Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.

Last update: Jan 01, 2023

Overview

PyLabel

pip install pylabel

PyLabel is a Python package to help you prepare image datasets for computer vision models including PyTorch and YOLOv5. It can translate bounding box annotations between different formats. (For example, COCO to YOLO.) And it includes an AI-assisted labeling tool that runs in a Jupyter notebook.

Translate: Convert annotation formats with a single line of code:
```
importer.ImportCoco(path_to_annotations).ExportToYoloV5()
```
Analyze: PyLabel stores annotatations in a pandas dataframe so you can easily perform analysis on image datasets.
Split: Divide image datasets into train, test, and val with stratification to get consistent class distribution.
Label: PyLabel also includes an image labeling tool that runs in a Jupyter notebook that can annotate images manually or perform automatic labeling using a pre-trained model.
Visualize: Render images from your dataset with bounding boxes overlaid so you can confirm the accuracy of the annotations.

Tutorial Notebooks

See PyLabel in action in these sample Jupyter notebooks:

About PyLabel

PyLabel is being developed by Jeremy Fraenkel, Alex Heaton, and Derek Topper as the Capstope project for the Master of Information and Data Science (MIDS) at the UC Berkeley School of Information. If you have any questions or feedback please create an issue. Please let us know how we can make PyLabel more useful.

Comments

"No objects to concatenate" conversion yolo to coco

I'm doing a yolo 2 coco conversion with yaml, everything works just fine until I do dataset.export.ExportToCoco(cat_id_index=0) i tried with cat_id_index=1 as well. The full error is as it follows: Traceback (most recent call last): File "/home/usuaris/imatge/ilias.khayat/TFG/yolo22coco.py", line 45, in <module> dataset.export.ExportToCoco(cat_id_index=1) File "/home/usuaris/imatge/ilias.khayat/TFG/pylabel/pylabel/exporter.py", line 744, in ExportToCoco mergedI = pd.concat(df_outputI, ignore_index=True) File "/home/usuaris/imatge/ilias.khayat/venv/detectron2-/lib/python3.9/site-packages/pandas/util/_decorators.py", line 311, in wrapper return func(*args, **kwargs) File "/home/usuaris/imatge/ilias.khayat/venv/detectron2-/lib/python3.9/site-packages/pandas/core/reshape/concat.py", line 347, in concat op = _Concatenator( File "/home/usuaris/imatge/ilias.khayat/venv/detectron2-/lib/python3.9/site-packages/pandas/core/reshape/concat.py", line 404, in __init__ raise ValueError("No objects to concatenate") ValueError: No objects to concatenate srun: error: gpic10: task 0: Exited with exit code 1 I'd appreciate any suggestions. `

opened by 88ili88 12

ValueError: not enough values to unpack (expected 5, got 0).

I think this happens with images that don't have annotations, meaning their respective .txt files are empty.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_24072/4001401144.py in <module>
      9 print("Here")
     10 dataset1 = importer.ImportYoloV5(path=path_to_annotations, path_to_images=path_to_images, cat_names=yoloclasses,
---> 11     img_ext="jpeg", name="view, cut, plan")
     12 print("Here 2")
     13 

~\Anaconda3\envs\yolov5\lib\site-packages\pylabel\importer.py in ImportYoloV5(path, img_ext, cat_names, path_to_images, name)
    300                     width_norm,
    301                     height_norm,
--> 302                 ) = line.split()
    303                 row["img_folder"] = path_to_images
    304                 row["img_filename"] = filename.name.replace("txt", img_ext)

ValueError: not enough values to unpack (expected 5, got 0)

opened by isspid 9

ValueError: cannot convert float NaN to integer

Occurs on calling visualize when bounding boxes have nan values:

from IPython.display import Image, display
display(ds.visualize.ShowBoundingBoxes(5))

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/Users/robin/Documents/GitHub/oil-storage-tank/pylabel.ipynb Cell 7' in <cell line: 2>()
      [1](vscode-notebook-cell:/Users/robin/Documents/GitHub/oil-storage-tank/pylabel.ipynb#ch0000002?line=0)[ from IPython.display import Image, display
----> ]()[2](vscode-notebook-cell:/Users/robin/Documents/GitHub/oil-storage-tank/pylabel.ipynb#ch0000002?line=1)[ display(ds.visualize.ShowBoundingBoxes(5))

File ~/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/visualize.py:34, in Visualize.ShowBoundingBoxes(self, img_id, img_filename)
     ]()[32](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/visualize.py?line=31)[ for index, row in df_single_img_annots.iterrows():
     ]()[33](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/visualize.py?line=32)[     labels.append(row['cat_name'])
---> ]()[34](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/visualize.py?line=33)[     bboxes.append([int(row['ann_bbox_xmin']),int(row['ann_bbox_ymin']),int(row['ann_bbox_xmax']),int(row['ann_bbox_ymax'])])
     ]()[36](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/visualize.py?line=35)[ img_with_boxes = bbv.draw_multiple_rectangles(img, bboxes)
     ]()[37](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/visualize.py?line=36)[ img_with_boxes = bbv.add_multiple_labels(img_with_boxes, labels, bboxes)

ValueError: cannot convert float NaN to integer]()

I guess this occurs as I have images with zero annotations

opened by robmarkcole 8

Cannot import PASCAL VOC dataset

The dataset is in PASCAL VOC format, as exported by CVAT. When importing, I receive the traceback

Traceback (most recent call last):
  File "path\to\convert_annotations.py", line 5, in <module>
    voc_data = importer.ImportVOC(voc_folder)
  File "path\to\.venv\lib\site-packages\pylabel\importer.py", line 207, in ImportVOC
    row["ann_pose"] = _GetValueOrBlank(o.find("pose"))
  File "path\to\.venv\lib\site-packages\pylabel\importer.py", line 27, in _GetValueOrBlank
    return element.text
AttributeError: 'NoneType' object has no attribute 'text'

Here is an example of an annotation xml exported by CVAT:

<?xml version="1.0"?>
<annotation>
<folder>myfolder</folder>
<filename>myfile.png</filename>
<source>
<database>Unknown</database>
<annotation>Unknown</annotation>
<image>Unknown</image>
</source>
<size>
<width>5328</width>
<height>4608</height>
<depth/>
</size>
<segmented>0</segmented>
<object>
<name>my_object</name>
<truncated>0</truncated>
<occluded>0</occluded>
<difficult>0</difficult>
<bndbox>
<xmin>127.06</xmin>
<ymin>632.19</ymin>
<xmax>161.12</xmax>
<ymax>666.86</ymax>
</bndbox>
<attributes>
<attribute>
<name>rotation</name>
<value>0.0</value>
</attribute>
</attributes>
</annotation>```

opened by emillundh 7

Category_id exported as string instead of int

Hi,

Thank you for create this nice utility!

I encountered an issue with ExportToCoco, the category_id in the exported json file is stored as a string instead of an integer, which caused trouble when registering datasets in detectron2. My quick fix was just cast all category_id to int.

opened by ytzeng1 7
Issue if image has no bounding boxes

Hi, I was converting from YOLO to VOC, and it seems that if an image doesnt have any bounding boxes, then it causes an error to occur. This is the error: ValueError: invalid literal for int() with base 10: '. This arises in the exporter.py script.

opened by n12iB 5
Cast `category_id` field value from `str` to `int`

This PR casts the category_id field value from str to int data type to be consistent with the COCO data format.

I have also checked the other fields and found everything is consistent with the format outlined in https://cocodataset.org/#format-data

Closes #46

opened by dnth 5
Cvat compatibility and bug fixes
CVAT Compatibility and over bug fixes

I tried using this package to convert my coco annotations of a custom data-set, which was annotated in CVAT, to YOLOV5 labels. I ran into some issues and have fixed them and hope they may be of use to others as well.

The issues are also explained in the commit messages.

importer.py: the tag "img_folder" was required to be in coco annotations but this is not a necessary tag, it is often left blank or not included at all, when it is not included a KeyError occured. There was an attempt at this in place with a call _GetValueOrBlank(images["img_folder"], path_to_images) which though still requires there to be an "img_folder" in images even if an alternative path was given by the user. Fixed by checking if this tag/column exists and creating it empty if it does not yet exist

Same issue as 1. only with img_depth, it is not required in the coco annotation format and is not given when exporting from cvat

exporter.py, ExportToYoloV5(): -default value of yaml_file=None leads to TypeError: expected str, ... fixed by setting default value to the recommended yaml_file='dataset.yaml' -fix to the issue that not using splits created "UnboundLocalError: local variable 'split_dir' referenced before assignment" fixed by setting split_dir = '' if no split is wanted

analize.py bug: i in cat_names was assumed to be str and caused error "AttributeError: 'float' object has no attribute 'strip'" fix: typecast str(i) before calling strip

Please let me know if there is anything you need me to change and thank you for creating this package.
bug good first issue
opened by landegt 5
_ReindexCatIds is broken
https://github.com/thovden/pylabel/blob/c17d2c644f1dcb85ef3f9835c8771b96b969a673/pylabel/shared.py#L39

The __ReindexCatIds function is broken, mainly because it's operating on a copy of a DataFrame.

The following code does not work in-place by default:

df = df.replace(r"^\s*$", np.nan, regex=True)

Hence, the rest of the function operates on a copy of the DataFrame, but the intention is to do in-place replacements of df['cat_id'].

I also suspect the intention of the following code is probably to coerce df['cat_id'] to an numeric type:

pd.to_numeric(df["cat_id"])

However, these values are not written back to the original data frame.

I'm going to cleanup this function a little and submit a PR.
opened by thovden 4

!_src.empty() in function 'cvtColor'

from IPython.display import Image, display
display(dataset.visualize.ShowBoundingBoxes(100))

---------------------------------------------------------------------------
error                                     Traceback (most recent call last)
/Users/robin/Documents/GitHub/oil-storage-tank/pylabel.ipynb Cell 3' in <cell line: 2>()
      [1](vscode-notebook-cell:/Users/robin/Documents/GitHub/oil-storage-tank/pylabel.ipynb#ch0000002?line=0)[ from IPython.display import Image, display
----> ]()[2](vscode-notebook-cell:/Users/robin/Documents/GitHub/oil-storage-tank/pylabel.ipynb#ch0000002?line=1)[ display(dataset.visualize.ShowBoundingBoxes(100))

File ~/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/visualize.py:27, in Visualize.ShowBoundingBoxes(self, img_id, img_filename)
     ]()[25](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/visualize.py?line=24)[ full_image_path = str(Path(ds.path_to_annotations, df_single_img_annots.iloc[0].img_folder, df_single_img_annots.iloc[0].img_filename))
     ]()[26](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/visualize.py?line=25)[ img = cv2.imread(str(full_image_path))
---> ]()[27](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/visualize.py?line=26)[ img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     ]()[29](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/visualize.py?line=28)[ labels = []
     ]()[30](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/visualize.py?line=29)[ bboxes = []

error: OpenCV(4.5.5) /Users/runner/work/opencv-python/opencv-python/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor']()

I was suspicious the img_path col is empty, but updating this doesn't resolve

opened by robmarkcole 4

keep images without annotation

Hi, thanks for opensourcing this great tool! It's quite helpful. Do you know if there is a walk-around that I can use to keep the image information for images that do not have annotations? For example, if one image does not have any annotation, ideally it will be kept in the "images" field when exported to the coco format.
question

opened by vincentlux 3

IndexError: index 8 is out of bounds for axis 0 with size 7

Code:

#Specify path to the coco.json file
path_to_annotations = "data/coco.json"
#Specify the path to the images (if they are in a different folder than the annotations)
path_to_images = "data/image_patches/"

#Import the dataset into the pylable schema 
dataset = importer.ImportCoco(path_to_annotations, path_to_images=path_to_images, name="tanks_coco")
dataset.df.head(5)

Error:

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
/Users/robin/Documents/GitHub/oil-storage-tank/pre-processing.ipynb Cell 7' in <cell line: 7>()
      [4](vscode-notebook-cell:/Users/robin/Documents/GitHub/oil-storage-tank/pre-processing.ipynb#ch0000010?line=3)[ path_to_images = "data/image_patches/"
      ]()[6](vscode-notebook-cell:/Users/robin/Documents/GitHub/oil-storage-tank/pre-processing.ipynb#ch0000010?line=5)[ #Import the dataset into the pylable schema 
----> ]()[7](vscode-notebook-cell:/Users/robin/Documents/GitHub/oil-storage-tank/pre-processing.ipynb#ch0000010?line=6)[ dataset = importer.ImportCoco(path_to_annotations, path_to_images=path_to_images, name="tanks_coco")
      ]()[8](vscode-notebook-cell:/Users/robin/Documents/GitHub/oil-storage-tank/pre-processing.ipynb#ch0000010?line=7)[ dataset.df.head(5)

File ~/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/importer.py:94, in ImportCoco(path, path_to_images, name)
     ]()[89](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/importer.py?line=88)[ df.ann_category_id = df.ann_category_id.astype(str)
     ]()[91](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/importer.py?line=90)[ df[
     ]()[92](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/importer.py?line=91)[     ["ann_bbox_xmin", "ann_bbox_ymin", "ann_bbox_width", "ann_bbox_height"]
     ]()[93](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/importer.py?line=92)[ ] = pd.DataFrame(df.ann_bbox.tolist(), index=df.index)
---> ]()[94](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/importer.py?line=93)[ df.insert(8, "ann_bbox_xmax", df["ann_bbox_xmin"] + df["ann_bbox_width"])
     ]()[95](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/importer.py?line=94)[ df.insert(10, "ann_bbox_ymax", df["ann_bbox_ymin"] + df["ann_bbox_height"])
     ]()[97](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/importer.py?line=96)[ # debug print(df.info())
     ]()[98](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/importer.py?line=97)[ 
     ]()[99](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pylabel/importer.py?line=98)[ # Join the annotions with the information about the image to add the image columns to the dataframe

File ~/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/frame.py:4439, in DataFrame.insert(self, loc, column, value, allow_duplicates)
   ]()[4436](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/frame.py?line=4435)[     raise TypeError("loc must be int")
   ]()[4438](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/frame.py?line=4437)[ value = self._sanitize_column(value)
-> ]()[4439](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/frame.py?line=4438)[ self._mgr.insert(loc, column, value)]()
File ~/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py:1230, in BlockManager.insert(self, loc, item, value)
   [1220](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py?line=1219)[ """
   ]()[1221](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py?line=1220)[ Insert item at selected position.
   ]()[1222](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py?line=1221)[ 
   (...)
   ]()[1227](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py?line=1226)[ value : np.ndarray or ExtensionArray
   ]()[1228](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py?line=1227)[ """
   ]()[1229](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py?line=1228)[ # insert to the axis; this could possibly raise a TypeError
-> ]()[1230](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py?line=1229)[ new_axis = self.items.insert(loc, item)
   ]()[1232](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py?line=1231)[ if value.ndim == 2:
   ]()[1233](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py?line=1232)[     value = value.T

File ~/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/indexes/base.py:6602, in Index.insert(self, loc, item)
   ]()[6595](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=6594)[ if arr.dtype != object or not isinstance(
   ]()[6596](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=6595)[     item, (tuple, np.datetime64, np.timedelta64)
   ]()[6597](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=6596)[ ):
   ]()[6598](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=6597)[     # with object-dtype we need to worry about numpy incorrectly casting
   ]()[6599](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=6598)[     # dt64/td64 to integer, also about treating tuples as sequences
   ]()[6600](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=6599)[     # special-casing dt64/td64 https://github.com/numpy/numpy/issues/12550
   ]()[6601](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=6600)[     casted = arr.dtype.type(item)
-> ]()[6602](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=6601)[     new_values = np.insert(arr, loc, casted)
   ]()[6604](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=6603)[ else:
   ]()[6605](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=6604)[     # No overload variant of "insert" matches argument types
   ]()[6606](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=6605)[     # "ndarray[Any, Any]", "int", "None"  [call-overload]
   ]()[6607](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=6606)[     new_values = np.insert(arr, loc, None)  # type: ignore[call-overload]

File <__array_function__ internals>:180, in insert(*args, **kwargs)

File ~/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/numpy/lib/function_base.py:5280, in insert(arr, obj, values, axis)
   ]()[5278](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/numpy/lib/function_base.py?line=5277)[ index = indices.item()
   ]()[5279](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/numpy/lib/function_base.py?line=5278)[ if index < -N or index > N:
-> ]()[5280](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/numpy/lib/function_base.py?line=5279)[     raise IndexError(f"index {obj} is out of bounds for axis {axis} "
   ]()[5281](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/numpy/lib/function_base.py?line=5280)[                      f"with size {N}")
   ]()[5282](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/numpy/lib/function_base.py?line=5281)[ if (index < 0):
   ]()[5283](file:///Users/robin/Documents/GitHub/oil-storage-tank/venv/lib/python3.9/site-packages/numpy/lib/function_base.py?line=5282)[     index += N

IndexError: index 8 is out of bounds for axis 0 with size 7]()

opened by robmarkcole 10

Error during exporting of split dataset (with null class values).

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\Anaconda3\envs\yolov5\lib\site-packages\pandas\core\ops\array_ops.py in na_arithmetic_op(left, right, op, is_cmp)
    142     try:
--> 143         result = expressions.evaluate(op, left, right)
    144     except TypeError:

~\Anaconda3\envs\yolov5\lib\site-packages\pandas\core\computation\expressions.py in evaluate(op, a, b, use_numexpr)
    232         if use_numexpr:
--> 233             return _evaluate(op, op_str, a, b)  # type: ignore
    234     return _evaluate_standard(op, op_str, a, b)

~\Anaconda3\envs\yolov5\lib\site-packages\pandas\core\computation\expressions.py in _evaluate_standard(op, op_str, a, b)
     67     with np.errstate(all="ignore"):
---> 68         return op(a, b)
     69 

TypeError: can't multiply sequence by non-int of type 'float'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_59916/1620987889.py in <module>
----> 1 dataset.export.ExportToYoloV5(output_path='model_training/labels',yaml_file='dataset.yaml', use_splits=True)

~\Anaconda3\envs\yolov5\lib\site-packages\pylabel\exporter.py in ExportToYoloV5(self, output_path, yaml_file, copy_images, use_splits, cat_id_index)
    486 
    487         yolo_dataset["center_x_scaled"] = (
--> 488             yolo_dataset["ann_bbox_xmin"] + (yolo_dataset["ann_bbox_width"] * 0.5)
    489         ) / yolo_dataset["img_width"]
    490         yolo_dataset["center_y_scaled"] = (

~\Anaconda3\envs\yolov5\lib\site-packages\pandas\core\ops\common.py in new_method(self, other)
     63         other = item_from_zerodim(other)
     64 
---> 65         return method(self, other)
     66 
     67     return new_method

~\Anaconda3\envs\yolov5\lib\site-packages\pandas\core\ops\__init__.py in wrapper(left, right)
    341         lvalues = extract_array(left, extract_numpy=True)
    342         rvalues = extract_array(right, extract_numpy=True)
--> 343         result = arithmetic_op(lvalues, rvalues, op)
    344 
    345         return left._construct_result(result, name=res_name)

~\Anaconda3\envs\yolov5\lib\site-packages\pandas\core\ops\array_ops.py in arithmetic_op(left, right, op)
    188     else:
    189         with np.errstate(all="ignore"):
--> 190             res_values = na_arithmetic_op(lvalues, rvalues, op)
    191 
    192     return res_values

~\Anaconda3\envs\yolov5\lib\site-packages\pandas\core\ops\array_ops.py in na_arithmetic_op(left, right, op, is_cmp)
    148             #  will handle complex numbers incorrectly, see GH#32047
    149             raise
--> 150         result = masked_arith_op(left, right, op)
    151 
    152     if is_cmp and (is_scalar(result) or result is NotImplemented):

~\Anaconda3\envs\yolov5\lib\site-packages\pandas\core\ops\array_ops.py in masked_arith_op(x, y, op)
    110         if mask.any():
    111             with np.errstate(all="ignore"):
--> 112                 result[mask] = op(xrav[mask], y)
    113 
    114     result, _ = maybe_upcast_putmask(result, ~mask, np.nan)

TypeError: can't multiply sequence by non-int of type 'float'

opened by isspid 8

Multiple image extension for YOLOv5 dataset
My Yolov5 dataset has got multiple image extensions files such as jpg, jpeg, png, etc. What is the best way to handle these cases?

dataset = importer.ImportYoloV5(path=path_to_annotations, path_to_images=path_to_images, cat_names=yoloclasses, img_ext="jpg", name="coco128")

in img_ext we can only specify one extension format.
opened by dnth 8
reindexing category id when exporting to coco json format

Instead of changing the category Ids, it might be a nice feature to add a "background" category at category_id = 0, that is how other libraries do it.

opened by Daniel-R-Armstrong 3

Releases(v0.1.42)

v0.1.42(Dec 29, 2022)
What's Changed

To resolve issue #66 with voc exporting by @alexheat in https://github.com/pylabel-project/pylabel/pull/67

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.41...v0.1.42
Source code(tar.gz)
Source code(zip)
v0.1.41(Aug 27, 2022)
This should resolve issue https://github.com/pylabel-project/pylabel/issues/61 "Cannot import PASCAL VOC dataset"

What's Changed

GetValueOrBlank should return a blank if the element is missing by @emillundh in https://github.com/pylabel-project/pylabel/pull/62

New Contributors

@emillundh made their first contribution in https://github.com/pylabel-project/pylabel/pull/62

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.40...v0.1.41
Source code(tar.gz)
Source code(zip)
v0.1.40(Jun 3, 2022)
What's Changed

Resolve issues #57 in ImportYoloV5WithYaml method by @alexheat in https://github.com/pylabel-project/pylabel/pull/59

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.39...v0.1.40
Source code(tar.gz)
Source code(zip)
v0.1.39(May 1, 2022)
This release will resolve issue https://github.com/pylabel-project/pylabel/issues/52 discovered by @thovden who also supplied a fix that resolved the issue. The ReindexCatIds function was broken. Additional tests have been added to catch this issue in the future.

What's Changed

Write converted cat_ids back to orignal data frame by @thovden in https://github.com/pylabel-project/pylabel/pull/53

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.38...v0.1.39
Source code(tar.gz)
Source code(zip)
v0.1.38(Apr 28, 2022)
This release will resolve issue #49 when there are file names with multiple periods discovered by @thovden who also supplied a fix that resolved the issue.

What's Changed

Ensure the annotation files have complete filenames. by @thovden in https://github.com/pylabel-project/pylabel/pull/50

New Contributors

@thovden made their first contribution in https://github.com/pylabel-project/pylabel/pull/50

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.37...v0.1.38
Source code(tar.gz)
Source code(zip)
v0.1.37(Apr 24, 2022)
This release (.37) will resolve an issue discovered by @dnth where category_ids were being exported as string values instead of ints. (Issue https://github.com/pylabel-project/pylabel/issues/46) @dnth supplied a fix that resolved the issue.

What's Changed

Cast category_id field value from str to int by @dnth in https://github.com/pylabel-project/pylabel/pull/47

New Contributors

@dnth made their first contribution in https://github.com/pylabel-project/pylabel/pull/47

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.36...v0.1.37
Source code(tar.gz)
Source code(zip)
v0.1.36(Apr 15, 2022)
What's Changed

This release should fix issues reported by users when importing coco files that had images without annotations. It should be able to handle those cases without errors and export annotation files even when there are no annotations.

Fixes issues with missing anots in coco files issue #35 by @alexheat in https://github.com/pylabel-project/pylabel/pull/44

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.35...v0.1.36
Source code(tar.gz)
Source code(zip)
v0.1.35(Mar 17, 2022)
What's Changed

Fix for issue #40 when importing Yolo files by @alexheat in https://github.com/pylabel-project/pylabel/pull/41

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.34...v0.1.35
Source code(tar.gz)
Source code(zip)
v0.1.34.2(Mar 16, 2022)

Re-releasing because publish action did not run.
Source code(tar.gz)
Source code(zip)
v0.1.34(Mar 16, 2022)
What's Changed

To fix errors when rendering images with no boxes by @alexheat in https://github.com/pylabel-project/pylabel/pull/36

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.33...v0.1.34
Source code(tar.gz)
Source code(zip)
v0.1.33(Feb 26, 2022)
What's Changed

Resolve issues #29 no annotations in Yolo file by @alexheat in https://github.com/pylabel-project/pylabel/pull/30

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.32...v0.1.33
Source code(tar.gz)
Source code(zip)
v0.1.32(Feb 20, 2022)
What's Changed

Add support for multiple image extension for YOLOv5 dataset by @alexheat in https://github.com/pylabel-project/pylabel/pull/27

This should resolve issue #26 (https://github.com/pylabel-project/pylabel/issues/26) by adding support to be able to import a Yolov5 dataset with images in multi image formats. For example jpg and png.

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.31...v0.1.32
Source code(tar.gz)
Source code(zip)
v0.1.31(Feb 11, 2022)
This release resolves issue #23

What's Changed

Update README.md by @alexheat in https://github.com/pylabel-project/pylabel/pull/19

Resolve Issue #23 by @alexheat in https://github.com/pylabel-project/pylabel/pull/24

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.29...v0.1.31
Source code(tar.gz)
Source code(zip)
v0.1.30(Jan 13, 2022)
This is a test of the auto publish action

What's Changed

Fixing tests that use nbmake by @alexheat in https://github.com/pylabel-project/pylabel/pull/12

Merge by @alexheat in https://github.com/pylabel-project/pylabel/pull/13

fix "Max Recursion Level Reached" issue by @Daniel-R-Armstrong in https://github.com/pylabel-project/pylabel/pull/14

Push latest work to main by @alexheat in https://github.com/pylabel-project/pylabel/pull/17

Push latest to main by @alexheat in https://github.com/pylabel-project/pylabel/pull/18

Update README.md by @alexheat in https://github.com/pylabel-project/pylabel/pull/19

Push latest to main by @alexheat in https://github.com/pylabel-project/pylabel/pull/20

New Contributors

@Daniel-R-Armstrong made their first contribution in https://github.com/pylabel-project/pylabel/pull/14

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.28...v0.1.30
Source code(tar.gz)
Source code(zip)
v0.1.29(Jan 12, 2022)
New Contributors

@Daniel-R-Armstrong made their first contribution in https://github.com/pylabel-project/pylabel/pull/14

What's Changed

Fixing tests that use nbmake by @alexheat in https://github.com/pylabel-project/pylabel/pull/12

Merge by @alexheat in https://github.com/pylabel-project/pylabel/pull/13

fix "Max Recursion Level Reached" issue by @Daniel-R-Armstrong in https://github.com/pylabel-project/pylabel/pull/14

Push latest work to main by @alexheat in https://github.com/pylabel-project/pylabel/pull/17

Push latest to main by @alexheat in https://github.com/pylabel-project/pylabel/pull/18

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.28...v0.1.29
Source code(tar.gz)
Source code(zip)
v0.1.28(Jan 12, 2022)

Added new property. analyze.class_name_id_map. See docs at https://pylabel.readthedocs.io/en/latest/pylabel.html#pylabel.analyze.Analyze.class_name_id_map

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.27...v0.1.28
Source code(tar.gz)
Source code(zip)
v0.1.26(Jan 11, 2022)

Fixed small bug caused when saving images with no annotations to Yolo format.

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.25...v0.1.26
Source code(tar.gz)
Source code(zip)
v0.1.25(Jan 6, 2022)

This release fixes a bug found in the VOC exporter by convert bbox coordinates to ints.

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.24...v0.1.25
Source code(tar.gz)
Source code(zip)
v0.1.24(Dec 30, 2021)
What's Changed

Added more doc strings and created documentation at https://pylabel.readthedocs.io/en/latest/. See new badge on readme to access the docs

Add workflow to enable CI on the package by @rajasvu in https://github.com/pylabel-project/pylabel/pull/8

Main by @alexheat in https://github.com/pylabel-project/pylabel/pull/10

New Contributors

@rajasvu made their first contribution in https://github.com/pylabel-project/pylabel/pull/8

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.22...v0.1.24
Source code(tar.gz)
Source code(zip)
v0.1.22(Dec 23, 2021)

The output of dataset.analyze.classes and dataset.analyze.class_ids are now sorted by the class id. For example:

` Class ids: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 69, 71, 72, 73, 74, 75, 76, 77, 79]

Classes: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'stop sign', 'bench', 'bird', 'cat', 'dog', 'horse', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'cell phone', 'microwave', 'oven', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'toothbrush'] `

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.21...v0.1.22
Source code(tar.gz)
Source code(zip)
v0.1.21(Dec 18, 2021)
What's Changed

ExportToCoco function mapping cat_ids to [index_base, index_base + nu… by @ytzeng1 in https://github.com/pylabel-project/pylabel/pull/7

Added new methods and parameters to handle non-continuous class ids, which cause problems when training models with many computer vision frameworks including Yolo and Detection. Use the dataset.ReindexCatIds() method to make all the cat_ids continuous starting from 0 or 1. Use the cat_id_index parameter in export functions to make the cat_ids of the output annotations continuous, without changing the the dataset itself.

New Contributors

@ytzeng1 made their first contribution in https://github.com/pylabel-project/pylabel/pull/7

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.20...v0.1.21
Source code(tar.gz)
Source code(zip)
v0.1.20(Dec 14, 2021)
This update partially fixes the issues mentioned in #6 (Category_id exported as string instead of int )

cat_id is now an int in the COCO export

iscrowd value defaults to 0 in COCO export

You can see an example of the COCO output by running this notebook https://github.com/pylabel-project/samples/blob/main/yolo2coco.ipynb

Thank you @ytzeng1 for reporting this issue

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.19...v0.1.20
Source code(tar.gz)
Source code(zip)
v0.1.19(Dec 5, 2021)

Fixes Issues #3 ImportCoco Fails if path_to_images is not specified
Source code(tar.gz)
Source code(zip)
v0.1.18(Dec 4, 2021)
What's Changed

Cvat compatibility and bug fixes by @landegt in https://github.com/pylabel-project/pylabel/pull/1

New Contributors

@landegt made their first contribution in https://github.com/pylabel-project/pylabel/pull/1

Full Changelog: https://github.com/pylabel-project/pylabel/compare/v0.1.14...v0.1.18
Source code(tar.gz)
Source code(zip)
v0.1.14(Nov 28, 2021)

Source code(tar.gz)
Source code(zip)
v0.1.11(Nov 17, 2021)

Source code(tar.gz)
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Owner

PyLabel Project

Home of the PyLabel Python package and labeling tool for computer vision annotations.

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Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. CVPR 2018

Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning Tensorflow code and models for the paper: Large Scale Fine-Grained Categ

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Code accompanying "Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity," accepted to IEEE SSCI ICES 2021

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A minimalist implementation of score-based diffusion model

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Predicts an answer in yes or no.

Oui-ou-non-prediction Predicts an answer in 'yes' or 'no'. It is based on the game 'effeuiller la marguerite' in which the person plucks flower petals

1 Jan 15, 2022

The implementation of 'Image synthesis via semantic composition'.

Image synthesis via semantic synthesis [Project Page] by Yi Wang, Lu Qi, Ying-Cong Chen, Xiangyu Zhang, Jiaya Jia. Introduction This repository gives

71 Jan 06, 2023

Code for "Causal autoregressive flows" - AISTATS, 2021

Code for "Causal Autoregressive Flow" This repository contains code to run and reproduce experiments presented in Causal Autoregressive Flows, present

35 Dec 16, 2022

RGB-stacking 🛑 🟩 🔷 for robotic manipulation

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543 Jan 08, 2023

B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search

B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search This is the offical implementation of the

0 Feb 07, 2022

FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data

FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data. Flexible EM-Inspired Discriminant Analysis is a robust supervised classification algorithm that performs well i

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Implement the Pareto Optimizer and pcgrad to make a self-adaptive loss for multi-task

multi-task_losses_optimizer Implement the Pareto Optimizer and pcgrad to make a self-adaptive loss for multi-task 已经实验过了，不会有cuda out of memory情况 ##Par

14 Dec 25, 2022

Official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition" in AAAI2022.

AimCLR This is an official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Reco

44 Dec 17, 2022

Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.

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PyLabel

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CVAT Compatibility and over bug fixes

Releases(v0.1.42)

v0.1.42(Dec 29, 2022)

What's Changed

v0.1.41(Aug 27, 2022)

What's Changed

New Contributors

v0.1.40(Jun 3, 2022)

What's Changed

v0.1.39(May 1, 2022)

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v0.1.38(Apr 28, 2022)

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New Contributors

v0.1.37(Apr 24, 2022)

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v0.1.36(Apr 15, 2022)

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v0.1.35(Mar 17, 2022)

What's Changed

v0.1.34.2(Mar 16, 2022)

v0.1.34(Mar 16, 2022)

What's Changed

v0.1.33(Feb 26, 2022)

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v0.1.32(Feb 20, 2022)

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v0.1.31(Feb 11, 2022)

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v0.1.30(Jan 13, 2022)

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New Contributors

v0.1.29(Jan 12, 2022)

New Contributors

What's Changed

v0.1.28(Jan 12, 2022)

v0.1.26(Jan 11, 2022)

v0.1.25(Jan 6, 2022)

v0.1.24(Dec 30, 2021)

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New Contributors

v0.1.22(Dec 23, 2021)

v0.1.21(Dec 18, 2021)

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New Contributors

v0.1.20(Dec 14, 2021)

v0.1.19(Dec 5, 2021)

v0.1.18(Dec 4, 2021)

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New Contributors

v0.1.14(Nov 28, 2021)

v0.1.11(Nov 17, 2021)

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PyLabel Project

Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. CVPR 2018

Code accompanying "Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity," accepted to IEEE SSCI ICES 2021

A minimalist implementation of score-based diffusion model

Predicts an answer in yes or no.

The implementation of 'Image synthesis via semantic composition'.

Code for "Causal autoregressive flows" - AISTATS, 2021

RGB-stacking 🛑 🟩 🔷 for robotic manipulation

FinRL­-Meta: A Universe for Data­-Driven Financial Reinforcement Learning. 🔥

B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search

FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data

Implement the Pareto Optimizer and pcgrad to make a self-adaptive loss for multi-task

Official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition" in AAAI2022.

Rotated Box Is Back : Accurate Box Proposal Network for Scene Text Detection

Deep functional residue identification

Flax is a neural network ecosystem for JAX that is designed for flexibility.

Code for our paper "Sematic Representation for Dialogue Modeling" in ACL2021

This is the code for the paper "Motion-Focused Contrastive Learning of Video Representations" (ICCV'21).

[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

FinRL-Meta: A Universe for Data-Driven Financial Reinforcement Learning. 🔥