Replaced undistortion with std::vectors as well.
This commit is contained in:
Daniel
236
media/RESULTS.md
236
media/RESULTS.md
@ -294,26 +294,26 @@ Results of the model in various experiments on different datasets. \
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```json
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{
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"img_loading": 0.0424014,
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"demosaicing": 0.000721194,
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"avg_time_2d": 0.0148998,
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"avg_time_3d": 0.000186437,
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"fps": 63.2615
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"img_loading": 0.0418024,
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"demosaicing": 0.000712412,
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"avg_time_2d": 0.014805,
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"avg_time_3d": 0.000291986,
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"fps": 63.2536
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}
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{
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"triangulator_calls": 301,
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"init_time": 7.22995e-06,
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"undistort_time": 2.88306e-05,
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"project_time": 3.50066e-06,
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"match_time": 1.16445e-05,
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"pairs_time": 6.11458e-06,
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"pair_scoring_time": 4.69781e-05,
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"grouping_time": 7.95871e-06,
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"full_time": 4.9667e-05,
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"merge_time": 1.32834e-05,
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"post_time": 9.24932e-06,
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"convert_time": 2.06937e-07,
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"total_time": 0.000184941
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"init_time": 6.44811e-06,
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"undistort_time": 4.05236e-05,
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"project_time": 3.04309e-06,
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"match_time": 1.07992e-05,
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"pairs_time": 6.09893e-06,
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"pair_scoring_time": 6.0547e-05,
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"grouping_time": 7.94037e-06,
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"full_time": 5.80405e-05,
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"merge_time": 1.34738e-05,
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"post_time": 9.23313e-06,
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"convert_time": 1.58432e-07,
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"total_time": 0.000216593
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}
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{
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"person_nums": {
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@ -332,25 +332,25 @@ Results of the model in various experiments on different datasets. \
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},
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"mpjpe": {
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"count": 477,
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"mean": 0.047957,
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||||
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|
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"median": 0.042546,
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"std": 0.014906,
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"sem": 0.000683,
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"std": 0.014958,
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"sem": 0.000686,
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"min": 0.03014,
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"max": 0.12344,
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"recall-0.025": 0.0,
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"recall-0.05": 0.698113,
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"recall-0.1": 0.985325,
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"recall-0.1": 0.987421,
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"recall-0.15": 1.0,
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"recall-0.25": 1.0,
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"recall-0.5": 1.0,
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"num_labels": 477,
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"ap-0.025": 0.0,
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"ap-0.05": 0.387635,
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"ap-0.1": 0.728518,
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"ap-0.15": 0.744056,
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"ap-0.25": 0.744056,
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"ap-0.5": 0.744056
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"ap-0.05": 0.387605,
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"ap-0.1": 0.730843,
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"ap-0.15": 0.744044,
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"ap-0.25": 0.744044,
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"ap-0.5": 0.744044
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},
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"head": {
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"count": 477,
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@ -402,13 +402,13 @@ Results of the model in various experiments on different datasets. \
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},
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"elbow_left": {
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"count": 477,
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"mean": 0.040748,
|
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"mean": 0.040782,
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"median": 0.032111,
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"std": 0.02924,
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"std": 0.029225,
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"sem": 0.00134,
|
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"min": 0.003362,
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"min": 0.003363,
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"max": 0.326353,
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"recall-0.025": 0.314465,
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"recall-0.025": 0.312369,
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"recall-0.05": 0.756813,
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"recall-0.1": 0.953878,
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"recall-0.15": 0.997904,
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@ -450,25 +450,25 @@ Results of the model in various experiments on different datasets. \
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},
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"wrist_right": {
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"count": 477,
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"mean": 0.059129,
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"median": 0.054282,
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|
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|
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|
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"median": 0.054488,
|
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"std": 0.034178,
|
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"sem": 0.001567,
|
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"min": 0.009831,
|
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"max": 0.371597,
|
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"recall-0.025": 0.109015,
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"recall-0.05": 0.419287,
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"recall-0.1": 0.899371,
|
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"recall-0.15": 0.981132,
|
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"recall-0.15": 0.979036,
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"recall-0.25": 0.997904,
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"recall-0.5": 1.0,
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"num_labels": 477
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},
|
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"hip_left": {
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"count": 477,
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|
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|
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"median": 0.042309,
|
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"std": 0.02636,
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"sem": 0.001208,
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"min": 0.006447,
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"max": 0.14256,
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@ -498,15 +498,15 @@ Results of the model in various experiments on different datasets. \
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},
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"knee_left": {
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"count": 477,
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"mean": 0.04045,
|
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"mean": 0.040396,
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"median": 0.037751,
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"std": 0.024566,
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"sem": 0.001126,
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"std": 0.02446,
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"sem": 0.001121,
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"min": 0.004904,
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"max": 0.190671,
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"recall-0.025": 0.259958,
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"recall-0.05": 0.748428,
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"recall-0.1": 0.972746,
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"recall-0.1": 0.974843,
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"recall-0.15": 0.989518,
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"recall-0.25": 1.0,
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"recall-0.5": 1.0,
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@ -564,7 +564,7 @@ Results of the model in various experiments on different datasets. \
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"num_labels": 6201,
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"recall-0.025": 0.20997,
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"recall-0.05": 0.61571,
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"recall-0.1": 0.94243,
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"recall-0.1": 0.94275,
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"recall-0.15": 0.98645,
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"recall-0.25": 0.99839,
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"recall-0.5": 1.0
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@ -5967,26 +5967,26 @@ Results of the model in various experiments on different datasets. \
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```json
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{
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"img_loading": 0.27609,
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"demosaicing": 0.0112097,
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"avg_time_2d": 0.0240132,
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"avg_time_3d": 0.000671032,
|
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"fps": 27.8599
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"img_loading": 0.282821,
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"demosaicing": 0.011311,
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"avg_time_2d": 0.0240066,
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"avg_time_3d": 0.00055045,
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"fps": 27.8799
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}
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{
|
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"triangulator_calls": 121,
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"init_time": 2.1143e-05,
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"undistort_time": 3.8166e-05,
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"project_time": 3.43309e-05,
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"match_time": 2.66535e-05,
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"pairs_time": 5.949e-05,
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"pair_scoring_time": 0.000217684,
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"grouping_time": 2.1364e-05,
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"full_time": 0.000180708,
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"merge_time": 2.17519e-05,
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"post_time": 1.14353e-05,
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||||
"convert_time": 5.88083e-07,
|
||||
"total_time": 0.000633601
|
||||
"init_time": 1.3244e-05,
|
||||
"undistort_time": 3.09483e-05,
|
||||
"project_time": 5.23027e-06,
|
||||
"match_time": 1.68509e-05,
|
||||
"pairs_time": 1.37968e-05,
|
||||
"pair_scoring_time": 0.000160537,
|
||||
"grouping_time": 1.73903e-05,
|
||||
"full_time": 0.000199044,
|
||||
"merge_time": 3.75236e-05,
|
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"post_time": 1.03264e-05,
|
||||
"convert_time": 1.91669e-07,
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"total_time": 0.000505528
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}
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{
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"person_nums": {
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@ -6005,21 +6005,21 @@ Results of the model in various experiments on different datasets. \
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},
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"mpjpe": {
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"count": 363,
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"mean": 0.025729,
|
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"median": 0.0249,
|
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"std": 0.007288,
|
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"sem": 0.000383,
|
||||
"mean": 0.0257,
|
||||
"median": 0.024739,
|
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"std": 0.007243,
|
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"sem": 0.000381,
|
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"min": 0.011333,
|
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"max": 0.051735,
|
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"recall-0.025": 0.506887,
|
||||
"recall-0.05": 0.99449,
|
||||
"recall-0.025": 0.515152,
|
||||
"recall-0.05": 0.997245,
|
||||
"recall-0.1": 1.0,
|
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"recall-0.15": 1.0,
|
||||
"recall-0.25": 1.0,
|
||||
"recall-0.5": 1.0,
|
||||
"num_labels": 363,
|
||||
"ap-0.025": 0.272651,
|
||||
"ap-0.05": 0.993013,
|
||||
"ap-0.025": 0.277608,
|
||||
"ap-0.05": 0.99638,
|
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"ap-0.1": 1.0,
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"ap-0.15": 1.0,
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"ap-0.25": 1.0,
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@ -6027,15 +6027,15 @@ Results of the model in various experiments on different datasets. \
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},
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"head": {
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"count": 363,
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"mean": 0.028076,
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||||
"median": 0.022721,
|
||||
"std": 0.017771,
|
||||
"sem": 0.000934,
|
||||
"mean": 0.027713,
|
||||
"median": 0.022633,
|
||||
"std": 0.017317,
|
||||
"sem": 0.00091,
|
||||
"min": 0.00109,
|
||||
"max": 0.112785,
|
||||
"recall-0.025": 0.550964,
|
||||
"recall-0.05": 0.884298,
|
||||
"recall-0.1": 0.997245,
|
||||
"max": 0.087763,
|
||||
"recall-0.025": 0.553719,
|
||||
"recall-0.05": 0.887052,
|
||||
"recall-0.1": 1.0,
|
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"recall-0.15": 1.0,
|
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"recall-0.25": 1.0,
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"recall-0.5": 1.0,
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@ -6043,14 +6043,14 @@ Results of the model in various experiments on different datasets. \
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},
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"shoulder_left": {
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"count": 363,
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"mean": 0.027266,
|
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"median": 0.02188,
|
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"std": 0.021199,
|
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"sem": 0.001114,
|
||||
"mean": 0.027215,
|
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|
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|
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"sem": 0.001113,
|
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"min": 0.002899,
|
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"max": 0.151257,
|
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"recall-0.025": 0.584022,
|
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"recall-0.05": 0.892562,
|
||||
"recall-0.05": 0.895317,
|
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"recall-0.1": 0.975207,
|
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"recall-0.15": 0.997245,
|
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"recall-0.25": 1.0,
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@ -6059,10 +6059,10 @@ Results of the model in various experiments on different datasets. \
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},
|
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"shoulder_right": {
|
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"count": 363,
|
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"mean": 0.023379,
|
||||
"mean": 0.023389,
|
||||
"median": 0.021151,
|
||||
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|
||||
"sem": 0.000672,
|
||||
"std": 0.012799,
|
||||
"sem": 0.000673,
|
||||
"min": 0.003682,
|
||||
"max": 0.101851,
|
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"recall-0.025": 0.61157,
|
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@ -6077,8 +6077,8 @@ Results of the model in various experiments on different datasets. \
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"count": 363,
|
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"mean": 0.022276,
|
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"median": 0.019385,
|
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"std": 0.014907,
|
||||
"sem": 0.000784,
|
||||
"std": 0.014902,
|
||||
"sem": 0.000783,
|
||||
"min": 0.001441,
|
||||
"max": 0.194618,
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"recall-0.025": 0.694215,
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@ -6091,9 +6091,9 @@ Results of the model in various experiments on different datasets. \
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},
|
||||
"elbow_right": {
|
||||
"count": 363,
|
||||
"mean": 0.018547,
|
||||
"median": 0.016646,
|
||||
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|
||||
"mean": 0.018528,
|
||||
"median": 0.016603,
|
||||
"std": 0.010173,
|
||||
"sem": 0.000535,
|
||||
"min": 0.001046,
|
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"max": 0.083441,
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@ -6107,13 +6107,13 @@ Results of the model in various experiments on different datasets. \
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},
|
||||
"wrist_left": {
|
||||
"count": 363,
|
||||
"mean": 0.023528,
|
||||
"mean": 0.023532,
|
||||
"median": 0.018873,
|
||||
"std": 0.018383,
|
||||
"std": 0.018388,
|
||||
"sem": 0.000966,
|
||||
"min": 0.00279,
|
||||
"max": 0.199397,
|
||||
"recall-0.025": 0.68595,
|
||||
"recall-0.025": 0.683196,
|
||||
"recall-0.05": 0.931129,
|
||||
"recall-0.1": 0.991736,
|
||||
"recall-0.15": 0.997245,
|
||||
@ -6123,9 +6123,9 @@ Results of the model in various experiments on different datasets. \
|
||||
},
|
||||
"wrist_right": {
|
||||
"count": 363,
|
||||
"mean": 0.01958,
|
||||
"mean": 0.019579,
|
||||
"median": 0.017651,
|
||||
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|
||||
"std": 0.011201,
|
||||
"sem": 0.000589,
|
||||
"min": 0.002333,
|
||||
"max": 0.076342,
|
||||
@ -6139,13 +6139,13 @@ Results of the model in various experiments on different datasets. \
|
||||
},
|
||||
"hip_left": {
|
||||
"count": 363,
|
||||
"mean": 0.031085,
|
||||
"median": 0.026292,
|
||||
"std": 0.016989,
|
||||
"mean": 0.031156,
|
||||
"median": 0.026379,
|
||||
"std": 0.016985,
|
||||
"sem": 0.000893,
|
||||
"min": 0.006012,
|
||||
"min": 0.006013,
|
||||
"max": 0.117111,
|
||||
"recall-0.025": 0.446281,
|
||||
"recall-0.025": 0.443526,
|
||||
"recall-0.05": 0.859504,
|
||||
"recall-0.1": 0.997245,
|
||||
"recall-0.15": 1.0,
|
||||
@ -6155,14 +6155,14 @@ Results of the model in various experiments on different datasets. \
|
||||
},
|
||||
"hip_right": {
|
||||
"count": 363,
|
||||
"mean": 0.031145,
|
||||
"median": 0.028754,
|
||||
"std": 0.016728,
|
||||
"sem": 0.000879,
|
||||
"mean": 0.03111,
|
||||
"median": 0.028792,
|
||||
"std": 0.01668,
|
||||
"sem": 0.000877,
|
||||
"min": 0.003451,
|
||||
"max": 0.138183,
|
||||
"recall-0.025": 0.38292,
|
||||
"recall-0.05": 0.900826,
|
||||
"recall-0.05": 0.903581,
|
||||
"recall-0.1": 0.99449,
|
||||
"recall-0.15": 1.0,
|
||||
"recall-0.25": 1.0,
|
||||
@ -6171,9 +6171,9 @@ Results of the model in various experiments on different datasets. \
|
||||
},
|
||||
"knee_left": {
|
||||
"count": 363,
|
||||
"mean": 0.028289,
|
||||
"mean": 0.028282,
|
||||
"median": 0.020833,
|
||||
"std": 0.023122,
|
||||
"std": 0.023126,
|
||||
"sem": 0.001215,
|
||||
"min": 0.001686,
|
||||
"max": 0.127237,
|
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@ -6203,14 +6203,14 @@ Results of the model in various experiments on different datasets. \
|
||||
},
|
||||
"ankle_left": {
|
||||
"count": 363,
|
||||
"mean": 0.028177,
|
||||
"median": 0.021538,
|
||||
"std": 0.022712,
|
||||
"sem": 0.001194,
|
||||
"mean": 0.028125,
|
||||
"median": 0.021539,
|
||||
"std": 0.02265,
|
||||
"sem": 0.00119,
|
||||
"min": 0.002656,
|
||||
"max": 0.178927,
|
||||
"recall-0.025": 0.575758,
|
||||
"recall-0.05": 0.895317,
|
||||
"recall-0.025": 0.578512,
|
||||
"recall-0.05": 0.900826,
|
||||
"recall-0.1": 0.980716,
|
||||
"recall-0.15": 0.99449,
|
||||
"recall-0.25": 1.0,
|
||||
@ -6219,10 +6219,10 @@ Results of the model in various experiments on different datasets. \
|
||||
},
|
||||
"ankle_right": {
|
||||
"count": 363,
|
||||
"mean": 0.029428,
|
||||
"median": 0.022095,
|
||||
"std": 0.027113,
|
||||
"sem": 0.001425,
|
||||
"mean": 0.029496,
|
||||
"median": 0.022264,
|
||||
"std": 0.027073,
|
||||
"sem": 0.001423,
|
||||
"min": 0.002482,
|
||||
"max": 0.263543,
|
||||
"recall-0.025": 0.584022,
|
||||
@ -6235,9 +6235,9 @@ Results of the model in various experiments on different datasets. \
|
||||
},
|
||||
"joint_recalls": {
|
||||
"num_labels": 4719,
|
||||
"recall-0.025": 0.60606,
|
||||
"recall-0.05": 0.91566,
|
||||
"recall-0.1": 0.98962,
|
||||
"recall-0.025": 0.60585,
|
||||
"recall-0.05": 0.9163,
|
||||
"recall-0.1": 0.99004,
|
||||
"recall-0.15": 0.99746,
|
||||
"recall-0.25": 0.99958,
|
||||
"recall-0.5": 1.0
|
||||
|
||||
@ -210,7 +210,6 @@ std::vector<std::vector<std::array<float, 4>>> TriangulatorInternal::triangulate
|
||||
stime = std::chrono::steady_clock::now();
|
||||
|
||||
// Undistort 2D poses
|
||||
#pragma omp parallel for
|
||||
for (size_t i = 0; i < cameras.size(); ++i)
|
||||
{
|
||||
undistort_poses(i_poses_2d[i], internal_cameras[i]);
|
||||
@ -604,6 +603,84 @@ void TriangulatorInternal::print_stats()
|
||||
|
||||
// =================================================================================================
|
||||
|
||||
void undistort_point_pinhole(std::array<float, 3> &p, const std::vector<float> &k)
|
||||
{
|
||||
// Use distortion coefficients: [k1, k2, p1, p2, k3]
|
||||
// https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/undistort.dispatch.cpp#L432
|
||||
|
||||
float x0 = p[0];
|
||||
float y0 = p[1];
|
||||
float x = x0;
|
||||
float y = y0;
|
||||
|
||||
// Iteratively refine the estimate for the undistorted point.
|
||||
int max_iterations = 5;
|
||||
for (int iter = 0; iter < max_iterations; ++iter)
|
||||
{
|
||||
float r2 = x * x + y * y;
|
||||
double icdist = 1.0 / (1 + ((k[4] * r2 + k[1]) * r2 + k[0]) * r2);
|
||||
if (icdist < 0)
|
||||
{
|
||||
x = x0;
|
||||
y = y0;
|
||||
break;
|
||||
}
|
||||
|
||||
float deltaX = 2 * k[2] * x * y + k[3] * (r2 + 2 * x * x);
|
||||
float deltaY = k[2] * (r2 + 2 * y * y) + 2 * k[3] * x * y;
|
||||
x = (x0 - deltaX) * icdist;
|
||||
y = (y0 - deltaY) * icdist;
|
||||
}
|
||||
|
||||
p[0] = x;
|
||||
p[1] = y;
|
||||
}
|
||||
|
||||
void undistort_point_fisheye(std::array<float, 3> &p, const std::vector<float> &k)
|
||||
{
|
||||
// Use distortion coefficients: [k1, k2, k3, k4]
|
||||
// https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/fisheye.cpp#L429
|
||||
|
||||
float theta_d = std::sqrt(p[0] * p[0] + p[1] * p[1]);
|
||||
float pi_half = std::numbers::pi * 0.5;
|
||||
theta_d = std::min(std::max(-pi_half, theta_d), pi_half);
|
||||
|
||||
if (theta_d < 1e-6)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
float scale = 0.0;
|
||||
float theta = theta_d;
|
||||
|
||||
int max_iterations = 5;
|
||||
for (int iter = 0; iter < max_iterations; ++iter)
|
||||
{
|
||||
float theta2 = theta * theta;
|
||||
float theta4 = theta2 * theta2;
|
||||
float theta6 = theta4 * theta2;
|
||||
float theta8 = theta4 * theta4;
|
||||
|
||||
float k0_theta2 = k[0] * theta2;
|
||||
float k1_theta4 = k[1] * theta4;
|
||||
float k2_theta6 = k[2] * theta6;
|
||||
float k3_theta8 = k[3] * theta8;
|
||||
|
||||
float theta_fix = (theta * (1 + k0_theta2 + k1_theta4 + k2_theta6 + k3_theta8) - theta_d) /
|
||||
(1 + 3 * k0_theta2 + 5 * k1_theta4 + 7 * k2_theta6 + 9 * k3_theta8);
|
||||
theta = theta - theta_fix;
|
||||
|
||||
if (std::fabs(theta_fix) < 1e-6)
|
||||
{
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
scale = std::tan(theta) / theta_d;
|
||||
p[0] *= scale;
|
||||
p[1] *= scale;
|
||||
}
|
||||
|
||||
void TriangulatorInternal::undistort_poses(
|
||||
std::vector<std::vector<std::array<float, 3>>> &poses_2d, CameraInternal &icam)
|
||||
{
|
||||
@ -624,44 +701,42 @@ void TriangulatorInternal::undistort_poses(
|
||||
icam.K, icam.DC, cv::Size(width, height), 1, cv::Size(width, height));
|
||||
}
|
||||
|
||||
// Convert vectors to single mat
|
||||
size_t num_persons = poses_2d.size();
|
||||
size_t num_joints = poses_2d[0].size();
|
||||
std::vector<int> dims = {(int)(num_persons * num_joints), 2};
|
||||
cv::Mat points_mat = cv::Mat(dims, CV_32F);
|
||||
float *mat_ptr = points_mat.ptr<float>(0);
|
||||
for (size_t i = 0; i < num_persons; ++i)
|
||||
{
|
||||
for (size_t j = 0; j < num_joints; ++j)
|
||||
{
|
||||
for (size_t k = 0; k < 2; ++k)
|
||||
{
|
||||
mat_ptr[i * num_joints * 2 + j * 2 + k] = poses_2d[i][j][k];
|
||||
}
|
||||
}
|
||||
}
|
||||
float ifx_old = 1.0 / icam.cam.K[0][0];
|
||||
float ify_old = 1.0 / icam.cam.K[1][1];
|
||||
float cx_old = icam.cam.K[0][2];
|
||||
float cy_old = icam.cam.K[1][2];
|
||||
float fx_new = newK.at<float>(0, 0);
|
||||
float fy_new = newK.at<float>(1, 1);
|
||||
float cx_new = newK.at<float>(0, 2);
|
||||
float cy_new = newK.at<float>(1, 2);
|
||||
|
||||
// Undistort all the points
|
||||
points_mat = points_mat.reshape(2, static_cast<int>(points_mat.rows));
|
||||
if (icam.cam.type == "fisheye")
|
||||
{
|
||||
cv::fisheye::undistortPoints(points_mat, points_mat, icam.K, icam.DC, cv::noArray(), newK);
|
||||
}
|
||||
else
|
||||
{
|
||||
cv::undistortPoints(points_mat, points_mat, icam.K, icam.DC, cv::noArray(), newK);
|
||||
}
|
||||
points_mat = points_mat.reshape(1, static_cast<int>(points_mat.rows));
|
||||
|
||||
// Overwrite the old coordinates with the undistorted ones
|
||||
size_t num_persons = poses_2d.size();
|
||||
size_t num_joints = poses_2d[0].size();
|
||||
for (size_t i = 0; i < num_persons; ++i)
|
||||
{
|
||||
for (size_t j = 0; j < num_joints; ++j)
|
||||
{
|
||||
for (size_t k = 0; k < 2; ++k)
|
||||
// Normalize the point using the original camera matrix
|
||||
poses_2d[i][j][0] = (poses_2d[i][j][0] - cx_old) * ifx_old;
|
||||
poses_2d[i][j][1] = (poses_2d[i][j][1] - cy_old) * ify_old;
|
||||
|
||||
// Undistort
|
||||
// Using own implementation is faster than using OpenCV, because it avoids the
|
||||
// overhead of creating cv::Mat objects and further unnecessary calculations for
|
||||
// additional distortion parameters and identity rotations in this usecase.
|
||||
if (icam.cam.type == "fisheye")
|
||||
{
|
||||
poses_2d[i][j][k] = mat_ptr[i * num_joints * 2 + j * 2 + k];
|
||||
undistort_point_fisheye(poses_2d[i][j], icam.cam.DC);
|
||||
}
|
||||
else
|
||||
{
|
||||
undistort_point_pinhole(poses_2d[i][j], icam.cam.DC);
|
||||
}
|
||||
|
||||
// Map the undistorted normalized point to the new image coordinates
|
||||
poses_2d[i][j][0] = (poses_2d[i][j][0] * fx_new) + cx_new;
|
||||
poses_2d[i][j][1] = (poses_2d[i][j][1] * fy_new) + cy_new;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user