From b958bc8bcf327a89c86c726f70291b8a2523813a Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 10 Oct 2024 12:47:41 +0200 Subject: [PATCH] Reset with index jumps. --- media/RESULTS.md | 188 ++++++++++++++++----------------- scripts/test_skelda_dataset.py | 6 +- 2 files changed, 99 insertions(+), 95 deletions(-) diff --git a/media/RESULTS.md b/media/RESULTS.md index 22f1eda..57ce6cc 100644 --- a/media/RESULTS.md +++ b/media/RESULTS.md @@ -6006,48 +6006,48 @@ Results of the model in various experiments on different datasets. "person_nums": { "total_frames": 121, "total_labels": 484, - "total_preds": 634, + "total_preds": 638, "considered_empty": 0, "valid_preds": 483, - "invalid_preds": 151, + "invalid_preds": 155, "missing": 1, - "invalid_fraction": 0.23817, - "precision": 0.76183, + "invalid_fraction": 0.24295, + "precision": 0.75705, "recall": 0.99793, - "f1": 0.86404, - "non_empty": 634 + "f1": 0.86096, + "non_empty": 638 }, "mpjpe": { "count": 483, - "mean": 0.03745, - "median": 0.035063, - "std": 0.014194, - "sem": 0.000647, + "mean": 0.037486, + "median": 0.034783, + "std": 0.014406, + "sem": 0.000656, "min": 0.01794, "max": 0.136715, - "recall-0.025": 0.163223, - "recall-0.05": 0.842975, + "recall-0.025": 0.165289, + "recall-0.05": 0.840909, "recall-0.1": 0.991736, "recall-0.15": 0.997934, "recall-0.25": 0.997934, "recall-0.5": 0.997934, "num_labels": 484, - "ap-0.025": 0.023957, - "ap-0.05": 0.580664, - "ap-0.1": 0.79534, - "ap-0.15": 0.804355, - "ap-0.25": 0.804355, - "ap-0.5": 0.804355 + "ap-0.025": 0.024574, + "ap-0.05": 0.576818, + "ap-0.1": 0.794001, + "ap-0.15": 0.803006, + "ap-0.25": 0.803006, + "ap-0.5": 0.803006 }, "head": { "count": 483, - "mean": 0.037576, - "median": 0.030619, - "std": 0.023474, - "sem": 0.001069, + "mean": 0.037728, + "median": 0.030655, + "std": 0.023551, + "sem": 0.001073, "min": 0.004382, "max": 0.136584, - "recall-0.025": 0.378099, + "recall-0.025": 0.371901, "recall-0.05": 0.721074, "recall-0.1": 0.977273, "recall-0.15": 0.997934, @@ -6057,13 +6057,13 @@ Results of the model in various experiments on different datasets. }, "shoulder_left": { "count": 483, - "mean": 0.039129, - "median": 0.032213, - "std": 0.022975, - "sem": 0.001047, + "mean": 0.039183, + "median": 0.032414, + "std": 0.022945, + "sem": 0.001045, "min": 0.004214, "max": 0.142662, - "recall-0.025": 0.31405, + "recall-0.025": 0.311983, "recall-0.05": 0.745868, "recall-0.1": 0.979339, "recall-0.15": 0.997934, @@ -6073,13 +6073,13 @@ Results of the model in various experiments on different datasets. }, "shoulder_right": { "count": 483, - "mean": 0.035508, - "median": 0.030929, - "std": 0.021175, - "sem": 0.000965, + "mean": 0.035627, + "median": 0.031518, + "std": 0.021128, + "sem": 0.000962, "min": 0.003681, "max": 0.180338, - "recall-0.025": 0.384298, + "recall-0.025": 0.378099, "recall-0.05": 0.778926, "recall-0.1": 0.987603, "recall-0.15": 0.995868, @@ -6089,14 +6089,14 @@ Results of the model in various experiments on different datasets. }, "elbow_left": { "count": 483, - "mean": 0.039093, - "median": 0.031479, - "std": 0.027871, - "sem": 0.001269, + "mean": 0.039216, + "median": 0.031488, + "std": 0.027873, + "sem": 0.00127, "min": 0.005996, "max": 0.237523, - "recall-0.025": 0.347107, - "recall-0.05": 0.737603, + "recall-0.025": 0.345041, + "recall-0.05": 0.735537, "recall-0.1": 0.966942, "recall-0.15": 0.987603, "recall-0.25": 0.997934, @@ -6105,13 +6105,13 @@ Results of the model in various experiments on different datasets. }, "elbow_right": { "count": 483, - "mean": 0.041455, - "median": 0.033784, - "std": 0.038189, - "sem": 0.001739, + "mean": 0.041639, + "median": 0.033826, + "std": 0.038231, + "sem": 0.001741, "min": 0.003944, "max": 0.443462, - "recall-0.025": 0.367769, + "recall-0.025": 0.363636, "recall-0.05": 0.741736, "recall-0.1": 0.954545, "recall-0.15": 0.979339, @@ -6121,47 +6121,47 @@ Results of the model in various experiments on different datasets. }, "wrist_left": { "count": 483, - "mean": 0.04895, - "median": 0.039104, - "std": 0.036788, - "sem": 0.001676, + "mean": 0.048731, + "median": 0.038931, + "std": 0.036668, + "sem": 0.00167, "min": 0.002618, "max": 0.309556, - "recall-0.025": 0.258264, - "recall-0.05": 0.650826, + "recall-0.025": 0.262397, + "recall-0.05": 0.654959, "recall-0.1": 0.913223, - "recall-0.15": 0.977273, + "recall-0.15": 0.979339, "recall-0.25": 0.995868, "recall-0.5": 0.997934, "num_labels": 484 }, "wrist_right": { "count": 481, - "mean": 0.050009, - "median": 0.040081, - "std": 0.040668, - "sem": 0.001856, + "mean": 0.050653, + "median": 0.039122, + "std": 0.044513, + "sem": 0.002032, "min": 0.003069, - "max": 0.35488, - "recall-0.025": 0.262397, + "max": 0.445609, + "recall-0.025": 0.268595, "recall-0.05": 0.63843, "recall-0.1": 0.900826, - "recall-0.15": 0.964876, - "recall-0.25": 0.987603, + "recall-0.15": 0.96281, + "recall-0.25": 0.985537, "recall-0.5": 0.993802, "num_labels": 484 }, "hip_left": { "count": 483, - "mean": 0.040026, + "mean": 0.039765, "median": 0.036626, - "std": 0.021724, - "sem": 0.00099, + "std": 0.02136, + "sem": 0.000973, "min": 0.004385, "max": 0.191835, - "recall-0.025": 0.231405, - "recall-0.05": 0.756198, - "recall-0.1": 0.979339, + "recall-0.025": 0.235537, + "recall-0.05": 0.758264, + "recall-0.1": 0.981405, "recall-0.15": 0.995868, "recall-0.25": 0.997934, "recall-0.5": 0.997934, @@ -6169,15 +6169,15 @@ Results of the model in various experiments on different datasets. }, "hip_right": { "count": 483, - "mean": 0.042645, - "median": 0.034828, - "std": 0.027596, - "sem": 0.001257, + "mean": 0.042641, + "median": 0.03475, + "std": 0.027897, + "sem": 0.001271, "min": 0.004215, "max": 0.149838, - "recall-0.025": 0.27686, - "recall-0.05": 0.71281, - "recall-0.1": 0.948347, + "recall-0.025": 0.28719, + "recall-0.05": 0.714876, + "recall-0.1": 0.946281, "recall-0.15": 0.997934, "recall-0.25": 0.997934, "recall-0.5": 0.997934, @@ -6185,9 +6185,9 @@ Results of the model in various experiments on different datasets. }, "knee_left": { "count": 483, - "mean": 0.025053, - "median": 0.021076, - "std": 0.018987, + "mean": 0.024984, + "median": 0.020915, + "std": 0.018998, "sem": 0.000865, "min": 0.001403, "max": 0.27912, @@ -6201,14 +6201,14 @@ Results of the model in various experiments on different datasets. }, "knee_right": { "count": 483, - "mean": 0.026099, - "median": 0.022868, - "std": 0.015211, - "sem": 0.000693, + "mean": 0.026066, + "median": 0.022746, + "std": 0.015356, + "sem": 0.000699, "min": 0.001415, "max": 0.094234, - "recall-0.025": 0.557851, - "recall-0.05": 0.919421, + "recall-0.025": 0.559917, + "recall-0.05": 0.917355, "recall-0.1": 0.997934, "recall-0.15": 0.997934, "recall-0.25": 0.997934, @@ -6217,9 +6217,9 @@ Results of the model in various experiments on different datasets. }, "ankle_left": { "count": 483, - "mean": 0.029784, + "mean": 0.029662, "median": 0.023999, - "std": 0.034378, + "std": 0.034381, "sem": 0.001566, "min": 0.002215, "max": 0.497796, @@ -6233,14 +6233,14 @@ Results of the model in various experiments on different datasets. }, "ankle_right": { "count": 483, - "mean": 0.029202, + "mean": 0.029111, "median": 0.026139, - "std": 0.017443, - "sem": 0.000795, + "std": 0.017356, + "sem": 0.000791, "min": 0.001964, "max": 0.103825, - "recall-0.025": 0.464876, - "recall-0.05": 0.880165, + "recall-0.025": 0.466942, + "recall-0.05": 0.882231, "recall-0.1": 0.993802, "recall-0.15": 0.997934, "recall-0.25": 0.997934, @@ -6250,17 +6250,17 @@ Results of the model in various experiments on different datasets. "joint_recalls": { "num_labels": 6292, "recall-0.025": 0.38207, - "recall-0.05": 0.77718, - "recall-0.1": 0.96615, - "recall-0.15": 0.99031, - "recall-0.25": 0.99603, + "recall-0.05": 0.77765, + "recall-0.1": 0.96631, + "recall-0.15": 0.99046, + "recall-0.25": 0.99587, "recall-0.5": 0.99762 } } { "total_parts": 6776, - "correct_parts": 6729, - "pcp": 0.993064 + "correct_parts": 6727, + "pcp": 0.992769 } ``` diff --git a/scripts/test_skelda_dataset.py b/scripts/test_skelda_dataset.py index fe32a59..3a18bf9 100644 --- a/scripts/test_skelda_dataset.py +++ b/scripts/test_skelda_dataset.py @@ -277,10 +277,13 @@ def main(): times = [] triangulator = spt.Triangulator(min_score=minscore, min_group_size=min_group_size) old_scene = "" + old_index = -1 for label in tqdm.tqdm(labels): images_2d = [] - if old_scene != label.get("scene", "") or dataset_use == "human36m_wb": + if old_scene != label.get("scene", "") or ( + old_index + datasets[dataset_use]["take_interval"] < label["index"] + ): # Reset last poses if scene changes old_scene = label.get("scene", "") triangulator.reset() @@ -328,6 +331,7 @@ def main(): time_3d = time.time() - start print("3D time:", time_3d) + old_index = label["index"] all_poses.append(np.array(poses3D)) all_ids.append(label["id"]) all_paths.append(label["imgpaths"])