IVALab Python Libraries
Collection of code for computer vision and robotics with specific API.
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A puzzle piece clustering method based on color. The feature extractor should be based on color. More...
Public Member Functions | |
def | __init__ (self, thePuzzle, extractor=Histogram(), theParams=ParamColorCluster) |
Constructor for the byColor class. More... | |
def | process (self) |
Extract color features from the data. More... | |
def | score (self, cluster_id_pred_dict, method='label') |
Score the clustering result. More... | |
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def | __init__ (self, *argv) |
Constructor for puzzle board. More... | |
def | addPiece (self, piece, ORIGINAL_ID=False) |
Add puzzle piece instance to the board. More... | |
def | addPieceFromImageAndMask (self, theImage, theMask, cLoc=None) |
Given a mask and an image of same base dimensions, use to instantiate a puzzle piece template. More... | |
def | addPieces (self, pieces) |
Add puzzle piece to board. More... | |
def | boundingBox (self) |
Iterate through pieces to get tight bounding box. More... | |
def | clear (self) |
def | display_cv (self, theImage=None, fh=None, ID_DISPLAY=False, CONTOUR_DISPLAY=False, BOUNDING_BOX=False, window_name='Puzzle') |
Display the puzzle board as an image using matplot library. More... | |
def | display_mp (self, theImage=None, ax=None, fh=None, ID_DISPLAY=False, CONTOUR_DISPLAY=False, BOUNDING_BOX=False) |
Display the puzzle board as an image using matplot library. More... | |
def | extents (self) |
Iterate through puzzle pieces to get tight bounding box extents of the board. More... | |
def | fromImageAndLabels (self, theImage, theLabels) |
Template | getPiece (self, id) |
Get puzzle piece instance based on id. More... | |
def | markMissing (self, indSetMeasured) |
Given set of indices to measured pieces, mark remaining as unmeasured. More... | |
def | offset (self, dr) |
Offset the location of the entire puzzle in the board. More... | |
def | pieceLocations (self, isCenter=False) |
Returns list/array of puzzle piece locations. More... | |
def | relabel (self, newLabels, idContinue) |
Relabel the puzzle piece IDs in the board using new label reassignments and adjust IDs for those without reassignment. More... | |
def | rmPiece (self, id) |
def | size (self) |
Number of pieces on the board. More... | |
def | testAdjacent (self, id_A, id_B, tauAdj) |
Check if two puzzle pieces are adjacent or not. More... | |
def | toImage (self, theImage=None, ID_DISPLAY=False, COLOR=(0, 0, 0), ID_COLOR=(255, 255, 255), CONTOUR_DISPLAY=True, BOUNDING_BOX=True) |
Uses puzzle piece locations to create an image for visualizing them. More... | |
Public Attributes | |
feaExtractor | |
feaLabel | |
feaLabel_dict | |
feature | |
feature_dict | |
params | |
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id_count | |
pieces | |
A puzzle piece clustering method based on color. The feature extractor should be based on color.
def __init__ | ( | self, | |
thePuzzle, | |||
extractor = Histogram() , |
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theParams = ParamColorCluster |
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) |
Constructor for the byColor class.
Args: thePuzzle: The input puzzle board. extractor: A matcher instance. theParams: The param for threshold.
def process | ( | self | ) |
Extract color features from the data.
Since this instance is a board, the presumption is that there is a measurement available in the stored board data. If there is nothing then there can be no clustering achieved.
def score | ( | self, | |
cluster_id_pred_dict, | |||
method = 'label' |
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) |
Score the clustering result.
See https://scikit-learn.org/stable/modules/clustering.html#overview-of-clustering-methods
Args: cluster_id_pred_dict: The predicted cluster id for each piece. method: The method to score the clustering result. ['label', 'histogram']
Returns: The score of the clustering result.
feaExtractor |
feaLabel |
feaLabel_dict |
feature |
feature_dict |
params |