diff --git a/.gitignore b/.gitignore
index 30ec818..7a71247 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,5 @@
-spt_wrap.*
-spt.py
+rpt_wrap.*
+rpt.py
*.bin
# Prerequisites
diff --git a/Dockerfile b/Dockerfile
index 1007af5..b2b00d1 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,4 +1,4 @@
-FROM nvcr.io/nvidia/tensorflow:22.08-tf2-py3
+FROM nvcr.io/nvidia/pytorch:23.02-py3
ARG DEBIAN_FRONTEND=noninteractive
ENV LANG=C.UTF-8
@@ -15,11 +15,9 @@ RUN apt-get update && apt-get install -y --no-install-recommends python3-tk
# Update pip to allow installation of skelda in editable mode
RUN pip3 install --upgrade --no-cache-dir pip
-# Install pytorch
-RUN pip3 install --upgrade --no-cache-dir "torch<2.0"
-RUN pip3 install --upgrade --no-cache-dir "torchvision<0.15"
-
# Install MMPose
+ENV FORCE_CUDA="1"
+ENV MMCV_WITH_OPS=1
RUN pip3 install --upgrade --no-cache-dir openmim
RUN mim install mmengine
RUN mim install "mmcv>=2,<2.2.0"
@@ -45,5 +43,5 @@ RUN apt-get update && apt-get install -y --no-install-recommends libopencv-dev
COPY ./skelda/ /skelda/
RUN pip3 install --no-cache-dir -e /skelda/
-WORKDIR /SimplePoseTriangulation/
+WORKDIR /RapidPoseTriangulation/
CMD ["/bin/bash"]
diff --git a/README.md b/README.md
index 4812eb3..6506675 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,14 @@
-# SimplePoseTriangulation
+# RapidPoseTriangulation
-Triangulation of multiple persons from multiple camera views.
+Fast triangulation of multiple persons from multiple camera views.
@@ -14,25 +17,25 @@ Triangulation of multiple persons from multiple camera views.
- Clone this project with submodules:
```bash
- git clone --recurse-submodules https://gitlab.com/Percipiote/SimplePoseTriangulation.git
- cd SimplePoseTriangulation/
+ git clone --recurse-submodules https://gitlab.com/Percipiote/RapidPoseTriangulation.git
+ cd RapidPoseTriangulation/
```
- Build docker container:
```bash
- docker build --progress=plain -t simpleposetriangulation .
+ docker build --progress=plain -t rapidposetriangulation .
./run_container.sh
```
- Test with samples:
```bash
- python3 /SimplePoseTriangulation/scripts/test_triangulate.py
+ python3 /RapidPoseTriangulation/scripts/test_triangulate.py
```
- Test with _skelda_ dataset:
```bash
export CUDA_VISIBLE_DEVICES=0
- python3 /SimplePoseTriangulation/scripts/test_skelda_dataset.py
+ python3 /RapidPoseTriangulation/scripts/test_skelda_dataset.py
```
@@ -40,5 +43,5 @@ Triangulation of multiple persons from multiple camera views.
## Debugging
```bash
-cd /SimplePoseTriangulation/swig/ && make all && cd ../tests/ && python3 test_interface.py
+cd /RapidPoseTriangulation/swig/ && make all && cd ../tests/ && python3 test_interface.py
```
diff --git a/media/2d-k.jpg b/media/2d-k.jpg
new file mode 100644
index 0000000..4801a4c
Binary files /dev/null and b/media/2d-k.jpg differ
diff --git a/media/2d-k.png b/media/2d-k.png
deleted file mode 100644
index 2312d51..0000000
Binary files a/media/2d-k.png and /dev/null differ
diff --git a/media/2d-p.jpg b/media/2d-p.jpg
new file mode 100644
index 0000000..8a23926
Binary files /dev/null and b/media/2d-p.jpg differ
diff --git a/media/2d-p.png b/media/2d-p.png
deleted file mode 100644
index 345a76e..0000000
Binary files a/media/2d-p.png and /dev/null differ
diff --git a/media/3d-p.jpg b/media/3d-p.jpg
new file mode 100644
index 0000000..4192b39
Binary files /dev/null and b/media/3d-p.jpg differ
diff --git a/media/3d-p.png b/media/3d-p.png
deleted file mode 100644
index 3610d4e..0000000
Binary files a/media/3d-p.png and /dev/null differ
diff --git a/media/RESULTS.md b/media/RESULTS.md
index 654bbdc..1004aaf 100644
--- a/media/RESULTS.md
+++ b/media/RESULTS.md
@@ -4,12 +4,11 @@ Results of the model in various experiments on different datasets.
### Human36m
-(duration 00:01:20)
```json
{
- "avg_time_2d": 0.07087800987696244,
- "avg_time_3d": 0.00041960861723301776,
- "avg_fps": 14.025713917519038
+ "avg_time_2d": 0.05260277602632167,
+ "avg_time_3d": 0.0003263081534434173,
+ "avg_fps": 18.8932042845038
}
{
"person_nums": {
@@ -28,51 +27,51 @@ Results of the model in various experiments on different datasets.
},
"mpjpe": {
"count": 600,
- "mean": 0.067044,
- "median": 0.059406,
- "std": 0.027778,
- "sem": 0.001135,
- "min": 0.043707,
- "max": 0.190247,
+ "mean": 0.067113,
+ "median": 0.059352,
+ "std": 0.027739,
+ "sem": 0.001133,
+ "min": 0.043176,
+ "max": 0.190437,
"recall-0.025": 0.0,
- "recall-0.05": 0.033333,
- "recall-0.1": 0.933333,
+ "recall-0.05": 0.036667,
+ "recall-0.1": 0.935,
"recall-0.15": 0.95,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
"num_labels": 600,
"ap-0.025": 0.0,
- "ap-0.05": 0.002029,
- "ap-0.1": 0.893774,
- "ap-0.15": 0.91673,
+ "ap-0.05": 0.002419,
+ "ap-0.1": 0.895969,
+ "ap-0.15": 0.916293,
"ap-0.25": 1.0,
"ap-0.5": 1.0
},
"nose": {
"count": 600,
- "mean": 0.117677,
- "median": 0.101523,
- "std": 0.042982,
- "sem": 0.001756,
- "min": 0.046466,
- "max": 0.271134,
+ "mean": 0.117755,
+ "median": 0.101583,
+ "std": 0.04303,
+ "sem": 0.001758,
+ "min": 0.045229,
+ "max": 0.270864,
"recall-0.025": 0.0,
- "recall-0.05": 0.003333,
+ "recall-0.05": 0.006667,
"recall-0.1": 0.485,
- "recall-0.15": 0.805,
+ "recall-0.15": 0.803333,
"recall-0.25": 0.995,
"recall-0.5": 1.0,
"num_labels": 600
},
"shoulder_left": {
"count": 600,
- "mean": 0.0334,
- "median": 0.025798,
- "std": 0.032123,
- "sem": 0.001312,
- "min": 0.003037,
- "max": 0.181945,
- "recall-0.025": 0.485,
+ "mean": 0.033354,
+ "median": 0.025457,
+ "std": 0.032091,
+ "sem": 0.001311,
+ "min": 0.003327,
+ "max": 0.181867,
+ "recall-0.025": 0.486667,
"recall-0.05": 0.871667,
"recall-0.1": 0.948333,
"recall-0.15": 0.965,
@@ -82,15 +81,15 @@ Results of the model in various experiments on different datasets.
},
"shoulder_right": {
"count": 600,
- "mean": 0.048216,
- "median": 0.033552,
- "std": 0.042119,
- "sem": 0.001721,
- "min": 0.003882,
- "max": 0.247362,
- "recall-0.025": 0.235,
- "recall-0.05": 0.758333,
- "recall-0.1": 0.901667,
+ "mean": 0.048518,
+ "median": 0.033796,
+ "std": 0.042293,
+ "sem": 0.001728,
+ "min": 0.004099,
+ "max": 0.247429,
+ "recall-0.025": 0.23,
+ "recall-0.05": 0.751667,
+ "recall-0.1": 0.9,
"recall-0.15": 0.943333,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -98,31 +97,31 @@ Results of the model in various experiments on different datasets.
},
"elbow_left": {
"count": 600,
- "mean": 0.042931,
- "median": 0.034599,
- "std": 0.034815,
- "sem": 0.001422,
- "min": 0.002682,
- "max": 0.193558,
+ "mean": 0.042901,
+ "median": 0.035019,
+ "std": 0.034783,
+ "sem": 0.001421,
+ "min": 0.002822,
+ "max": 0.195221,
"recall-0.025": 0.271667,
- "recall-0.05": 0.806667,
+ "recall-0.05": 0.811667,
"recall-0.1": 0.943333,
- "recall-0.15": 0.956667,
+ "recall-0.15": 0.955,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
"num_labels": 600
},
"elbow_right": {
"count": 600,
- "mean": 0.043934,
- "median": 0.03334,
- "std": 0.036065,
- "sem": 0.001474,
- "min": 0.00667,
- "max": 0.285024,
- "recall-0.025": 0.245,
- "recall-0.05": 0.806667,
- "recall-0.1": 0.928333,
+ "mean": 0.043838,
+ "median": 0.032965,
+ "std": 0.035921,
+ "sem": 0.001468,
+ "min": 0.006648,
+ "max": 0.282707,
+ "recall-0.025": 0.243333,
+ "recall-0.05": 0.803333,
+ "recall-0.1": 0.93,
"recall-0.15": 0.945,
"recall-0.25": 0.998333,
"recall-0.5": 1.0,
@@ -130,15 +129,15 @@ Results of the model in various experiments on different datasets.
},
"wrist_left": {
"count": 600,
- "mean": 0.040681,
- "median": 0.024264,
- "std": 0.042258,
- "sem": 0.001727,
- "min": 0.001517,
- "max": 0.18631,
- "recall-0.025": 0.513333,
- "recall-0.05": 0.746667,
- "recall-0.1": 0.91,
+ "mean": 0.040735,
+ "median": 0.024289,
+ "std": 0.042165,
+ "sem": 0.001723,
+ "min": 0.001529,
+ "max": 0.186262,
+ "recall-0.025": 0.515,
+ "recall-0.05": 0.745,
+ "recall-0.1": 0.911667,
"recall-0.15": 0.945,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -146,30 +145,30 @@ Results of the model in various experiments on different datasets.
},
"wrist_right": {
"count": 600,
- "mean": 0.043196,
- "median": 0.025854,
- "std": 0.047962,
- "sem": 0.00196,
- "min": 0.001698,
- "max": 0.425617,
- "recall-0.025": 0.471667,
- "recall-0.05": 0.77,
- "recall-0.1": 0.893333,
- "recall-0.15": 0.92,
+ "mean": 0.043487,
+ "median": 0.025787,
+ "std": 0.048637,
+ "sem": 0.001987,
+ "min": 0.002077,
+ "max": 0.425327,
+ "recall-0.025": 0.476667,
+ "recall-0.05": 0.768333,
+ "recall-0.1": 0.89,
+ "recall-0.15": 0.915,
"recall-0.25": 0.998333,
"recall-0.5": 1.0,
"num_labels": 600
},
"hip_left": {
"count": 600,
- "mean": 0.089857,
- "median": 0.087197,
- "std": 0.033035,
- "sem": 0.00135,
- "min": 0.01535,
- "max": 0.236764,
- "recall-0.025": 0.006667,
- "recall-0.05": 0.043333,
+ "mean": 0.089948,
+ "median": 0.087326,
+ "std": 0.032876,
+ "sem": 0.001343,
+ "min": 0.013928,
+ "max": 0.236753,
+ "recall-0.025": 0.005,
+ "recall-0.05": 0.04,
"recall-0.1": 0.823333,
"recall-0.15": 0.95,
"recall-0.25": 1.0,
@@ -178,46 +177,46 @@ Results of the model in various experiments on different datasets.
},
"hip_right": {
"count": 600,
- "mean": 0.114649,
- "median": 0.115077,
- "std": 0.026326,
- "sem": 0.001076,
- "min": 0.046172,
- "max": 0.234935,
+ "mean": 0.114678,
+ "median": 0.11506,
+ "std": 0.026245,
+ "sem": 0.001072,
+ "min": 0.046225,
+ "max": 0.234061,
"recall-0.025": 0.0,
"recall-0.05": 0.001667,
- "recall-0.1": 0.251667,
- "recall-0.15": 0.946667,
+ "recall-0.1": 0.25,
+ "recall-0.15": 0.948333,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
"num_labels": 600
},
"knee_left": {
"count": 600,
- "mean": 0.06083,
- "median": 0.045088,
- "std": 0.061419,
- "sem": 0.00251,
- "min": 0.01946,
- "max": 0.425374,
+ "mean": 0.06065,
+ "median": 0.045019,
+ "std": 0.060798,
+ "sem": 0.002484,
+ "min": 0.019487,
+ "max": 0.420267,
"recall-0.025": 0.046667,
- "recall-0.05": 0.6,
- "recall-0.1": 0.92,
- "recall-0.15": 0.921667,
+ "recall-0.05": 0.601667,
+ "recall-0.1": 0.918333,
+ "recall-0.15": 0.923333,
"recall-0.25": 0.98,
"recall-0.5": 1.0,
"num_labels": 600
},
"knee_right": {
"count": 600,
- "mean": 0.051154,
- "median": 0.043249,
- "std": 0.035226,
- "sem": 0.001439,
- "min": 0.017082,
- "max": 0.259899,
+ "mean": 0.051123,
+ "median": 0.043066,
+ "std": 0.035171,
+ "sem": 0.001437,
+ "min": 0.017039,
+ "max": 0.258443,
"recall-0.025": 0.036667,
- "recall-0.05": 0.733333,
+ "recall-0.05": 0.731667,
"recall-0.1": 0.943333,
"recall-0.15": 0.945,
"recall-0.25": 0.998333,
@@ -226,15 +225,15 @@ Results of the model in various experiments on different datasets.
},
"ankle_left": {
"count": 598,
- "mean": 0.098141,
- "median": 0.086283,
- "std": 0.049814,
- "sem": 0.002039,
- "min": 0.039216,
- "max": 0.495347,
+ "mean": 0.098132,
+ "median": 0.086223,
+ "std": 0.049848,
+ "sem": 0.00204,
+ "min": 0.039526,
+ "max": 0.495917,
"recall-0.025": 0.0,
"recall-0.05": 0.001667,
- "recall-0.1": 0.843333,
+ "recall-0.1": 0.841667,
"recall-0.15": 0.938333,
"recall-0.25": 0.98,
"recall-0.5": 0.996667,
@@ -242,45 +241,44 @@ Results of the model in various experiments on different datasets.
},
"ankle_right": {
"count": 600,
- "mean": 0.085402,
- "median": 0.068683,
- "std": 0.058021,
- "sem": 0.002371,
- "min": 0.032987,
- "max": 0.463238,
+ "mean": 0.08584,
+ "median": 0.068684,
+ "std": 0.059413,
+ "sem": 0.002428,
+ "min": 0.033422,
+ "max": 0.462949,
"recall-0.025": 0.0,
"recall-0.05": 0.016667,
- "recall-0.1": 0.885,
- "recall-0.15": 0.906667,
- "recall-0.25": 0.98,
+ "recall-0.1": 0.883333,
+ "recall-0.15": 0.905,
+ "recall-0.25": 0.978333,
"recall-0.5": 1.0,
"num_labels": 600
},
"joint_recalls": {
"num_labels": 7800,
"recall-0.025": 0.17769,
- "recall-0.05": 0.47333,
- "recall-0.1": 0.82038,
- "recall-0.15": 0.92962,
- "recall-0.25": 0.99423,
+ "recall-0.05": 0.47295,
+ "recall-0.1": 0.81987,
+ "recall-0.15": 0.92872,
+ "recall-0.25": 0.99397,
"recall-0.5": 0.99974
}
}
{
"total_parts": 8400,
- "correct_parts": 8089,
- "pcp": 0.962976
+ "correct_parts": 8084,
+ "pcp": 0.962381
}
```
### Shelf
-(duration 00:00:56)
```json
{
- "avg_time_2d": 0.09936108867737026,
- "avg_time_3d": 0.0006794069231170969,
- "avg_fps": 9.995952079181109
+ "avg_time_2d": 0.08998977933143013,
+ "avg_time_3d": 0.0006411935865264578,
+ "avg_fps": 11.033755545195868
}
{
"person_nums": {
@@ -299,36 +297,36 @@ Results of the model in various experiments on different datasets.
},
"mpjpe": {
"count": 477,
- "mean": 0.048328,
- "median": 0.043186,
+ "mean": 0.048298,
+ "median": 0.043007,
"std": 0.01475,
"sem": 0.000676,
- "min": 0.029586,
- "max": 0.106122,
+ "min": 0.029813,
+ "max": 0.10567,
"recall-0.025": 0.0,
- "recall-0.05": 0.675052,
+ "recall-0.05": 0.681342,
"recall-0.1": 0.989518,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
"num_labels": 477,
"ap-0.025": 0.0,
- "ap-0.05": 0.345013,
- "ap-0.1": 0.702867,
- "ap-0.15": 0.714372,
- "ap-0.25": 0.714372,
- "ap-0.5": 0.714372
+ "ap-0.05": 0.349635,
+ "ap-0.1": 0.705917,
+ "ap-0.15": 0.717347,
+ "ap-0.25": 0.717347,
+ "ap-0.5": 0.717347
},
"head": {
"count": 477,
- "mean": 0.055213,
- "median": 0.049788,
- "std": 0.025973,
- "sem": 0.00119,
- "min": 0.006373,
- "max": 0.147303,
+ "mean": 0.05524,
+ "median": 0.049786,
+ "std": 0.026004,
+ "sem": 0.001192,
+ "min": 0.006316,
+ "max": 0.147291,
"recall-0.025": 0.077568,
- "recall-0.05": 0.505241,
+ "recall-0.05": 0.503145,
"recall-0.1": 0.91195,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
@@ -337,14 +335,14 @@ Results of the model in various experiments on different datasets.
},
"shoulder_left": {
"count": 477,
- "mean": 0.04442,
- "median": 0.038938,
- "std": 0.020658,
- "sem": 0.000947,
- "min": 0.006714,
- "max": 0.138512,
- "recall-0.025": 0.146751,
- "recall-0.05": 0.70021,
+ "mean": 0.04431,
+ "median": 0.038744,
+ "std": 0.020582,
+ "sem": 0.000943,
+ "min": 0.006704,
+ "max": 0.138498,
+ "recall-0.025": 0.150943,
+ "recall-0.05": 0.702306,
"recall-0.1": 0.983229,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
@@ -353,12 +351,12 @@ Results of the model in various experiments on different datasets.
},
"shoulder_right": {
"count": 477,
- "mean": 0.050591,
- "median": 0.045337,
- "std": 0.024506,
+ "mean": 0.050558,
+ "median": 0.045659,
+ "std": 0.024497,
"sem": 0.001123,
- "min": 0.00639,
- "max": 0.15065,
+ "min": 0.006395,
+ "max": 0.150544,
"recall-0.025": 0.106918,
"recall-0.05": 0.555556,
"recall-0.1": 0.947589,
@@ -369,14 +367,14 @@ Results of the model in various experiments on different datasets.
},
"elbow_left": {
"count": 477,
- "mean": 0.040792,
- "median": 0.034056,
- "std": 0.025923,
+ "mean": 0.040842,
+ "median": 0.034008,
+ "std": 0.025918,
"sem": 0.001188,
- "min": 0.005533,
- "max": 0.152226,
+ "min": 0.005511,
+ "max": 0.150408,
"recall-0.025": 0.301887,
- "recall-0.05": 0.746331,
+ "recall-0.05": 0.748428,
"recall-0.1": 0.958071,
"recall-0.15": 0.997904,
"recall-0.25": 1.0,
@@ -385,15 +383,15 @@ Results of the model in various experiments on different datasets.
},
"elbow_right": {
"count": 477,
- "mean": 0.05355,
- "median": 0.044068,
- "std": 0.042016,
- "sem": 0.001926,
- "min": 0.002858,
- "max": 0.244706,
- "recall-0.025": 0.259958,
- "recall-0.05": 0.559748,
- "recall-0.1": 0.888889,
+ "mean": 0.053537,
+ "median": 0.044085,
+ "std": 0.041924,
+ "sem": 0.001922,
+ "min": 0.002875,
+ "max": 0.235324,
+ "recall-0.025": 0.262055,
+ "recall-0.05": 0.555556,
+ "recall-0.1": 0.890985,
"recall-0.15": 0.958071,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -401,15 +399,15 @@ Results of the model in various experiments on different datasets.
},
"wrist_left": {
"count": 477,
- "mean": 0.061591,
- "median": 0.053276,
- "std": 0.046099,
- "sem": 0.002113,
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+ "recall-0.1": 0.91195,
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@@ -417,15 +415,15 @@ Results of the model in various experiments on different datasets.
},
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- "median": 0.053924,
- "std": 0.030037,
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+ "mean": 0.058056,
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+ "recall-0.05": 0.446541,
+ "recall-0.1": 0.899371,
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@@ -433,15 +431,15 @@ Results of the model in various experiments on different datasets.
},
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- "median": 0.043075,
- "std": 0.026357,
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+ "mean": 0.0478,
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- "recall-0.05": 0.620545,
- "recall-0.1": 0.958071,
+ "recall-0.05": 0.616352,
+ "recall-0.1": 0.960168,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -449,14 +447,14 @@ Results of the model in various experiments on different datasets.
},
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+ "recall-0.05": 0.421384,
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@@ -465,14 +463,14 @@ Results of the model in various experiments on different datasets.
},
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+ "recall-0.05": 0.767296,
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"recall-0.25": 1.0,
@@ -481,14 +479,14 @@ Results of the model in various experiments on different datasets.
},
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- "mean": 0.039398,
- "median": 0.034816,
- "std": 0.023356,
+ "mean": 0.039442,
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+ "min": 0.006866,
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@@ -497,14 +495,14 @@ Results of the model in various experiments on different datasets.
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- "recall-0.05": 0.819706,
+ "mean": 0.037161,
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@@ -513,14 +511,14 @@ Results of the model in various experiments on different datasets.
},
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@@ -530,8 +528,8 @@ Results of the model in various experiments on different datasets.
"joint_recalls": {
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- "recall-0.05": 0.61893,
- "recall-0.1": 0.9384,
+ "recall-0.05": 0.62038,
+ "recall-0.1": 0.93872,
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"recall-0.5": 1.0
@@ -546,60 +544,59 @@ Results of the model in various experiments on different datasets.
### Campus
-(duration 00:00:29)
```json
{
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- "avg_time_3d": 0.00043942793360296283,
- "avg_fps": 17.586164294063575
+ "avg_time_2d": 0.04950667327305056,
+ "avg_time_3d": 0.0003714437754649036,
+ "avg_fps": 20.04887231463288
}
{
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+ "total_preds": 472,
"considered_empty": 0,
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- "invalid_preds": 97,
+ "invalid_preds": 96,
"missing": 0,
- "invalid_fraction": 0.20507,
- "precision": 0.79493,
+ "invalid_fraction": 0.20339,
+ "precision": 0.79661,
"recall": 1.0,
- "f1": 0.88575,
- "non_empty": 473
+ "f1": 0.88679,
+ "non_empty": 472
},
"mpjpe": {
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- "median": 0.076036,
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- "max": 0.158395,
+ "mean": 0.078549,
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+ "sem": 0.000984,
+ "min": 0.049002,
+ "max": 0.158403,
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- "recall-0.1": 0.890957,
+ "recall-0.1": 0.888298,
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"recall-0.25": 1.0,
"recall-0.5": 1.0,
"num_labels": 376,
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- "ap-0.05": 0.000324,
- "ap-0.1": 0.774956,
- "ap-0.15": 0.89001,
- "ap-0.25": 0.898696,
- "ap-0.5": 0.898696
+ "ap-0.05": 0.000331,
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+ "ap-0.15": 0.897845,
+ "ap-0.25": 0.906537,
+ "ap-0.5": 0.906537
},
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@@ -608,15 +605,15 @@ Results of the model in various experiments on different datasets.
},
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+ "recall-0.05": 0.398936,
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"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -624,12 +621,12 @@ Results of the model in various experiments on different datasets.
},
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@@ -640,15 +637,15 @@ Results of the model in various experiments on different datasets.
},
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"recall-0.5": 1.0,
@@ -656,31 +653,31 @@ Results of the model in various experiments on different datasets.
},
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"recall-0.5": 1.0,
@@ -688,12 +685,12 @@ Results of the model in various experiments on different datasets.
},
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@@ -704,15 +701,15 @@ Results of the model in various experiments on different datasets.
},
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- "recall-0.025": 0.029255,
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+ "mean": 0.081631,
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+ "recall-0.05": 0.167553,
+ "recall-0.1": 0.707447,
"recall-0.15": 0.992021,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -720,14 +717,14 @@ Results of the model in various experiments on different datasets.
},
"hip_right": {
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- "mean": 0.087186,
- "median": 0.083851,
- "std": 0.026263,
- "sem": 0.001356,
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- "max": 0.207002,
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+ "recall-0.05": 0.06117,
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"recall-0.25": 1.0,
@@ -736,14 +733,14 @@ Results of the model in various experiments on different datasets.
},
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"recall-0.25": 1.0,
@@ -752,13 +749,13 @@ Results of the model in various experiments on different datasets.
},
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- "std": 0.026175,
- "sem": 0.001352,
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@@ -767,337 +764,331 @@ Results of the model in various experiments on different datasets.
"num_labels": 376
},
"ankle_left": {
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- "mean": 0.091737,
- "median": 0.060063,
- "std": 0.100196,
- "sem": 0.005202,
- "min": 0.007394,
- "max": 0.493162,
+ "count": 371,
+ "mean": 0.090595,
+ "median": 0.059925,
+ "std": 0.098272,
+ "sem": 0.005109,
+ "min": 0.007433,
+ "max": 0.493218,
"recall-0.025": 0.06383,
- "recall-0.05": 0.297872,
- "recall-0.1": 0.816489,
+ "recall-0.05": 0.295213,
+ "recall-0.1": 0.819149,
"recall-0.15": 0.882979,
"recall-0.25": 0.904255,
- "recall-0.5": 0.989362,
+ "recall-0.5": 0.986702,
"num_labels": 376
},
"ankle_right": {
- "count": 372,
- "mean": 0.080393,
- "median": 0.052772,
- "std": 0.090361,
- "sem": 0.004691,
- "min": 0.006244,
- "max": 0.496601,
+ "count": 371,
+ "mean": 0.081171,
+ "median": 0.052557,
+ "std": 0.092463,
+ "sem": 0.004807,
+ "min": 0.006365,
+ "max": 0.495104,
"recall-0.025": 0.071809,
- "recall-0.05": 0.470745,
- "recall-0.1": 0.845745,
- "recall-0.15": 0.901596,
- "recall-0.25": 0.920213,
- "recall-0.5": 0.989362,
+ "recall-0.05": 0.465426,
+ "recall-0.1": 0.843085,
+ "recall-0.15": 0.898936,
+ "recall-0.25": 0.914894,
+ "recall-0.5": 0.986702,
"num_labels": 376
},
"joint_recalls": {
"num_labels": 4888,
- "recall-0.025": 0.04255,
- "recall-0.05": 0.28703,
- "recall-0.1": 0.78969,
+ "recall-0.025": 0.04194,
+ "recall-0.05": 0.2856,
+ "recall-0.1": 0.78928,
"recall-0.15": 0.93412,
- "recall-0.25": 0.98261,
- "recall-0.5": 0.99836
+ "recall-0.25": 0.9822,
+ "recall-0.5": 0.99755
}
}
{
"total_parts": 5264,
- "correct_parts": 4962,
- "pcp": 0.942629
+ "correct_parts": 4958,
+ "pcp": 0.941869
}
```
### MVOR
-(duration 00:02:31)
```json
{
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- "avg_time_3d": 0.0004480181680164738,
- "avg_fps": 18.04581185125563
+ "avg_time_2d": 0.04502476590515531,
+ "avg_time_3d": 0.0003439047417463509,
+ "avg_fps": 22.041642079021162
}
{
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- "total_preds": 998,
+ "total_preds": 1002,
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- "invalid_preds": 237,
+ "invalid_preds": 241,
"missing": 300,
- "invalid_fraction": 0.23747,
- "precision": 0.76253,
+ "invalid_fraction": 0.24052,
+ "precision": 0.75948,
"recall": 0.71725,
- "f1": 0.73919,
- "non_empty": 998
+ "f1": 0.73776,
+ "non_empty": 1002
},
"mpjpe": {
"count": 761,
- "mean": 0.115727,
- "median": 0.094907,
- "std": 0.067543,
- "sem": 0.00245,
- "min": 0.040489,
- "max": 0.497874,
+ "mean": 0.11541,
+ "median": 0.095862,
+ "std": 0.066157,
+ "sem": 0.0024,
+ "min": 0.041161,
+ "max": 0.498007,
"recall-0.025": 0.0,
"recall-0.05": 0.005655,
"recall-0.1": 0.38737,
"recall-0.15": 0.590952,
- "recall-0.25": 0.688973,
+ "recall-0.25": 0.690858,
"recall-0.5": 0.717248,
"num_labels": 1061,
"ap-0.025": 0.0,
- "ap-0.05": 9.8e-05,
- "ap-0.1": 0.249132,
- "ap-0.15": 0.489733,
- "ap-0.25": 0.615775,
- "ap-0.5": 0.652046
+ "ap-0.05": 9.7e-05,
+ "ap-0.1": 0.249015,
+ "ap-0.15": 0.488567,
+ "ap-0.25": 0.61615,
+ "ap-0.5": 0.650766
},
"head": {
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{
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}
```
### Panoptic
-(duration 00:02:28)
```json
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}
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@@ -1106,15 +1097,15 @@ Results of the model in various experiments on different datasets.
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@@ -1122,15 +1113,15 @@ Results of the model in various experiments on different datasets.
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@@ -1138,62 +1129,62 @@ Results of the model in various experiments on different datasets.
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@@ -1202,15 +1193,15 @@ Results of the model in various experiments on different datasets.
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@@ -1218,30 +1209,30 @@ Results of the model in various experiments on different datasets.
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@@ -1249,377 +1240,379 @@ Results of the model in various experiments on different datasets.
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- "recall-0.025": 0.369863,
- "recall-0.05": 0.684247,
- "recall-0.1": 0.772603,
- "recall-0.15": 0.813699,
- "recall-0.25": 0.896575,
- "recall-0.5": 0.992466,
+ "count": 1450,
+ "mean": 0.078982,
+ "median": 0.03334,
+ "std": 0.105487,
+ "sem": 0.002771,
+ "min": 0.001095,
+ "max": 0.498037,
+ "recall-0.025": 0.367808,
+ "recall-0.05": 0.682192,
+ "recall-0.1": 0.771233,
+ "recall-0.15": 0.811644,
+ "recall-0.25": 0.895205,
+ "recall-0.5": 0.993151,
"num_labels": 1460
},
"joint_recalls": {
"num_labels": 18990,
- "recall-0.025": 0.48773,
- "recall-0.05": 0.75039,
- "recall-0.1": 0.92507,
- "recall-0.15": 0.95956,
- "recall-0.25": 0.98157,
- "recall-0.5": 0.99768
+ "recall-0.025": 0.48694,
+ "recall-0.05": 0.75013,
+ "recall-0.1": 0.92459,
+ "recall-0.15": 0.96003,
+ "recall-0.25": 0.98136,
+ "recall-0.5": 0.99763
}
}
{
"total_parts": 20444,
- "correct_parts": 19978,
- "pcp": 0.977206
+ "correct_parts": 19972,
+ "pcp": 0.976913
}
```
-(3 cameras)
+##### 3 cameras
+
```json
{
- "avg_time_2d": 0.0669327892908236,
- "avg_time_3d": 0.0005383119350526391,
- "avg_fps": 14.82116019793796
+ "avg_time_2d": 0.06035172067037443,
+ "avg_time_3d": 0.00048815215506204746,
+ "avg_fps": 16.43658925568811
}
{
"person_nums": {
"total_frames": 420,
"total_labels": 1466,
- "total_preds": 1429,
+ "total_preds": 1432,
"considered_empty": 0,
- "valid_preds": 1412,
- "invalid_preds": 17,
- "missing": 54,
- "invalid_fraction": 0.0119,
- "precision": 0.9881,
- "recall": 0.96317,
- "f1": 0.97547,
- "non_empty": 1429
+ "valid_preds": 1416,
+ "invalid_preds": 16,
+ "missing": 50,
+ "invalid_fraction": 0.01117,
+ "precision": 0.98883,
+ "recall": 0.96589,
+ "f1": 0.97723,
+ "non_empty": 1432
},
"mpjpe": {
- "count": 1412,
- "mean": 0.075792,
- "median": 0.065401,
- "std": 0.04425,
- "sem": 0.001178,
- "min": 0.020974,
- "max": 0.442572,
+ "count": 1416,
+ "mean": 0.075878,
+ "median": 0.065403,
+ "std": 0.043901,
+ "sem": 0.001167,
+ "min": 0.021024,
+ "max": 0.442732,
"recall-0.025": 0.004775,
- "recall-0.05": 0.273533,
- "recall-0.1": 0.796044,
- "recall-0.15": 0.907913,
- "recall-0.25": 0.948158,
- "recall-0.5": 0.963165,
+ "recall-0.05": 0.271487,
+ "recall-0.1": 0.794679,
+ "recall-0.15": 0.911323,
+ "recall-0.25": 0.951569,
+ "recall-0.5": 0.965894,
"num_labels": 1466,
- "ap-0.025": 0.001689,
- "ap-0.05": 0.185397,
- "ap-0.1": 0.735355,
- "ap-0.15": 0.886829,
- "ap-0.25": 0.934464,
- "ap-0.5": 0.951885
+ "ap-0.025": 0.001729,
+ "ap-0.05": 0.180768,
+ "ap-0.1": 0.732669,
+ "ap-0.15": 0.890679,
+ "ap-0.25": 0.938419,
+ "ap-0.5": 0.9553
},
"nose": {
- "count": 1411,
- "mean": 0.023942,
- "median": 0.018414,
- "std": 0.018239,
- "sem": 0.000486,
- "min": 0.001916,
- "max": 0.23494,
- "recall-0.025": 0.625427,
- "recall-0.05": 0.90499,
- "recall-0.1": 0.956254,
- "recall-0.15": 0.962406,
- "recall-0.25": 0.964457,
- "recall-0.5": 0.964457,
+ "count": 1415,
+ "mean": 0.023812,
+ "median": 0.018331,
+ "std": 0.017907,
+ "sem": 0.000476,
+ "min": 0.001934,
+ "max": 0.234974,
+ "recall-0.025": 0.632262,
+ "recall-0.05": 0.907724,
+ "recall-0.1": 0.959672,
+ "recall-0.15": 0.965824,
+ "recall-0.25": 0.967191,
+ "recall-0.5": 0.967191,
"num_labels": 1463
},
"shoulder_left": {
- "count": 1412,
- "mean": 0.024262,
- "median": 0.02112,
- "std": 0.018593,
- "sem": 0.000495,
- "min": 0.002094,
- "max": 0.31592,
+ "count": 1416,
+ "mean": 0.02429,
+ "median": 0.021182,
+ "std": 0.018533,
+ "sem": 0.000493,
+ "min": 0.002092,
+ "max": 0.315725,
"recall-0.025": 0.571623,
- "recall-0.05": 0.912688,
- "recall-0.1": 0.959072,
- "recall-0.15": 0.961119,
- "recall-0.25": 0.961119,
- "recall-0.5": 0.963165,
+ "recall-0.05": 0.914734,
+ "recall-0.1": 0.961801,
+ "recall-0.15": 0.963847,
+ "recall-0.25": 0.963847,
+ "recall-0.5": 0.965894,
"num_labels": 1466
},
"shoulder_right": {
- "count": 1411,
- "mean": 0.029836,
- "median": 0.024318,
- "std": 0.023709,
- "sem": 0.000631,
- "min": 0.00406,
- "max": 0.302527,
- "recall-0.025": 0.496928,
- "recall-0.05": 0.867577,
- "recall-0.1": 0.948123,
- "recall-0.15": 0.956997,
- "recall-0.25": 0.961092,
- "recall-0.5": 0.96314,
+ "count": 1415,
+ "mean": 0.03001,
+ "median": 0.024274,
+ "std": 0.024761,
+ "sem": 0.000658,
+ "min": 0.003865,
+ "max": 0.302059,
+ "recall-0.025": 0.502389,
+ "recall-0.05": 0.868942,
+ "recall-0.1": 0.950171,
+ "recall-0.15": 0.959044,
+ "recall-0.25": 0.96314,
+ "recall-0.5": 0.96587,
"num_labels": 1465
},
"elbow_left": {
- "count": 1411,
- "mean": 0.036684,
- "median": 0.023627,
- "std": 0.036215,
+ "count": 1415,
+ "mean": 0.036771,
+ "median": 0.023677,
+ "std": 0.036234,
"sem": 0.000964,
- "min": 0.001498,
- "max": 0.359129,
+ "min": 0.001509,
+ "max": 0.35915,
"recall-0.025": 0.511263,
- "recall-0.05": 0.759044,
+ "recall-0.05": 0.759727,
"recall-0.1": 0.894881,
- "recall-0.15": 0.946758,
- "recall-0.25": 0.961092,
- "recall-0.5": 0.96314,
+ "recall-0.15": 0.950171,
+ "recall-0.25": 0.963823,
+ "recall-0.5": 0.96587,
"num_labels": 1465
},
"elbow_right": {
- "count": 1411,
- "mean": 0.037083,
- "median": 0.022473,
- "std": 0.042933,
- "sem": 0.001143,
- "min": 0.001047,
- "max": 0.414541,
- "recall-0.025": 0.550923,
- "recall-0.05": 0.784005,
- "recall-0.1": 0.902939,
- "recall-0.15": 0.922078,
- "recall-0.25": 0.962406,
- "recall-0.5": 0.964457,
+ "count": 1415,
+ "mean": 0.037368,
+ "median": 0.022564,
+ "std": 0.043946,
+ "sem": 0.001169,
+ "min": 0.001064,
+ "max": 0.417028,
+ "recall-0.025": 0.548189,
+ "recall-0.05": 0.784689,
+ "recall-0.1": 0.903623,
+ "recall-0.15": 0.924812,
+ "recall-0.25": 0.964457,
+ "recall-0.5": 0.967191,
"num_labels": 1463
},
"wrist_left": {
- "count": 1376,
- "mean": 0.057384,
- "median": 0.022777,
- "std": 0.075996,
- "sem": 0.002049,
- "min": 0.001727,
- "max": 0.4856,
+ "count": 1380,
+ "mean": 0.057057,
+ "median": 0.023098,
+ "std": 0.074873,
+ "sem": 0.002016,
+ "min": 0.001825,
+ "max": 0.467986,
"recall-0.025": 0.505579,
- "recall-0.05": 0.635286,
+ "recall-0.05": 0.638075,
"recall-0.1": 0.799163,
- "recall-0.15": 0.863319,
- "recall-0.25": 0.929568,
- "recall-0.5": 0.959554,
+ "recall-0.15": 0.866806,
+ "recall-0.25": 0.933752,
+ "recall-0.5": 0.962343,
"num_labels": 1434
},
"wrist_right": {
- "count": 1403,
- "mean": 0.051906,
- "median": 0.027982,
- "std": 0.065859,
- "sem": 0.001759,
- "min": 0.0006,
- "max": 0.453971,
- "recall-0.025": 0.43956,
- "recall-0.05": 0.645604,
- "recall-0.1": 0.845467,
+ "count": 1407,
+ "mean": 0.052599,
+ "median": 0.028103,
+ "std": 0.067423,
+ "sem": 0.001798,
+ "min": 0.000678,
+ "max": 0.453996,
+ "recall-0.025": 0.4375,
+ "recall-0.05": 0.646291,
+ "recall-0.1": 0.846154,
"recall-0.15": 0.908654,
- "recall-0.25": 0.940934,
- "recall-0.5": 0.963599,
+ "recall-0.25": 0.942308,
+ "recall-0.5": 0.966346,
"num_labels": 1456
},
"hip_left": {
- "count": 1410,
- "mean": 0.069404,
- "median": 0.063958,
- "std": 0.034045,
- "sem": 0.000907,
- "min": 0.004913,
- "max": 0.205973,
+ "count": 1414,
+ "mean": 0.069345,
+ "median": 0.063987,
+ "std": 0.033986,
+ "sem": 0.000904,
+ "min": 0.004843,
+ "max": 0.206544,
"recall-0.025": 0.03959,
"recall-0.05": 0.257338,
- "recall-0.1": 0.842321,
- "recall-0.15": 0.913993,
- "recall-0.25": 0.962457,
- "recall-0.5": 0.962457,
+ "recall-0.1": 0.843686,
+ "recall-0.15": 0.916724,
+ "recall-0.25": 0.965188,
+ "recall-0.5": 0.965188,
"num_labels": 1465
},
"hip_right": {
- "count": 1411,
- "mean": 0.079895,
- "median": 0.071167,
- "std": 0.03891,
+ "count": 1415,
+ "mean": 0.079898,
+ "median": 0.071309,
+ "std": 0.038974,
"sem": 0.001036,
- "min": 0.010114,
- "max": 0.304525,
- "recall-0.025": 0.01296,
- "recall-0.05": 0.206003,
- "recall-0.1": 0.749659,
- "recall-0.15": 0.892906,
- "recall-0.25": 0.961119,
- "recall-0.5": 0.962483,
+ "min": 0.010034,
+ "max": 0.299462,
+ "recall-0.025": 0.013643,
+ "recall-0.05": 0.208049,
+ "recall-0.1": 0.752387,
+ "recall-0.15": 0.894952,
+ "recall-0.25": 0.963165,
+ "recall-0.5": 0.965211,
"num_labels": 1466
},
"knee_left": {
- "count": 1392,
- "mean": 0.08272,
- "median": 0.055753,
- "std": 0.078906,
- "sem": 0.002116,
- "min": 0.006729,
- "max": 0.491598,
- "recall-0.025": 0.096928,
- "recall-0.05": 0.389078,
+ "count": 1398,
+ "mean": 0.083457,
+ "median": 0.055943,
+ "std": 0.079924,
+ "sem": 0.002138,
+ "min": 0.006682,
+ "max": 0.499765,
+ "recall-0.025": 0.094881,
+ "recall-0.05": 0.392491,
"recall-0.1": 0.741297,
- "recall-0.15": 0.800683,
- "recall-0.25": 0.90785,
- "recall-0.5": 0.950171,
+ "recall-0.15": 0.802048,
+ "recall-0.25": 0.91058,
+ "recall-0.5": 0.954266,
"num_labels": 1465
},
"knee_right": {
- "count": 1392,
- "mean": 0.081912,
- "median": 0.056033,
- "std": 0.076692,
- "sem": 0.002056,
- "min": 0.005476,
- "max": 0.496602,
- "recall-0.025": 0.078821,
- "recall-0.05": 0.382454,
+ "count": 1396,
+ "mean": 0.082016,
+ "median": 0.055961,
+ "std": 0.07626,
+ "sem": 0.002042,
+ "min": 0.005483,
+ "max": 0.495027,
+ "recall-0.025": 0.07745,
+ "recall-0.05": 0.381768,
"recall-0.1": 0.749143,
- "recall-0.15": 0.827964,
- "recall-0.25": 0.911583,
- "recall-0.5": 0.954078,
+ "recall-0.15": 0.829335,
+ "recall-0.25": 0.91501,
+ "recall-0.5": 0.95682,
"num_labels": 1459
},
"ankle_left": {
- "count": 1319,
- "mean": 0.161869,
- "median": 0.131406,
- "std": 0.140066,
- "sem": 0.003858,
- "min": 0.000769,
- "max": 0.49962,
- "recall-0.025": 0.198223,
+ "count": 1324,
+ "mean": 0.162633,
+ "median": 0.132173,
+ "std": 0.140662,
+ "sem": 0.003867,
+ "min": 0.000753,
+ "max": 0.499578,
+ "recall-0.025": 0.197539,
"recall-0.05": 0.293233,
- "recall-0.1": 0.372522,
- "recall-0.15": 0.498975,
+ "recall-0.1": 0.374573,
+ "recall-0.15": 0.501025,
"recall-0.25": 0.649351,
- "recall-0.5": 0.901572,
+ "recall-0.5": 0.90499,
"num_labels": 1463
},
"ankle_right": {
- "count": 1311,
- "mean": 0.149307,
- "median": 0.106447,
- "std": 0.131489,
- "sem": 0.003633,
- "min": 0.001734,
- "max": 0.497141,
- "recall-0.025": 0.161644,
- "recall-0.05": 0.29589,
- "recall-0.1": 0.427397,
- "recall-0.15": 0.541096,
- "recall-0.25": 0.681507,
- "recall-0.5": 0.897945,
+ "count": 1317,
+ "mean": 0.151178,
+ "median": 0.10686,
+ "std": 0.132903,
+ "sem": 0.003664,
+ "min": 0.001721,
+ "max": 0.496921,
+ "recall-0.025": 0.158904,
+ "recall-0.05": 0.295205,
+ "recall-0.1": 0.426712,
+ "recall-0.15": 0.539041,
+ "recall-0.25": 0.679452,
+ "recall-0.5": 0.902055,
"num_labels": 1460
},
"joint_recalls": {
"num_labels": 18990,
- "recall-0.025": 0.32928,
- "recall-0.05": 0.56372,
- "recall-0.1": 0.78336,
- "recall-0.15": 0.84576,
- "recall-0.25": 0.9039,
- "recall-0.5": 0.95129
+ "recall-0.025": 0.32944,
+ "recall-0.05": 0.56482,
+ "recall-0.1": 0.78468,
+ "recall-0.15": 0.8475,
+ "recall-0.25": 0.906,
+ "recall-0.5": 0.95424
}
}
{
"total_parts": 20444,
- "correct_parts": 18309,
- "pcp": 0.895568
+ "correct_parts": 18352,
+ "pcp": 0.897672
}
```
-(10 cameras)
+##### 10 cameras
+
```json
{
- "avg_time_2d": 0.20779247690991656,
- "avg_time_3d": 0.0016487220438515268,
- "avg_fps": 4.774609794994247
+ "avg_time_2d": 0.18815787420040223,
+ "avg_time_3d": 0.001592859989259301,
+ "avg_fps": 5.270071835403125
}
{
"person_nums": {
"total_frames": 420,
"total_labels": 1466,
- "total_preds": 1523,
+ "total_preds": 1493,
"considered_empty": 0,
"valid_preds": 1465,
- "invalid_preds": 58,
+ "invalid_preds": 28,
"missing": 1,
- "invalid_fraction": 0.03808,
- "precision": 0.96192,
+ "invalid_fraction": 0.01875,
+ "precision": 0.98125,
"recall": 0.99932,
- "f1": 0.98026,
- "non_empty": 1523
+ "f1": 0.9902,
+ "non_empty": 1493
},
"mpjpe": {
"count": 1465,
- "mean": 0.03698,
- "median": 0.032157,
- "std": 0.017197,
- "sem": 0.000449,
- "min": 0.013848,
- "max": 0.136363,
- "recall-0.025": 0.191678,
- "recall-0.05": 0.854025,
- "recall-0.1": 0.988404,
+ "mean": 0.035896,
+ "median": 0.03129,
+ "std": 0.017035,
+ "sem": 0.000445,
+ "min": 0.012795,
+ "max": 0.13346,
+ "recall-0.025": 0.238745,
+ "recall-0.05": 0.858799,
+ "recall-0.1": 0.991132,
"recall-0.15": 0.999318,
"recall-0.25": 0.999318,
"recall-0.5": 0.999318,
"num_labels": 1466,
- "ap-0.025": 0.089714,
- "ap-0.05": 0.813775,
- "ap-0.1": 0.976169,
- "ap-0.15": 0.988475,
- "ap-0.25": 0.988475,
- "ap-0.5": 0.988475
+ "ap-0.025": 0.138828,
+ "ap-0.05": 0.833731,
+ "ap-0.1": 0.980837,
+ "ap-0.15": 0.990118,
+ "ap-0.25": 0.990118,
+ "ap-0.5": 0.990118
},
"nose": {
"count": 1462,
- "mean": 0.012463,
- "median": 0.010896,
- "std": 0.007875,
- "sem": 0.000206,
- "min": 0.001275,
- "max": 0.124831,
- "recall-0.025": 0.954887,
- "recall-0.05": 0.992481,
+ "mean": 0.011192,
+ "median": 0.010004,
+ "std": 0.006886,
+ "sem": 0.00018,
+ "min": 0.001687,
+ "max": 0.130269,
+ "recall-0.025": 0.976077,
+ "recall-0.05": 0.995215,
"recall-0.1": 0.998633,
"recall-0.15": 0.999316,
"recall-0.25": 0.999316,
@@ -1628,15 +1621,15 @@ Results of the model in various experiments on different datasets.
},
"shoulder_left": {
"count": 1465,
- "mean": 0.018708,
- "median": 0.016682,
- "std": 0.009587,
- "sem": 0.000251,
- "min": 0.001103,
- "max": 0.11138,
- "recall-0.025": 0.799454,
- "recall-0.05": 0.991132,
- "recall-0.1": 0.998636,
+ "mean": 0.018481,
+ "median": 0.016756,
+ "std": 0.008734,
+ "sem": 0.000228,
+ "min": 0.001637,
+ "max": 0.082437,
+ "recall-0.025": 0.817872,
+ "recall-0.05": 0.991814,
+ "recall-0.1": 0.999318,
"recall-0.15": 0.999318,
"recall-0.25": 0.999318,
"recall-0.5": 0.999318,
@@ -1644,14 +1637,14 @@ Results of the model in various experiments on different datasets.
},
"shoulder_right": {
"count": 1464,
- "mean": 0.020508,
- "median": 0.019301,
- "std": 0.008862,
- "sem": 0.000232,
- "min": 0.000512,
- "max": 0.087963,
- "recall-0.025": 0.767918,
- "recall-0.05": 0.990444,
+ "mean": 0.019939,
+ "median": 0.018826,
+ "std": 0.008763,
+ "sem": 0.000229,
+ "min": 0.002124,
+ "max": 0.085937,
+ "recall-0.025": 0.822526,
+ "recall-0.05": 0.989761,
"recall-0.1": 0.999317,
"recall-0.15": 0.999317,
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@@ -1660,15 +1653,15 @@ Results of the model in various experiments on different datasets.
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@@ -1676,15 +1669,15 @@ Results of the model in various experiments on different datasets.
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@@ -1692,63 +1685,63 @@ Results of the model in various experiments on different datasets.
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@@ -1756,82 +1749,82 @@ Results of the model in various experiments on different datasets.
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}
```
@@ -1840,269 +1833,269 @@ Results of the model in various experiments on different datasets.
(duration 00:01:45)
```json
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+ "recall-0.15": 0.57971,
+ "recall-0.25": 0.699275,
+ "recall-0.5": 0.833333,
"num_labels": 276
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- "recall-0.15": 0.52093,
- "recall-0.25": 0.627907,
- "recall-0.5": 0.762791,
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+ "recall-0.1": 0.418605,
+ "recall-0.15": 0.576744,
+ "recall-0.25": 0.683721,
+ "recall-0.5": 0.804651,
"num_labels": 215
},
"ankle_left": {
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+ "recall-0.15": 0.037879,
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"num_labels": 132
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+ "recall-0.25": 0.055118,
+ "recall-0.5": 0.448819,
"num_labels": 127
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"joint_recalls": {
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- "recall-0.025": 0.25712,
- "recall-0.05": 0.49652,
- "recall-0.1": 0.65492,
- "recall-0.15": 0.71595,
- "recall-0.25": 0.75028,
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+ "recall-0.25": 0.79403,
+ "recall-0.5": 0.84205
}
}
{
"total_parts": 4313,
- "correct_parts": 3239,
- "pcp": 0.750985
+ "correct_parts": 3411,
+ "pcp": 0.790865
}
```
@@ -2111,58 +2104,58 @@ Results of the model in various experiments on different datasets.
(duration 00:01:51)
```json
{
- "avg_time_2d": 0.12212131857872009,
- "avg_time_3d": 0.0008241891860961914,
- "avg_fps": 8.133684737086208
+ "avg_time_2d": 0.11152931094169617,
+ "avg_time_3d": 0.0007880079746246338,
+ "avg_fps": 8.903346426431572
}
{
"person_nums": {
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"total_labels": 630,
- "total_preds": 631,
+ "total_preds": 630,
"considered_empty": 0,
"valid_preds": 630,
- "invalid_preds": 1,
+ "invalid_preds": 0,
"missing": 0,
- "invalid_fraction": 0.00158,
- "precision": 0.99842,
+ "invalid_fraction": 0.0,
+ "precision": 1.0,
"recall": 1.0,
- "f1": 0.99921,
- "non_empty": 631
+ "f1": 1.0,
+ "non_empty": 630
},
"mpjpe": {
"count": 630,
- "mean": 0.056103,
- "median": 0.051456,
- "std": 0.018372,
+ "mean": 0.0559,
+ "median": 0.051047,
+ "std": 0.018383,
"sem": 0.000733,
- "min": 0.028965,
- "max": 0.14306,
+ "min": 0.029149,
+ "max": 0.144155,
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- "recall-0.05": 0.439683,
- "recall-0.1": 0.949206,
+ "recall-0.05": 0.446032,
+ "recall-0.1": 0.947619,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
"num_labels": 630,
"ap-0.025": 0.0,
- "ap-0.05": 0.224824,
- "ap-0.1": 0.928154,
- "ap-0.15": 0.999746,
- "ap-0.25": 0.999746,
- "ap-0.5": 0.999746
+ "ap-0.05": 0.23272,
+ "ap-0.1": 0.928677,
+ "ap-0.15": 1.0,
+ "ap-0.25": 1.0,
+ "ap-0.5": 1.0
},
"head": {
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- "mean": 0.040759,
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- "std": 0.013741,
- "sem": 0.000562,
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- "recall-0.025": 0.125418,
- "recall-0.05": 0.767559,
- "recall-0.1": 0.998328,
+ "mean": 0.040407,
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+ "recall-0.025": 0.113712,
+ "recall-0.05": 0.777592,
+ "recall-0.1": 1.0,
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"recall-0.25": 1.0,
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@@ -2170,15 +2163,15 @@ Results of the model in various experiments on different datasets.
},
"shoulder_left": {
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- "mean": 0.062835,
- "median": 0.060457,
- "std": 0.019397,
- "sem": 0.000773,
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+ "mean": 0.062874,
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+ "sem": 0.000771,
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+ "max": 0.139185,
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- "recall-0.1": 0.97619,
+ "recall-0.05": 0.284127,
+ "recall-0.1": 0.971429,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -2186,15 +2179,15 @@ Results of the model in various experiments on different datasets.
},
"shoulder_right": {
"count": 630,
- "mean": 0.066268,
- "median": 0.06401,
- "std": 0.01988,
- "sem": 0.000793,
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- "max": 0.146825,
+ "mean": 0.066032,
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+ "sem": 0.000785,
+ "min": 0.026785,
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- "recall-0.1": 0.934921,
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+ "recall-0.1": 0.938095,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -2202,14 +2195,14 @@ Results of the model in various experiments on different datasets.
},
"elbow_left": {
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- "mean": 0.052366,
- "median": 0.049508,
- "std": 0.02021,
- "sem": 0.000806,
- "min": 0.010131,
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- "recall-0.025": 0.05873,
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+ "mean": 0.05202,
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+ "recall-0.025": 0.052381,
+ "recall-0.05": 0.528571,
"recall-0.1": 0.974603,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
@@ -2218,31 +2211,31 @@ Results of the model in various experiments on different datasets.
},
"elbow_right": {
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- "mean": 0.055804,
- "median": 0.048387,
- "std": 0.032053,
- "sem": 0.001279,
- "min": 0.004074,
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- "recall-0.025": 0.084261,
- "recall-0.05": 0.529412,
- "recall-0.1": 0.920509,
- "recall-0.15": 0.969793,
+ "mean": 0.055264,
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+ "sem": 0.00126,
+ "min": 0.00774,
+ "max": 0.230646,
+ "recall-0.025": 0.100159,
+ "recall-0.05": 0.527822,
+ "recall-0.1": 0.922099,
+ "recall-0.15": 0.972973,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
"num_labels": 629
},
"wrist_left": {
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- "mean": 0.048061,
- "median": 0.041971,
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- "min": 0.007895,
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- "recall-0.025": 0.134921,
- "recall-0.05": 0.642857,
- "recall-0.1": 0.950794,
+ "mean": 0.047936,
+ "median": 0.041232,
+ "std": 0.026637,
+ "sem": 0.001062,
+ "min": 0.003013,
+ "max": 0.213528,
+ "recall-0.025": 0.133333,
+ "recall-0.05": 0.650794,
+ "recall-0.1": 0.953968,
"recall-0.15": 0.988889,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -2250,15 +2243,15 @@ Results of the model in various experiments on different datasets.
},
"wrist_right": {
"count": 625,
- "mean": 0.052694,
- "median": 0.047416,
- "std": 0.0259,
- "sem": 0.001037,
- "min": 0.008634,
- "max": 0.226556,
- "recall-0.025": 0.0816,
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- "recall-0.1": 0.9488,
+ "mean": 0.052226,
+ "median": 0.046644,
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+ "sem": 0.001027,
+ "min": 0.007235,
+ "max": 0.226558,
+ "recall-0.025": 0.0864,
+ "recall-0.05": 0.552,
+ "recall-0.1": 0.9472,
"recall-0.15": 0.9952,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -2266,14 +2259,14 @@ Results of the model in various experiments on different datasets.
},
"hip_left": {
"count": 630,
- "mean": 0.057289,
- "median": 0.054137,
- "std": 0.020575,
- "sem": 0.00082,
- "min": 0.014001,
- "max": 0.17071,
- "recall-0.025": 0.025397,
- "recall-0.05": 0.401587,
+ "mean": 0.056856,
+ "median": 0.053491,
+ "std": 0.020361,
+ "sem": 0.000812,
+ "min": 0.014996,
+ "max": 0.170692,
+ "recall-0.025": 0.019048,
+ "recall-0.05": 0.404762,
"recall-0.1": 0.960317,
"recall-0.15": 0.995238,
"recall-0.25": 1.0,
@@ -2282,14 +2275,14 @@ Results of the model in various experiments on different datasets.
},
"hip_right": {
"count": 629,
- "mean": 0.055246,
- "median": 0.050996,
- "std": 0.023089,
- "sem": 0.000921,
- "min": 0.004999,
- "max": 0.145424,
- "recall-0.025": 0.069952,
- "recall-0.05": 0.480127,
+ "mean": 0.055372,
+ "median": 0.050585,
+ "std": 0.023477,
+ "sem": 0.000937,
+ "min": 0.004628,
+ "max": 0.146936,
+ "recall-0.025": 0.060413,
+ "recall-0.05": 0.492846,
"recall-0.1": 0.957075,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
@@ -2298,31 +2291,31 @@ Results of the model in various experiments on different datasets.
},
"knee_left": {
"count": 628,
- "mean": 0.045669,
- "median": 0.034743,
- "std": 0.046074,
+ "mean": 0.045803,
+ "median": 0.034827,
+ "std": 0.046081,
"sem": 0.00184,
- "min": 0.003593,
- "max": 0.364064,
- "recall-0.025": 0.291401,
- "recall-0.05": 0.724522,
+ "min": 0.002265,
+ "max": 0.364051,
+ "recall-0.025": 0.288217,
+ "recall-0.05": 0.734076,
"recall-0.1": 0.941083,
- "recall-0.15": 0.961783,
+ "recall-0.15": 0.963376,
"recall-0.25": 0.984076,
"recall-0.5": 1.0,
"num_labels": 628
},
"knee_right": {
"count": 629,
- "mean": 0.053711,
- "median": 0.036065,
- "std": 0.072301,
- "sem": 0.002885,
- "min": 0.002669,
- "max": 0.496679,
- "recall-0.025": 0.252782,
- "recall-0.05": 0.72973,
- "recall-0.1": 0.934817,
+ "mean": 0.053706,
+ "median": 0.036714,
+ "std": 0.07183,
+ "sem": 0.002866,
+ "min": 0.002678,
+ "max": 0.494376,
+ "recall-0.025": 0.27027,
+ "recall-0.05": 0.710652,
+ "recall-0.1": 0.933227,
"recall-0.15": 0.952305,
"recall-0.25": 0.958665,
"recall-0.5": 1.0,
@@ -2330,14 +2323,14 @@ Results of the model in various experiments on different datasets.
},
"ankle_left": {
"count": 619,
- "mean": 0.065863,
- "median": 0.050491,
- "std": 0.072779,
- "sem": 0.002928,
- "min": 0.012793,
- "max": 0.493666,
+ "mean": 0.065155,
+ "median": 0.050506,
+ "std": 0.071537,
+ "sem": 0.002878,
+ "min": 0.014226,
+ "max": 0.482565,
"recall-0.025": 0.035541,
- "recall-0.05": 0.489499,
+ "recall-0.05": 0.494346,
"recall-0.1": 0.945073,
"recall-0.15": 0.959612,
"recall-0.25": 0.962843,
@@ -2346,15 +2339,15 @@ Results of the model in various experiments on different datasets.
},
"ankle_right": {
"count": 601,
- "mean": 0.054029,
- "median": 0.047411,
- "std": 0.042569,
- "sem": 0.001738,
- "min": 0.011391,
- "max": 0.47977,
- "recall-0.025": 0.096463,
- "recall-0.05": 0.522508,
- "recall-0.1": 0.92926,
+ "mean": 0.054338,
+ "median": 0.047418,
+ "std": 0.043058,
+ "sem": 0.001758,
+ "min": 0.009373,
+ "max": 0.4761,
+ "recall-0.025": 0.098071,
+ "recall-0.05": 0.525723,
+ "recall-0.1": 0.930868,
"recall-0.15": 0.946945,
"recall-0.25": 0.954984,
"recall-0.5": 0.966238,
@@ -2362,298 +2355,30 @@ Results of the model in various experiments on different datasets.
},
"joint_recalls": {
"num_labels": 8129,
- "recall-0.025": 0.09595,
- "recall-0.05": 0.52749,
- "recall-0.1": 0.95092,
- "recall-0.15": 0.98155,
+ "recall-0.025": 0.09632,
+ "recall-0.05": 0.5334,
+ "recall-0.1": 0.95104,
+ "recall-0.15": 0.98216,
"recall-0.25": 0.98905,
"recall-0.5": 0.99742
}
}
{
"total_parts": 8725,
- "correct_parts": 8585,
- "pcp": 0.983954
+ "correct_parts": 8587,
+ "pcp": 0.984183
}
```
### H3WB
-(default joints, duration 00:00:41)
-```json
-{
- "avg_time_2d": 0.07019692094702469,
- "avg_time_3d": 0.0003231123874061986,
- "avg_fps": 14.180367658898387
-}
-{
- "person_nums": {
- "total_frames": 200,
- "total_labels": 200,
- "total_preds": 201,
- "considered_empty": 0,
- "valid_preds": 200,
- "invalid_preds": 1,
- "missing": 0,
- "invalid_fraction": 0.00498,
- "precision": 0.99502,
- "recall": 1.0,
- "f1": 0.99751,
- "non_empty": 201
- },
- "mpjpe": {
- "count": 200,
- "mean": 0.024971,
- "median": 0.023763,
- "std": 0.008862,
- "sem": 0.000628,
- "min": 0.012001,
- "max": 0.074839,
- "recall-0.025": 0.57,
- "recall-0.05": 0.985,
- "recall-0.1": 1.0,
- "recall-0.15": 1.0,
- "recall-0.25": 1.0,
- "recall-0.5": 1.0,
- "num_labels": 200,
- "ap-0.025": 0.469072,
- "ap-0.05": 0.984418,
- "ap-0.1": 1.0,
- "ap-0.15": 1.0,
- "ap-0.25": 1.0,
- "ap-0.5": 1.0
- },
- "nose": {
- "count": 200,
- "mean": 0.043706,
- "median": 0.035621,
- "std": 0.033787,
- "sem": 0.002395,
- "min": 0.004482,
- "max": 0.189964,
- "recall-0.025": 0.37,
- "recall-0.05": 0.71,
- "recall-0.1": 0.93,
- "recall-0.15": 0.985,
- "recall-0.25": 1.0,
- "recall-0.5": 1.0,
- "num_labels": 200
- },
- "shoulder_left": {
- "count": 200,
- "mean": 0.019033,
- "median": 0.016999,
- "std": 0.010274,
- "sem": 0.000728,
- "min": 0.00327,
- "max": 0.064981,
- "recall-0.025": 0.77,
- "recall-0.05": 0.975,
- "recall-0.1": 1.0,
- "recall-0.15": 1.0,
- "recall-0.25": 1.0,
- "recall-0.5": 1.0,
- "num_labels": 200
- },
- "shoulder_right": {
- "count": 200,
- "mean": 0.026164,
- "median": 0.023125,
- "std": 0.012796,
- "sem": 0.000907,
- "min": 0.006157,
- "max": 0.075377,
- "recall-0.025": 0.57,
- "recall-0.05": 0.935,
- "recall-0.1": 1.0,
- "recall-0.15": 1.0,
- "recall-0.25": 1.0,
- "recall-0.5": 1.0,
- "num_labels": 200
- },
- "elbow_left": {
- "count": 200,
- "mean": 0.015216,
- "median": 0.011591,
- "std": 0.013076,
- "sem": 0.000927,
- "min": 0.001038,
- "max": 0.098801,
- "recall-0.025": 0.875,
- "recall-0.05": 0.98,
- "recall-0.1": 1.0,
- "recall-0.15": 1.0,
- "recall-0.25": 1.0,
- "recall-0.5": 1.0,
- "num_labels": 200
- },
- "elbow_right": {
- "count": 200,
- "mean": 0.020709,
- "median": 0.014512,
- "std": 0.018279,
- "sem": 0.001296,
- "min": 0.002652,
- "max": 0.118555,
- "recall-0.025": 0.735,
- "recall-0.05": 0.915,
- "recall-0.1": 0.995,
- "recall-0.15": 1.0,
- "recall-0.25": 1.0,
- "recall-0.5": 1.0,
- "num_labels": 200
- },
- "wrist_left": {
- "count": 200,
- "mean": 0.024578,
- "median": 0.017257,
- "std": 0.02688,
- "sem": 0.001905,
- "min": 0.000802,
- "max": 0.261247,
- "recall-0.025": 0.725,
- "recall-0.05": 0.91,
- "recall-0.1": 0.975,
- "recall-0.15": 0.995,
- "recall-0.25": 0.995,
- "recall-0.5": 1.0,
- "num_labels": 200
- },
- "wrist_right": {
- "count": 200,
- "mean": 0.032118,
- "median": 0.023494,
- "std": 0.029205,
- "sem": 0.00207,
- "min": 0.001103,
- "max": 0.23071,
- "recall-0.025": 0.525,
- "recall-0.05": 0.83,
- "recall-0.1": 0.97,
- "recall-0.15": 0.995,
- "recall-0.25": 1.0,
- "recall-0.5": 1.0,
- "num_labels": 200
- },
- "hip_left": {
- "count": 200,
- "mean": 0.036467,
- "median": 0.034929,
- "std": 0.014313,
- "sem": 0.001015,
- "min": 0.006136,
- "max": 0.09849,
- "recall-0.025": 0.215,
- "recall-0.05": 0.845,
- "recall-0.1": 1.0,
- "recall-0.15": 1.0,
- "recall-0.25": 1.0,
- "recall-0.5": 1.0,
- "num_labels": 200
- },
- "hip_right": {
- "count": 200,
- "mean": 0.034682,
- "median": 0.033326,
- "std": 0.013764,
- "sem": 0.000976,
- "min": 0.003115,
- "max": 0.076881,
- "recall-0.025": 0.25,
- "recall-0.05": 0.845,
- "recall-0.1": 1.0,
- "recall-0.15": 1.0,
- "recall-0.25": 1.0,
- "recall-0.5": 1.0,
- "num_labels": 200
- },
- "knee_left": {
- "count": 200,
- "mean": 0.018318,
- "median": 0.013632,
- "std": 0.014531,
- "sem": 0.00103,
- "min": 0.002782,
- "max": 0.100895,
- "recall-0.025": 0.78,
- "recall-0.05": 0.97,
- "recall-0.1": 0.995,
- "recall-0.15": 1.0,
- "recall-0.25": 1.0,
- "recall-0.5": 1.0,
- "num_labels": 200
- },
- "knee_right": {
- "count": 200,
- "mean": 0.017774,
- "median": 0.0126,
- "std": 0.021883,
- "sem": 0.001551,
- "min": 0.001628,
- "max": 0.248436,
- "recall-0.025": 0.84,
- "recall-0.05": 0.96,
- "recall-0.1": 0.99,
- "recall-0.15": 0.995,
- "recall-0.25": 1.0,
- "recall-0.5": 1.0,
- "num_labels": 200
- },
- "ankle_left": {
- "count": 200,
- "mean": 0.017516,
- "median": 0.013814,
- "std": 0.022897,
- "sem": 0.001623,
- "min": 0.002513,
- "max": 0.232845,
- "recall-0.025": 0.91,
- "recall-0.05": 0.96,
- "recall-0.1": 0.985,
- "recall-0.15": 0.99,
- "recall-0.25": 1.0,
- "recall-0.5": 1.0,
- "num_labels": 200
- },
- "ankle_right": {
- "count": 200,
- "mean": 0.018347,
- "median": 0.013634,
- "std": 0.022266,
- "sem": 0.001578,
- "min": 0.001808,
- "max": 0.264653,
- "recall-0.025": 0.835,
- "recall-0.05": 0.965,
- "recall-0.1": 0.99,
- "recall-0.15": 0.995,
- "recall-0.25": 0.995,
- "recall-0.5": 1.0,
- "num_labels": 200
- },
- "joint_recalls": {
- "num_labels": 2600,
- "recall-0.025": 0.64577,
- "recall-0.05": 0.90769,
- "recall-0.1": 0.98692,
- "recall-0.15": 0.99654,
- "recall-0.25": 0.99923,
- "recall-0.5": 1.0
- }
-}
-{
- "total_parts": 2800,
- "correct_parts": 2797,
- "pcp": 0.998929
-}
-```
+##### default joints
-(duration 00:00:54)
```json
{
- "avg_time_2d": 0.08644666295302542,
- "avg_time_3d": 0.0006727946432013261,
- "avg_fps": 11.478492033716602
+ "avg_time_2d": 0.05200650064568771,
+ "avg_time_3d": 0.000292415367929559,
+ "avg_fps": 19.120855195920814
}
{
"person_nums": {
@@ -2672,12 +2397,282 @@ Results of the model in various experiments on different datasets.
},
"mpjpe": {
"count": 200,
- "mean": 0.048869,
- "median": 0.041028,
- "std": 0.02694,
- "sem": 0.00191,
- "min": 0.021878,
- "max": 0.244768,
+ "mean": 0.02515,
+ "median": 0.023689,
+ "std": 0.010002,
+ "sem": 0.000709,
+ "min": 0.01255,
+ "max": 0.107625,
+ "recall-0.025": 0.56,
+ "recall-0.05": 0.98,
+ "recall-0.1": 0.995,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
+ "num_labels": 200,
+ "ap-0.025": 0.45717,
+ "ap-0.05": 0.979363,
+ "ap-0.1": 0.994825,
+ "ap-0.15": 1.0,
+ "ap-0.25": 1.0,
+ "ap-0.5": 1.0
+ },
+ "nose": {
+ "count": 200,
+ "mean": 0.043369,
+ "median": 0.03534,
+ "std": 0.033311,
+ "sem": 0.002361,
+ "min": 0.004699,
+ "max": 0.19073,
+ "recall-0.025": 0.37,
+ "recall-0.05": 0.715,
+ "recall-0.1": 0.935,
+ "recall-0.15": 0.985,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
+ "num_labels": 200
+ },
+ "shoulder_left": {
+ "count": 200,
+ "mean": 0.01896,
+ "median": 0.016589,
+ "std": 0.010208,
+ "sem": 0.000724,
+ "min": 0.003772,
+ "max": 0.069414,
+ "recall-0.025": 0.785,
+ "recall-0.05": 0.975,
+ "recall-0.1": 1.0,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
+ "num_labels": 200
+ },
+ "shoulder_right": {
+ "count": 200,
+ "mean": 0.026151,
+ "median": 0.023182,
+ "std": 0.012763,
+ "sem": 0.000905,
+ "min": 0.004487,
+ "max": 0.075418,
+ "recall-0.025": 0.575,
+ "recall-0.05": 0.945,
+ "recall-0.1": 1.0,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
+ "num_labels": 200
+ },
+ "elbow_left": {
+ "count": 200,
+ "mean": 0.01524,
+ "median": 0.011716,
+ "std": 0.0131,
+ "sem": 0.000929,
+ "min": 0.001021,
+ "max": 0.098887,
+ "recall-0.025": 0.87,
+ "recall-0.05": 0.98,
+ "recall-0.1": 1.0,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
+ "num_labels": 200
+ },
+ "elbow_right": {
+ "count": 200,
+ "mean": 0.020908,
+ "median": 0.014473,
+ "std": 0.019387,
+ "sem": 0.001374,
+ "min": 0.000895,
+ "max": 0.12409,
+ "recall-0.025": 0.73,
+ "recall-0.05": 0.92,
+ "recall-0.1": 0.99,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
+ "num_labels": 200
+ },
+ "wrist_left": {
+ "count": 200,
+ "mean": 0.024447,
+ "median": 0.017245,
+ "std": 0.026566,
+ "sem": 0.001883,
+ "min": 0.000603,
+ "max": 0.261032,
+ "recall-0.025": 0.72,
+ "recall-0.05": 0.905,
+ "recall-0.1": 0.975,
+ "recall-0.15": 0.995,
+ "recall-0.25": 0.995,
+ "recall-0.5": 1.0,
+ "num_labels": 200
+ },
+ "wrist_right": {
+ "count": 200,
+ "mean": 0.032044,
+ "median": 0.023165,
+ "std": 0.029318,
+ "sem": 0.002078,
+ "min": 0.001198,
+ "max": 0.230805,
+ "recall-0.025": 0.53,
+ "recall-0.05": 0.83,
+ "recall-0.1": 0.97,
+ "recall-0.15": 0.995,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
+ "num_labels": 200
+ },
+ "hip_left": {
+ "count": 200,
+ "mean": 0.036425,
+ "median": 0.034978,
+ "std": 0.0142,
+ "sem": 0.001007,
+ "min": 0.006217,
+ "max": 0.098359,
+ "recall-0.025": 0.2,
+ "recall-0.05": 0.85,
+ "recall-0.1": 1.0,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
+ "num_labels": 200
+ },
+ "hip_right": {
+ "count": 200,
+ "mean": 0.034687,
+ "median": 0.033267,
+ "std": 0.013631,
+ "sem": 0.000966,
+ "min": 0.003129,
+ "max": 0.076945,
+ "recall-0.025": 0.25,
+ "recall-0.05": 0.845,
+ "recall-0.1": 1.0,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
+ "num_labels": 200
+ },
+ "knee_left": {
+ "count": 200,
+ "mean": 0.018635,
+ "median": 0.013488,
+ "std": 0.017566,
+ "sem": 0.001245,
+ "min": 0.002544,
+ "max": 0.181286,
+ "recall-0.025": 0.785,
+ "recall-0.05": 0.97,
+ "recall-0.1": 0.995,
+ "recall-0.15": 0.995,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
+ "num_labels": 200
+ },
+ "knee_right": {
+ "count": 200,
+ "mean": 0.018306,
+ "median": 0.012878,
+ "std": 0.021929,
+ "sem": 0.001555,
+ "min": 0.002418,
+ "max": 0.24868,
+ "recall-0.025": 0.82,
+ "recall-0.05": 0.96,
+ "recall-0.1": 0.99,
+ "recall-0.15": 0.995,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
+ "num_labels": 200
+ },
+ "ankle_left": {
+ "count": 200,
+ "mean": 0.018595,
+ "median": 0.013493,
+ "std": 0.033845,
+ "sem": 0.002399,
+ "min": 0.003314,
+ "max": 0.444766,
+ "recall-0.025": 0.9,
+ "recall-0.05": 0.955,
+ "recall-0.1": 0.99,
+ "recall-0.15": 0.995,
+ "recall-0.25": 0.995,
+ "recall-0.5": 1.0,
+ "num_labels": 200
+ },
+ "ankle_right": {
+ "count": 200,
+ "mean": 0.019189,
+ "median": 0.013539,
+ "std": 0.033179,
+ "sem": 0.002352,
+ "min": 0.001806,
+ "max": 0.445265,
+ "recall-0.025": 0.83,
+ "recall-0.05": 0.955,
+ "recall-0.1": 0.99,
+ "recall-0.15": 0.995,
+ "recall-0.25": 0.995,
+ "recall-0.5": 1.0,
+ "num_labels": 200
+ },
+ "joint_recalls": {
+ "num_labels": 2600,
+ "recall-0.025": 0.64308,
+ "recall-0.05": 0.90808,
+ "recall-0.1": 0.98731,
+ "recall-0.15": 0.99654,
+ "recall-0.25": 0.99885,
+ "recall-0.5": 1.0
+ }
+}
+{
+ "total_parts": 2800,
+ "correct_parts": 2795,
+ "pcp": 0.998214
+}
+```
+
+##### whole-body
+
+```json
+{
+ "avg_time_2d": 0.0930509040230199,
+ "avg_time_3d": 0.000683552340457314,
+ "avg_fps": 10.668435480355983
+}
+{
+ "person_nums": {
+ "total_frames": 200,
+ "total_labels": 200,
+ "total_preds": 200,
+ "considered_empty": 0,
+ "valid_preds": 200,
+ "invalid_preds": 0,
+ "missing": 0,
+ "invalid_fraction": 0.0,
+ "precision": 1.0,
+ "recall": 1.0,
+ "f1": 1.0,
+ "non_empty": 200
+ },
+ "mpjpe": {
+ "count": 200,
+ "mean": 0.0489,
+ "median": 0.041571,
+ "std": 0.02696,
+ "sem": 0.001911,
+ "min": 0.021811,
+ "max": 0.245554,
"recall-0.025": 0.02,
"recall-0.05": 0.68,
"recall-0.1": 0.96,
@@ -2686,38 +2681,38 @@ Results of the model in various experiments on different datasets.
"recall-0.5": 1.0,
"num_labels": 200,
"ap-0.025": 0.004893,
- "ap-0.05": 0.612983,
- "ap-0.1": 0.95333,
- "ap-0.15": 0.982961,
+ "ap-0.05": 0.608555,
+ "ap-0.1": 0.953375,
+ "ap-0.15": 0.982941,
"ap-0.25": 1.0,
"ap-0.5": 1.0
},
"nose": {
"count": 200,
- "mean": 0.051588,
- "median": 0.04253,
- "std": 0.041215,
- "sem": 0.002922,
- "min": 0.004778,
- "max": 0.258996,
- "recall-0.025": 0.285,
- "recall-0.05": 0.61,
+ "mean": 0.052155,
+ "median": 0.042304,
+ "std": 0.042473,
+ "sem": 0.003011,
+ "min": 0.004724,
+ "max": 0.260082,
+ "recall-0.025": 0.29,
+ "recall-0.05": 0.605,
"recall-0.1": 0.875,
- "recall-0.15": 0.975,
+ "recall-0.15": 0.965,
"recall-0.25": 0.995,
"recall-0.5": 1.0,
"num_labels": 200
},
"eye_left": {
"count": 200,
- "mean": 0.044718,
- "median": 0.034878,
- "std": 0.031444,
- "sem": 0.002229,
+ "mean": 0.0447,
+ "median": 0.034844,
+ "std": 0.031528,
+ "sem": 0.002235,
"min": 0.005777,
- "max": 0.164617,
+ "max": 0.164646,
"recall-0.025": 0.325,
- "recall-0.05": 0.675,
+ "recall-0.05": 0.68,
"recall-0.1": 0.915,
"recall-0.15": 0.99,
"recall-0.25": 1.0,
@@ -2726,14 +2721,14 @@ Results of the model in various experiments on different datasets.
},
"eye_right": {
"count": 200,
- "mean": 0.051131,
- "median": 0.044672,
- "std": 0.033522,
- "sem": 0.002376,
- "min": 0.004727,
- "max": 0.194151,
+ "mean": 0.051839,
+ "median": 0.044697,
+ "std": 0.033931,
+ "sem": 0.002405,
+ "min": 0.0047,
+ "max": 0.189378,
"recall-0.025": 0.23,
- "recall-0.05": 0.595,
+ "recall-0.05": 0.585,
"recall-0.1": 0.905,
"recall-0.15": 0.99,
"recall-0.25": 1.0,
@@ -2742,15 +2737,15 @@ Results of the model in various experiments on different datasets.
},
"ear_left": {
"count": 200,
- "mean": 0.034799,
- "median": 0.026707,
- "std": 0.028328,
- "sem": 0.002008,
- "min": 0.004074,
- "max": 0.196783,
- "recall-0.025": 0.445,
- "recall-0.05": 0.82,
- "recall-0.1": 0.97,
+ "mean": 0.034269,
+ "median": 0.025962,
+ "std": 0.028064,
+ "sem": 0.001989,
+ "min": 0.004571,
+ "max": 0.183472,
+ "recall-0.025": 0.485,
+ "recall-0.05": 0.825,
+ "recall-0.1": 0.965,
"recall-0.15": 0.99,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -2758,15 +2753,15 @@ Results of the model in various experiments on different datasets.
},
"ear_right": {
"count": 200,
- "mean": 0.04394,
- "median": 0.037015,
- "std": 0.027701,
- "sem": 0.001964,
- "min": 0.003835,
- "max": 0.133839,
- "recall-0.025": 0.28,
+ "mean": 0.043564,
+ "median": 0.037037,
+ "std": 0.027275,
+ "sem": 0.001933,
+ "min": 0.003843,
+ "max": 0.13407,
+ "recall-0.025": 0.29,
"recall-0.05": 0.67,
- "recall-0.1": 0.965,
+ "recall-0.1": 0.97,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -2774,13 +2769,13 @@ Results of the model in various experiments on different datasets.
},
"shoulder_left": {
"count": 200,
- "mean": 0.020137,
- "median": 0.018308,
- "std": 0.01136,
- "sem": 0.000805,
- "min": 0.001587,
- "max": 0.096788,
- "recall-0.025": 0.74,
+ "mean": 0.020277,
+ "median": 0.018464,
+ "std": 0.011523,
+ "sem": 0.000817,
+ "min": 0.001476,
+ "max": 0.096754,
+ "recall-0.025": 0.725,
"recall-0.05": 0.985,
"recall-0.1": 1.0,
"recall-0.15": 1.0,
@@ -2790,13 +2785,13 @@ Results of the model in various experiments on different datasets.
},
"shoulder_right": {
"count": 200,
- "mean": 0.023694,
- "median": 0.019472,
- "std": 0.014816,
- "sem": 0.00105,
- "min": 0.001051,
- "max": 0.091146,
- "recall-0.025": 0.655,
+ "mean": 0.023771,
+ "median": 0.01965,
+ "std": 0.014833,
+ "sem": 0.001051,
+ "min": 0.00089,
+ "max": 0.088781,
+ "recall-0.025": 0.645,
"recall-0.05": 0.93,
"recall-0.1": 1.0,
"recall-0.15": 1.0,
@@ -2806,14 +2801,14 @@ Results of the model in various experiments on different datasets.
},
"elbow_left": {
"count": 200,
- "mean": 0.021653,
- "median": 0.0159,
- "std": 0.019525,
- "sem": 0.001384,
- "min": 0.001613,
- "max": 0.131488,
- "recall-0.025": 0.755,
- "recall-0.05": 0.95,
+ "mean": 0.021929,
+ "median": 0.016049,
+ "std": 0.019549,
+ "sem": 0.001386,
+ "min": 0.001185,
+ "max": 0.128758,
+ "recall-0.025": 0.74,
+ "recall-0.05": 0.945,
"recall-0.1": 0.985,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
@@ -2822,14 +2817,14 @@ Results of the model in various experiments on different datasets.
},
"elbow_right": {
"count": 200,
- "mean": 0.025526,
- "median": 0.018237,
- "std": 0.022042,
- "sem": 0.001562,
- "min": 0.001078,
- "max": 0.117042,
- "recall-0.025": 0.625,
- "recall-0.05": 0.88,
+ "mean": 0.025547,
+ "median": 0.018257,
+ "std": 0.022194,
+ "sem": 0.001573,
+ "min": 0.000989,
+ "max": 0.116739,
+ "recall-0.025": 0.63,
+ "recall-0.05": 0.885,
"recall-0.1": 0.975,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
@@ -2838,14 +2833,14 @@ Results of the model in various experiments on different datasets.
},
"wrist_left": {
"count": 200,
- "mean": 0.031578,
- "median": 0.020573,
- "std": 0.037562,
- "sem": 0.002663,
- "min": 0.003098,
- "max": 0.344977,
+ "mean": 0.031359,
+ "median": 0.020701,
+ "std": 0.037293,
+ "sem": 0.002644,
+ "min": 0.001713,
+ "max": 0.345022,
"recall-0.025": 0.59,
- "recall-0.05": 0.855,
+ "recall-0.05": 0.86,
"recall-0.1": 0.955,
"recall-0.15": 0.97,
"recall-0.25": 0.995,
@@ -2854,30 +2849,30 @@ Results of the model in various experiments on different datasets.
},
"wrist_right": {
"count": 200,
- "mean": 0.043504,
- "median": 0.028684,
- "std": 0.054739,
- "sem": 0.00388,
- "min": 0.002623,
- "max": 0.453945,
- "recall-0.025": 0.455,
- "recall-0.05": 0.76,
- "recall-0.1": 0.92,
+ "mean": 0.044186,
+ "median": 0.028162,
+ "std": 0.056426,
+ "sem": 0.004,
+ "min": 0.002667,
+ "max": 0.453742,
+ "recall-0.025": 0.46,
+ "recall-0.05": 0.75,
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@@ -3062,15 +3057,15 @@ Results of the model in various experiments on different datasets.
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@@ -3222,15 +3217,15 @@ Results of the model in various experiments on different datasets.
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@@ -3238,15 +3233,15 @@ Results of the model in various experiments on different datasets.
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@@ -3254,30 +3249,30 @@ Results of the model in various experiments on different datasets.
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@@ -3286,31 +3281,31 @@ Results of the model in various experiments on different datasets.
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@@ -4534,31 +4529,31 @@ Results of the model in various experiments on different datasets.
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@@ -4566,14 +4561,14 @@ Results of the model in various experiments on different datasets.
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@@ -4598,15 +4593,15 @@ Results of the model in various experiments on different datasets.
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@@ -4614,29 +4609,29 @@ Results of the model in various experiments on different datasets.
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@@ -4646,63 +4641,63 @@ Results of the model in various experiments on different datasets.
},
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},
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},
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@@ -4710,46 +4705,46 @@ Results of the model in various experiments on different datasets.
},
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@@ -4758,46 +4753,46 @@ Results of the model in various experiments on different datasets.
},
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},
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@@ -4806,46 +4801,46 @@ Results of the model in various experiments on different datasets.
},
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+ "recall-0.05": 0.6,
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@@ -4854,14 +4849,14 @@ Results of the model in various experiments on different datasets.
},
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"recall-0.25": 1.0,
@@ -4871,378 +4866,380 @@ Results of the model in various experiments on different datasets.
"body_errors": {
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- "mean": 0.030619,
- "median": 0.023676,
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},
"face": {
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},
"hand": {
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}
},
"joint_recalls": {
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- "recall-0.025": 0.38254,
- "recall-0.05": 0.69548,
- "recall-0.1": 0.90996,
- "recall-0.15": 0.96386,
- "recall-0.25": 0.98581,
+ "recall-0.025": 0.3825,
+ "recall-0.05": 0.69544,
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+ "recall-0.25": 0.9857,
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}
}
{
"total_parts": 2800,
- "correct_parts": 2791,
- "pcp": 0.996786
+ "correct_parts": 2790,
+ "pcp": 0.996429
}
```
### EgoHumans
-(tagging)
+##### tagging
+
```json
{
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- "avg_time_3d": 0.0010031461715698242,
- "avg_fps": 6.106866494558892
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+ "avg_time_3d": 0.0009679979748196072,
+ "avg_fps": 6.655202410170138
}
{
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+ "total_preds": 355,
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- "invalid_preds": 7,
- "missing": 48,
- "invalid_fraction": 0.0195,
- "precision": 0.9805,
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- "f1": 0.92754,
- "non_empty": 359
+ "valid_preds": 353,
+ "invalid_preds": 2,
+ "missing": 47,
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+ "precision": 0.99437,
+ "recall": 0.8825,
+ "f1": 0.9351,
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},
"mpjpe": {
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- "num_labels": 400,
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- "ap-0.5": 0.879958
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- "head": {
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+ "recall-0.5": 0.8825,
+ "num_labels": 400,
+ "ap-0.025": 0.037299,
+ "ap-0.05": 0.685899,
+ "ap-0.1": 0.865057,
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+ "ap-0.25": 0.8825,
+ "ap-0.5": 0.8825
+ },
+ "head": {
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+ "count": 353,
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- "sem": 0.001691,
- "min": 0.002855,
- "max": 0.337234,
- "recall-0.025": 0.2375,
- "recall-0.05": 0.63,
- "recall-0.1": 0.8275,
- "recall-0.15": 0.875,
- "recall-0.25": 0.8775,
- "recall-0.5": 0.88,
- "num_labels": 400
- },
- "knee_left": {
- "count": 352,
- "mean": 0.026271,
- "median": 0.022014,
- "std": 0.016314,
- "sem": 0.000871,
- "min": 0.00144,
- "max": 0.108472,
- "recall-0.025": 0.5125,
- "recall-0.05": 0.815,
- "recall-0.1": 0.8775,
- "recall-0.15": 0.88,
"recall-0.25": 0.88,
"recall-0.5": 0.88,
"num_labels": 400
},
- "knee_right": {
+ "elbow_right": {
+ "count": 353,
+ "mean": 0.037493,
+ "median": 0.029876,
+ "std": 0.04065,
+ "sem": 0.002167,
+ "min": 0.004951,
+ "max": 0.494585,
+ "recall-0.025": 0.3575,
+ "recall-0.05": 0.7225,
+ "recall-0.1": 0.8475,
+ "recall-0.15": 0.87,
+ "recall-0.25": 0.8775,
+ "recall-0.5": 0.8825,
+ "num_labels": 400
+ },
+ "wrist_left": {
+ "count": 353,
+ "mean": 0.032765,
+ "median": 0.025804,
+ "std": 0.031045,
+ "sem": 0.001655,
+ "min": 0.001774,
+ "max": 0.297486,
+ "recall-0.025": 0.4125,
+ "recall-0.05": 0.78,
+ "recall-0.1": 0.845,
+ "recall-0.15": 0.8725,
+ "recall-0.25": 0.8775,
+ "recall-0.5": 0.8825,
+ "num_labels": 400
+ },
+ "wrist_right": {
"count": 352,
- "mean": 0.031668,
- "median": 0.024086,
- "std": 0.037861,
- "sem": 0.002021,
- "min": 0.000614,
- "max": 0.466897,
- "recall-0.025": 0.4625,
- "recall-0.05": 0.79,
- "recall-0.1": 0.865,
- "recall-0.15": 0.8675,
- "recall-0.25": 0.8725,
+ "mean": 0.036987,
+ "median": 0.022997,
+ "std": 0.05094,
+ "sem": 0.002719,
+ "min": 0.000935,
+ "max": 0.394496,
+ "recall-0.025": 0.4825,
+ "recall-0.05": 0.7225,
+ "recall-0.1": 0.835,
+ "recall-0.15": 0.8525,
+ "recall-0.25": 0.8625,
"recall-0.5": 0.88,
"num_labels": 400
},
+ "hip_left": {
+ "count": 353,
+ "mean": 0.046009,
+ "median": 0.037949,
+ "std": 0.033242,
+ "sem": 0.001772,
+ "min": 0.006021,
+ "max": 0.347255,
+ "recall-0.025": 0.2275,
+ "recall-0.05": 0.6225,
+ "recall-0.1": 0.815,
+ "recall-0.15": 0.8775,
+ "recall-0.25": 0.88,
+ "recall-0.5": 0.8825,
+ "num_labels": 400
+ },
+ "hip_right": {
+ "count": 353,
+ "mean": 0.043704,
+ "median": 0.035638,
+ "std": 0.031666,
+ "sem": 0.001688,
+ "min": 0.003099,
+ "max": 0.3352,
+ "recall-0.025": 0.225,
+ "recall-0.05": 0.64,
+ "recall-0.1": 0.83,
+ "recall-0.15": 0.8775,
+ "recall-0.25": 0.88,
+ "recall-0.5": 0.8825,
+ "num_labels": 400
+ },
+ "knee_left": {
+ "count": 353,
+ "mean": 0.026158,
+ "median": 0.022194,
+ "std": 0.016673,
+ "sem": 0.000889,
+ "min": 0.001406,
+ "max": 0.12144,
+ "recall-0.025": 0.5125,
+ "recall-0.05": 0.8175,
+ "recall-0.1": 0.8775,
+ "recall-0.15": 0.8825,
+ "recall-0.25": 0.8825,
+ "recall-0.5": 0.8825,
+ "num_labels": 400
+ },
+ "knee_right": {
+ "count": 353,
+ "mean": 0.031454,
+ "median": 0.023803,
+ "std": 0.037654,
+ "sem": 0.002007,
+ "min": 0.004796,
+ "max": 0.467974,
+ "recall-0.025": 0.4775,
+ "recall-0.05": 0.795,
+ "recall-0.1": 0.8675,
+ "recall-0.15": 0.87,
+ "recall-0.25": 0.875,
+ "recall-0.5": 0.8825,
+ "num_labels": 400
+ },
"ankle_left": {
- "count": 352,
- "mean": 0.038157,
- "median": 0.030181,
- "std": 0.040799,
- "sem": 0.002178,
- "min": 0.002653,
- "max": 0.45785,
+ "count": 353,
+ "mean": 0.03851,
+ "median": 0.029955,
+ "std": 0.04292,
+ "sem": 0.002288,
+ "min": 0.003123,
+ "max": 0.459378,
"recall-0.025": 0.3325,
- "recall-0.05": 0.7325,
+ "recall-0.05": 0.73,
"recall-0.1": 0.855,
- "recall-0.15": 0.8675,
+ "recall-0.15": 0.87,
"recall-0.25": 0.8725,
- "recall-0.5": 0.88,
+ "recall-0.5": 0.8825,
"num_labels": 400
},
"ankle_right": {
- "count": 351,
- "mean": 0.049452,
- "median": 0.035286,
- "std": 0.049636,
- "sem": 0.002653,
- "min": 0.002213,
- "max": 0.409273,
- "recall-0.025": 0.225,
- "recall-0.05": 0.605,
- "recall-0.1": 0.7875,
- "recall-0.15": 0.8525,
- "recall-0.25": 0.8625,
- "recall-0.5": 0.8775,
+ "count": 352,
+ "mean": 0.048881,
+ "median": 0.035266,
+ "std": 0.048,
+ "sem": 0.002562,
+ "min": 0.004365,
+ "max": 0.409384,
+ "recall-0.025": 0.2425,
+ "recall-0.05": 0.6025,
+ "recall-0.1": 0.795,
+ "recall-0.15": 0.855,
+ "recall-0.25": 0.8675,
+ "recall-0.5": 0.88,
"num_labels": 400
},
"joint_recalls": {
"num_labels": 5200,
- "recall-0.025": 0.32788,
- "recall-0.05": 0.70308,
- "recall-0.1": 0.84769,
- "recall-0.15": 0.86981,
- "recall-0.25": 0.87404,
- "recall-0.5": 0.87923
+ "recall-0.025": 0.34154,
+ "recall-0.05": 0.71077,
+ "recall-0.1": 0.85038,
+ "recall-0.15": 0.87231,
+ "recall-0.25": 0.87635,
+ "recall-0.5": 0.88192
}
}
{
"total_parts": 5600,
- "correct_parts": 4880,
- "pcp": 0.871429
+ "correct_parts": 4893,
+ "pcp": 0.87375
}
```
-(legoassemble)
+##### legoassemble
+
```json
{
- "avg_time_2d": 0.16489756859100616,
- "avg_time_3d": 0.0009790734127835112,
- "avg_fps": 6.028576343962604
+ "avg_time_2d": 0.1481909300829913,
+ "avg_time_3d": 0.0009748033575109533,
+ "avg_fps": 6.703952556227466
}
{
"person_nums": {
"total_frames": 121,
"total_labels": 363,
- "total_preds": 369,
+ "total_preds": 363,
"considered_empty": 0,
"valid_preds": 363,
- "invalid_preds": 6,
+ "invalid_preds": 0,
"missing": 0,
- "invalid_fraction": 0.01626,
- "precision": 0.98374,
+ "invalid_fraction": 0.0,
+ "precision": 1.0,
"recall": 1.0,
- "f1": 0.9918,
- "non_empty": 369
+ "f1": 1.0,
+ "non_empty": 363
},
"mpjpe": {
"count": 363,
- "mean": 0.030185,
- "median": 0.028973,
- "std": 0.008398,
- "sem": 0.000441,
- "min": 0.013843,
- "max": 0.086047,
- "recall-0.025": 0.283747,
+ "mean": 0.029236,
+ "median": 0.027834,
+ "std": 0.008449,
+ "sem": 0.000444,
+ "min": 0.013702,
+ "max": 0.065791,
+ "recall-0.025": 0.341598,
"recall-0.05": 0.975207,
"recall-0.1": 1.0,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
"num_labels": 363,
- "ap-0.025": 0.08155,
- "ap-0.05": 0.962913,
- "ap-0.1": 0.999647,
- "ap-0.15": 0.999647,
- "ap-0.25": 0.999647,
- "ap-0.5": 0.999647
+ "ap-0.025": 0.119962,
+ "ap-0.05": 0.963366,
+ "ap-0.1": 1.0,
+ "ap-0.15": 1.0,
+ "ap-0.25": 1.0,
+ "ap-0.5": 1.0
},
"head": {
"count": 363,
- "mean": 0.030491,
- "median": 0.025346,
- "std": 0.019468,
- "sem": 0.001023,
- "min": 0.001475,
- "max": 0.1476,
- "recall-0.025": 0.490358,
- "recall-0.05": 0.842975,
- "recall-0.1": 0.997245,
+ "mean": 0.029497,
+ "median": 0.024784,
+ "std": 0.01864,
+ "sem": 0.00098,
+ "min": 0.00309,
+ "max": 0.090403,
+ "recall-0.025": 0.509642,
+ "recall-0.05": 0.862259,
+ "recall-0.1": 1.0,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -5250,30 +5247,30 @@ Results of the model in various experiments on different datasets.
},
"shoulder_left": {
"count": 363,
- "mean": 0.033709,
- "median": 0.027672,
- "std": 0.021187,
- "sem": 0.001114,
- "min": 0.003916,
- "max": 0.145934,
- "recall-0.025": 0.426997,
- "recall-0.05": 0.831956,
+ "mean": 0.03253,
+ "median": 0.0262,
+ "std": 0.021667,
+ "sem": 0.001139,
+ "min": 0.003877,
+ "max": 0.153738,
+ "recall-0.025": 0.454545,
+ "recall-0.05": 0.84022,
"recall-0.1": 0.977961,
- "recall-0.15": 1.0,
+ "recall-0.15": 0.997245,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
"num_labels": 363
},
"shoulder_right": {
"count": 363,
- "mean": 0.030587,
- "median": 0.029255,
- "std": 0.015422,
- "sem": 0.000811,
- "min": 0.003011,
- "max": 0.108366,
- "recall-0.025": 0.393939,
- "recall-0.05": 0.895317,
+ "mean": 0.029017,
+ "median": 0.027969,
+ "std": 0.014883,
+ "sem": 0.000782,
+ "min": 0.002833,
+ "max": 0.10701,
+ "recall-0.025": 0.435262,
+ "recall-0.05": 0.911846,
"recall-0.1": 0.997245,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
@@ -5282,13 +5279,13 @@ Results of the model in various experiments on different datasets.
},
"elbow_left": {
"count": 363,
- "mean": 0.023632,
- "median": 0.020758,
- "std": 0.016124,
- "sem": 0.000847,
- "min": 0.000934,
- "max": 0.23055,
- "recall-0.025": 0.655647,
+ "mean": 0.023168,
+ "median": 0.020768,
+ "std": 0.01597,
+ "sem": 0.000839,
+ "min": 0.003257,
+ "max": 0.230605,
+ "recall-0.025": 0.647383,
"recall-0.05": 0.969697,
"recall-0.1": 0.997245,
"recall-0.15": 0.997245,
@@ -5298,15 +5295,15 @@ Results of the model in various experiments on different datasets.
},
"elbow_right": {
"count": 363,
- "mean": 0.021278,
- "median": 0.019411,
- "std": 0.013729,
- "sem": 0.000722,
- "min": 0.001225,
- "max": 0.117625,
- "recall-0.025": 0.738292,
+ "mean": 0.020239,
+ "median": 0.018368,
+ "std": 0.012649,
+ "sem": 0.000665,
+ "min": 0.001404,
+ "max": 0.11648,
+ "recall-0.025": 0.768595,
"recall-0.05": 0.977961,
- "recall-0.1": 0.991736,
+ "recall-0.1": 0.99449,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -5314,47 +5311,47 @@ Results of the model in various experiments on different datasets.
},
"wrist_left": {
"count": 363,
- "mean": 0.026442,
- "median": 0.021244,
- "std": 0.022425,
- "sem": 0.001179,
- "min": 0.002722,
- "max": 0.236533,
- "recall-0.025": 0.61708,
- "recall-0.05": 0.906336,
+ "mean": 0.024941,
+ "median": 0.019975,
+ "std": 0.021321,
+ "sem": 0.001121,
+ "min": 0.001322,
+ "max": 0.236003,
+ "recall-0.025": 0.672176,
+ "recall-0.05": 0.933884,
"recall-0.1": 0.991736,
- "recall-0.15": 0.991736,
+ "recall-0.15": 0.99449,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
"num_labels": 363
},
"wrist_right": {
"count": 363,
- "mean": 0.02119,
- "median": 0.017154,
- "std": 0.019268,
- "sem": 0.001013,
- "min": 0.00099,
- "max": 0.294118,
+ "mean": 0.020419,
+ "median": 0.016684,
+ "std": 0.015538,
+ "sem": 0.000817,
+ "min": 0.001359,
+ "max": 0.166627,
"recall-0.025": 0.730028,
"recall-0.05": 0.964187,
- "recall-0.1": 0.997245,
+ "recall-0.1": 0.99449,
"recall-0.15": 0.997245,
- "recall-0.25": 0.997245,
+ "recall-0.25": 1.0,
"recall-0.5": 1.0,
"num_labels": 363
},
"hip_left": {
"count": 363,
- "mean": 0.043545,
- "median": 0.041298,
- "std": 0.019574,
- "sem": 0.001029,
- "min": 0.002097,
- "max": 0.123427,
- "recall-0.025": 0.15978,
- "recall-0.05": 0.694215,
- "recall-0.1": 0.986226,
+ "mean": 0.042888,
+ "median": 0.040724,
+ "std": 0.019565,
+ "sem": 0.001028,
+ "min": 0.003411,
+ "max": 0.112183,
+ "recall-0.025": 0.181818,
+ "recall-0.05": 0.727273,
+ "recall-0.1": 0.991736,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -5362,15 +5359,15 @@ Results of the model in various experiments on different datasets.
},
"hip_right": {
"count": 363,
- "mean": 0.040411,
- "median": 0.036517,
- "std": 0.020654,
- "sem": 0.001086,
- "min": 0.002313,
- "max": 0.133689,
- "recall-0.025": 0.206612,
- "recall-0.05": 0.730028,
- "recall-0.1": 0.991736,
+ "mean": 0.039792,
+ "median": 0.036989,
+ "std": 0.020807,
+ "sem": 0.001094,
+ "min": 0.004596,
+ "max": 0.133553,
+ "recall-0.025": 0.247934,
+ "recall-0.05": 0.746556,
+ "recall-0.1": 0.986226,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
@@ -5378,30 +5375,30 @@ Results of the model in various experiments on different datasets.
},
"knee_left": {
"count": 363,
- "mean": 0.032471,
- "median": 0.023837,
- "std": 0.027178,
- "sem": 0.001428,
- "min": 0.004028,
- "max": 0.161111,
- "recall-0.025": 0.53168,
- "recall-0.05": 0.842975,
- "recall-0.1": 0.958678,
- "recall-0.15": 0.99449,
+ "mean": 0.032229,
+ "median": 0.022692,
+ "std": 0.02766,
+ "sem": 0.001454,
+ "min": 0.003214,
+ "max": 0.15128,
+ "recall-0.025": 0.556474,
+ "recall-0.05": 0.85124,
+ "recall-0.1": 0.950413,
+ "recall-0.15": 0.997245,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
"num_labels": 363
},
"knee_right": {
"count": 363,
- "mean": 0.026508,
- "median": 0.022923,
- "std": 0.015493,
- "sem": 0.000814,
- "min": 0.003672,
- "max": 0.084957,
- "recall-0.025": 0.53719,
- "recall-0.05": 0.917355,
+ "mean": 0.025683,
+ "median": 0.022733,
+ "std": 0.01495,
+ "sem": 0.000786,
+ "min": 0.001921,
+ "max": 0.084441,
+ "recall-0.025": 0.561983,
+ "recall-0.05": 0.92011,
"recall-0.1": 1.0,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
@@ -5410,30 +5407,30 @@ Results of the model in various experiments on different datasets.
},
"ankle_left": {
"count": 363,
- "mean": 0.032367,
- "median": 0.026819,
- "std": 0.023367,
- "sem": 0.001228,
- "min": 0.001118,
- "max": 0.167208,
- "recall-0.025": 0.4573,
- "recall-0.05": 0.842975,
- "recall-0.1": 0.975207,
- "recall-0.15": 0.99449,
+ "mean": 0.030772,
+ "median": 0.024894,
+ "std": 0.020907,
+ "sem": 0.001099,
+ "min": 0.001513,
+ "max": 0.148207,
+ "recall-0.025": 0.506887,
+ "recall-0.05": 0.859504,
+ "recall-0.1": 0.983471,
+ "recall-0.15": 1.0,
"recall-0.25": 1.0,
"recall-0.5": 1.0,
"num_labels": 363
},
"ankle_right": {
"count": 363,
- "mean": 0.029771,
- "median": 0.024989,
- "std": 0.02062,
- "sem": 0.001084,
- "min": 0.002353,
- "max": 0.160106,
- "recall-0.025": 0.501377,
- "recall-0.05": 0.878788,
+ "mean": 0.028893,
+ "median": 0.024299,
+ "std": 0.020574,
+ "sem": 0.001081,
+ "min": 0.002098,
+ "max": 0.160231,
+ "recall-0.025": 0.534435,
+ "recall-0.05": 0.884298,
"recall-0.1": 0.986226,
"recall-0.15": 0.997245,
"recall-0.25": 1.0,
@@ -5442,75 +5439,76 @@ Results of the model in various experiments on different datasets.
},
"joint_recalls": {
"num_labels": 4719,
- "recall-0.025": 0.49396,
- "recall-0.05": 0.86734,
- "recall-0.1": 0.98729,
- "recall-0.15": 0.99682,
- "recall-0.25": 0.99958,
+ "recall-0.025": 0.52214,
+ "recall-0.05": 0.87921,
+ "recall-0.1": 0.98707,
+ "recall-0.15": 0.99725,
+ "recall-0.25": 1.0,
"recall-0.5": 1.0
}
}
{
"total_parts": 5082,
- "correct_parts": 5080,
- "pcp": 0.999606
+ "correct_parts": 5081,
+ "pcp": 0.999803
}
```
-(fencing)
+##### fencing
+
```json
{
- "avg_time_2d": 0.2504721008814298,
- "avg_time_3d": 0.002506994284116305,
- "avg_fps": 3.9528957890596197
+ "avg_time_2d": 0.2371039772645021,
+ "avg_time_3d": 0.002369839411515456,
+ "avg_fps": 4.175821865957451
}
{
"person_nums": {
"total_frames": 166,
"total_labels": 332,
- "total_preds": 916,
+ "total_preds": 925,
"considered_empty": 0,
"valid_preds": 332,
- "invalid_preds": 584,
+ "invalid_preds": 593,
"missing": 0,
- "invalid_fraction": 0.63755,
- "precision": 0.36245,
+ "invalid_fraction": 0.64108,
+ "precision": 0.35892,
"recall": 1.0,
- "f1": 0.53205,
- "non_empty": 916
+ "f1": 0.52824,
+ "non_empty": 925
},
"mpjpe": {
"count": 332,
- "mean": 0.028478,
- "median": 0.026298,
- "std": 0.008428,
- "sem": 0.000463,
- "min": 0.015013,
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@@ -5519,30 +5517,30 @@ Results of the model in various experiments on different datasets.
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@@ -5551,15 +5549,15 @@ Results of the model in various experiments on different datasets.
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@@ -5567,14 +5565,14 @@ Results of the model in various experiments on different datasets.
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@@ -5583,31 +5581,31 @@ Results of the model in various experiments on different datasets.
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@@ -5615,15 +5613,15 @@ Results of the model in various experiments on different datasets.
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@@ -5631,15 +5629,15 @@ Results of the model in various experiments on different datasets.
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@@ -5647,14 +5645,14 @@ Results of the model in various experiments on different datasets.
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@@ -5663,15 +5661,15 @@ Results of the model in various experiments on different datasets.
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+ "recall-0.1": 1.0,
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@@ -5679,15 +5677,15 @@ Results of the model in various experiments on different datasets.
},
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@@ -5695,15 +5693,15 @@ Results of the model in various experiments on different datasets.
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@@ -5711,10 +5709,10 @@ Results of the model in various experiments on different datasets.
},
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"recall-0.5": 1.0
}
@@ -5726,77 +5724,78 @@ Results of the model in various experiments on different datasets.
}
```
-(basketball)
+##### basketball
+
```json
{
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- "avg_time_3d": 0.0014095330238342286,
- "avg_fps": 5.90006633787833
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+ "avg_time_3d": 0.0012971425056457519,
+ "avg_fps": 6.378415522070676
}
{
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+ "total_preds": 477,
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- "invalid_preds": 195,
+ "invalid_preds": 148,
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+ "invalid_fraction": 0.31027,
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- "f1": 0.77049,
- "non_empty": 524
+ "f1": 0.81537,
+ "non_empty": 477
},
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- "recall-0.05": 0.733333,
+ "mean": 0.042431,
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- "recall-0.25": 0.993939,
+ "recall-0.15": 0.99697,
+ "recall-0.25": 0.99697,
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- "ap-0.1": 0.670817,
- "ap-0.15": 0.677332,
- "ap-0.25": 0.677332,
- "ap-0.5": 0.682795
+ "ap-0.025": 0.005288,
+ "ap-0.05": 0.472824,
+ "ap-0.1": 0.709707,
+ "ap-0.15": 0.722388,
+ "ap-0.25": 0.722388,
+ "ap-0.5": 0.722388
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- "median": 0.044052,
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+ "recall-0.05": 0.618182,
+ "recall-0.1": 0.963636,
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@@ -5804,94 +5803,94 @@ Results of the model in various experiments on different datasets.
},
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+ "recall-0.15": 0.975758,
+ "recall-0.25": 0.99697,
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@@ -5900,473 +5899,475 @@ Results of the model in various experiments on different datasets.
},
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- "recall-0.05": 0.690909,
- "recall-0.1": 0.957576,
- "recall-0.15": 0.990909,
+ "mean": 0.042825,
+ "median": 0.038835,
+ "std": 0.02514,
+ "sem": 0.001388,
+ "min": 0.004561,
+ "max": 0.195982,
+ "recall-0.025": 0.245455,
+ "recall-0.05": 0.684848,
+ "recall-0.1": 0.966667,
+ "recall-0.15": 0.993939,
"recall-0.25": 0.99697,
"recall-0.5": 0.99697,
"num_labels": 330
},
"knee_left": {
"count": 329,
- "mean": 0.033741,
- "median": 0.029039,
- "std": 0.023577,
- "sem": 0.001302,
- "min": 0.000983,
- "max": 0.254384,
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- "recall-0.1": 0.981818,
- "recall-0.15": 0.993939,
- "recall-0.25": 0.993939,
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+ "sem": 0.001132,
+ "min": 0.002074,
+ "max": 0.121802,
+ "recall-0.025": 0.430303,
+ "recall-0.05": 0.80303,
+ "recall-0.1": 0.987879,
+ "recall-0.15": 0.99697,
+ "recall-0.25": 0.99697,
"recall-0.5": 0.99697,
"num_labels": 330
},
"knee_right": {
"count": 329,
- "mean": 0.035159,
- "median": 0.028309,
- "std": 0.033604,
- "sem": 0.001855,
- "min": 0.002281,
- "max": 0.477361,
- "recall-0.025": 0.436364,
+ "mean": 0.033203,
+ "median": 0.027414,
+ "std": 0.02114,
+ "sem": 0.001167,
+ "min": 0.000654,
+ "max": 0.118668,
+ "recall-0.025": 0.439394,
"recall-0.05": 0.815152,
- "recall-0.1": 0.972727,
- "recall-0.15": 0.990909,
- "recall-0.25": 0.993939,
+ "recall-0.1": 0.984848,
+ "recall-0.15": 0.99697,
+ "recall-0.25": 0.99697,
"recall-0.5": 0.99697,
"num_labels": 330
},
"ankle_left": {
- "count": 328,
- "mean": 0.031468,
- "median": 0.026176,
- "std": 0.020199,
- "sem": 0.001117,
- "min": 0.002204,
- "max": 0.158101,
- "recall-0.025": 0.448485,
- "recall-0.05": 0.863636,
- "recall-0.1": 0.975758,
- "recall-0.15": 0.990909,
- "recall-0.25": 0.993939,
- "recall-0.5": 0.993939,
+ "count": 329,
+ "mean": 0.03161,
+ "median": 0.025931,
+ "std": 0.021618,
+ "sem": 0.001194,
+ "min": 0.001687,
+ "max": 0.16244,
+ "recall-0.025": 0.472727,
+ "recall-0.05": 0.866667,
+ "recall-0.1": 0.978788,
+ "recall-0.15": 0.993939,
+ "recall-0.25": 0.99697,
+ "recall-0.5": 0.99697,
"num_labels": 330
},
"ankle_right": {
- "count": 328,
- "mean": 0.033299,
- "median": 0.028298,
- "std": 0.01954,
- "sem": 0.001081,
- "min": 0.006668,
- "max": 0.133288,
- "recall-0.025": 0.384848,
- "recall-0.05": 0.839394,
- "recall-0.1": 0.981818,
- "recall-0.15": 0.993939,
- "recall-0.25": 0.993939,
- "recall-0.5": 0.993939,
+ "count": 329,
+ "mean": 0.032908,
+ "median": 0.027387,
+ "std": 0.020224,
+ "sem": 0.001117,
+ "min": 0.006919,
+ "max": 0.121939,
+ "recall-0.025": 0.418182,
+ "recall-0.05": 0.845455,
+ "recall-0.1": 0.978788,
+ "recall-0.15": 0.99697,
+ "recall-0.25": 0.99697,
+ "recall-0.5": 0.99697,
"num_labels": 330
},
"joint_recalls": {
"num_labels": 4290,
- "recall-0.025": 0.26247,
- "recall-0.05": 0.69044,
- "recall-0.1": 0.94802,
- "recall-0.15": 0.98462,
- "recall-0.25": 0.99277,
- "recall-0.5": 0.99557
+ "recall-0.025": 0.29161,
+ "recall-0.05": 0.70256,
+ "recall-0.1": 0.95594,
+ "recall-0.15": 0.98695,
+ "recall-0.25": 0.99534,
+ "recall-0.5": 0.99697
}
}
{
"total_parts": 4620,
- "correct_parts": 4570,
- "pcp": 0.989177
+ "correct_parts": 4577,
+ "pcp": 0.990693
}
```
-(volleyball)
+##### volleyball
+
```json
{
- "avg_time_2d": 0.3441050417788394,
- "avg_time_3d": 0.0033576402578267966,
- "avg_fps": 2.878006910378003
+ "avg_time_2d": 0.3177612257433367,
+ "avg_time_3d": 0.0035632279542115357,
+ "avg_fps": 3.11211919445529
}
{
"person_nums": {
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"total_labels": 484,
- "total_preds": 633,
+ "total_preds": 639,
"considered_empty": 0,
- "valid_preds": 483,
- "invalid_preds": 150,
- "missing": 1,
- "invalid_fraction": 0.23697,
- "precision": 0.76303,
- "recall": 0.99793,
- "f1": 0.86482,
- "non_empty": 633
+ "valid_preds": 484,
+ "invalid_preds": 155,
+ "missing": 0,
+ "invalid_fraction": 0.24257,
+ "precision": 0.75743,
+ "recall": 1.0,
+ "f1": 0.86198,
+ "non_empty": 639
},
"mpjpe": {
- "count": 483,
- "mean": 0.035428,
- "median": 0.032584,
- "std": 0.013121,
- "sem": 0.000598,
- "min": 0.017908,
- "max": 0.12169,
- "recall-0.025": 0.216942,
- "recall-0.05": 0.876033,
- "recall-0.1": 0.993802,
- "recall-0.15": 0.997934,
- "recall-0.25": 0.997934,
- "recall-0.5": 0.997934,
+ "count": 484,
+ "mean": 0.033532,
+ "median": 0.030036,
+ "std": 0.014617,
+ "sem": 0.000665,
+ "min": 0.016099,
+ "max": 0.146128,
+ "recall-0.025": 0.322314,
+ "recall-0.05": 0.886364,
+ "recall-0.1": 0.995868,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
"num_labels": 484,
- "ap-0.025": 0.041041,
- "ap-0.05": 0.626763,
- "ap-0.1": 0.797009,
- "ap-0.15": 0.803699,
- "ap-0.25": 0.803699,
- "ap-0.5": 0.803699
+ "ap-0.025": 0.088374,
+ "ap-0.05": 0.640853,
+ "ap-0.1": 0.800023,
+ "ap-0.15": 0.805685,
+ "ap-0.25": 0.805685,
+ "ap-0.5": 0.805685
},
"head": {
- "count": 483,
- "mean": 0.036562,
- "median": 0.030386,
- "std": 0.022881,
- "sem": 0.001042,
- "min": 0.004382,
- "max": 0.136584,
- "recall-0.025": 0.392562,
- "recall-0.05": 0.739669,
- "recall-0.1": 0.979339,
- "recall-0.15": 0.997934,
- "recall-0.25": 0.997934,
- "recall-0.5": 0.997934,
+ "count": 484,
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+ "min": 0.002192,
+ "max": 0.108731,
+ "recall-0.025": 0.444215,
+ "recall-0.05": 0.770661,
+ "recall-0.1": 0.997934,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
"num_labels": 484
},
"shoulder_left": {
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- "mean": 0.034722,
- "median": 0.029123,
- "std": 0.020485,
- "sem": 0.000933,
- "min": 0.004214,
- "max": 0.142662,
- "recall-0.025": 0.369835,
- "recall-0.05": 0.81405,
+ "count": 484,
+ "mean": 0.034665,
+ "median": 0.028747,
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+ "sem": 0.000905,
+ "min": 0.005085,
+ "max": 0.139146,
+ "recall-0.025": 0.380165,
+ "recall-0.05": 0.811983,
"recall-0.1": 0.987603,
- "recall-0.15": 0.997934,
- "recall-0.25": 0.997934,
- "recall-0.5": 0.997934,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
"num_labels": 484
},
"shoulder_right": {
- "count": 483,
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- "sem": 0.000838,
- "min": 0.00347,
- "max": 0.110679,
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- "recall-0.05": 0.826446,
- "recall-0.1": 0.993802,
- "recall-0.15": 0.997934,
- "recall-0.25": 0.997934,
- "recall-0.5": 0.997934,
+ "count": 484,
+ "mean": 0.032068,
+ "median": 0.028047,
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+ "sem": 0.000842,
+ "min": 0.003465,
+ "max": 0.113248,
+ "recall-0.025": 0.431818,
+ "recall-0.05": 0.830579,
+ "recall-0.1": 0.995868,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
"num_labels": 484
},
"elbow_left": {
- "count": 483,
- "mean": 0.035706,
- "median": 0.029341,
- "std": 0.024522,
- "sem": 0.001117,
- "min": 0.004211,
- "max": 0.237523,
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- "recall-0.05": 0.77686,
- "recall-0.1": 0.981405,
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- "recall-0.5": 0.997934,
+ "count": 484,
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+ "min": 0.003353,
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+ "recall-0.05": 0.81405,
+ "recall-0.1": 0.985537,
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+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
"num_labels": 484
},
"elbow_right": {
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- "min": 0.003944,
- "max": 0.362552,
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- "recall-0.05": 0.774793,
+ "mean": 0.035603,
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+ "min": 0.003971,
+ "max": 0.295815,
+ "recall-0.025": 0.431818,
+ "recall-0.05": 0.809917,
"recall-0.1": 0.964876,
- "recall-0.15": 0.981405,
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+ "recall-0.15": 0.987603,
+ "recall-0.25": 0.995868,
"recall-0.5": 0.997934,
"num_labels": 484
},
"wrist_left": {
- "count": 483,
- "mean": 0.047139,
- "median": 0.03772,
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- "sem": 0.00162,
- "min": 0.003421,
- "max": 0.314461,
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- "recall-0.05": 0.669421,
- "recall-0.1": 0.923554,
- "recall-0.15": 0.979339,
- "recall-0.25": 0.995868,
- "recall-0.5": 0.997934,
+ "count": 484,
+ "mean": 0.04083,
+ "median": 0.03162,
+ "std": 0.031707,
+ "sem": 0.001443,
+ "min": 0.004432,
+ "max": 0.298529,
+ "recall-0.025": 0.357438,
+ "recall-0.05": 0.737603,
+ "recall-0.1": 0.954545,
+ "recall-0.15": 0.991736,
+ "recall-0.25": 0.997934,
+ "recall-0.5": 1.0,
"num_labels": 484
},
"wrist_right": {
- "count": 482,
- "mean": 0.047946,
- "median": 0.036566,
- "std": 0.041584,
- "sem": 0.001896,
- "min": 0.002446,
- "max": 0.39908,
- "recall-0.025": 0.289256,
- "recall-0.05": 0.652893,
- "recall-0.1": 0.919421,
- "recall-0.15": 0.969008,
- "recall-0.25": 0.987603,
- "recall-0.5": 0.995868,
+ "count": 481,
+ "mean": 0.040278,
+ "median": 0.032104,
+ "std": 0.034452,
+ "sem": 0.001573,
+ "min": 0.002532,
+ "max": 0.342783,
+ "recall-0.025": 0.338843,
+ "recall-0.05": 0.762397,
+ "recall-0.1": 0.942149,
+ "recall-0.15": 0.977273,
+ "recall-0.25": 0.989669,
+ "recall-0.5": 0.993802,
"num_labels": 484
},
"hip_left": {
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- "median": 0.036117,
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- "min": 0.005977,
- "max": 0.133493,
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- "recall-0.05": 0.774793,
+ "count": 484,
+ "mean": 0.036552,
+ "median": 0.033883,
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+ "sem": 0.00087,
+ "min": 0.002281,
+ "max": 0.132653,
+ "recall-0.025": 0.278926,
+ "recall-0.05": 0.807851,
"recall-0.1": 0.985537,
- "recall-0.15": 0.997934,
- "recall-0.25": 0.997934,
- "recall-0.5": 0.997934,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
"num_labels": 484
},
"hip_right": {
- "count": 483,
- "mean": 0.040711,
- "median": 0.033421,
- "std": 0.026405,
- "sem": 0.001203,
- "min": 0.005419,
- "max": 0.149838,
- "recall-0.025": 0.291322,
- "recall-0.05": 0.75,
- "recall-0.1": 0.958678,
+ "count": 484,
+ "mean": 0.041649,
+ "median": 0.032928,
+ "std": 0.028497,
+ "sem": 0.001297,
+ "min": 0.003156,
+ "max": 0.150148,
+ "recall-0.025": 0.293388,
+ "recall-0.05": 0.764463,
+ "recall-0.1": 0.946281,
"recall-0.15": 0.997934,
- "recall-0.25": 0.997934,
- "recall-0.5": 0.997934,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
"num_labels": 484
},
"knee_left": {
- "count": 483,
- "mean": 0.025004,
- "median": 0.0211,
- "std": 0.018866,
- "sem": 0.000859,
- "min": 0.001613,
- "max": 0.27912,
- "recall-0.025": 0.595041,
- "recall-0.05": 0.93595,
- "recall-0.1": 0.993802,
+ "count": 484,
+ "mean": 0.023332,
+ "median": 0.019111,
+ "std": 0.019579,
+ "sem": 0.000891,
+ "min": 0.000898,
+ "max": 0.230698,
+ "recall-0.025": 0.704545,
+ "recall-0.05": 0.923554,
+ "recall-0.1": 0.995868,
"recall-0.15": 0.995868,
- "recall-0.25": 0.995868,
- "recall-0.5": 0.997934,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
"num_labels": 484
},
"knee_right": {
- "count": 483,
- "mean": 0.025695,
- "median": 0.022859,
- "std": 0.014864,
- "sem": 0.000677,
- "min": 0.001415,
- "max": 0.094234,
- "recall-0.025": 0.576446,
- "recall-0.05": 0.92562,
+ "count": 484,
+ "mean": 0.024527,
+ "median": 0.020468,
+ "std": 0.01538,
+ "sem": 0.0007,
+ "min": 0.003418,
+ "max": 0.101846,
+ "recall-0.025": 0.628099,
+ "recall-0.05": 0.923554,
"recall-0.1": 0.997934,
- "recall-0.15": 0.997934,
- "recall-0.25": 0.997934,
- "recall-0.5": 0.997934,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
"num_labels": 484
},
"ankle_left": {
- "count": 483,
- "mean": 0.028829,
- "median": 0.023676,
- "std": 0.033788,
- "sem": 0.001539,
- "min": 0.001752,
- "max": 0.497796,
- "recall-0.025": 0.530992,
- "recall-0.05": 0.92562,
- "recall-0.1": 0.981405,
- "recall-0.15": 0.991736,
- "recall-0.25": 0.993802,
- "recall-0.5": 0.997934,
+ "count": 484,
+ "mean": 0.027217,
+ "median": 0.023029,
+ "std": 0.030318,
+ "sem": 0.001379,
+ "min": 0.00176,
+ "max": 0.49629,
+ "recall-0.025": 0.56405,
+ "recall-0.05": 0.931818,
+ "recall-0.1": 0.991736,
+ "recall-0.15": 0.995868,
+ "recall-0.25": 0.995868,
+ "recall-0.5": 1.0,
"num_labels": 484
},
"ankle_right": {
- "count": 483,
- "mean": 0.028136,
- "median": 0.02508,
- "std": 0.016445,
- "sem": 0.000749,
- "min": 0.001964,
- "max": 0.103825,
- "recall-0.025": 0.495868,
- "recall-0.05": 0.890496,
- "recall-0.1": 0.993802,
- "recall-0.15": 0.997934,
- "recall-0.25": 0.997934,
- "recall-0.5": 0.997934,
+ "count": 484,
+ "mean": 0.026743,
+ "median": 0.024481,
+ "std": 0.015442,
+ "sem": 0.000703,
+ "min": 0.001085,
+ "max": 0.084476,
+ "recall-0.025": 0.528926,
+ "recall-0.05": 0.909091,
+ "recall-0.1": 1.0,
+ "recall-0.15": 1.0,
+ "recall-0.25": 1.0,
+ "recall-0.5": 1.0,
"num_labels": 484
},
"joint_recalls": {
"num_labels": 6292,
- "recall-0.025": 0.40782,
- "recall-0.05": 0.8034,
- "recall-0.1": 0.9733,
- "recall-0.15": 0.99189,
- "recall-0.25": 0.99603,
- "recall-0.5": 0.99777
+ "recall-0.025": 0.4466,
+ "recall-0.05": 0.82962,
+ "recall-0.1": 0.97966,
+ "recall-0.15": 0.99507,
+ "recall-0.25": 0.99825,
+ "recall-0.5": 0.99936
}
}
{
"total_parts": 6776,
- "correct_parts": 6740,
- "pcp": 0.994687
+ "correct_parts": 6755,
+ "pcp": 0.996901
}
```
-(badminton)
+##### badminton
+
```json
{
- "avg_time_2d": 0.3149814826215623,
- "avg_time_3d": 0.0030397500605941507,
- "avg_fps": 3.144444135273953
+ "avg_time_2d": 0.2823985504966251,
+ "avg_time_3d": 0.003012508326183165,
+ "avg_fps": 3.5037184758171196
}
{
"person_nums": {
"total_frames": 183,
"total_labels": 732,
- "total_preds": 756,
+ "total_preds": 751,
"considered_empty": 0,
"valid_preds": 732,
- "invalid_preds": 24,
+ "invalid_preds": 19,
"missing": 0,
- "invalid_fraction": 0.03175,
- "precision": 0.96825,
+ "invalid_fraction": 0.0253,
+ "precision": 0.9747,
"recall": 1.0,
- "f1": 0.98387,
- "non_empty": 756
+ "f1": 0.98719,
+ "non_empty": 751
},
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@@ -6374,15 +6375,15 @@ Results of the model in various experiments on different datasets.
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@@ -6390,47 +6391,47 @@ Results of the model in various experiments on different datasets.
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@@ -6438,14 +6439,14 @@ Results of the model in various experiments on different datasets.
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@@ -6454,14 +6455,14 @@ Results of the model in various experiments on different datasets.
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@@ -6470,15 +6471,15 @@ Results of the model in various experiments on different datasets.
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@@ -6486,30 +6487,30 @@ Results of the model in various experiments on different datasets.
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@@ -6518,12 +6519,12 @@ Results of the model in various experiments on different datasets.
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}
}
{
@@ -6533,12 +6534,13 @@ Results of the model in various experiments on different datasets.
}
```
-(tennis)
+##### tennis
+
```json
{
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- "avg_time_3d": 0.0018767069654855115,
- "avg_fps": 2.7662303185137875
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+ "avg_time_3d": 0.0019383890586986876,
+ "avg_fps": 3.3880382039335584
}
{
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@@ -6557,36 +6559,36 @@ Results of the model in various experiments on different datasets.
},
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@@ -6595,15 +6597,15 @@ Results of the model in various experiments on different datasets.
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@@ -6611,14 +6613,14 @@ Results of the model in various experiments on different datasets.
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@@ -6627,15 +6629,15 @@ Results of the model in various experiments on different datasets.
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@@ -6643,14 +6645,14 @@ Results of the model in various experiments on different datasets.
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@@ -6659,14 +6661,14 @@ Results of the model in various experiments on different datasets.
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@@ -6675,30 +6677,30 @@ Results of the model in various experiments on different datasets.
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"recall-0.25": 1.0,
@@ -6707,13 +6709,13 @@ Results of the model in various experiments on different datasets.
},
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@@ -6723,14 +6725,14 @@ Results of the model in various experiments on different datasets.
},
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- "mean": 0.015798,
- "median": 0.014138,
- "std": 0.008394,
- "sem": 0.000442,
- "min": 0.001037,
- "max": 0.063199,
- "recall-0.025": 0.864641,
- "recall-0.05": 0.997238,
+ "mean": 0.014274,
+ "median": 0.012875,
+ "std": 0.007508,
+ "sem": 0.000395,
+ "min": 0.00133,
+ "max": 0.047529,
+ "recall-0.025": 0.91989,
+ "recall-0.05": 1.0,
"recall-0.1": 1.0,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
@@ -6739,14 +6741,14 @@ Results of the model in various experiments on different datasets.
},
"knee_right": {
"count": 362,
- "mean": 0.018264,
- "median": 0.015093,
- "std": 0.017603,
- "sem": 0.000926,
- "min": 0.001885,
- "max": 0.268255,
- "recall-0.025": 0.828729,
- "recall-0.05": 0.977901,
+ "mean": 0.015968,
+ "median": 0.013439,
+ "std": 0.016304,
+ "sem": 0.000858,
+ "min": 0.002223,
+ "max": 0.259639,
+ "recall-0.025": 0.88674,
+ "recall-0.05": 0.986188,
"recall-0.1": 0.994475,
"recall-0.15": 0.997238,
"recall-0.25": 0.997238,
@@ -6755,14 +6757,14 @@ Results of the model in various experiments on different datasets.
},
"ankle_left": {
"count": 362,
- "mean": 0.03303,
- "median": 0.031631,
- "std": 0.013751,
- "sem": 0.000724,
- "min": 0.004951,
- "max": 0.092434,
- "recall-0.025": 0.29558,
- "recall-0.05": 0.900552,
+ "mean": 0.027315,
+ "median": 0.025773,
+ "std": 0.010493,
+ "sem": 0.000552,
+ "min": 0.006963,
+ "max": 0.072852,
+ "recall-0.025": 0.444751,
+ "recall-0.05": 0.961326,
"recall-0.1": 1.0,
"recall-0.15": 1.0,
"recall-0.25": 1.0,
@@ -6771,15 +6773,15 @@ Results of the model in various experiments on different datasets.
},
"ankle_right": {
"count": 362,
- "mean": 0.031596,
- "median": 0.027104,
- "std": 0.030751,
- "sem": 0.001618,
- "min": 0.003664,
- "max": 0.496076,
- "recall-0.025": 0.441989,
- "recall-0.05": 0.864641,
- "recall-0.1": 0.986188,
+ "mean": 0.025082,
+ "median": 0.021269,
+ "std": 0.02841,
+ "sem": 0.001495,
+ "min": 0.001559,
+ "max": 0.492964,
+ "recall-0.025": 0.624309,
+ "recall-0.05": 0.964088,
+ "recall-0.1": 0.994475,
"recall-0.15": 0.994475,
"recall-0.25": 0.997238,
"recall-0.5": 1.0,
@@ -6787,9 +6789,9 @@ Results of the model in various experiments on different datasets.
},
"joint_recalls": {
"num_labels": 4706,
- "recall-0.025": 0.55312,
- "recall-0.05": 0.92414,
- "recall-0.1": 0.99299,
+ "recall-0.025": 0.59839,
+ "recall-0.05": 0.94411,
+ "recall-0.1": 0.99426,
"recall-0.15": 0.99745,
"recall-0.25": 0.99894,
"recall-0.5": 1.0
@@ -6797,7 +6799,7 @@ Results of the model in various experiments on different datasets.
}
{
"total_parts": 5068,
- "correct_parts": 5057,
- "pcp": 0.99783
+ "correct_parts": 5059,
+ "pcp": 0.998224
}
```
diff --git a/spt/camera.cpp b/rpt/camera.cpp
similarity index 100%
rename from spt/camera.cpp
rename to rpt/camera.cpp
diff --git a/spt/camera.hpp b/rpt/camera.hpp
similarity index 100%
rename from spt/camera.hpp
rename to rpt/camera.hpp
diff --git a/spt/interface.cpp b/rpt/interface.cpp
similarity index 100%
rename from spt/interface.cpp
rename to rpt/interface.cpp
diff --git a/spt/interface.hpp b/rpt/interface.hpp
similarity index 100%
rename from spt/interface.hpp
rename to rpt/interface.hpp
diff --git a/spt/triangulator.cpp b/rpt/triangulator.cpp
similarity index 92%
rename from spt/triangulator.cpp
rename to rpt/triangulator.cpp
index ac2b585..28e4618 100644
--- a/spt/triangulator.cpp
+++ b/rpt/triangulator.cpp
@@ -1220,8 +1220,7 @@ std::vector>> TriangulatorInte
cv::Mat &group_pose = std::get<1>(group);
// Calculate average joint distance
- float dist_sum = 0.0;
- size_t count = 0;
+ std::vector dists;
for (size_t row = 0; row < num_joints; ++row)
{
const float *pose_3d_ptr = pose_3d.ptr(row);
@@ -1236,15 +1235,20 @@ std::vector>> TriangulatorInte
float dy = pose_3d_ptr[1] - group_pose_ptr[1];
float dz = pose_3d_ptr[2] - group_pose_ptr[2];
float dist_sq = dx * dx + dy * dy + dz * dz;
- dist_sum += std::sqrt(dist_sq);
- count++;
+ dists.push_back(std::sqrt(dist_sq));
}
}
-
- if (count > 0)
+ if (dists.size() >= 5)
+ {
+ // Drop highest value to reduce influence of outliers
+ auto max_it = std::max_element(dists.begin(), dists.end());
+ dists.erase(max_it);
+ }
+ if (dists.size() > 0)
{
// Check if the average joint distance is close enough
- float avg_dist = dist_sum / count;
+ float avg_dist = std::accumulate(dists.begin(), dists.end(), 0.0);
+ avg_dist /= static_cast(dists.size());
if (avg_dist < max_joint_avg_dist && avg_dist < best_dist)
{
best_dist = avg_dist;
@@ -1310,7 +1314,113 @@ std::vector>> TriangulatorInte
}
}
- return groups;
+ // Merge close groups
+ // Depending on the inital group creation, one or more groups can be created that in the end
+ // share the same persons, even if they had a larger distance at the beginning
+ // So merge them similar to the group assignment before
+ std::vector>> merged_groups;
+ for (size_t i = 0; i < groups.size(); ++i)
+ {
+ size_t num_joints = std::get<1>(groups[i]).rows;
+ auto &group = groups[i];
+ auto &group_visible_counts = per_group_visible_counts[i];
+
+ float best_dist = std::numeric_limits::infinity();
+ int best_group = -1;
+
+ for (size_t j = 0; j < merged_groups.size(); ++j)
+ {
+ auto &merged_group = merged_groups[j];
+
+ // Calculate average joint distance
+ std::vector dists;
+ for (size_t row = 0; row < num_joints; ++row)
+ {
+ const float *group_pose_ptr = std::get<1>(group).ptr(row);
+ const float *merged_pose_ptr = std::get<1>(merged_group).ptr(row);
+
+ float score1 = group_pose_ptr[3];
+ float score2 = merged_pose_ptr[3];
+
+ if (score1 > min_score && score2 > min_score)
+ {
+ float dx = group_pose_ptr[0] - merged_pose_ptr[0];
+ float dy = group_pose_ptr[1] - merged_pose_ptr[1];
+ float dz = group_pose_ptr[2] - merged_pose_ptr[2];
+ float dist_sq = dx * dx + dy * dy + dz * dz;
+ dists.push_back(std::sqrt(dist_sq));
+ }
+ }
+ if (dists.size() >= 5)
+ {
+ // Drop highest value to reduce influence of outliers
+ auto max_it = std::max_element(dists.begin(), dists.end());
+ dists.erase(max_it);
+ }
+ if (dists.size() > 0)
+ {
+ // Check if the average joint distance is close enough
+ float avg_dist = std::accumulate(dists.begin(), dists.end(), 0.0);
+ avg_dist /= static_cast(dists.size());
+ if (avg_dist < max_joint_avg_dist && avg_dist < best_dist)
+ {
+ best_dist = avg_dist;
+ best_group = static_cast(j);
+ }
+ }
+ }
+
+ if (best_group == -1)
+ {
+ // Create a new group
+ merged_groups.push_back(group);
+ }
+ else
+ {
+ // Update existing group
+ auto &merged_group = merged_groups[best_group];
+ cv::Point3f &merged_center = std::get<0>(merged_group);
+ cv::Mat &merged_group_pose = std::get<1>(merged_group);
+ std::vector &merged_group_indices = std::get<2>(merged_group);
+
+ float n_elems1 = static_cast(merged_group_indices.size());
+ float n_elems2 = static_cast(std::get<2>(group).size());
+ float inv1 = n_elems1 / (n_elems1 + n_elems2);
+ float inv2 = n_elems2 / (n_elems1 + n_elems2);
+
+ // Update group center
+ merged_center.x = (merged_center.x * inv1 + std::get<0>(group).x * inv2);
+ merged_center.y = (merged_center.y * inv1 + std::get<0>(group).y * inv2);
+ merged_center.z = (merged_center.z * inv1 + std::get<0>(group).z * inv2);
+
+ // Update group pose
+ for (size_t row = 0; row < num_joints; ++row)
+ {
+ const float *group_pose_ptr = std::get<1>(group).ptr(row);
+ float *merged_pose_ptr = merged_group_pose.ptr(row);
+
+ if (group_pose_ptr[3] > min_score)
+ {
+ float j_elems1 = static_cast(group_visible_counts[row]);
+ float j_elems2 = static_cast(per_group_visible_counts[best_group][row]);
+ float inv1 = j_elems1 / (j_elems1 + j_elems2);
+ float inv2 = j_elems2 / (j_elems1 + j_elems2);
+
+ merged_pose_ptr[0] = (merged_pose_ptr[0] * inv1 + group_pose_ptr[0] * inv2);
+ merged_pose_ptr[1] = (merged_pose_ptr[1] * inv1 + group_pose_ptr[1] * inv2);
+ merged_pose_ptr[2] = (merged_pose_ptr[2] * inv1 + group_pose_ptr[2] * inv2);
+ merged_pose_ptr[3] = (merged_pose_ptr[3] * inv1 + group_pose_ptr[3] * inv2);
+ group_visible_counts[row]++;
+ }
+ }
+
+ // Merge indices
+ merged_group_indices.insert(
+ merged_group_indices.end(), std::get<2>(group).begin(), std::get<2>(group).end());
+ }
+ }
+
+ return merged_groups;
}
// =================================================================================================
@@ -1424,7 +1534,7 @@ cv::Mat TriangulatorInternal::merge_group(const std::vector &poses_3d,
}
// Select the best-k proposals for each joint that are closest to the initial pose
- int keep_best = 3;
+ int keep_best = std::max(3, num_poses / 3);
cv::Mat best_k_mask = cv::Mat::zeros(num_poses, num_joints, CV_8U);
for (int j = 0; j < num_joints; ++j)
{
diff --git a/spt/triangulator.hpp b/rpt/triangulator.hpp
similarity index 100%
rename from spt/triangulator.hpp
rename to rpt/triangulator.hpp
diff --git a/run_container.sh b/run_container.sh
old mode 100644
new mode 100755
index 5b4fb8d..348883b
--- a/run_container.sh
+++ b/run_container.sh
@@ -3,9 +3,9 @@
xhost +
docker run --privileged --rm --network host -it \
--gpus all --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 \
- --volume "$(pwd)"/:/SimplePoseTriangulation/ \
+ --volume "$(pwd)"/:/RapidPoseTriangulation/ \
--volume "$(pwd)"/../datasets/:/datasets/ \
--volume "$(pwd)"/skelda/:/skelda/ \
--volume /tmp/.X11-unix:/tmp/.X11-unix \
--env DISPLAY --env QT_X11_NO_MITSHM=1 \
- simpleposetriangulation
+ rapidposetriangulation
diff --git a/scripts/draw_utils.py b/scripts/draw_utils.py
deleted file mode 100644
index 1489dba..0000000
--- a/scripts/draw_utils.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-
-from skelda import utils_view
-
-# ==================================================================================================
-
-
-def show_poses2d(bodies, images, joint_names, title=""):
- num_imgs = len(images)
- rowbreak = int(num_imgs / 2.0 + 0.5)
- fig, axs = plt.subplots(2, rowbreak, figsize=(30, 20))
- fig.suptitle(title, fontsize=20)
-
- if isinstance(bodies, np.ndarray):
- bodies = bodies.tolist()
-
- # Draw skeletons into images
- for i, image in enumerate(images):
- colors = plt.cm.hsv(np.linspace(0, 1, len(bodies[i]), endpoint=False)).tolist()
- colors = [[int(c[0] * 255), int(c[1] * 255), int(c[2] * 255)] for c in colors]
-
- for j, body in enumerate(bodies[i]):
- image = utils_view.draw_body_in_image(image, body, joint_names, colors[j])
-
- # Rescale image range for plotting
- images = [img / 255.0 for img in images]
-
- if rowbreak == 1:
- axs[0].imshow(images[0])
- if len(images) == 2:
- axs[1].imshow(images[1])
- else:
- # Optionally delete last empty plot
- fig.delaxes(axs[1])
-
- else:
- for i in range(rowbreak):
- axs[0][i].imshow(images[i])
- if i + rowbreak < num_imgs:
- axs[1][i].imshow(images[i + rowbreak])
- else:
- # Optionally delete last empty plot
- fig.delaxes(axs[1][i])
-
- return fig
diff --git a/scripts/test_skelda_dataset.py b/scripts/test_skelda_dataset.py
index fe32a59..32c1dbc 100644
--- a/scripts/test_skelda_dataset.py
+++ b/scripts/test_skelda_dataset.py
@@ -10,10 +10,10 @@ import tqdm
import test_triangulate
import utils_2d_pose
-from skelda import evals, utils_pose
+from skelda import evals
-sys.path.append("/SimplePoseTriangulation/swig/")
-import spt
+sys.path.append("/RapidPoseTriangulation/swig/")
+import rpt
# ==================================================================================================
@@ -100,7 +100,7 @@ if dataset_use.endswith("_wb"):
# eval_joints[eval_joints.index("head")] = "nose"
eval_joints = list(joint_names_2d)
-# output_dir = "/SimplePoseTriangulation/data/testoutput/"
+# output_dir = "/RapidPoseTriangulation/data/testoutput/"
output_dir = ""
# ==================================================================================================
@@ -197,10 +197,6 @@ def load_labels(dataset: dict):
if take_interval > 1:
labels = [l for i, l in enumerate(labels) if i % take_interval == 0]
- # Filter joints
- fj_func = lambda x: utils_pose.filter_joints_3d(x, eval_joints)
- labels = list(map(fj_func, labels))
-
return labels
@@ -273,14 +269,16 @@ def main():
print("\nRunning predictions ...")
all_poses = []
all_ids = []
- all_paths = []
times = []
- triangulator = spt.Triangulator(min_score=minscore, min_group_size=min_group_size)
+ triangulator = rpt.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()
@@ -319,18 +317,18 @@ def main():
if sum(np.sum(p) for p in poses_2d) == 0:
poses3D = np.zeros([1, len(joint_names_3d), 4]).tolist()
else:
- spt_cameras = spt.convert_cameras(label["cameras"])
+ rpt_cameras = rpt.convert_cameras(label["cameras"])
roomparams = [label["room_size"], label["room_center"]]
poses3D = triangulator.triangulate_poses(
- poses_2d, spt_cameras, roomparams, joint_names_2d
+ poses_2d, rpt_cameras, roomparams, joint_names_2d
)
time_3d = time.time() - start
print("3D time:", time_3d)
- all_poses.append(np.array(poses3D))
+ old_index = label["index"]
+ all_poses.append(np.array(poses3D).tolist())
all_ids.append(label["id"])
- all_paths.append(label["imgpaths"])
times.append((time_2d, time_3d))
# Print per-step triangulation timings
diff --git a/scripts/test_triangulate.py b/scripts/test_triangulate.py
index 0490d60..01d8ab9 100644
--- a/scripts/test_triangulate.py
+++ b/scripts/test_triangulate.py
@@ -9,12 +9,11 @@ import cv2
import matplotlib
import numpy as np
-import draw_utils
import utils_2d_pose
-from skelda import utils_pose
+from skelda import utils_pose, utils_view
-sys.path.append("/SimplePoseTriangulation/swig/")
-import spt
+sys.path.append("/RapidPoseTriangulation/swig/")
+import rpt
# ==================================================================================================
@@ -324,14 +323,14 @@ def main():
print("2D time:", time.time() - stime)
# print([np.array(p).round(6).tolist() for p in poses_2d])
- fig1 = draw_utils.show_poses2d(
- poses_2d, np.array(images_2d), joint_names_2d, "2D detections"
+ fig1 = utils_view.draw_many_images(
+ sample["imgpaths_color"], [], [], poses_2d, joint_names_2d, "2D detections"
)
fig1.savefig(os.path.join(dirpath, "2d-k.png"), dpi=fig1.dpi)
# draw_utils.utils_view.show_plots()
if len(images_2d) == 1:
- draw_utils.utils_view.show_plots()
+ utils_view.show_plots()
continue
# Get 3D poses
@@ -339,9 +338,9 @@ def main():
poses3D = np.zeros([1, len(joint_names_3d), 4])
poses2D = np.zeros([len(images_2d), 1, len(joint_names_3d), 3])
else:
- cameras = spt.convert_cameras(camparams)
+ cameras = rpt.convert_cameras(camparams)
roomp = [roomparams["room_size"], roomparams["room_center"]]
- triangulator = spt.Triangulator(min_score=0.95)
+ triangulator = rpt.Triangulator(min_score=0.95)
stime = time.time()
poses_3d = triangulator.triangulate_poses(
@@ -361,15 +360,13 @@ def main():
# print(poses2D)
# print(poses3D.round(3).tolist())
- fig2 = draw_utils.utils_view.show_poses3d(
- poses3D, joint_names_3d, roomparams, camparams
- )
- fig3 = draw_utils.show_poses2d(
- poses2D, np.array(images_2d), joint_names_3d, "2D reprojections"
+ fig2 = utils_view.draw_poses3d(poses3D, joint_names_3d, roomparams, camparams)
+ fig3 = utils_view.draw_many_images(
+ sample["imgpaths_color"], [], [], poses2D, joint_names_3d, "2D projections"
)
fig2.savefig(os.path.join(dirpath, "3d-p.png"), dpi=fig2.dpi)
fig3.savefig(os.path.join(dirpath, "2d-p.png"), dpi=fig3.dpi)
- draw_utils.utils_view.show_plots()
+ utils_view.show_plots()
# ==================================================================================================
diff --git a/skelda b/skelda
index 87caa79..0127e9d 160000
--- a/skelda
+++ b/skelda
@@ -1 +1 @@
-Subproject commit 87caa796fdc9e338efdaca85a7994734d8221880
+Subproject commit 0127e9de87d1cdb33fac50337f18e365638e8770
diff --git a/swig/Makefile b/swig/Makefile
index 8ae5c14..cea52c3 100644
--- a/swig/Makefile
+++ b/swig/Makefile
@@ -7,7 +7,7 @@ PYTHONL = -Xlinker -export-dynamic
# Default super-target
all:
- cd ../spt/ && g++ $(FLAGS) -std=c++2a -I/usr/include/opencv4 -c *.cpp ; cd ../swig/
- swig -c++ -python -keyword -o spt_wrap.cxx spt.i
- g++ $(FLAGS) $(PYTHONI) -c spt_wrap.cxx -o spt_wrap.o
- g++ $(FLAGS) $(PYTHONL) -shared ../spt/*.o spt_wrap.o -lopencv_core -lopencv_imgproc -lopencv_calib3d -o _spt.so
+ cd ../rpt/ && g++ $(FLAGS) -std=c++2a -I/usr/include/opencv4 -c *.cpp ; cd ../swig/
+ swig -c++ -python -keyword -o rpt_wrap.cxx rpt.i
+ g++ $(FLAGS) $(PYTHONI) -c rpt_wrap.cxx -o rpt_wrap.o
+ g++ $(FLAGS) $(PYTHONL) -shared ../rpt/*.o rpt_wrap.o -lopencv_core -lopencv_imgproc -lopencv_calib3d -o _rpt.so
diff --git a/swig/spt.i b/swig/rpt.i
similarity index 93%
rename from swig/spt.i
rename to swig/rpt.i
index ce0f7ef..4bebcbf 100644
--- a/swig/spt.i
+++ b/swig/rpt.i
@@ -1,8 +1,8 @@
-%module spt
+%module rpt
%{
// Includes the header in the wrapper code
- #include "../spt/camera.hpp"
- #include "../spt/interface.hpp"
+ #include "../rpt/camera.hpp"
+ #include "../rpt/interface.hpp"
%}
// Some modules need extra imports beside the main .hpp file
@@ -47,8 +47,8 @@ namespace std {
#pragma SWIG nowarn=511
// Parse the header file to generate wrappers
-%include "../spt/camera.hpp"
-%include "../spt/interface.hpp"
+%include "../rpt/camera.hpp"
+%include "../rpt/interface.hpp"
// Add additional Python code to the module
%pythoncode %{
diff --git a/tests/test_interface.py b/tests/test_interface.py
index 631e47c..c571421 100644
--- a/tests/test_interface.py
+++ b/tests/test_interface.py
@@ -5,7 +5,7 @@ import time
import numpy as np
sys.path.append("../swig/")
-import spt
+import rpt
# ==================================================================================================
@@ -14,7 +14,7 @@ def main():
print("")
# Test camera structure
- camera = spt.Camera()
+ camera = rpt.Camera()
camera.name = "Camera 1"
camera.K = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
camera.DC = [0, 0, 0, 0, 0]
@@ -49,18 +49,18 @@ def main():
"shoulder_middle",
"head",
]
- cpath = "/SimplePoseTriangulation/data/h1/sample.json"
- ppath = "/SimplePoseTriangulation/tests/poses_h1.json"
+ cpath = "/RapidPoseTriangulation/data/h1/sample.json"
+ ppath = "/RapidPoseTriangulation/tests/poses_h1.json"
with open(cpath, "r") as file:
cdata = json.load(file)
with open(ppath, "r") as file:
pdata = json.load(file)
cams = cdata["cameras"]
poses_2d = pdata["2D"]
- cameras = spt.convert_cameras(cams)
+ cameras = rpt.convert_cameras(cams)
# Run triangulation
- triangulator = spt.Triangulator(min_score=0.95)
+ triangulator = rpt.Triangulator(min_score=0.95)
stime = time.time()
poses_3d = triangulator.triangulate_poses(
poses_2d, cameras, roomparams, joint_names
@@ -71,15 +71,15 @@ def main():
# Load input data
roomparams = [[5.6, 6.4, 2.4], [0, -0.5, 1.2]]
- cpath = "/SimplePoseTriangulation/data/p1/sample.json"
- ppath = "/SimplePoseTriangulation/tests/poses_p1.json"
+ cpath = "/RapidPoseTriangulation/data/p1/sample.json"
+ ppath = "/RapidPoseTriangulation/tests/poses_p1.json"
with open(cpath, "r") as file:
cdata = json.load(file)
with open(ppath, "r") as file:
pdata = json.load(file)
cams = cdata["cameras"]
poses_2d = pdata["2D"]
- cameras = spt.convert_cameras(cams)
+ cameras = rpt.convert_cameras(cams)
# Run triangulation
triangulator.reset()
@@ -102,15 +102,15 @@ def main():
# Load input data
roomparams = [[6.0, 5.0, 2.0], [1.5, 1.0, -0.5]]
- cpath = "/SimplePoseTriangulation/data/e1/sample.json"
- ppath = "/SimplePoseTriangulation/tests/poses_e1.json"
+ cpath = "/RapidPoseTriangulation/data/e1/sample.json"
+ ppath = "/RapidPoseTriangulation/tests/poses_e1.json"
with open(cpath, "r") as file:
cdata = json.load(file)
with open(ppath, "r") as file:
pdata = json.load(file)
cams = cdata["cameras"]
poses_2d = pdata["2D"]
- cameras = spt.convert_cameras(cams)
+ cameras = rpt.convert_cameras(cams)
# Run triangulation
triangulator.reset()
- 
- 
+ 
+
+ 
+ 