[kitti-evaluation-toolkit-format-labels-dumping] Minor code changes to dump prediction labels in KITTI evaluation toolkit expected format.

.gitignore

@@ -1,134 +1,135 @@
-dataset/
-logs/
-checkpoints/
-results/
-src/.idea/
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-pip-wheel-metadata/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
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-.webassets-cache
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-# Scrapy stuff:
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-
-# Sphinx documentation
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-
-# PyBuilder
-target/
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-# Jupyter Notebook
-.ipynb_checkpoints
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-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-.python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow
-__pypackages__/
-
-# Celery stuff
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-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
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-# Spyder project settings
-.spyderproject
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-# Rope project settings
-.ropeproject
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-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
+dataset/
+logs/
+checkpoints/
+results/
+src/.idea/
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+.vscode/settings.json

src/data_process/kitti_bev_utils.py

@@ -141,7 +141,7 @@ def build_yolo_target(labels):
 def inverse_yolo_target(targets, bc):
     labels = []
     for t in targets:
-        c, y, x, w, l, im, re = t
+        c, y, x, w, l, im, re, conf = t
         z, h = -1.55, 1.5
         if c == 1:
             h = 1.8
@@ -154,7 +154,7 @@ def inverse_yolo_target(targets, bc):
         l = l * (bc["maxX"] - bc["minX"])
         w -= 0.3
         l -= 0.3
-        labels.append([c, x, y, z, h, w, l, - np.arctan2(im, re) - 2 * np.pi])
+        labels.append([c, x, y, z, h, w, l, - np.arctan2(im, re) - 2 * np.pi, conf])
 
     return np.array(labels)
 

src/data_process/transformation.py

@@ -93,15 +93,15 @@ def camera_to_lidar_box(boxes, V2C=None, R0=None, P2=None):
 
 
 def lidar_to_camera_box(boxes, V2C=None, R0=None, P2=None):
-    # (N, 7) -> (N, 7) x,y,z,h,w,l,r
+    # (N, 8) -> (N, 8) x,y,z,h,w,l,r,conf
     ret = []
     for box in boxes:
-        x, y, z, h, w, l, rz = box
-        (x, y, z), h, w, l, ry = lidar_to_camera(
-            x, y, z, V2C=V2C, R0=R0, P2=P2), h, w, l, -rz - np.pi / 2
+        x, y, z, h, w, l, rz, conf = box
+        (x, y, z), h, w, l, ry, conf = lidar_to_camera(
+            x, y, z, V2C=V2C, R0=R0, P2=P2), h, w, l, -rz - np.pi / 2, conf
         # ry = angle_in_limit(ry)
-        ret.append([x, y, z, h, w, l, ry])
-    return np.array(ret).reshape(-1, 7)
+        ret.append([x, y, z, h, w, l, ry, conf])
+    return np.array(ret).reshape(-1, 8)
 
 
 def center_to_corner_box2d(boxes_center, coordinate='lidar'):

src/test.py

@@ -24,7 +24,7 @@
 from data_process import kitti_data_utils, kitti_bev_utils
 from data_process.kitti_dataloader import create_test_dataloader
 from models.model_utils import create_model
-from utils.misc import make_folder
+from utils.misc import make_folder, dump_predicted_labels
 from utils.evaluation_utils import post_processing, rescale_boxes, post_processing_v2
 from utils.misc import time_synchronized
 from utils.visualization_utils import show_image_with_boxes, merge_rgb_to_bev, predictions_to_kitti_format
@@ -38,7 +38,7 @@ def parse_test_configs():
                         help='The name of the model architecture')
     parser.add_argument('--cfgfile', type=str, default='./config/cfg/complex_yolov4.cfg', metavar='PATH',
                         help='The path for cfgfile (only for darknet)')
-    parser.add_argument('--pretrained_path', type=str, default=None, metavar='PATH',
+    parser.add_argument('--pretrained_path', type=str, default='None', metavar='PATH',
                         help='the path of the pretrained checkpoint')
     parser.add_argument('--use_giou_loss', action='store_true',
                         help='If true, use GIoU loss during training. If false, use MSE loss for training')
@@ -82,7 +82,13 @@ def parse_test_configs():
 
     if configs.save_test_output:
         configs.results_dir = os.path.join(configs.working_dir, 'results', configs.saved_fn)
+        configs.results_dir_images = os.path.join(configs.working_dir, 'results', configs.saved_fn, 'images')
+        configs.results_dir_labels = os.path.join(configs.working_dir, 'results', configs.saved_fn, 'labels')
+
         make_folder(configs.results_dir)
+        if configs.output_format == 'image':
+            make_folder(configs.results_dir_images)
+            make_folder(configs.results_dir_labels)
 
     return configs
 
@@ -95,9 +101,9 @@ def parse_test_configs():
     model.print_network()
     print('\n\n' + '-*=' * 30 + '\n\n')
     assert os.path.isfile(configs.pretrained_path), "No file at {}".format(configs.pretrained_path)
-    model.load_state_dict(torch.load(configs.pretrained_path))
 
     configs.device = torch.device('cpu' if configs.no_cuda else 'cuda:{}'.format(configs.gpu_idx))
+    model.load_state_dict(torch.load(configs.pretrained_path, map_location=configs.device))
     model = model.to(device=configs.device)
 
     out_cap = None
@@ -144,7 +150,9 @@ def parse_test_configs():
             if configs.save_test_output:
                 if configs.output_format == 'image':
                     img_fn = os.path.basename(img_paths[0])[:-4]
-                    cv2.imwrite(os.path.join(configs.results_dir, '{}.jpg'.format(img_fn)), out_img)
+                    cv2.imwrite(os.path.join(configs.results_dir_images, '{}.jpg'.format(img_fn)), out_img)
+                    dump_predicted_labels(os.path.join(configs.results_dir_labels, '{}.txt'.format(img_fn)), objects_pred)
+
                 elif configs.output_format == 'video':
                     if out_cap is None:
                         out_cap_h, out_cap_w = out_img.shape[:2]

src/utils/misc.py

@@ -8,6 +8,33 @@ def make_folder(folder_name):
     # or os.makedirs(folder_name, exist_ok=True)
 
 
+def dump_predicted_labels(file_path, predictions):
+    """ Dumps the output labels (KITTI format) on the provided path """
+    with open(file_path, 'w') as out_label_file:
+        for predicted_box_parameters in predictions:
+            out_label = []
+            out_label.append(predicted_box_parameters.type) # BB class
+            out_label.append(predicted_box_parameters.truncation) # BB truncation
+            out_label.append(predicted_box_parameters.occlusion) # BB occlusion level
+            out_label.append(predicted_box_parameters.alpha) # BB observation angle
+            out_label.append(predicted_box_parameters.box2d[0]) # 2D BB top
+            out_label.append(predicted_box_parameters.box2d[1]) # 2D BB left
+            out_label.append(predicted_box_parameters.box2d[2]) # 2D BB bottom
+            out_label.append(predicted_box_parameters.box2d[3]) # 2D BB right
+            out_label.append(predicted_box_parameters.h) # 3D BB height
+            out_label.append(predicted_box_parameters.w) # 3D BB width
+            out_label.append(predicted_box_parameters.l) # 3D BB length
+            out_label.append(predicted_box_parameters.t[0]) # 3D BB x
+            out_label.append(predicted_box_parameters.t[1]) # 3D BB y
+            out_label.append(predicted_box_parameters.t[2]) # 3D BB z 
+            out_label.append(predicted_box_parameters.ry) # BB rotation around y-axis
+            out_label.append(predicted_box_parameters.score) # BB prediction score
+
+            for box_param in out_label:
+                out_label_file.write(str(box_param)+' ')
+            out_label_file.write('\n')
+
+
 class AverageMeter(object):
     """Computes and stores the average and current value"""