pyrf package

Submodules

pyrf._pyrf module

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pyrf._pyrf.C_ARRAY_CHAR

alias of LP_c_char_p

pyrf._pyrf.C_ARRAY_FLOAT

alias of LP_c_float

pyrf._pyrf.RESULTS_ARRAY

IMPORTANT:

For functions that return void, use Python None as the return value. For functions that take no parameters, use the Python empty list [].

alias of ndpointer_<u8_1d_ALIGNED_C_CONTIGUOUS_WRITEABLE

class pyrf._pyrf.Random_Forest_Detector(rf, verbose=False)

Bases: object

detect(rf, forest, input_gpath_list, **kwargs)

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

Parameters:

• forest (object) – the forest obejct that you want to use during detection
• input_gpath_list (list of str) – the list of image paths that you want to test

Kwargs:

output_gpath_list (list of str, optional): the paralell list of output

image paths for detection debugging or results; defaults to None

When this list is None no images are outputted for any test images, whereas the list can be a parallel list where some values are strings and others are None

output_scale_gpath_list (list of str, optional): the paralell list of output

scale image paths for detection debugging or results; defaults to None

When this list is None no images are outputted for any test images, whereas the list can be a parallel list where some values are strings and others are None

mode (int, optional): the mode that the detector outputs; detaults to 0

0 - Hough Voting - the output is a Hough image that predicts the

locations of the obejct centeroids

0 - Classification Map - the output is a classification probability

map across the entire image where no regression information is utilized

sensitivity (float, optional): the sensitivity of the detector;

mode = 0 - defaults to 255.0 mode = 1 - defaults to 255.0

scale_list (list of float, optional): the list of floats that specifies the scales

to try during testing; defaults to [1.33, 1.00, 0.75, 0.56, 0.42, 0.32, 0.24, 0.18]

scale > 1.0 - Upscale the image scale = 1.0 - Original image size scale < 1.0 - Downscale the image

The list of scales highly impacts the performance of the detector and should be carefully chosen

The scales are applied to BOTH the width and the height of the image in order to scale the image and an interpolation of CV_INTER_LINEAR is used

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

nms_min_area_contour (int, optional): the minimum size of a centroid

candidate region; defaults to 300

nms_min_area_overlap (float, optional, DEPRICATED): the allowable overlap in

bounding box predictions; defaults to 0.75

serial (bool, optional): flag to signify if to run detection in serial;

len(input_gpath_list) >= batch_size - defaults to False len(input_gpath_list) < batch_size - defaults to False

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:

centerx (int): the x position of the object’s centroid

Note that the center of the bounding box and the location of the object’s centroid can be different

centery (int): the y position of the obejct’s centroid

Note that the center of the bounding box and the location of the object’s centroid can be different

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 confidence (float): the confidence that this bounding box is of

the class specified by the trees used during testing

suppressed (bool, DEPRICATED): the flag of if this bounding

box has been marked to be suppressed by the detection algorithm

dump(rf, file)

UNIMPLEMENTED

Returns:None

dumps(rf)

UNIMPLEMENTED

Returns:string

forest(rf, tree_path_list, **kwargs)

Create the forest object by loading a list of tree paths.

Parameters:

• tree_path_list (list of str) – list of tree paths as strings
• serial (bool, optional) – flag to signify if to load the forest in serial; defaults to False
• verbose (bool, optional) – verbose flag; defaults to object’s verbose or selectively enabled for this function

Returns:

forest – the forest object of the loaded trees

Return type:

object

load(file)

UNIMPLEMENTED

Returns:detector (object)

loads(string)

UNIMPLEMENTED

Returns:detector (object)

train(rf, train_pos_cpath_list, train_neg_cpath_list, trees_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

chips_prob_flip_horizontally (float, optional): The probability

that a chips is flipped horizontally before training to make the training set invariant to horizontal flips in the image; defaults to 0.5; 0.0 <= chips_prob_flip_horizontally <= 1.0

chips_prob_flip_vertically (float, optional): The probability

that a chips is flipped vertivcally before training to make the training set invariant to vertical flips in the image; defaults to 0.5; 0.0 <= chips_prob_flip_vertically <= 1.0

patch_width (int, optional): the width of the patches for extraction

in the tree; defaults to 32; patch_width > 0

patch_height (int, optional): the height of the patches for extraction

in the tree; defaults to 32; patch_height > 0

patch_density (float, optional): the number of patches to extract from

each chip as a function of density; the density is calculated as:

samples = patch_density * [(chip_width * chip_height) / (patch_width * patch_height)]

and specifies how many times a particular pixel is sampled from the chip; defaults to 4.0; patch_density > 0

trees_num (int, optional): the number of trees to train in parallel;

defaults to 10

trees_offset (int, optional): the tree number that begins the sequence

of when a tree is trained; defaults to None

If None is specified, the trees_offset value is automatically guessed by using the number of files in trees_path

Tree model files are overwritten if the offset has overlap with previouly generated trees

trees_max_depth (int, optional): the maximum depth of the tree during

training, this can used for regularization; defaults to 16

trees_max_patches (int, optional): the maximum number of patches that

should be extracted for training between positives AND negatives (the detector attempts to balance between the number of positive and negative patches to be roughly the same in quantity); defaults to 64000

trees_leaf_size (int, optional): the number of patches in a node that

specifies the threshold for becoming a leaf; defaults to 20

A node becomes a leaf under two conditions:

1.) The maximum tree depth has been reached (trees_max_depth) 2.) The patches in the node is less than trees_leaf_size and

is stopped prematurely

trees_pixel_tests (int, optional): the number of pixel tests to perform

at each node; defaults to 2000

trees_prob_optimize_mode (float, optional): The probability of the

tree optimizing between classification and regression; defaults to 0.5

serial (bool, optional): flag to signify if to run training in serial;

defaults to False

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

selectively enabled for this function

Returns:None

train_folder(rf, train_pos_path, train_neg_path, trees_path, **kwargs)

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class pyrf._pyrf.Random_Forest_Detector(rf, **kwargs)

Bases: object

detect(rf, forest, image_fpath, result_fpath)

detect_many(rf, forest, image_fpath_list, result_fpath_list)

WIP

load(rf, tree_path, prefix, num_trees=10)

retrain(rf)

save(rf)

segment(rf)

set_detect_params(rf, **kwargs)

train(rf, database_path, category, pos_path, neg_path, val_path, test_path, trees_path, **kwargs)

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pyrf._pyrf.VERBOSE_RF = False

Bindings for C Variable Types

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pyrf._pyrf.arrptr_to_np(c_arrptr, shape, arr_t, dtype)

Casts an array pointer from C to numpy Input:

c_arrpt - an array pointer returned from C shape - shape of that array pointer arr_t - the ctypes datatype of c_arrptr

Avi’s code

pyrf._pyrf.extract_2darr_list(size_list, ptr_list, arr_t, arr_dtype, arr_dim)

size_list - contains the size of each output 2d array ptr_list - an array of pointers to the head of each output 2d

array (which was allocated in C)

arr_t - the C pointer type arr_dtype - the numpy array type arr_dim - the number of columns in each output 2d array

pyrf._pyrf.load_pyrf_clib(rebuild=False)

Loads the pyrf dynamic library and defines its functions

pyrf._pyrf.results_array_t

This defines the default constructor parameters for the algorithm. These values may be overwritten by passing in a dictionary to the class constructor using kwargs

constructor_parameters = [

(parameter type, parameter name, parameter default value),

]

IMPORTANT: The order of this list must match the C++ constructor parameter order

alias of ndpointer_<u8_1d_ALIGNED_C_CONTIGUOUS_WRITEABLE

pyrf._pyrf.str_list_t

alias of LP_c_char_p

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pyrf.pyrf_helpers module

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pyrf.pyrf_helpers.ensuredir(path)

pyrf.pyrf_helpers.get_training_data_from_ibeis(dataset_path, category, pos_path, neg_path, val_path, test_path, **kwargs)

pyrf.pyrf_helpers.rmtreedir(path)

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Module contents