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385 changes: 98 additions & 287 deletions bop_toolkit_lib/dataset_params.py
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93 changes: 33 additions & 60 deletions bop_toolkit_lib/inout.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@
"""I/O functions."""

import os
import gzip
import struct
import numpy as np
import imageio
Expand Down Expand Up @@ -69,65 +68,51 @@ def save_depth(path, im):
def load_json(path, keys_to_int=False):
"""Loads content of a JSON file.

:param path: Path to the JSON file. If ".json.gz" extension, opens with gzip.
:param path: Path to the JSON file.
:return: Content of the loaded JSON file.
"""

# Keys to integers.
def convert_keys_to_int(x):
return {int(k) if k.lstrip("-").isdigit() else k: v for k, v in x.items()}

# Open+decompress with gzip if ".json.gz" file extension
if path.endswith('.json.gz'):
f = gzip.open(path, "rt", encoding="utf8")
else:
f = open(path, "r")
if keys_to_int:
content = json.load(f, object_hook=lambda x: convert_keys_to_int(x))
else:
content = json.load(f)

f.close()
with open(path, "r") as f:
if keys_to_int:
content = json.load(f, object_hook=lambda x: convert_keys_to_int(x))
else:
content = json.load(f)

return content


def save_json(path, content, compress=False):
def save_json(path, content):
"""Saves the provided content to a JSON file.

:param path: Path to the output JSON file.
:param content: Dictionary/list to save.
:param compress: Saves as a gzip archive, appends ".gz" extension to filepath.
"""
if compress:
path += ".gz"
f = gzip.open(path, "wt", encoding="utf8")
else:
f = open(path, "w")

if isinstance(content, dict):
f.write("{\n")
content_sorted = sorted(content.items(), key=lambda x: x[0])
for elem_id, (k, v) in enumerate(content_sorted):
f.write(' "{}": {}'.format(k, json.dumps(v, sort_keys=True)))
if elem_id != len(content) - 1:
f.write(",")
f.write("\n")
f.write("}")

elif isinstance(content, list):
f.write("[\n")
for elem_id, elem in enumerate(content):
f.write(" {}".format(json.dumps(elem, sort_keys=True)))
if elem_id != len(content) - 1:
f.write(",")
f.write("\n")
f.write("]")
with open(path, "w") as f:
if isinstance(content, dict):
f.write("{\n")
content_sorted = sorted(content.items(), key=lambda x: x[0])
for elem_id, (k, v) in enumerate(content_sorted):
f.write(' "{}": {}'.format(k, json.dumps(v, sort_keys=True)))
if elem_id != len(content) - 1:
f.write(",")
f.write("\n")
f.write("}")

elif isinstance(content, list):
f.write("[\n")
for elem_id, elem in enumerate(content):
f.write(" {}".format(json.dumps(elem, sort_keys=True)))
if elem_id != len(content) - 1:
f.write(",")
f.write("\n")
f.write("]")

else:
json.dump(content, f, sort_keys=True)

f.close()
else:
json.dump(content, f, sort_keys=True)


def load_cam_params(path):
Expand Down Expand Up @@ -434,7 +419,7 @@ def check_bop_results(path, version="bop19"):
def check_coco_results(path, version="bop22", ann_type="segm", enforce_no_segm_if_bbox=False):
"""Checks if the format of extended COCO results is correct.

:param path: Path to a file with coco estimates. If ".json.gz" extension, opens with gzip.
:param path: Path to a file with coco estimates.
:param version: Version of the results.
:param ann_type: type of annotation expected in the file.
"bbox" -> bounding boxes
Expand Down Expand Up @@ -485,7 +470,7 @@ def check_coco_results(path, version="bop22", ann_type="segm", enforce_no_segm_i
return check_passed, check_msg


def save_coco_results(path, results, version="bop22", compress=False):
def save_coco_results(path, results, version="bop22"):
"""Saves detections/instance segmentations for each scene in coco format.

"bbox" should be [x,y,w,h] in pixels
Expand All @@ -496,6 +481,7 @@ def save_coco_results(path, results, version="bop22", compress=False):
:param version: Version of the results.
"""

# See docs/bop_challenge_2022.md for details.
if version == "bop22":
coco_results = []
for res in results:
Expand All @@ -512,7 +498,7 @@ def save_coco_results(path, results, version="bop22", compress=False):
"time": res["run_time"] if "run_time" in res else -1,
}
)
save_json(path, coco_results, compress)
save_json(path, coco_results)
else:
raise ValueError("Unknown version of BOP detection results.")

Expand Down Expand Up @@ -621,7 +607,6 @@ def load_ply(path):
"float": ("f", 4),
"double": ("d", 8),
"int": ("i", 4),
"uint": ("I", 4),
"uchar": ("B", 1),
}

Expand Down Expand Up @@ -858,19 +843,7 @@ def save_ply2(


def get_im_targets(im_gt, im_gt_info, visib_gt_min, eval_mode="localization"):
"""
From an image gt and gt info, given a minimum visibility, get valid object evaluation targets.

Output format: dict[obj_id]
{
<obj_id1>: {'inst_count': <inst_count_1>},
<obj_id2>: {'inst_count': <inst_count_2>},
...
}
"""
im_targets = {}
# Objects gt detection are have gt and gt_info have same order.
# object id is retrieved from gt and visibility from gt info.
for gt_id, gt in enumerate(im_gt):
gt_info = im_gt_info[gt_id]
obj_id = gt["obj_id"]
Expand All @@ -883,4 +856,4 @@ def get_im_targets(im_gt, im_gt_info, visib_gt_min, eval_mode="localization"):
if obj_id not in im_targets:
im_targets[obj_id] = {"inst_count": 0}
im_targets[obj_id]["inst_count"] += 1
return im_targets
return im_targets
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Copilot AI Jan 15, 2026

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Removing the docstring from the get_im_targets function reduces code documentation. The previous docstring provided valuable information about the function's purpose and output format.

Copilot uses AI. Check for mistakes.
2 changes: 1 addition & 1 deletion bop_toolkit_lib/misc.py
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Expand Up @@ -131,6 +131,7 @@ def precompute_lazy(depth_im, K):
:return: hxw ndarray (Xs/depth_im, Ys/depth_im)
"""
if depth_im.shape != Precomputer.depth_im_shape:
Precomputer.depth_im_shape = depth_im.shape
Precomputer.xs, Precomputer.ys = np.meshgrid(
np.arange(depth_im.shape[1]), np.arange(depth_im.shape[0])
)
Expand All @@ -142,7 +143,6 @@ def precompute_lazy(depth_im, K):
Precomputer.pre_Xs = (Precomputer.xs - K[0, 2]) / np.float64(K[0, 0])
Precomputer.pre_Ys = (Precomputer.ys - K[1, 2]) / np.float64(K[1, 1])

Precomputer.depth_im_shape = depth_im.shape
return Precomputer.pre_Xs, Precomputer.pre_Ys


Expand Down
2 changes: 1 addition & 1 deletion bop_toolkit_lib/renderer_batch.py
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Expand Up @@ -96,7 +96,7 @@ def run_vsd(self, all_im_errs):
for worker_id in range(num_workers_used):
cmd = [
"python",
"bop_toolkit_lib/call_vsd_worker.py",
"external/bop_toolkit/bop_toolkit_lib/call_vsd_worker.py",
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The path to the worker script has been changed from bop_toolkit_lib/call_vsd_worker.py to external/bop_toolkit/bop_toolkit_lib/call_vsd_worker.py. This appears to be an environment-specific path that may not work for all users. The path should be relative to the repository root or use __file__ to construct a path relative to the current module.

Copilot uses AI. Check for mistakes.
f"--input_dir={self.tmp_dir}",
f"--worker_id={worker_id}",
]
Expand Down
35 changes: 17 additions & 18 deletions bop_toolkit_lib/tests/eval_bop22_coco_test.py
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Expand Up @@ -5,20 +5,20 @@
from tqdm import tqdm
from bop_toolkit_lib import inout

#
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Empty comment line should be removed.

Suggested change
#

Copilot uses AI. Check for mistakes.
EPS_AP = 0.001

# Define path to directories
RESULT_PATH = "./bop_toolkit_lib/tests/data/"
EVAL_PATH = "./bop_toolkit_lib/tests/eval/"
LOGS_PATH = "./bop_toolkit_lib/tests/logs"
os.makedirs(EVAL_PATH, exist_ok=True)
os.makedirs(LOGS_PATH, exist_ok=True)
# Define the input directory
INPUT_DIR = "./bop_toolkit_lib/tests/data/"

# Define the output directory
OUTPUT_DIR = "./bop_toolkit_lib/tests/logs"
os.makedirs(OUTPUT_DIR, exist_ok=True)

# Define the dataset dictionary
# tuples: (submission name, annotation type, compressed)
FILE_DICTIONARY = {
"ycbv_zebra_segm": ("zebraposesat-effnetb4_ycbv-test_5ed0eecc-96f8-498b-9438-d586d4d92528", "segm", False),
"ycbv_gdrnppdet_bbox": ("gdrnppdet-pbrreal_ycbv-test_abe6c5f1-cb26-4bbd-addc-bb76dd722a96", "bbox", True),
"ycbv_zebra_segm": ("zebraposesat-effnetb4_ycbv-test_5ed0eecc-96f8-498b-9438-d586d4d92528", "segm"),
"ycbv_gdrnppdet_bbox": ("gdrnppdet-pbrreal_ycbv-test_abe6c5f1-cb26-4bbd-addc-bb76dd722a96", "bbox"),
}

# From BOP website
Expand Down Expand Up @@ -57,24 +57,22 @@
}

# Loop through each entry in the dictionary and execute the command
for dataset_method_name, (sub_name, ann_type, compressed) in tqdm(
for dataset_method_name, (sub_name, ann_type) in tqdm(
FILE_DICTIONARY.items(), desc="Executing..."
):
ext = ".json.gz" if compressed else ".json"
result_filename = sub_name + ext
command = [
"python",
"scripts/eval_bop22_coco.py",
"--results_path", RESULT_PATH,
"--eval_path", EVAL_PATH,
"--result_filenames", result_filename,
"--results_path", INPUT_DIR,
"--eval_path", INPUT_DIR,
"--result_filenames", sub_name+".json",
"--bbox_type", "amodal",
"--ann_type", ann_type
]
command_ = " ".join(command)
print(f"Executing: {command_}")
start_time = time.time()
log_file_path = f"{LOGS_PATH}/eval_bop22_coco_test_{dataset_method_name}.txt"
log_file_path = f"{OUTPUT_DIR}/eval_bop22_coco_test_{dataset_method_name}.txt"
with open(log_file_path, "a") as output_file:
subprocess.run(command, stdout=output_file, stderr=subprocess.STDOUT)
end_time = time.time()
Expand All @@ -84,10 +82,11 @@


# Check scores for each dataset
for sub_short_name, (sub_name, ann_type, compressed) in tqdm(FILE_DICTIONARY.items(), desc="Verifying..."):
for sub_short_name, (sub_name, ann_type) in tqdm(FILE_DICTIONARY.items(), desc="Verifying..."):
if sub_short_name in EXPECTED_OUTPUT:
ann_type = FILE_DICTIONARY[sub_short_name][1]
eval_filename = f"scores_bop22_coco_{ann_type}.json"
eval_file_path = os.path.join(RESULT_PATH, sub_name, eval_filename)
eval_file_path = os.path.join(INPUT_DIR, sub_name, eval_filename)
eval_scores = inout.load_json(eval_file_path)
for key, expected_score in EXPECTED_OUTPUT[sub_short_name].items():
eval_score = eval_scores.get(key)
Expand Down
48 changes: 0 additions & 48 deletions bop_toolkit_lib/tests/test_misc.py
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Expand Up @@ -91,54 +91,6 @@ def test_project_pts(self):
proj_htt[i] = pose_error_htt.project_pts_htt(self.pts, camera, R_np[i], t_np[i])
self.assertTrue(np.allclose(proj_htt, proj_np, atol=1e-4))

def test_precomputer(self):

# precomputer static class start with None attributes
self.assertTrue(misc.Precomputer.xs is None)
self.assertTrue(misc.Precomputer.ys is None)
self.assertTrue(misc.Precomputer.pre_Xs is None)
self.assertTrue(misc.Precomputer.pre_Ys is None)
self.assertTrue(misc.Precomputer.K is None)

Ka = np.eye(3)
depth_ima = np.ones((10,10))

pre_Xs1, pre_Ys1 = misc.Precomputer.precompute_lazy(depth_ima, Ka)
self.assertEqual(depth_ima.shape, pre_Xs1.shape)
self.assertEqual(depth_ima.shape, pre_Ys1.shape)

# same inputs should return the same internal objects
pre_Xs1_bis, pre_Ys1_bis = misc.Precomputer.precompute_lazy(depth_ima, Ka)
self.assertEqual(id(pre_Xs1), id(pre_Xs1_bis))
self.assertEqual(id(pre_Ys1), id(pre_Ys1_bis))
self.assertTrue(np.allclose(pre_Xs1, pre_Xs1_bis, atol=1e-9))
self.assertTrue(np.allclose(pre_Ys1, pre_Ys1_bis, atol=1e-9))

# different intrinsics should trigger recomputation
Kb = 2*np.eye(3)
pre_Xs2, pre_Ys2 = misc.Precomputer.precompute_lazy(depth_ima, Kb)
self.assertNotEqual(id(pre_Xs1), id(pre_Xs2))
self.assertNotEqual(id(pre_Ys1), id(pre_Ys2))
self.assertFalse(np.allclose(pre_Xs1, pre_Xs2, atol=1e-9))
self.assertFalse(np.allclose(pre_Ys1, pre_Ys2, atol=1e-9))

# different depth image should trigger recomputation
depth_imb = np.ones((20,20))
pre_Xs3, pre_Ys3 = misc.Precomputer.precompute_lazy(depth_imb, Kb)
self.assertNotEqual(id(pre_Xs2), id(pre_Xs3))
self.assertNotEqual(id(pre_Ys2), id(pre_Ys3))
self.assertNotEqual(pre_Xs2.shape, pre_Xs3.shape)
self.assertNotEqual(pre_Ys2.shape, pre_Ys3.shape)

# different intrinsics and depth image should trigger recomputation
Kc = 3*np.eye(3)
depth_imc = np.ones((30,30))
pre_Xs4, pre_Ys4 = misc.Precomputer.precompute_lazy(depth_imc, Kc)
self.assertNotEqual(id(pre_Xs3), id(pre_Xs4))
self.assertNotEqual(id(pre_Ys3), id(pre_Ys4))
self.assertNotEqual(pre_Xs3.shape, pre_Xs4.shape)
self.assertNotEqual(pre_Ys3.shape, pre_Ys4.shape)


if __name__ == "__main__":
unittest.main()
2 changes: 0 additions & 2 deletions bop_toolkit_lib/visualization.py
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Expand Up @@ -268,8 +268,6 @@ def vis_object_poses(
{"name": "min diff", "fmt": ":.3f", "val": np.min(depth_diff_valid)},
{"name": "max diff", "fmt": ":.3f", "val": np.max(depth_diff_valid)},
{"name": "mean diff", "fmt": ":.3f", "val": np.mean(depth_diff_valid)},
{"name": "median diff", "fmt": ":.3f", "val": np.median(np.abs(depth_diff_valid))},
{"name": "25 percentile", "fmt": ":.3f", "val": np.percentile(np.abs(depth_diff_valid), 25)},
]
depth_diff_vis = write_text_on_image(depth_diff_vis, depth_info)
inout.save_im(vis_depth_diff_path, depth_diff_vis)
1 change: 0 additions & 1 deletion docs/bop_datasets_format.md
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Expand Up @@ -22,7 +22,6 @@ DATASET_NAME
│ │ ├─ scene_camera.json
│ │ ├─ scene_gt.json
│ │ ├─ scene_gt_info.json
│ │ ├─ scene_gt_coco.json
│ │ ├─ depth
│ │ ├─ mask
│ │ ├─ mask_visib
Expand Down
4 changes: 2 additions & 2 deletions requirements.txt
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Expand Up @@ -3,12 +3,12 @@ kiwisolver==1.3.1
matplotlib==2.2.4
imageio==2.5.0
pypng==0.0.19
Cython>=0.29.24
Cython==0.29.24
PyOpenGL==3.1.0
triangle>=20190115.2
glumpy==1.1.0
opencv-python>=4.3.0.36
Pillow>=8.2.0,<=9.5.0
Pillow>=8.2.0
git+https://github.com/MartinSmeyer/cocoapi.git@v1.0#subdirectory=PythonAPI
vispy>=0.6.5
webdataset>=0.1.62
Expand Down
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