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Implemented custom intrinsic matrix undistortion.

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Daniel
2025-02-28 12:56:28 +01:00
parent 0f2d597899
commit 1d259846fc
5 changed files with 638 additions and 434 deletions
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@ -294,72 +294,72 @@ Results of the model in various experiments on different datasets. \
```json ```json
{ {
"img_loading": 0.180589, "img_loading": 0.0424103,
"demosaicing": 0.000695076, "demosaicing": 0.000724716,
"avg_time_2d": 0.0152607, "avg_time_2d": 0.01494,
"avg_time_3d": 0.000150192, "avg_time_3d": 0.000128772,
"fps": 62.0888 "fps": 63.3173
} }
{ {
"triangulator_calls": 301, "triangulator_calls": 301,
"init_time": 3.53967e-06, "init_time": 1.60891e-06,
"undistort_time": 3.48582e-05, "undistort_time": 2.57178e-05,
"project_time": 2.18348e-06, "project_time": 2.22848e-06,
"match_time": 8.45481e-06, "match_time": 8.41567e-06,
"pairs_time": 4.53164e-06, "pairs_time": 4.53139e-06,
"pair_scoring_time": 3.10183e-05, "pair_scoring_time": 2.67118e-05,
"grouping_time": 4.6499e-06, "grouping_time": 4.63213e-06,
"full_time": 3.33672e-05, "full_time": 2.72313e-05,
"merge_time": 1.02807e-05, "merge_time": 1.03292e-05,
"post_time": 7.00402e-06, "post_time": 7.36791e-06,
"convert_time": 1.11306e-07, "convert_time": 1.27439e-07,
"total_time": 0.000140236 "total_time": 0.00011914
} }
{ {
"person_nums": { "person_nums": {
"total_frames": 301, "total_frames": 301,
"total_labels": 477, "total_labels": 477,
"total_preds": 829, "total_preds": 827,
"considered_empty": 0, "considered_empty": 0,
"valid_preds": 477, "valid_preds": 477,
"invalid_preds": 352, "invalid_preds": 350,
"missing": 0, "missing": 0,
"invalid_fraction": 0.42461, "invalid_fraction": 0.42322,
"precision": 0.57539, "precision": 0.57678,
"recall": 1.0, "recall": 1.0,
"f1": 0.73047, "f1": 0.7316,
"non_empty": 829 "non_empty": 827
}, },
"mpjpe": { "mpjpe": {
"count": 477, "count": 477,
"mean": 0.047984, "mean": 0.047983,
"median": 0.042648, "median": 0.042569,
"std": 0.014812, "std": 0.01486,
"sem": 0.000679, "sem": 0.000681,
"min": 0.03012, "min": 0.03012,
"max": 0.116312, "max": 0.116311,
"recall-0.025": 0.0, "recall-0.025": 0.0,
"recall-0.05": 0.70021, "recall-0.05": 0.70021,
"recall-0.1": 0.985325, "recall-0.1": 0.983229,
"recall-0.15": 1.0, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
"recall-0.5": 1.0, "recall-0.5": 1.0,
"num_labels": 477, "num_labels": 477,
"ap-0.025": 0.0, "ap-0.025": 0.0,
"ap-0.05": 0.39114, "ap-0.05": 0.389102,
"ap-0.1": 0.735434, "ap-0.1": 0.729848,
"ap-0.15": 0.751482, "ap-0.15": 0.747198,
"ap-0.25": 0.751482, "ap-0.25": 0.747198,
"ap-0.5": 0.751482 "ap-0.5": 0.747198
}, },
"head": { "head": {
"count": 477, "count": 477,
"mean": 0.054212, "mean": 0.054217,
"median": 0.050157, "median": 0.050158,
"std": 0.024854, "std": 0.024847,
"sem": 0.001139, "sem": 0.001139,
"min": 0.005599, "min": 0.005598,
"max": 0.180565, "max": 0.180564,
"recall-0.025": 0.081761, "recall-0.025": 0.081761,
"recall-0.05": 0.496855, "recall-0.05": 0.496855,
"recall-0.1": 0.937107, "recall-0.1": 0.937107,
@ -370,11 +370,11 @@ Results of the model in various experiments on different datasets. \
}, },
"shoulder_left": { "shoulder_left": {
"count": 477, "count": 477,
"mean": 0.042435, "mean": 0.042429,
"median": 0.03702, "median": 0.037021,
"std": 0.02058, "std": 0.020584,
"sem": 0.000943, "sem": 0.000943,
"min": 0.00431, "min": 0.004311,
"max": 0.136587, "max": 0.136587,
"recall-0.025": 0.161426, "recall-0.025": 0.161426,
"recall-0.05": 0.727463, "recall-0.05": 0.727463,
@ -386,14 +386,14 @@ Results of the model in various experiments on different datasets. \
}, },
"shoulder_right": { "shoulder_right": {
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"mean": 0.049634, "mean": 0.049623,
"median": 0.045795, "median": 0.045796,
"std": 0.023121, "std": 0.02312,
"sem": 0.00106, "sem": 0.00106,
"min": 0.00535, "min": 0.005349,
"max": 0.14745, "max": 0.147448,
"recall-0.025": 0.100629, "recall-0.025": 0.100629,
"recall-0.05": 0.559748, "recall-0.05": 0.561845,
"recall-0.1": 0.955975, "recall-0.1": 0.955975,
"recall-0.15": 1.0, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
@ -402,12 +402,12 @@ Results of the model in various experiments on different datasets. \
}, },
"elbow_left": { "elbow_left": {
"count": 477, "count": 477,
"mean": 0.040763, "mean": 0.04078,
"median": 0.032063, "median": 0.032063,
"std": 0.029259, "std": 0.029273,
"sem": 0.001341, "sem": 0.001342,
"min": 0.003449, "min": 0.003449,
"max": 0.326227, "max": 0.326226,
"recall-0.025": 0.316562, "recall-0.025": 0.316562,
"recall-0.05": 0.756813, "recall-0.05": 0.756813,
"recall-0.1": 0.953878, "recall-0.1": 0.953878,
@ -418,13 +418,13 @@ Results of the model in various experiments on different datasets. \
}, },
"elbow_right": { "elbow_right": {
"count": 477, "count": 477,
"mean": 0.053368, "mean": 0.053274,
"median": 0.045043, "median": 0.044357,
"std": 0.040851, "std": 0.040895,
"sem": 0.001872, "sem": 0.001874,
"min": 0.003529, "min": 0.003528,
"max": 0.244051, "max": 0.244052,
"recall-0.025": 0.255765, "recall-0.025": 0.257862,
"recall-0.05": 0.561845, "recall-0.05": 0.561845,
"recall-0.1": 0.901468, "recall-0.1": 0.901468,
"recall-0.15": 0.958071, "recall-0.15": 0.958071,
@ -434,9 +434,9 @@ Results of the model in various experiments on different datasets. \
}, },
"wrist_left": { "wrist_left": {
"count": 477, "count": 477,
"mean": 0.060002, "mean": 0.059994,
"median": 0.053953, "median": 0.053953,
"std": 0.03861, "std": 0.038609,
"sem": 0.00177, "sem": 0.00177,
"min": 0.002051, "min": 0.002051,
"max": 0.322481, "max": 0.322481,
@ -450,12 +450,12 @@ Results of the model in various experiments on different datasets. \
}, },
"wrist_right": { "wrist_right": {
"count": 477, "count": 477,
"mean": 0.059207, "mean": 0.059177,
"median": 0.054405, "median": 0.054405,
"std": 0.033578, "std": 0.033566,
"sem": 0.001539, "sem": 0.001538,
"min": 0.009618, "min": 0.009618,
"max": 0.371667, "max": 0.371666,
"recall-0.025": 0.115304, "recall-0.025": 0.115304,
"recall-0.05": 0.415094, "recall-0.05": 0.415094,
"recall-0.1": 0.899371, "recall-0.1": 0.899371,
@ -466,15 +466,15 @@ Results of the model in various experiments on different datasets. \
}, },
"hip_left": { "hip_left": {
"count": 477, "count": 477,
"mean": 0.047948, "mean": 0.048042,
"median": 0.042251, "median": 0.042252,
"std": 0.026295, "std": 0.026486,
"sem": 0.001205, "sem": 0.001214,
"min": 0.006475, "min": 0.006475,
"max": 0.145903, "max": 0.145904,
"recall-0.025": 0.188679, "recall-0.025": 0.190776,
"recall-0.05": 0.618449, "recall-0.05": 0.618449,
"recall-0.1": 0.953878, "recall-0.1": 0.951782,
"recall-0.15": 1.0, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
"recall-0.5": 1.0, "recall-0.5": 1.0,
@ -482,15 +482,15 @@ Results of the model in various experiments on different datasets. \
}, },
"hip_right": { "hip_right": {
"count": 477, "count": 477,
"mean": 0.058483, "mean": 0.058447,
"median": 0.05753, "median": 0.05753,
"std": 0.023762, "std": 0.0237,
"sem": 0.001089, "sem": 0.001086,
"min": 0.005137, "min": 0.005137,
"max": 0.132318, "max": 0.132317,
"recall-0.025": 0.098532, "recall-0.025": 0.098532,
"recall-0.05": 0.39413, "recall-0.05": 0.39413,
"recall-0.1": 0.943396, "recall-0.1": 0.945493,
"recall-0.15": 1.0, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
"recall-0.5": 1.0, "recall-0.5": 1.0,
@ -498,15 +498,15 @@ Results of the model in various experiments on different datasets. \
}, },
"knee_left": { "knee_left": {
"count": 477, "count": 477,
"mean": 0.040438, "mean": 0.040484,
"median": 0.03808, "median": 0.038079,
"std": 0.024403, "std": 0.024499,
"sem": 0.001118, "sem": 0.001123,
"min": 0.004928, "min": 0.004927,
"max": 0.190069, "max": 0.190068,
"recall-0.025": 0.257862, "recall-0.025": 0.257862,
"recall-0.05": 0.748428, "recall-0.05": 0.748428,
"recall-0.1": 0.974843, "recall-0.1": 0.972746,
"recall-0.15": 0.989518, "recall-0.15": 0.989518,
"recall-0.25": 1.0, "recall-0.25": 1.0,
"recall-0.5": 1.0, "recall-0.5": 1.0,
@ -514,12 +514,12 @@ Results of the model in various experiments on different datasets. \
}, },
"knee_right": { "knee_right": {
"count": 477, "count": 477,
"mean": 0.040168, "mean": 0.040167,
"median": 0.03623, "median": 0.036232,
"std": 0.023114, "std": 0.023115,
"sem": 0.001059, "sem": 0.001059,
"min": 0.00733, "min": 0.00733,
"max": 0.184933, "max": 0.184932,
"recall-0.025": 0.310273, "recall-0.025": 0.310273,
"recall-0.05": 0.708595, "recall-0.05": 0.708595,
"recall-0.1": 0.976939, "recall-0.1": 0.976939,
@ -530,15 +530,15 @@ Results of the model in various experiments on different datasets. \
}, },
"ankle_left": { "ankle_left": {
"count": 477, "count": 477,
"mean": 0.036353, "mean": 0.036403,
"median": 0.028172, "median": 0.028172,
"std": 0.030783, "std": 0.03066,
"sem": 0.001411, "sem": 0.001405,
"min": 0.004787, "min": 0.004789,
"max": 0.223747, "max": 0.223748,
"recall-0.025": 0.433962, "recall-0.025": 0.433962,
"recall-0.05": 0.81761, "recall-0.05": 0.81761,
"recall-0.1": 0.945493, "recall-0.1": 0.947589,
"recall-0.15": 0.983229, "recall-0.15": 0.983229,
"recall-0.25": 1.0, "recall-0.25": 1.0,
"recall-0.5": 1.0, "recall-0.5": 1.0,
@ -546,14 +546,14 @@ Results of the model in various experiments on different datasets. \
}, },
"ankle_right": { "ankle_right": {
"count": 477, "count": 477,
"mean": 0.040777, "mean": 0.040745,
"median": 0.030897, "median": 0.030898,
"std": 0.037254, "std": 0.03726,
"sem": 0.001708, "sem": 0.001708,
"min": 0.003323, "min": 0.003323,
"max": 0.27012, "max": 0.270118,
"recall-0.025": 0.303983, "recall-0.025": 0.301887,
"recall-0.05": 0.802935, "recall-0.05": 0.805031,
"recall-0.1": 0.930818, "recall-0.1": 0.930818,
"recall-0.15": 0.968553, "recall-0.15": 0.968553,
"recall-0.25": 0.997904, "recall-0.25": 0.997904,
@ -562,8 +562,8 @@ Results of the model in various experiments on different datasets. \
}, },
"joint_recalls": { "joint_recalls": {
"num_labels": 6201, "num_labels": 6201,
"recall-0.025": 0.21093, "recall-0.025": 0.21158,
"recall-0.05": 0.6149, "recall-0.05": 0.61538,
"recall-0.1": 0.94275, "recall-0.1": 0.94275,
"recall-0.15": 0.98645, "recall-0.15": 0.98645,
"recall-0.25": 0.99871, "recall-0.25": 0.99871,
@ -5967,26 +5967,26 @@ Results of the model in various experiments on different datasets. \
```json ```json
{ {
"img_loading": 0.282821, "img_loading": 0.287326,
"demosaicing": 0.011311, "demosaicing": 0.0112221,
"avg_time_2d": 0.0240066, "avg_time_2d": 0.0240407,
"avg_time_3d": 0.00055045, "avg_time_3d": 0.00023535,
"fps": 27.8799 "fps": 28.1705
} }
{ {
"triangulator_calls": 121, "triangulator_calls": 121,
"init_time": 1.3244e-05, "init_time": 2.2796e-06,
"undistort_time": 3.09483e-05, "undistort_time": 2.07935e-05,
"project_time": 5.23027e-06, "project_time": 3.8538e-06,
"match_time": 1.68509e-05, "match_time": 1.31121e-05,
"pairs_time": 1.37968e-05, "pairs_time": 9.31819e-06,
"pair_scoring_time": 0.000160537, "pair_scoring_time": 6.0523e-05,
"grouping_time": 1.73903e-05, "grouping_time": 1.10819e-05,
"full_time": 0.000199044, "full_time": 7.05671e-05,
"merge_time": 3.75236e-05, "merge_time": 2.99701e-05,
"post_time": 1.03264e-05, "post_time": 7.38921e-06,
"convert_time": 1.91669e-07, "convert_time": 1.13331e-07,
"total_time": 0.000505528 "total_time": 0.000229241
} }
{ {
"person_nums": { "person_nums": {
@ -6005,21 +6005,21 @@ Results of the model in various experiments on different datasets. \
}, },
"mpjpe": { "mpjpe": {
"count": 363, "count": 363,
"mean": 0.0257, "mean": 0.025723,
"median": 0.024739, "median": 0.024899,
"std": 0.007243, "std": 0.00724,
"sem": 0.000381, "sem": 0.000381,
"min": 0.011333, "min": 0.011774,
"max": 0.051735, "max": 0.052562,
"recall-0.025": 0.515152, "recall-0.025": 0.504132,
"recall-0.05": 0.997245, "recall-0.05": 0.997245,
"recall-0.1": 1.0, "recall-0.1": 1.0,
"recall-0.15": 1.0, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
"recall-0.5": 1.0, "recall-0.5": 1.0,
"num_labels": 363, "num_labels": 363,
"ap-0.025": 0.277608, "ap-0.025": 0.274693,
"ap-0.05": 0.99638, "ap-0.05": 0.996479,
"ap-0.1": 1.0, "ap-0.1": 1.0,
"ap-0.15": 1.0, "ap-0.15": 1.0,
"ap-0.25": 1.0, "ap-0.25": 1.0,
@ -6027,14 +6027,14 @@ Results of the model in various experiments on different datasets. \
}, },
"head": { "head": {
"count": 363, "count": 363,
"mean": 0.027713, "mean": 0.02732,
"median": 0.022633, "median": 0.022736,
"std": 0.017317, "std": 0.016828,
"sem": 0.00091, "sem": 0.000884,
"min": 0.00109, "min": 0.00115,
"max": 0.087763, "max": 0.085467,
"recall-0.025": 0.553719, "recall-0.025": 0.573003,
"recall-0.05": 0.887052, "recall-0.05": 0.895317,
"recall-0.1": 1.0, "recall-0.1": 1.0,
"recall-0.15": 1.0, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
@ -6043,30 +6043,30 @@ Results of the model in various experiments on different datasets. \
}, },
"shoulder_left": { "shoulder_left": {
"count": 363, "count": 363,
"mean": 0.027215, "mean": 0.027043,
"median": 0.021616, "median": 0.021957,
"std": 0.021167, "std": 0.020841,
"sem": 0.001113, "sem": 0.001095,
"min": 0.002899, "min": 0.002793,
"max": 0.151257, "max": 0.149897,
"recall-0.025": 0.584022, "recall-0.025": 0.589532,
"recall-0.05": 0.895317, "recall-0.05": 0.887052,
"recall-0.1": 0.975207, "recall-0.1": 0.986226,
"recall-0.15": 0.997245, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
"recall-0.5": 1.0, "recall-0.5": 1.0,
"num_labels": 363 "num_labels": 363
}, },
"shoulder_right": { "shoulder_right": {
"count": 363, "count": 363,
"mean": 0.023389, "mean": 0.022955,
"median": 0.021151, "median": 0.020511,
"std": 0.012799, "std": 0.012805,
"sem": 0.000673, "sem": 0.000673,
"min": 0.003682, "min": 0.003064,
"max": 0.101851, "max": 0.101875,
"recall-0.025": 0.61157, "recall-0.025": 0.628099,
"recall-0.05": 0.955923, "recall-0.05": 0.961433,
"recall-0.1": 0.997245, "recall-0.1": 0.997245,
"recall-0.15": 1.0, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
@ -6075,13 +6075,13 @@ Results of the model in various experiments on different datasets. \
}, },
"elbow_left": { "elbow_left": {
"count": 363, "count": 363,
"mean": 0.022276, "mean": 0.02252,
"median": 0.019385, "median": 0.019538,
"std": 0.014902, "std": 0.015672,
"sem": 0.000783, "sem": 0.000824,
"min": 0.001441, "min": 0.001254,
"max": 0.194618, "max": 0.207405,
"recall-0.025": 0.694215, "recall-0.025": 0.680441,
"recall-0.05": 0.961433, "recall-0.05": 0.961433,
"recall-0.1": 0.997245, "recall-0.1": 0.997245,
"recall-0.15": 0.997245, "recall-0.15": 0.997245,
@ -6091,14 +6091,14 @@ Results of the model in various experiments on different datasets. \
}, },
"elbow_right": { "elbow_right": {
"count": 363, "count": 363,
"mean": 0.018528, "mean": 0.018549,
"median": 0.016603, "median": 0.016702,
"std": 0.010173, "std": 0.010286,
"sem": 0.000535, "sem": 0.000541,
"min": 0.001046, "min": 0.002522,
"max": 0.083441, "max": 0.082821,
"recall-0.025": 0.801653, "recall-0.025": 0.807163,
"recall-0.05": 0.988981, "recall-0.05": 0.991736,
"recall-0.1": 1.0, "recall-0.1": 1.0,
"recall-0.15": 1.0, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
@ -6107,14 +6107,14 @@ Results of the model in various experiments on different datasets. \
}, },
"wrist_left": { "wrist_left": {
"count": 363, "count": 363,
"mean": 0.023532, "mean": 0.023641,
"median": 0.018873, "median": 0.018944,
"std": 0.018388, "std": 0.018362,
"sem": 0.000966, "sem": 0.000965,
"min": 0.00279, "min": 0.002721,
"max": 0.199397, "max": 0.199952,
"recall-0.025": 0.683196, "recall-0.025": 0.688705,
"recall-0.05": 0.931129, "recall-0.05": 0.928375,
"recall-0.1": 0.991736, "recall-0.1": 0.991736,
"recall-0.15": 0.997245, "recall-0.15": 0.997245,
"recall-0.25": 1.0, "recall-0.25": 1.0,
@ -6123,14 +6123,14 @@ Results of the model in various experiments on different datasets. \
}, },
"wrist_right": { "wrist_right": {
"count": 363, "count": 363,
"mean": 0.019579, "mean": 0.019745,
"median": 0.017651, "median": 0.017758,
"std": 0.011201, "std": 0.011172,
"sem": 0.000589, "sem": 0.000587,
"min": 0.002333, "min": 0.001634,
"max": 0.076342, "max": 0.076393,
"recall-0.025": 0.760331, "recall-0.025": 0.760331,
"recall-0.05": 0.977961, "recall-0.05": 0.975207,
"recall-0.1": 1.0, "recall-0.1": 1.0,
"recall-0.15": 1.0, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
@ -6139,14 +6139,14 @@ Results of the model in various experiments on different datasets. \
}, },
"hip_left": { "hip_left": {
"count": 363, "count": 363,
"mean": 0.031156, "mean": 0.03119,
"median": 0.026379, "median": 0.026563,
"std": 0.016985, "std": 0.017008,
"sem": 0.000893, "sem": 0.000894,
"min": 0.006013, "min": 0.005051,
"max": 0.117111, "max": 0.116967,
"recall-0.025": 0.443526, "recall-0.025": 0.438017,
"recall-0.05": 0.859504, "recall-0.05": 0.848485,
"recall-0.1": 0.997245, "recall-0.1": 0.997245,
"recall-0.15": 1.0, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
@ -6155,15 +6155,15 @@ Results of the model in various experiments on different datasets. \
}, },
"hip_right": { "hip_right": {
"count": 363, "count": 363,
"mean": 0.03111, "mean": 0.03118,
"median": 0.028792, "median": 0.028448,
"std": 0.01668, "std": 0.01705,
"sem": 0.000877, "sem": 0.000896,
"min": 0.003451, "min": 0.003029,
"max": 0.138183, "max": 0.138279,
"recall-0.025": 0.38292, "recall-0.025": 0.371901,
"recall-0.05": 0.903581, "recall-0.05": 0.900826,
"recall-0.1": 0.99449, "recall-0.1": 0.991736,
"recall-0.15": 1.0, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
"recall-0.5": 1.0, "recall-0.5": 1.0,
@ -6171,15 +6171,15 @@ Results of the model in various experiments on different datasets. \
}, },
"knee_left": { "knee_left": {
"count": 363, "count": 363,
"mean": 0.028282, "mean": 0.028395,
"median": 0.020833, "median": 0.020711,
"std": 0.023126, "std": 0.02313,
"sem": 0.001215, "sem": 0.001216,
"min": 0.001686, "min": 0.001921,
"max": 0.127237, "max": 0.12193,
"recall-0.025": 0.625344, "recall-0.025": 0.633609,
"recall-0.05": 0.859504, "recall-0.05": 0.856749,
"recall-0.1": 0.983471, "recall-0.1": 0.977961,
"recall-0.15": 1.0, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
"recall-0.5": 1.0, "recall-0.5": 1.0,
@ -6187,14 +6187,14 @@ Results of the model in various experiments on different datasets. \
}, },
"knee_right": { "knee_right": {
"count": 363, "count": 363,
"mean": 0.023698, "mean": 0.023775,
"median": 0.020666, "median": 0.020622,
"std": 0.013376, "std": 0.013654,
"sem": 0.000703, "sem": 0.000718,
"min": 0.001969, "min": 0.002869,
"max": 0.066531, "max": 0.067651,
"recall-0.025": 0.597796, "recall-0.025": 0.592287,
"recall-0.05": 0.944904, "recall-0.05": 0.939394,
"recall-0.1": 1.0, "recall-0.1": 1.0,
"recall-0.15": 1.0, "recall-0.15": 1.0,
"recall-0.25": 1.0, "recall-0.25": 1.0,
@ -6203,14 +6203,14 @@ Results of the model in various experiments on different datasets. \
}, },
"ankle_left": { "ankle_left": {
"count": 363, "count": 363,
"mean": 0.028125, "mean": 0.028379,
"median": 0.021539, "median": 0.022211,
"std": 0.02265, "std": 0.022536,
"sem": 0.00119, "sem": 0.001184,
"min": 0.002656, "min": 0.002738,
"max": 0.178927, "max": 0.179868,
"recall-0.025": 0.578512, "recall-0.025": 0.570248,
"recall-0.05": 0.900826, "recall-0.05": 0.895317,
"recall-0.1": 0.980716, "recall-0.1": 0.980716,
"recall-0.15": 0.99449, "recall-0.15": 0.99449,
"recall-0.25": 1.0, "recall-0.25": 1.0,
@ -6219,13 +6219,13 @@ Results of the model in various experiments on different datasets. \
}, },
"ankle_right": { "ankle_right": {
"count": 363, "count": 363,
"mean": 0.029496, "mean": 0.029711,
"median": 0.022264, "median": 0.022134,
"std": 0.027073, "std": 0.026933,
"sem": 0.001423, "sem": 0.001416,
"min": 0.002482, "min": 0.003345,
"max": 0.263543, "max": 0.265506,
"recall-0.025": 0.584022, "recall-0.025": 0.573003,
"recall-0.05": 0.865014, "recall-0.05": 0.865014,
"recall-0.1": 0.969697, "recall-0.1": 0.969697,
"recall-0.15": 0.99449, "recall-0.15": 0.99449,
@ -6235,10 +6235,10 @@ Results of the model in various experiments on different datasets. \
}, },
"joint_recalls": { "joint_recalls": {
"num_labels": 4719, "num_labels": 4719,
"recall-0.025": 0.60585, "recall-0.025": 0.60776,
"recall-0.05": 0.9163, "recall-0.05": 0.91524,
"recall-0.1": 0.99004, "recall-0.1": 0.99046,
"recall-0.15": 0.99746, "recall-0.15": 0.99788,
"recall-0.25": 0.99958, "recall-0.25": 0.99958,
"recall-0.5": 1.0 "recall-0.5": 1.0
} }

1
rpt/camera.cpp
View File

@ -56,7 +56,6 @@ std::string Camera::to_string() const
out << "'R': " << print_matrix(R) << ", "; out << "'R': " << print_matrix(R) << ", ";
out << "'T': " << print_matrix(T) << ", "; out << "'T': " << print_matrix(T) << ", ";
out << "'P': " << print_matrix(P) << ", ";
out << "'width': " << width << ", "; out << "'width': " << width << ", ";
out << "'height': " << height << ", "; out << "'height': " << height << ", ";

1
rpt/camera.hpp
View File

@ -14,7 +14,6 @@ struct Camera
std::vector<float> DC; std::vector<float> DC;
std::array<std::array<float, 3>, 3> R; std::array<std::array<float, 3>, 3> R;
std::array<std::array<float, 1>, 3> T; std::array<std::array<float, 1>, 3> T;
std::array<std::array<float, 3>, 4> P;
int width; int width;
int height; int height;
std::string type; std::string type;

575
rpt/triangulator.cpp
View File

@ -123,22 +123,372 @@ CameraInternal::CameraInternal(const Camera &cam)
{ {
this->cam = cam; this->cam = cam;
// Convert camera matrix to cv::Mat for OpenCV this->invK = invert3x3(cam.K);
K = cv::Mat(3, 3, CV_32FC1, const_cast<float *>(&cam.K[0][0])).clone(); this->invR = transpose3x3(cam.R);
DC = cv::Mat(cam.DC.size(), 1, CV_32FC1, const_cast<float *>(cam.DC.data())).clone();
R = cv::Mat(3, 3, CV_32FC1, const_cast<float *>(&cam.R[0][0])).clone(); // Camera center:
T = cv::Mat(3, 1, CV_32FC1, const_cast<float *>(&cam.T[0][0])).clone(); // C = -(Rᵀ * t) = -(Rᵀ * (R * (T * -1))) = -(Rᵀ * (R * -T)) = -(Rᵀ * -R * T) = -(-T) = T
this->center = {cam.T[0][0], cam.T[1][0], cam.T[2][0]};
} }
// ================================================================================================= // =================================================================================================
void CameraInternal::update_projection_matrix() std::array<std::array<float, 3>, 3> CameraInternal::transpose3x3(
const std::array<std::array<float, 3>, 3> &M)
{ {
// Calculate opencv-style projection matrix return {{{M[0][0], M[1][0], M[2][0]},
cv::Mat Tr, RT; {M[0][1], M[1][1], M[2][1]},
Tr = R * (T * -1); {M[0][2], M[1][2], M[2][2]}}};
cv::hconcat(R, Tr, RT); }
P = K * RT;
// =================================================================================================
std::array<std::array<float, 3>, 3> CameraInternal::invert3x3(
const std::array<std::array<float, 3>, 3> &M)
{
// Compute the inverse using the adjugate method
// See: https://scicomp.stackexchange.com/a/29206
std::array<std::array<float, 3>, 3> adj = {
{{
M[1][1] * M[2][2] - M[1][2] * M[2][1],
M[0][2] * M[2][1] - M[0][1] * M[2][2],
M[0][1] * M[1][2] - M[0][2] * M[1][1],
},
{
M[1][2] * M[2][0] - M[1][0] * M[2][2],
M[0][0] * M[2][2] - M[0][2] * M[2][0],
M[0][2] * M[1][0] - M[0][0] * M[1][2],
},
{
M[1][0] * M[2][1] - M[1][1] * M[2][0],
M[0][1] * M[2][0] - M[0][0] * M[2][1],
M[0][0] * M[1][1] - M[0][1] * M[1][0],
}}};
float det = M[0][0] * adj[0][0] + M[0][1] * adj[1][0] + M[0][2] * adj[2][0];
if (std::fabs(det) < 1e-6f)
{
return {{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}};
}
float idet = 1.0f / det;
std::array<std::array<float, 3>, 3> inv = {
{{
adj[0][0] * idet,
adj[0][1] * idet,
adj[0][2] * idet,
},
{
adj[1][0] * idet,
adj[1][1] * idet,
adj[1][2] * idet,
},
{
adj[2][0] * idet,
adj[2][1] * idet,
adj[2][2] * idet,
}}};
return inv;
}
// =================================================================================================
void CameraInternal::undistort_point_pinhole(std::array<float, 3> &p, const std::vector<float> &k)
{
// Following: cv::cvUndistortPointsInternal
// Uses only the 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 CameraInternal::undistort_point_fisheye(std::array<float, 3> &p, const std::vector<float> &k)
{
// Following: cv::fisheye::undistortPoints
// Uses only the 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;
}
// =================================================================================================
std::array<std::array<float, 3>, 3> CameraInternal::calc_optimal_camera_matrix_fisheye(
float balance, std::pair<int, int> new_size)
{
// Following: cv::fisheye::estimateNewCameraMatrixForUndistortRectify
// https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/fisheye.cpp#L630
float fov_scale = 1.0;
float w = static_cast<float>(cam.width);
float h = static_cast<float>(cam.height);
balance = std::min(std::max(balance, 0.0f), 1.0f);
// Define four key points at the middle of each edge
std::vector<std ::array<float, 2>> pts = {
{w * 0.5f, 0.0},
{w, h * 0.5f},
{w * 0.5f, h},
{0.0, h * 0.5f}};
// Extract intrinsic parameters
float fx = cam.K[0][0];
float fy = cam.K[1][1];
float cx = cam.K[0][2];
float cy = cam.K[1][2];
// Undistort the edge points
for (auto &pt : pts)
{
std::array<float, 3> p_normed = {(pt[0] - cx) / fx, (pt[1] - cy) / fy, 1.0};
undistort_point_fisheye(p_normed, cam.DC);
pt[0] = p_normed[0];
pt[1] = p_normed[1];
}
// Compute center mass of the undistorted edge points
float sum_x = 0.0, sum_y = 0.0;
for (const auto &pt : pts)
{
sum_x += pt[0];
sum_y += pt[1];
}
float cn_x = sum_x / pts.size();
float cn_y = sum_y / pts.size();
// Convert to identity ratio
float aspect_ratio = fx / fy;
cn_y *= aspect_ratio;
for (auto &pt : pts)
pt[1] *= aspect_ratio;
// Find the bounding box of the undistorted points
float minx = std::numeric_limits<float>::max();
float miny = std::numeric_limits<float>::max();
float maxx = -std::numeric_limits<float>::max();
float maxy = -std::numeric_limits<float>::max();
for (const auto &pt : pts)
{
minx = std::min(minx, pt[0]);
maxx = std::max(maxx, pt[0]);
miny = std::min(miny, pt[1]);
maxy = std::max(maxy, pt[1]);
}
// Calculate candidate focal lengths
float f1 = (w * 0.5) / (cn_x - minx);
float f2 = (w * 0.5) / (maxx - cn_x);
float f3 = (h * 0.5 * aspect_ratio) / (cn_y - miny);
float f4 = (h * 0.5 * aspect_ratio) / (maxy - cn_y);
float fmin = std::min({f1, f2, f3, f4});
float fmax = std::max({f1, f2, f3, f4});
// Blend the candidate focal lengths
float f_val = balance * fmin + (1.0f - balance) * fmax;
if (fov_scale > 0.0f)
f_val /= fov_scale;
// Compute new intrinsic parameters
float new_fx = f_val;
float new_fy = f_val;
float new_cx = -cn_x * f_val + (w * 0.5);
float new_cy = -cn_y * f_val + (h * aspect_ratio * 0.5);
// Restore aspect ratio
new_fy /= aspect_ratio;
new_cy /= aspect_ratio;
// Optionally scale parameters to new new image size
if (new_size.first > 0 && new_size.second > 0)
{
float rx = static_cast<float>(new_size.first) / w;
float ry = static_cast<float>(new_size.second) / h;
new_fx *= rx;
new_fy *= ry;
new_cx *= rx;
new_cy *= ry;
}
std::array<std::array<float, 3>, 3> newK = {
{{new_fx, 0.0, new_cx},
{0.0, new_fy, new_cy},
{0.0, 0.0, 1.0}}};
return newK;
}
// =================================================================================================
std::array<std::array<float, 3>, 3> CameraInternal::calc_optimal_camera_matrix_pinhole(
float alpha, std::pair<int, int> new_size)
{
// Following: cv::getOptimalNewCameraMatrix
// https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/calibration_base.cpp#L1565
bool center_principal_point = false;
bool use_pix_roi = false;
float w = static_cast<float>(cam.width);
float h = static_cast<float>(cam.height);
alpha = std::min(std::max(alpha, 0.0f), 1.0f);
if (center_principal_point || use_pix_roi)
{
// Not implemented
exit(1);
}
// Define key points
const size_t N = 9;
std::vector<std ::array<float, 2>> pts;
pts.reserve(N * N);
for (size_t y = 0; y < N; ++y)
{
for (size_t x = 0; x < N; ++x)
{
pts.push_back({x * (w - 1) / (N - 1), y * (h - 1) / (N - 1)});
}
}
// Extract intrinsic parameters
float fx = cam.K[0][0];
float fy = cam.K[1][1];
float cx = cam.K[0][2];
float cy = cam.K[1][2];
// Undistort the key points
for (auto &pt : pts)
{
std::array<float, 3> p_normed = {(pt[0] - cx) / fx, (pt[1] - cy) / fy, 1.0};
undistort_point_pinhole(p_normed, cam.DC);
pt[0] = p_normed[0];
pt[1] = p_normed[1];
}
// Get inscribed and circumscribed rectangles in normalized coordinates
float iX0 = -std::numeric_limits<float>::max();
float iX1 = std::numeric_limits<float>::max();
float iY0 = -std::numeric_limits<float>::max();
float iY1 = std::numeric_limits<float>::max();
float oX0 = std::numeric_limits<float>::max();
float oX1 = -std::numeric_limits<float>::max();
float oY0 = std::numeric_limits<float>::max();
float oY1 = -std::numeric_limits<float>::max();
size_t k = 0;
for (size_t y = 0; y < N; ++y)
{
for (size_t x = 0; x < N; ++x)
{
auto &pt = pts[k];
k += 1;
oX0 = std::min(oX0, pt[0]);
oX1 = std::max(oX1, pt[0]);
oY0 = std::min(oY0, pt[1]);
oY1 = std::max(oY1, pt[1]);
if (x == 0)
iX0 = std::max(iX0, pt[0]);
if (x == N - 1)
iX1 = std::min(iX1, pt[0]);
if (y == 0)
iY0 = std::max(iY0, pt[1]);
if (y == N - 1)
iY1 = std::min(iY1, pt[1]);
}
}
float inner_width = iX1 - iX0;
float inner_height = iY1 - iY0;
float outer_width = oX1 - oX0;
float outer_height = oY1 - oY0;
// Projection mapping inner rectangle to viewport
float fx0 = (new_size.first - 1) / inner_width;
float fy0 = (new_size.second - 1) / inner_height;
float cx0 = -fx0 * iX0;
float cy0 = -fy0 * iY0;
// Projection mapping outer rectangle to viewport
float fx1 = (new_size.first - 1) / outer_width;
float fy1 = (new_size.second - 1) / outer_height;
float cx1 = -fx1 * oX0;
float cy1 = -fy1 * oY0;
// Interpolate between the two optimal projections
float new_fx = fx0 * (1 - alpha) + fx1 * alpha;
float new_fy = fy0 * (1 - alpha) + fy1 * alpha;
float new_cx = cx0 * (1 - alpha) + cx1 * alpha;
float new_cy = cy0 * (1 - alpha) + cy1 * alpha;
std::array<std::array<float, 3>, 3> newK = {
{{new_fx, 0.0, new_cx},
{0.0, new_fy, new_cy},
{0.0, 0.0, 1.0}}};
return newK;
} }
// ================================================================================================= // =================================================================================================
@ -213,7 +563,6 @@ std::vector<std::vector<std::array<float, 4>>> TriangulatorInternal::triangulate
for (size_t i = 0; i < cameras.size(); ++i) for (size_t i = 0; i < cameras.size(); ++i)
{ {
undistort_poses(i_poses_2d[i], internal_cameras[i]); undistort_poses(i_poses_2d[i], internal_cameras[i]);
internal_cameras[i].update_projection_matrix();
} }
elapsed = std::chrono::steady_clock::now() - stime; elapsed = std::chrono::steady_clock::now() - stime;
@ -601,84 +950,6 @@ 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( void TriangulatorInternal::undistort_poses(
std::vector<std::vector<std::array<float, 3>>> &poses_2d, CameraInternal &icam) std::vector<std::vector<std::array<float, 3>>> &poses_2d, CameraInternal &icam)
{ {
@ -686,27 +957,25 @@ void TriangulatorInternal::undistort_poses(
int height = icam.cam.height; int height = icam.cam.height;
// Undistort camera matrix // Undistort camera matrix
cv::Mat newK; // As with the undistortion, the own implementation avoids some overhead compared to OpenCV
std::array<std::array<float, 3>, 3> newK;
if (icam.cam.type == "fisheye") if (icam.cam.type == "fisheye")
{ {
cv::fisheye::estimateNewCameraMatrixForUndistortRectify( newK = icam.calc_optimal_camera_matrix_fisheye(1.0, {width, height});
icam.K, icam.DC, cv::Size(width, height), cv::Matx33d::eye(),
newK, 1.0, cv::Size(width, height), 1.0);
} }
else else
{ {
newK = cv::getOptimalNewCameraMatrix( newK = icam.calc_optimal_camera_matrix_pinhole(1.0, {width, height});
icam.K, icam.DC, cv::Size(width, height), 1, cv::Size(width, height));
} }
float ifx_old = 1.0 / icam.cam.K[0][0]; float ifx_old = 1.0 / icam.cam.K[0][0];
float ify_old = 1.0 / icam.cam.K[1][1]; float ify_old = 1.0 / icam.cam.K[1][1];
float cx_old = icam.cam.K[0][2]; float cx_old = icam.cam.K[0][2];
float cy_old = icam.cam.K[1][2]; float cy_old = icam.cam.K[1][2];
float fx_new = newK.at<float>(0, 0); float fx_new = newK[0][0];
float fy_new = newK.at<float>(1, 1); float fy_new = newK[1][1];
float cx_new = newK.at<float>(0, 2); float cx_new = newK[0][2];
float cy_new = newK.at<float>(1, 2); float cy_new = newK[1][2];
// Undistort all the points // Undistort all the points
size_t num_persons = poses_2d.size(); size_t num_persons = poses_2d.size();
@ -725,11 +994,11 @@ void TriangulatorInternal::undistort_poses(
// additional distortion parameters and identity rotations in this usecase. // additional distortion parameters and identity rotations in this usecase.
if (icam.cam.type == "fisheye") if (icam.cam.type == "fisheye")
{ {
undistort_point_fisheye(poses_2d[i][j], icam.cam.DC); CameraInternal::undistort_point_fisheye(poses_2d[i][j], icam.cam.DC);
} }
else else
{ {
undistort_point_pinhole(poses_2d[i][j], icam.cam.DC); CameraInternal::undistort_point_pinhole(poses_2d[i][j], icam.cam.DC);
} }
// Map the undistorted normalized point to the new image coordinates // Map the undistorted normalized point to the new image coordinates
@ -754,23 +1023,15 @@ void TriangulatorInternal::undistort_poses(
} }
} }
// Update the camera matrix // Update the camera intrinsics
icam.K = newK.clone(); icam.cam.K = newK;
for (size_t i = 0; i < 3; ++i) icam.invK = CameraInternal::invert3x3(newK);
{
for (size_t j = 0; j < 3; ++j)
{
icam.cam.K[i][j] = newK.at<float>(i, j);
}
}
if (icam.cam.type == "fisheye") if (icam.cam.type == "fisheye")
{ {
icam.DC = cv::Mat::zeros(4, 1, CV_32F);
icam.cam.DC = {0.0, 0.0, 0.0, 0.0}; icam.cam.DC = {0.0, 0.0, 0.0, 0.0};
} }
else else
{ {
icam.DC = cv::Mat::zeros(5, 1, CV_32F);
icam.cam.DC = {0.0, 0.0, 0.0, 0.0, 0.0}; icam.cam.DC = {0.0, 0.0, 0.0, 0.0, 0.0};
} }
} }
@ -1008,62 +1269,6 @@ std::vector<float> TriangulatorInternal::score_projection(
// ================================================================================================= // =================================================================================================
/* Compute the inverse using the adjugate method */
std::array<std::array<float, 3>, 3> invert3x3(const std::array<std::array<float, 3>, 3> &M)
{
// See: https://scicomp.stackexchange.com/a/29206
std::array<std::array<float, 3>, 3> adj = {
{{
M[1][1] * M[2][2] - M[1][2] * M[2][1],
M[0][2] * M[2][1] - M[0][1] * M[2][2],
M[0][1] * M[1][2] - M[0][2] * M[1][1],
},
{
M[1][2] * M[2][0] - M[1][0] * M[2][2],
M[0][0] * M[2][2] - M[0][2] * M[2][0],
M[0][2] * M[1][0] - M[0][0] * M[1][2],
},
{
M[1][0] * M[2][1] - M[1][1] * M[2][0],
M[0][1] * M[2][0] - M[0][0] * M[2][1],
M[0][0] * M[1][1] - M[0][1] * M[1][0],
}}};
float det = M[0][0] * adj[0][0] + M[0][1] * adj[1][0] + M[0][2] * adj[2][0];
if (std::fabs(det) < 1e-6f)
{
return {{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}};
}
float idet = 1.0f / det;
std::array<std::array<float, 3>, 3> inv = {
{{
adj[0][0] * idet,
adj[0][1] * idet,
adj[0][2] * idet,
},
{
adj[1][0] * idet,
adj[1][1] * idet,
adj[1][2] * idet,
},
{
adj[2][0] * idet,
adj[2][1] * idet,
adj[2][2] * idet,
}}};
return inv;
}
std::array<std::array<float, 3>, 3> transpose3x3(const std::array<std::array<float, 3>, 3> &M)
{
return {{{M[0][0], M[1][0], M[2][0]},
{M[0][1], M[1][1], M[2][1]},
{M[0][2], M[1][2], M[2][2]}}};
}
float dot(const std::array<float, 3> &a, const std::array<float, 3> &b) float dot(const std::array<float, 3> &a, const std::array<float, 3> &b)
{ {
return a[0] * b[0] + a[1] * b[1] + a[2] * b[2]; return a[0] * b[0] + a[1] * b[1] + a[2] * b[2];
@ -1102,40 +1307,30 @@ std::array<float, 3> mat_mul_vec(
return res; return res;
} }
/* Compute camera center and corresponding ray direction */ std::array<float, 3> calc_ray_dir(const CameraInternal &icam, const std::array<float, 2> &pt)
std::tuple<std::array<float, 3>, std::array<float, 3>> calc_center_and_ray(
const CameraInternal &icam,
const std::array<float, 2> &pt)
{ {
// Compute Rᵀ and t // Compute normalized ray direction from the point
auto R_transpose = transpose3x3(icam.cam.R); std::array<float, 3> uv1 = {pt[0], pt[1], 1.0};
std::array<float, 3> t = {icam.cam.T[0][0], icam.cam.T[1][0], icam.cam.T[2][0]}; auto d = mat_mul_vec(icam.invR, mat_mul_vec(icam.invK, uv1));
t = mat_mul_vec(icam.cam.R, multiply(t, -1.0f)); auto ray_dir = normalize(d);
// Camera center: C = -Rᵀ * t return ray_dir;
auto C = multiply(mat_mul_vec(R_transpose, t), -1.0f);
// Compute ray direction:
std::array<float, 3> uv1 = {pt[0], pt[1], 1.0f};
auto K_inv = invert3x3(icam.cam.K);
auto d = mat_mul_vec(R_transpose, mat_mul_vec(K_inv, uv1));
auto rayDir = normalize(d);
return std::make_tuple(C, rayDir);
} }
/* Triangulate two points by computing their two rays and the midpoint of their closest approach */
std::array<float, 3> triangulate_midpoint( std::array<float, 3> triangulate_midpoint(
const CameraInternal &icam1, const CameraInternal &icam1,
const CameraInternal &icam2, const CameraInternal &icam2,
const std::array<float, 2> &pt1, const std::array<float, 2> &pt1,
const std::array<float, 2> &pt2) const std::array<float, 2> &pt2)
{ {
// Triangulate two points by computing their two rays and the midpoint of their closest approach
// See: https://en.wikipedia.org/wiki/Skew_lines#Nearest_points // See: https://en.wikipedia.org/wiki/Skew_lines#Nearest_points
// Obtain the camera centers and ray directions for both views // Obtain the camera centers and ray directions for both views
auto [p1, d1] = calc_center_and_ray(icam1, pt1); std::array<float, 3> p1 = icam1.center;
auto [p2, d2] = calc_center_and_ray(icam2, pt2); std::array<float, 3> p2 = icam2.center;
std::array<float, 3> d1 = calc_ray_dir(icam1, pt1);
std::array<float, 3> d2 = calc_ray_dir(icam2, pt2);
// Compute the perpendicular plane vectors // Compute the perpendicular plane vectors
std::array<float, 3> n = cross(d1, d2); std::array<float, 3> n = cross(d1, d2);

23
rpt/triangulator.hpp
View File

@ -17,13 +17,24 @@ public:
CameraInternal(const Camera &cam); CameraInternal(const Camera &cam);
Camera cam; Camera cam;
cv::Mat K;
cv::Mat DC;
cv::Mat R;
cv::Mat T;
cv::Mat P;
void update_projection_matrix(); std::array<std::array<float, 3>, 3> invK;
std::array<std::array<float, 3>, 3> invR;
std::array<float, 3> center;
static std::array<std::array<float, 3>, 3> transpose3x3(
const std::array<std::array<float, 3>, 3> &M);
static std::array<std::array<float, 3>, 3> invert3x3(
const std::array<std::array<float, 3>, 3> &M);
static void undistort_point_pinhole(std::array<float, 3> &p, const std::vector<float> &k);
static void undistort_point_fisheye(std::array<float, 3> &p, const std::vector<float> &k);
std::array<std::array<float, 3>, 3> calc_optimal_camera_matrix_fisheye(
float balance, std::pair<int, int> new_size);
std::array<std::array<float, 3>, 3> calc_optimal_camera_matrix_pinhole(
float alpha, std::pair<int, int> new_size);
}; };
// ================================================================================================= // =================================================================================================