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sp_configs.py
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sp_configs.py
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import json
from copy import deepcopy
pdfdict = {"lanet": "https://openaccess.thecvf.com/content/ACCV2022/papers/Wang_Rethinking_Low-level_Features_for_Interest_Point_Detection_and_Description_ACCV_2022_paper.pdf",
"kp2d": "https://openreview.net/pdf?id=Skx82ySYPH",
"superpoint_indep": "https://arxiv.org/abs/2007.15122",
"superpoint": "https://arxiv.org/abs/1712.07629",
}
repo_dict = {"lanet": "https://github.com/wangch-g/lanet",
"kp2d":"https://github.com/TRI-ML/KP2D",
"superpoint": "https://github.com/magicleap/SuperPointPretrainedNetwork",
"superpoint_indep": "https://github.com/eric-yyjau/pytorch-superpoint",
}
method_name_dict = {"lanet": "LANet",
"kp2d": "KeypointNet",
"superpoint_indep": "DeepFEPE SuperPoint",
"superpoint": "MagicLeap SuperPoint"}
metadata_dict = {
"publish_anonymously": False,
"authors": "Submitted by benchmark orgs, implementation by paper authors",
"contact_email": "[email protected]",
"method_name": "",
"method_description":
r"""Baseline, PUT 2048 features
Matched using the built-in matcher (bidirectional filter with the 'both' strategy,
hopefully optimal inlier and ratio test thresholds) with DEGENSAC""",
"link_to_website": "",
"link_to_pdf": ""
}
config_common_dict = {"json_label": "rootsiftfix",
"keypoint": "korniadogfix",
"descriptor": "rootsift",
"num_keypoints": 2048}
matcher_template_dict = {
"method": "nn",
"distance": "L2",
"flann": False,
"num_nn": 1,
"filtering": {
"type": "snn_ratio_pairwise",
"threshold": 0.99,
},
"symmetric": {
"enabled": True,
"reduce": "both",
}
}
geom_template_dict = {"method": "cmp-degensac-f",
"threshold": 0.75,
"confidence": 0.999999,
"max_iter": 100000,
"error_type": "sampson",
"degeneracy_check": True,
}
base_config = {
"metadata": metadata_dict,
"config_common": config_common_dict,
"config_phototourism_stereo": {
"use_custom_matches": False,
"matcher": deepcopy(matcher_template_dict),
"outlier_filter": { "method": "none" },
"geom": deepcopy(geom_template_dict)
},
"config_pragueparks_stereo": {
"use_custom_matches": False,
"matcher": deepcopy(matcher_template_dict),
"outlier_filter": { "method": "none" },
"geom": deepcopy(geom_template_dict)
},
}
if __name__ == '__main__':
kp = 'kp2d'
for v in ['v0','v1', 'v2', 'v3', 'v4']:
for norm in ['', '_norm']:
json_fn = f'{kp}_{v}{norm}_1024'
json_fn2 = f'{kp}_{v}{norm}_1024'.replace('_','-')
md = deepcopy(metadata_dict)
md['link_to_website'] = repo_dict[kp]
md['link_to_pdf'] = pdfdict[kp]
md['method_name'] = method_name_dict[kp]
md['method_description']=md['method_description'].replace('PUT', method_name_dict[kp])
common = {"json_label": json_fn2,
"keypoint": json_fn2,
"descriptor": json_fn2,
"num_keypoints": 2048}
base_config = {
"metadata": md,
"config_common": common,
"config_phototourism_stereo": {
"use_custom_matches": False,
"matcher": deepcopy(matcher_template_dict),
"outlier_filter": { "method": "none" },
"geom": deepcopy(geom_template_dict)
},
"config_pragueparks_stereo": {
"use_custom_matches": False,
"matcher": deepcopy(matcher_template_dict),
"outlier_filter": { "method": "none" },
"geom": deepcopy(geom_template_dict)
}
}
with open(f'OUT_JSON/{json_fn}.json', 'w') as f:
json.dump([base_config], f, indent=2)
match_ths = [0.85, 0.9, 0.95, 0.99]
inl_ths = [0.5, 0.75, 1.0, 1.25]
configs = []
for match_th in match_ths:
for inl_th in inl_ths:
current_config = deepcopy(base_config)
for dset in ['phototourism', 'pragueparks']:
current_config[f'config_{dset}_stereo']['geom']['threshold'] = inl_th
current_config[f'config_{dset}_stereo']['matcher']['filtering']['threshold'] = match_th
label = current_config['config_common']['json_label']
current_config['config_common']['json_label'] = f'{label}-snnth-{match_th}-inlth-{inl_th}'
configs.append(current_config)
with open(f'OUT_JSON_RANSAC/{json_fn}_tuning.json', 'w') as f:
json.dump(configs, f, indent=2)
kp = 'superpoint'
for v in ['_magicleap']:
for norm in ['', '_norm']:
json_fn = f'{kp}{v}{norm}_1024'
json_fn2 = f'{kp}{v}{norm}_1024'.replace('_','-')
md = deepcopy(metadata_dict)
md['link_to_website'] = repo_dict[kp]
md['link_to_pdf'] = pdfdict[kp]
md['method_name'] = method_name_dict[kp]
md['method_description']=md['method_description'].replace('PUT', method_name_dict[kp])
common = {"json_label": json_fn2,
"keypoint": json_fn2,
"descriptor": json_fn2,
"num_keypoints": 2048}
base_config = {
"metadata": md,
"config_common": common,
"config_phototourism_stereo": {
"use_custom_matches": False,
"matcher": deepcopy(matcher_template_dict),
"outlier_filter": { "method": "none" },
"geom": deepcopy(geom_template_dict)
},
"config_pragueparks_stereo": {
"use_custom_matches": False,
"matcher": deepcopy(matcher_template_dict),
"outlier_filter": { "method": "none" },
"geom": deepcopy(geom_template_dict)
}
}
with open(f'OUT_JSON/{json_fn}.json', 'w') as f:
json.dump([base_config], f, indent=2)
match_ths = [0.85, 0.9, 0.95, 0.99]
inl_ths = [0.5, 0.75, 1.0, 1.25, 1.5]
configs = []
for match_th in match_ths:
for inl_th in inl_ths:
current_config = deepcopy(base_config)
for dset in ['phototourism', 'pragueparks']:
current_config[f'config_{dset}_stereo']['geom']['threshold'] = inl_th
current_config[f'config_{dset}_stereo']['matcher']['filtering']['threshold'] = match_th
label = current_config['config_common']['json_label']
current_config['config_common']['json_label'] = f'{label}-snnth-{match_th}-inlth-{inl_th}'
configs.append(current_config)
with open(f'OUT_JSON_RANSAC/{json_fn}_tuning.json', 'w') as f:
json.dump(configs, f, indent=2)
kp = 'lanet'
for v in ['v0','v1']:
for norm in ['', '_norm']:
json_fn = f'{kp}_{v}{norm}_1024'
json_fn2 = f'{kp}_{v}{norm}_1024'.replace('_','-')
md = deepcopy(metadata_dict)
md['link_to_website'] = repo_dict[kp]
md['link_to_pdf'] = pdfdict[kp]
md['method_name'] = method_name_dict[kp]
md['method_description']=md['method_description'].replace('PUT', method_name_dict[kp])
common = {"json_label": json_fn2,
"keypoint": json_fn2,
"descriptor": json_fn2,
"num_keypoints": 2048}
base_config = {
"metadata": md,
"config_common": common,
"config_phototourism_stereo": {
"use_custom_matches": False,
"matcher": deepcopy(matcher_template_dict),
"outlier_filter": { "method": "none" },
"geom": deepcopy(geom_template_dict)
},
"config_pragueparks_stereo": {
"use_custom_matches": False,
"matcher": deepcopy(matcher_template_dict),
"outlier_filter": { "method": "none" },
"geom": deepcopy(geom_template_dict)
}
}
with open(f'OUT_JSON/{json_fn}.json', 'w') as f:
json.dump([base_config], f, indent=2)
match_ths = [0.85, 0.9, 0.95, 0.99]
inl_ths = [0.5, 0.75, 1.0, 1.25]
configs = []
for match_th in match_ths:
for inl_th in inl_ths:
current_config = deepcopy(base_config)
for dset in ['phototourism', 'pragueparks']:
current_config[f'config_{dset}_stereo']['geom']['threshold'] = inl_th
current_config[f'config_{dset}_stereo']['matcher']['filtering']['threshold'] = match_th
label = current_config['config_common']['json_label']
current_config['config_common']['json_label'] = f'{label}-snnth-{match_th}-inlth-{inl_th}'
configs.append(current_config)
with open(f'OUT_JSON_RANSAC/{json_fn}_tuning.json', 'w') as f:
json.dump(configs, f, indent=2)
kp = 'superpoint_indep'
for v in ['coco', 'kitty']:
for norm in ['', '_norm']:
for sp in ['', '_subpix']:
json_fn = f'{kp}_{v}{norm}_1024{sp}'
json_fn2 = f'{kp}_{v}{norm}_1024{sp}'.replace('_','-')
md = deepcopy(metadata_dict)
md['link_to_website'] = repo_dict[kp]
md['link_to_pdf'] = pdfdict[kp]
md['method_name'] = method_name_dict[kp]
md['method_description']=md['method_description'].replace('PUT', method_name_dict[kp])
common = {"json_label": json_fn2,
"keypoint": json_fn2,
"descriptor": json_fn2,
"num_keypoints": 2048}
base_config = {
"metadata": md,
"config_common": common,
"config_phototourism_stereo": {
"use_custom_matches": False,
"matcher": deepcopy(matcher_template_dict),
"outlier_filter": { "method": "none" },
"geom": deepcopy(geom_template_dict)
},
"config_pragueparks_stereo": {
"use_custom_matches": False,
"matcher": deepcopy(matcher_template_dict),
"outlier_filter": { "method": "none" },
"geom": deepcopy(geom_template_dict)
}
}
with open(f'OUT_JSON/{json_fn}.json', 'w') as f:
json.dump([base_config], f, indent=2)
match_ths = [0.85, 0.9, 0.95, 0.99]
inl_ths = [0.5, 0.75, 1.0, 1.25]
configs = []
for match_th in match_ths:
for inl_th in inl_ths:
current_config = deepcopy(base_config)
for dset in ['phototourism', 'pragueparks']:
current_config[f'config_{dset}_stereo']['geom']['threshold'] = inl_th
current_config[f'config_{dset}_stereo']['matcher']['filtering']['threshold'] = match_th
label = current_config['config_common']['json_label']
current_config['config_common']['json_label'] = f'{label}-snnth-{match_th}-inlth-{inl_th}'
configs.append(current_config)
with open(f'OUT_JSON_RANSAC/{json_fn}_tuning.json', 'w') as f:
json.dump(configs, f, indent=2)