Merge branch '91-evaluation-on-external-detection-refactor' into next…

…-pack

docs/book/megapose/download_data.md

@@ -1,19 +1,31 @@
  # Download example data for minimal testing
 
-```
+```sh
 cd $HAPPYPOSE_DATA_DIR
 wget https://memmo-data.laas.fr/static/examples.tar.xz
 tar xf examples.tar.xz
 ```
 
  # Download pre-trained pose estimation models
 
-Download pose estimation models to $HAPPYPOSE_DATA_DIR/megapose-models:
+Download pose estimation models to `$HAPPYPOSE_DATA_DIR/megapose-models`:
 
-```
+```sh
 python -m happypose.toolbox.utils.download --megapose_models
 ```
 
+# Download pre-trained detection models
+Megapose can use pretrained detectors from CosyPose, which can be downloaded to `$HAPPYPOSE_DATA_DIR/experiments`:
+
+python -m happypose.toolbox.utils.download --cosypose_model detector-bop-hb-pbr--497808
+python -m happypose.toolbox.utils.download --cosypose_model detector-bop-hope-pbr--15246
+python -m happypose.toolbox.utils.download --cosypose_model detector-bop-icbin-pbr--947409
+python -m happypose.toolbox.utils.download --cosypose_model detector-bop-itodd-pbr--509908
+python -m happypose.toolbox.utils.download --cosypose_model detector-bop-lmo-pbr--517542
+python -m happypose.toolbox.utils.download --cosypose_model detector-bop-tless-pbr--873074
+python -m happypose.toolbox.utils.download --cosypose_model detector-bop-tudl-pbr--728047
+python -m happypose.toolbox.utils.download --cosypose_model detector-bop-ycbv-pbr--970850
+
 # Dataset
 
 ## Dataset information

docs/book/megapose/evaluate.md

@@ -2,17 +2,41 @@
 
 Please make sure you followed the steps relative to the evaluation in the main readme.
 
-An example to run the evaluation on `YCBV` dataset. Several datasets can be added to the list.
+## Evaluating with Megapose detector
+Run a detector part of Megapose pipeline to detect bounding boxes in the image dataset at run-time.
 
 ```
-python -m happypose.pose_estimators.megapose.src.megapose.scripts.run_full_megapose_eval detector_run_id=bop_pbr coarse_run_id=coarse-rgb-906902141 refiner_run_id=refiner-rgb-653307694 ds_names=[ycbv.bop19] result_id=fastsam_kbestdet_1posehyp detection_coarse_types=[["sam","SO3_grid"]] inference.n_pose_hypotheses=1 skip_inference=true run_bop_eval=true
+python -m happypose.pose_estimators.megapose.scripts.run_full_megapose_eval detector_run_id=bop_pbr coarse_run_id=coarse-rgb-906902141 refiner_run_id=refiner-rgb-653307694 ds_names=[ycbv.bop19,lmo.bop19,tless.bop19,tudl.bop19,icbin.bop19,hb.bop19,itodd.bop19] result_id=detector_1posehyp detection_coarse_types=[["detector","SO3_grid"]] inference.n_pose_hypotheses=1 skip_inference=false run_bop_eval=true
+```
+
+## Evaluating with external detections
+
+First step: download external detections from bop website (such as default detections for task 1 and 4). You should have one csv file for every bop dataset.
+Place these in a directory of your choice and define the environment variable.
+
+```sh
+EXTERNAL_DETECTIONS_DIR = /path/to/saved/detections/
+```
+
+Megapose expects a json file named `bop_detections_filenames.json` placed in `EXTERNAL_DETECTIONS_DIR` mapping bop dataset names to the csv file names, e.g. for CNOS detection (default detection for task 4, bop23):
+
+```json
+{
+    "ycbv": "cnos-fastsam_ycbv-test_f4f2127c-6f59-447c-95b3-28e1e591f1a1.json",
+    "lmo": "cnos-fastsam_lmo-test_3cb298ea-e2eb-4713-ae9e-5a7134c5da0f.json",
+    "tless": "cnos-fastsam_tless-test_8ca61cb0-4472-4f11-bce7-1362a12d396f.json",
+    "tudl": "cnos-fastsam_tudl-test_c48a2a95-1b41-4a51-9920-a667cb3d7149.json",
+    "icbin": "cnos-fastsam_icbin-test_f21a9faf-7ef2-4325-885f-f4b6460f4432.json",
+    "itodd": "cnos-fastsam_itodd-test_df32d45b-301c-4fc9-8769-797904dd9325.json",
+    "hb": "cnos-fastsam_hb-test_db836947-020a-45bd-8ec5-c95560b68011.json"
+}
 ```
 
 To reproduce the results we obtained for the BOP-Challenge, please run the following commands :
 
 ```sh
 # RGB 1 hyp
-python -m happypose.pose_estimators.megapose.src.megapose.scripts.run_full_megapose_eval detector_run_id=bop_pbr coarse_run_id=coarse-rgb-906902141 refiner_run_id=refiner-rgb-653307694 ds_names=[ycbv.bop19,lmo.bop19,tless.bop19,tudl.bop19,icbin.bop19,hb.bop19,itodd.bop19] result_id=fastsam_kbestdet_1posehyp detection_coarse_types=[["sam","SO3_grid"]] inference.n_pose_hypotheses=1 skip_inference=False run_bop_eval=true
+python -m happypose.pose_estimators.megapose.scripts.run_full_megapose_eval coarse_run_id=coarse-rgb-906902141 refiner_run_id=refiner-rgb-653307694 ds_names=[ycbv.bop19,lmo.bop19,tless.bop19,tudl.bop19,icbin.bop19,hb.bop19,itodd.bop19] result_id=exte_det_1posehyp detection_coarse_types=[["exte","SO3_grid"]] inference.n_pose_hypotheses=1 skip_inference=False run_bop_eval=true
 ```
 
 Results :
@@ -22,7 +46,7 @@ Results :
 
 ```sh
 # RGB 5 hyp
-python -m happypose.pose_estimators.megapose.src.megapose.scripts.run_full_megapose_eval detector_run_id=bop_pbr coarse_run_id=coarse-rgb-906902141 refiner_run_id=refiner-rgb-653307694 ds_names=[ycbv.bop19,lmo.bop19,tless.bop19,tudl.bop19,icbin.bop19,hb.bop19,itodd.bop19] result_id=fastsam_kbestdet_5posehyp detection_coarse_types=[["sam","SO3_grid"]] inference.n_pose_hypotheses=5 skip_inference=False run_bop_eval=true
+python -m happypose.pose_estimators.megapose.scripts.run_full_megapose_eval coarse_run_id=coarse-rgb-906902141 refiner_run_id=refiner-rgb-653307694 ds_names=[ycbv.bop19,lmo.bop19,tless.bop19,tudl.bop19,icbin.bop19,hb.bop19,itodd.bop19] result_id=exte_det_5posehyp detection_coarse_types=[["exte","SO3_grid"]] inference.n_pose_hypotheses=5 skip_inference=False run_bop_eval=true
 ```
 
 Results :
@@ -31,7 +55,7 @@ Results :
 
 ```sh
 # RGB-D 5 hyp
-python -m torch.distributed.run  --nproc_per_node gpu -m happypose.pose_estimators.megapose.src.megapose.scripts.run_full_megapose_eval detector_run_id=bop_pbr coarse_run_id=coarse-rgb-906902141 refiner_run_id=refiner-rgb-653307694 ds_names=[tless.bop19,tudl.bop19,icbin.bop19,hb.bop19,itodd.bop19] result_id=fastsam_kbestdet_5posehyp_teaserpp detection_coarse_types=[["sam","SO3_grid"]] inference.n_pose_hypotheses=5 inference.run_depth_refiner=true inference.depth_refiner=teaserpp skip_inference=False run_bop_eval=True
+python -m torch.distributed.run  --nproc_per_node gpu -m happypose.pose_estimators.megapose.scripts.run_full_megapose_eval coarse_run_id=coarse-rgb-906902141 refiner_run_id=refiner-rgb-653307694 ds_names=[tless.bop19,tudl.bop19,icbin.bop19,hb.bop19,itodd.bop19] result_id=exte_det_5posehyp_teaserpp detection_coarse_types=[["exte","SO3_grid"]] inference.n_pose_hypotheses=5 inference.run_depth_refiner=true inference.depth_refiner=teaserpp skip_inference=False run_bop_eval=True
 ```
 
 Results :
@@ -104,5 +128,5 @@ conda activate happypose_pytorch3d
 
 cd happypose
 
-python -m torch.distributed.run  --nproc_per_node gpu -m happypose.pose_estimators.megapose.src.megapose.scripts.run_full_megapose_eval detector_run_id=bop_pbr coarse_run_id=coarse-rgb-906902141 refiner_run_id=refiner-rgb-653307694 ds_names=[lmo.bop19] result_id=fastsam_kbestdet_1posehyp detection_coarse_types=[["sam","SO3_grid"]] inference.n_pose_hypotheses=1 skip_inference=False run_bop_eval=true
+python -m torch.distributed.run  --nproc_per_node gpu -m happypose.pose_estimators.megapose.scripts.run_full_megapose_eval coarse_run_id=coarse-rgb-906902141 refiner_run_id=refiner-rgb-653307694 ds_names=[lmo.bop19] result_id=exte_det_1posehyp detection_coarse_types=[["exte","SO3_grid"]] inference.n_pose_hypotheses=1 skip_inference=False run_bop_eval=true
 ```