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d37bcbdc92
Introduce Cropping to OpenCV Stitching Tool * Introduced Cropping * integrate https://github.com/opencv/opencv/pull/21413/ * fixed failing subsetter tests * updated stuff
95 lines
3.5 KiB
Python
95 lines
3.5 KiB
Python
from itertools import chain
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import math
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import cv2 as cv
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import numpy as np
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from .feature_matcher import FeatureMatcher
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from .stitching_error import StitchingError
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class Subsetter:
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DEFAULT_CONFIDENCE_THRESHOLD = 1
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DEFAULT_MATCHES_GRAPH_DOT_FILE = None
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def __init__(self,
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confidence_threshold=DEFAULT_CONFIDENCE_THRESHOLD,
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matches_graph_dot_file=DEFAULT_MATCHES_GRAPH_DOT_FILE):
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self.confidence_threshold = confidence_threshold
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self.save_file = matches_graph_dot_file
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def subset(self, img_names, img_sizes, imgs, features, matches):
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self.save_matches_graph_dot_file(img_names, matches)
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indices = self.get_indices_to_keep(features, matches)
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img_names = Subsetter.subset_list(img_names, indices)
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img_sizes = Subsetter.subset_list(img_sizes, indices)
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imgs = Subsetter.subset_list(imgs, indices)
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features = Subsetter.subset_list(features, indices)
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matches = Subsetter.subset_matches(matches, indices)
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return img_names, img_sizes, imgs, features, matches
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def save_matches_graph_dot_file(self, img_names, pairwise_matches):
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if self.save_file:
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with open(self.save_file, 'w') as filehandler:
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filehandler.write(self.get_matches_graph(img_names,
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pairwise_matches)
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)
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def get_matches_graph(self, img_names, pairwise_matches):
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return cv.detail.matchesGraphAsString(img_names, pairwise_matches,
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self.confidence_threshold)
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def get_indices_to_keep(self, features, pairwise_matches):
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indices = cv.detail.leaveBiggestComponent(features,
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pairwise_matches,
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self.confidence_threshold)
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if len(indices) < 2:
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raise StitchingError("No match exceeds the "
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"given confidence theshold.")
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return indices
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@staticmethod
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def subset_list(list_to_subset, indices):
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return [list_to_subset[i] for i in indices]
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@staticmethod
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def subset_matches(pairwise_matches, indices):
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indices_to_delete = Subsetter.get_indices_to_delete(
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math.sqrt(len(pairwise_matches)),
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indices
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)
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matches_matrix = FeatureMatcher.get_matches_matrix(pairwise_matches)
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matches_matrix_subset = Subsetter.subset_matrix(matches_matrix,
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indices_to_delete)
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matches_subset = Subsetter.matrix_rows_to_list(matches_matrix_subset)
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return matches_subset
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@staticmethod
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def get_indices_to_delete(nr_elements, indices_to_keep):
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return list(set(range(int(nr_elements))) - set(indices_to_keep))
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@staticmethod
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def subset_matrix(matrix_to_subset, indices_to_delete):
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for idx, idx_to_delete in enumerate(indices_to_delete):
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matrix_to_subset = Subsetter.delete_index_from_matrix(
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matrix_to_subset,
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idx_to_delete-idx # matrix shape reduced by one at each step
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)
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return matrix_to_subset
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@staticmethod
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def delete_index_from_matrix(matrix, idx):
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mask = np.ones(matrix.shape[0], bool)
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mask[idx] = 0
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return matrix[mask, :][:, mask]
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@staticmethod
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def matrix_rows_to_list(matrix):
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return list(chain.from_iterable(matrix.tolist()))
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