pydarknet package

Submodules

pydarknet.__main__ module

pydarknet.__main__.main()

pydarknet._pydarknet module

pydarknet._pydarknet.CONFIG_URL_DICT = {'template': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.template.cfg', 'original': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.5.cfg', 'old': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.5.cfg', 'v1': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.5.cfg', 'v2': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.12.cfg', 'v3': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.29.cfg', 'lynx': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.lynx.cfg', 'cheetah': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.cheetah.cfg', 'seaturtle': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.sea_turtle.cfg', 'sandtiger': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.shark_sandtiger.cfg', 'hammerhead': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.shark_hammerhead.cfg', 'whalefluke': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.whale_fluke.cfg', 'whalefluke_v2': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.whale_fluke.v2.cfg', 'sea': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.sea.cfg', 'candidacy': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.candidacy.cfg', 'default': 'https://wildbookiarepository.azureedge.net/models/detect.yolo.29.cfg', None: 'https://wildbookiarepository.azureedge.net/models/detect.yolo.29.cfg'}

Bindings for C Variable Types

pydarknet._pydarknet.C_ARRAY_CHAR

alias of pydarknet._pydarknet.LP_c_char_p

pydarknet._pydarknet.C_ARRAY_FLOAT

alias of pydarknet._pydarknet.LP_c_float

class pydarknet._pydarknet.Darknet_YOLO_Detector(config_filepath=None, weights_filepath=None, classes_filepath=None, verbose=True, quiet=False)

Bases: object

detect(input_gpath_list, **kwargs)

Run detection with a given loaded forest on a list of images

Parameters:

• input_gpath_list (list of str) – the list of image paths that you want
• test (to) –
• config_filepath (str, optional) – the network definition for YOLO to use
• weights_filepath (str, optional) – the network weights for YOLO to use

Kwargs:

sensitivity (float, optional): the sensitivity of the detector, which accepts a value between 0.0 and 1.0; defaults to 0.0 batch_size (int, optional): the number of images to test at a single time in paralell (if None, the number of CPUs is used); defaults to None verbose (bool, optional): verbose flag; defaults to object’s verbose or selectively enabled for this function

Yields:

(str, (list of dict)) – tuple of the input image path and a list of dictionaries specifying the detected bounding boxes

The dictionaries returned by this function are of the form:

xtl (int): the top left x position of the bounding box ytl (int): the top left y position of the bounding box width (int): the width of the bounding box height (int): the hiehgt of the bounding box class (str): the most probably class detected by the network confidence (float): the confidence that this bounding box is of the class specified by the trees used during testing

CommandLine:

python -m pydarknet._pydarknet detect –show

Example

>>> # DISABLE_DOCTEST
>>> from pydarknet._pydarknet import *  # NOQA
>>> dpath = '/media/raid/work/WS_ALL/localizer_backup/'
>>> weights_filepath = join(dpath, 'detect.yolo.2.39000.weights')
>>> config_filepath = join(dpath, 'detect.yolo.2.cfg')
>>> dark = Darknet_YOLO_Detector(config_filepath=config_filepath,
>>>                              weights_filepath=weights_filepath)
>>> input_gpath_list = [u'/media/raid/work/WS_ALL/_ibsdb/images/0cb41f1e-d746-3052-ded4-555e11eb718b.jpg']
>>> kwargs = {}
>>> (input_gpath, result_list_) = dark.detect(input_gpath_list)
>>> result = ('(input_gpath, result_list_) = %s' % (ut.repr2((input_gpath, result_list_)),))
>>> print(result)
>>> ut.quit_if_noshow()
>>> import wbia.plottool as pt
>>> ut.show_if_requested()

dump(file)

UNIMPLEMENTED

dumps()

UNIMPLEMENTED

load(file)

UNIMPLEMENTED

loads(string)

UNIMPLEMENTED

train(voc_path, weights_path, **kwargs)

Train a new forest with the given positive chips and negative chips.

Parameters:

• train_pos_chip_path_list (list of str) – list of positive training chips
• train_neg_chip_path_list (list of str) – list of negative training chips
• trees_path (str) – string path of where the newly trained trees are to be saved

Kwargs:

chips_norm_width (int, optional): Chip normalization width for resizing;

the chip is resized to have a width of chips_norm_width and whatever resulting height in order to best match the original aspect ratio; defaults to 128

If both chips_norm_width and chips_norm_height are specified, the original aspect ratio of the chip is not respected

chips_norm_height (int, optional): Chip normalization height for resizing;

the chip is resized to have a height of chips_norm_height and whatever resulting width in order to best match the original aspect ratio; defaults to None

If both chips_norm_width and chips_norm_height are specified, the original aspect ratio of the chip is not respected

verbose (bool, optional): verbose flag; defaults to object’s verbose or

selectively enabled for this function

Returns:None

pydarknet._pydarknet.RESULTS_ARRAY

alias of numpy.ctypeslib.ndpointer_<u8_1d_ALIGNED_C_CONTIGUOUS_WRITEABLE

pydarknet._pydarknet.test_pydarknet()

CommandLine:

python -m pydarknet._pydarknet –exec-test_pydarknet –show

Example

>>> # ENABLE_DOCTEST
>>> from pydarknet._pydarknet import *  # NOQA
>>> test_pydarknet()
>>> ut.quit_if_noshow()
>>> ut.show_if_requested()

pydarknet._pydarknet.test_pydarknet2(input_gpath_list=None, config_filepath=None, weights_filepath=None, classes_filepath=None)

CommandLine:

python -m pydarknet._pydarknet test_pydarknet2 –show

python -m pydarknet test_pydarknet2 –show

–input_gpath_list=[“~/work/WS_ALL/_ibsdb/images/0cb41f1e-d746-3052-ded4-555e11eb718b.jpg”] –config_filepath=”~/work/WS_ALL/localizer_backup/detect.yolo.2.cfg” –weights_filepath=”~/work/WS_ALL/localizer_backup/detect.yolo.2.39000.weights” –classes_filepath=”~/work/WS_ALL/localizer_backup/detect.yolo.2.cfg.classes”

Ignore:

>>> # Load in the second command line strings for faster testing
>>> from pydarknet._pydarknet import *  # NOQA
>>> cmdstr = ut.get_func_docblocks(test_pydarknet2)['CommandLine:'].split('\n\n')[1]
>>> ut.aug_sysargv(cmdstr)

Example

>>> # DISABLE_DOCTEST
>>> from pydarknet._pydarknet import *  # NOQA
>>> funckw = ut.argparse_funckw(test_pydarknet2)
>>> exec(ut.execstr_dict(funckw), globals())
>>> output_fpaths = test_pydarknet2(**funckw)
>>> ut.quit_if_noshow()
>>> import wbia.plottool as pt
>>> inter = pt.MultiImageInteraction(output_fpaths)
>>> inter.start()
>>> ut.show_if_requested()

pydarknet.ctypes_interface module

pydarknet.ctypes_interface.find_lib_fpath(libname, root_dir, recurse_down=True, verbose=False)

Search for the library

pydarknet.ctypes_interface.get_lib_dpath_list(root_dir)

returns possible lib locations

Parameters:root_dir (str) – deepest directory to look for a library (dll, so, dylib) Returns:plausible directories to look for libraries Return type:list

pydarknet.ctypes_interface.get_lib_fname_list(libname)

Parameters:libname (str) – library name (e.g. ‘hesaff’, not ‘libhesaff’) Returns:list of plausible library file names Return type:list

pydarknet.ctypes_interface.load_clib(libname, root_dir)

Does the work.

Parameters:

• libname (str) – library name (e.g. ‘hesaff’, not ‘libhesaff’)
• root_dir (str) – the deepest directory searched for the library file (dll, dylib, or so).

Returns:

clib a ctypes object used to interface with the library

Return type:

ctypes.cdll

pydarknet.pydarknet_helpers module

pydarknet.pydarknet_helpers.ensure_bytes_strings(str_list)

Module contents