color, width=1), showlegend=False)
-    fig.add_trace(pyramid)
-
-
-def plot_camera_colmap(
-        fig: go.Figure,
-        image: pycolmap.Image,
-        camera: pycolmap.Camera,
-        name: Optional[str] = None,
-        **kwargs):
-    """Plot a camera frustum from PyCOLMAP objects"""
-    intr = calib(camera.params)
-    if intr[0][0] > 10000:
-        print("Bad camera")
-        return
-    plot_camera(
-        fig,
-        qvec2rotmat(image.qvec).T,
-        t_to_proj_center(image.qvec, image.tvec),
-        intr,#calibration_matrix(),
-        name=name or str(image.id),
-        **kwargs)
-
-
-def plot_cameras(
-        fig: go.Figure,
-        reconstruction,#: pycolmap.Reconstruction,
-        **kwargs):
-    """Plot a camera as a cone with camera frustum."""
-    for image_id, image in reconstruction["images"].items():
-        plot_camera_colmap(
-            fig, image, reconstruction["cameras"][image.camera_id], **kwargs)
-
-
-def plot_reconstruction(
-        fig: go.Figure,
-        rec,
-        color: str = 'rgb(0, 0, 255)',
-        name: Optional[str] = None,
-        points: bool = True,
-        cameras: bool = True,
-        cs: float = 1.0,
-        single_color_points=False,
-        camera_color='rgba(0, 255, 0, 0.5)'):
-    # rec is result of loading reconstruction from "read_write_colmap.py"
-    # Filter outliers
-    xyzs = []
-    rgbs = []
-    for k, p3D in rec['points'].items():
-        xyzs.append(p3D.xyz)
-        rgbs.append(p3D.rgb)
-
-    if points:
-        plot_points(fig, np.array(xyzs), color=color if single_color_points else np.array(rgbs), ps=1, name=name)
-    if cameras:
-        plot_cameras(fig, rec, color=camera_color, legendgroup=name, size=cs)
-
-
-def plot_pointcloud(
-        fig: go.Figure,
-        pts: np.ndarray,
-        colors: np.ndarray,
-        ps: int = 2,
-        name: Optional[str] = None):
-    """Plot a set of 3D points."""
-    plot_points(fig, np.array(pts), color=colors, ps=ps, name=name)
-
-
-def plot_triangle_mesh(
-        fig: go.Figure,
-        vert: np.ndarray,
-        colors: np.ndarray,
-        triangles: np.ndarray,
-        name: Optional[str] = None):
-    """Plot a triangle mesh."""
-    tr = go.Mesh3d(
-        x=vert[:,0],
-        y=vert[:,1],
-        z=vert[:,2],
-        vertexcolor = np.clip(255*colors, 0, 255),
-        # i, j and k give the vertices of triangles
-        # here we represent the 4 triangles of the tetrahedron surface
-        i=triangles[:,0],
-        j=triangles[:,1],
-        k=triangles[:,2],
-        name=name,
-        showscale=False
-    )
-    fig.add_trace(tr)
-
-def plot_estimate_and_gt(pred_vertices, pred_connections, gt_vertices=None, gt_connections=None):
-    fig3d = init_figure()
-    c1 = (30, 20, 255)
-    img_color = [c1 for _ in range(len(pred_vertices))]
-    plot_points(fig3d, pred_vertices, color = img_color, ps = 10)  
-    lines = []
-    for c in pred_connections:
-        v1 = pred_vertices[c[0]]
-        v2 = pred_vertices[c[1]]
-        lines.append(np.stack([v1, v2], axis=0))
-    plot_lines_3d(fig3d, np.array(lines), img_color, ps=4)  
-    if gt_vertices is not None:
-        c2 = (30, 255, 20)
-        img_color2 = [c2 for _ in range(len(gt_vertices))]
-        plot_points(fig3d, gt_vertices, color = img_color2, ps = 10)  
-        if gt_connections is not None:
-            gt_lines = []
-            for c in gt_connections:
-                v1 = gt_vertices[c[0]]
-                v2 = gt_vertices[c[1]]
-                gt_lines.append(np.stack([v1, v2], axis=0))
-        plot_lines_3d(fig3d, np.array(gt_lines), img_color2, ps=4)        
-    fig3d.show()
-    return fig3d