Python skimage.measure.ransac() Examples

def get_inliers(loc1, desc1, loc2, desc2):
    n_feat1, n_feat2 = loc1.shape[0], loc2.shape[0]

    # from scipy.spatial import cKDTree
    KD_THRESH = 0.8
    d1_tree = cKDTree(desc1)
    distances, indices = d1_tree.query(desc2, distance_upper_bound=KD_THRESH)

    loc2_to_use = np.array([
        loc2[i, ] for i in range(n_feat2) if indices[i] != n_feat1])
    loc1_to_use = np.array([
        loc1[indices[i], ] for i in range(n_feat2) if indices[i] != n_feat1])

    np.random.seed(114514)

    # from skimage.measure import ransac as _ransac
    # from skimage.transform import AffineTransform
    model_robust, inliers = _ransac(
        (loc1_to_use, loc2_to_use),
        AffineTransform,
        min_samples=3,
        residual_threshold=20,
        max_trials=1000)

    return sum(inliers) 

def register_image_pair(idx, path_img_target, path_img_source, path_out):
    """ register two images together

    :param int idx: empty parameter for using the function in parallel
    :param str path_img_target: path to the target image
    :param str path_img_source: path to the source image
    :param str path_out: path for exporting the output
    :return tuple(str,float):
    """
    start = time.time()
    # load and denoise reference image
    img_target = io.imread(path_img_target)
    img_target = denoise_wavelet(img_target, wavelet_levels=7, multichannel=True)
    img_target_gray = rgb2gray(img_target)

    # load and denoise moving image
    img_source = io.imread(path_img_source)
    img_source = denoise_bilateral(img_source, sigma_color=0.05,
                                   sigma_spatial=2, multichannel=True)
    img_source_gray = rgb2gray(img_source)

    # detect ORB features on both images
    detector_target = ORB(n_keypoints=150)
    detector_source = ORB(n_keypoints=150)
    detector_target.detect_and_extract(img_target_gray)
    detector_source.detect_and_extract(img_source_gray)
    matches = match_descriptors(detector_target.descriptors,
                                detector_source.descriptors)
    # robustly estimate affine transform model with RANSAC
    model, _ = ransac((detector_target.keypoints[matches[:, 0]],
                       detector_source.keypoints[matches[:, 1]]),
                      AffineTransform, min_samples=25, max_trials=500,
                      residual_threshold=0.95)

    # warping source image with estimated transformations
    img_warped = warp(img_target, model.inverse, output_shape=img_target.shape[:2])
    path_img_warped = os.path.join(path_out, NAME_IMAGE_WARPED % idx)
    io.imsave(path_img_warped, img_warped)
    # summarise experiment
    execution_time = time.time() - start
    return path_img_warped, execution_time 

def ransac_linefit_sklearn(points):
        """
        Use sklearn ransac function to fit the line
        :param points:
        :return: skimage ransac return the model param set ('origin', 'direction')
        """
        # robustly fit line only using inlier data with RANSAC algorithm
        model_robust, inliers = ransac(points, LineModelND, min_samples=2,
                                       residual_threshold=1, max_trials=1000)
        return model_robust, inliers 

def segment_fit_ellipse_ransac(seg, centers, fn_preproc_points, nb_inliers=0.6,
                               thr_overlap=SEGM_OVERLAP):
    """ segment eggs using ellipse fitting and RANDSAC strategy

    :param ndarray seg: input image / segmentation
    :param [[int, int]] centers: position of centres / seeds
    :param fn_preproc_points: function for detection boundary points
    :param float nb_inliers: ratio of inliers for RANSAC
    :param float thr_overlap: threshold for removing overlapping segmentations
    :return (ndarray, [[int, int]]): resulting segmentation, updated centres
    """
    points_centers = fn_preproc_points(seg, centers)

    centres_new, ell_params = [], []
    segm = np.zeros_like(seg)
    for i, points in enumerate(points_centers):
        lb = i + 1
        nb_min = int(len(points) * nb_inliers)
        ransac_model, _ = measure.ransac(points, EllipseModel,
                                         min_samples=nb_min,
                                         residual_threshold=15,
                                         max_trials=250)
        if not ransac_model:
            continue
        logging.debug('ellipse params: %r', ransac_model.params)
        segm = ell_fit.add_overlap_ellipse(segm, ransac_model.params, lb,
                                           thr_overlap)

        if np.any(segm == lb):
            centres_new.append(centers[i])
            ell_params.append(ransac_model.params)

    dict_export = {'ellipses.csv': pd.DataFrame(ell_params, columns=COLUMNS_ELLIPSE)}
    return segm, np.array(centres_new), dict_export 

def estimateF(img1, img2):
    
    np.random.seed(0)

    #img1, img2, groundtruth_disp = data.stereo_motorcycle()

    img1_gray, img2_gray = map(rgb2gray, (img1, img2))
    
    descriptor_extractor = ORB()

    descriptor_extractor.detect_and_extract(img1_gray)
    keypoints_left = descriptor_extractor.keypoints
    descriptors_left = descriptor_extractor.descriptors

    descriptor_extractor.detect_and_extract(img2_gray)
    keypoints_right = descriptor_extractor.keypoints
    descriptors_right = descriptor_extractor.descriptors

    matches = match_descriptors(descriptors_left, descriptors_right,
                            cross_check=True)

    # Estimate the epipolar geometry between the left and right image.

    model, inliers = ransac((keypoints_left[matches[:, 0]],
                         keypoints_right[matches[:, 1]]),
                        FundamentalMatrixTransform, min_samples=8,
                        residual_threshold=1, max_trials=5000)

    inlier_keypoints_left = keypoints_left[matches[inliers, 0]]
    inlier_keypoints_right = keypoints_right[matches[inliers, 1]]

    print("Number of matches:", matches.shape[0])
    print("Number of inliers:", inliers.sum())

    # Visualize the results.
    '''
    fig, ax = plt.subplots(nrows=2, ncols=1)

    plt.gray()

    plot_matches(ax[0], img1, img2, keypoints_left, keypoints_right,
                 matches[inliers], only_matches=True)
    ax[0].axis("off")
    ax[0].set_title("Inlier correspondences")
    
    plt.show()
    '''
    #import pdb; pdb.set_trace()
    
    return model, matches.shape[0], inliers.sum(), inlier_keypoints_left, inlier_keypoints_right 

def estimateF(img1, img2):
    
    np.random.seed(0)

    #img1, img2, groundtruth_disp = data.stereo_motorcycle()

    img1_gray, img2_gray = map(rgb2gray, (img1, img2))
    
    descriptor_extractor = ORB()

    descriptor_extractor.detect_and_extract(img1_gray)
    keypoints_left = descriptor_extractor.keypoints
    descriptors_left = descriptor_extractor.descriptors

    descriptor_extractor.detect_and_extract(img2_gray)
    keypoints_right = descriptor_extractor.keypoints
    descriptors_right = descriptor_extractor.descriptors

    matches = match_descriptors(descriptors_left, descriptors_right,
                            cross_check=True)

    # Estimate the epipolar geometry between the left and right image.

    model, inliers = ransac((keypoints_left[matches[:, 0]],
                         keypoints_right[matches[:, 1]]),
                        FundamentalMatrixTransform, min_samples=8,
                        residual_threshold=1, max_trials=5000)

    inlier_keypoints_left = keypoints_left[matches[inliers, 0]]
    inlier_keypoints_right = keypoints_right[matches[inliers, 1]]

    print("Number of matches:", matches.shape[0])
    print("Number of inliers:", inliers.sum())

    # Visualize the results.
    '''
    fig, ax = plt.subplots(nrows=2, ncols=1)

    plt.gray()

    plot_matches(ax[0], img1, img2, keypoints_left, keypoints_right,
                 matches[inliers], only_matches=True)
    ax[0].axis("off")
    ax[0].set_title("Inlier correspondences")
    
    plt.show()
    '''
    #import pdb; pdb.set_trace()
    
    return model, matches.shape[0], inliers.sum(), inlier_keypoints_left, inlier_keypoints_right 

def match_frames(f1, f2):
  bf = cv2.BFMatcher(cv2.NORM_HAMMING)
  matches = bf.knnMatch(f1.des, f2.des, k=2)

  # Lowe's ratio test
  ret = []
  idx1, idx2 = [], []
  idx1s, idx2s = set(), set()

  for m,n in matches:
    if m.distance < 0.75*n.distance:
      p1 = f1.kps[m.queryIdx]
      p2 = f2.kps[m.trainIdx]

      # be within orb distance 32
      if m.distance < 32:
        # keep around indices
        # TODO: refactor this to not be O(N^2)
        if m.queryIdx not in idx1s and m.trainIdx not in idx2s:
          idx1.append(m.queryIdx)
          idx2.append(m.trainIdx)
          idx1s.add(m.queryIdx)
          idx2s.add(m.trainIdx)
          ret.append((p1, p2))

  # no duplicates
  assert(len(set(idx1)) == len(idx1))
  assert(len(set(idx2)) == len(idx2))

  assert len(ret) >= 8
  ret = np.array(ret)
  idx1 = np.array(idx1)
  idx2 = np.array(idx2)

  # fit matrix
  model, inliers = ransac((ret[:, 0], ret[:, 1]),
                          EssentialMatrixTransform,
                          min_samples=8,
                          residual_threshold=RANSAC_RESIDUAL_THRES,
                          max_trials=RANSAC_MAX_TRIALS)
  print("Matches:  %d -> %d -> %d -> %d" % (len(f1.des), len(matches), len(inliers), sum(inliers)))
  return idx1[inliers], idx2[inliers], fundamentalToRt(model.params) 

def main(unused_argv):
  tf.logging.set_verbosity(tf.logging.INFO)

  # Read features.
  locations_1, _, descriptors_1, _, _ = feature_io.ReadFromFile(
      cmd_args.features_1_path)
  num_features_1 = locations_1.shape[0]
  tf.logging.info("Loaded image 1's %d features" % num_features_1)
  locations_2, _, descriptors_2, _, _ = feature_io.ReadFromFile(
      cmd_args.features_2_path)
  num_features_2 = locations_2.shape[0]
  tf.logging.info("Loaded image 2's %d features" % num_features_2)

  # Find nearest-neighbor matches using a KD tree.
  d1_tree = cKDTree(descriptors_1)
  distances, indices = d1_tree.query(
      descriptors_2, distance_upper_bound=_DISTANCE_THRESHOLD)

  # Select feature locations for putative matches.
  locations_2_to_use = np.array([
      locations_2[i,] for i in range(num_features_2)
      if indices[i] != num_features_1
  ])
  locations_1_to_use = np.array([
      locations_1[indices[i],] for i in range(num_features_2)
      if indices[i] != num_features_1
  ])

  # Perform geometric verification using RANSAC.
  model_robust, inliers = ransac(
      (locations_1_to_use, locations_2_to_use),
      AffineTransform,
      min_samples=3,
      residual_threshold=20,
      max_trials=1000)

  tf.logging.info('Found %d inliers' % sum(inliers))

  # Visualize correspondences, and save to file.
  fig, ax = plt.subplots()
  img_1 = mpimg.imread(cmd_args.image_1_path)
  img_2 = mpimg.imread(cmd_args.image_2_path)
  inlier_idxs = np.nonzero(inliers)[0]
  plot_matches(
      ax,
      img_1,
      img_2,
      locations_1_to_use,
      locations_2_to_use,
      np.column_stack((inlier_idxs, inlier_idxs)),
      matches_color='b')
  ax.axis('off')
  ax.set_title('DELF correspondences')
  plt.savefig(cmd_args.output_image) 

def main(unused_argv):
  tf.logging.set_verbosity(tf.logging.INFO)

  # Read features.
  locations_1, _, descriptors_1, _, _ = feature_io.ReadFromFile(
      cmd_args.features_1_path)
  num_features_1 = locations_1.shape[0]
  tf.logging.info("Loaded image 1's %d features" % num_features_1)
  locations_2, _, descriptors_2, _, _ = feature_io.ReadFromFile(
      cmd_args.features_2_path)
  num_features_2 = locations_2.shape[0]
  tf.logging.info("Loaded image 2's %d features" % num_features_2)

  # Find nearest-neighbor matches using a KD tree.
  d1_tree = cKDTree(descriptors_1)
  _, indices = d1_tree.query(
      descriptors_2, distance_upper_bound=_DISTANCE_THRESHOLD)

  # Select feature locations for putative matches.
  locations_2_to_use = np.array([
      locations_2[i,]
      for i in range(num_features_2)
      if indices[i] != num_features_1
  ])
  locations_1_to_use = np.array([
      locations_1[indices[i],]
      for i in range(num_features_2)
      if indices[i] != num_features_1
  ])

  # Perform geometric verification using RANSAC.
  _, inliers = ransac(
      (locations_1_to_use, locations_2_to_use),
      AffineTransform,
      min_samples=3,
      residual_threshold=20,
      max_trials=1000)

  tf.logging.info('Found %d inliers' % sum(inliers))

  # Visualize correspondences, and save to file.
  _, ax = plt.subplots()
  img_1 = mpimg.imread(cmd_args.image_1_path)
  img_2 = mpimg.imread(cmd_args.image_2_path)
  inlier_idxs = np.nonzero(inliers)[0]
  plot_matches(
      ax,
      img_1,
      img_2,
      locations_1_to_use,
      locations_2_to_use,
      np.column_stack((inlier_idxs, inlier_idxs)),
      matches_color='b')
  ax.axis('off')
  ax.set_title('DELF correspondences')
  plt.savefig(cmd_args.output_image) 

def main(unused_argv):
  tf.logging.set_verbosity(tf.logging.INFO)

  # Read features.
  locations_1, _, descriptors_1, _, _ = feature_io.ReadFromFile(
      cmd_args.features_1_path)
  num_features_1 = locations_1.shape[0]
  tf.logging.info("Loaded image 1's %d features" % num_features_1)
  locations_2, _, descriptors_2, _, _ = feature_io.ReadFromFile(
      cmd_args.features_2_path)
  num_features_2 = locations_2.shape[0]
  tf.logging.info("Loaded image 2's %d features" % num_features_2)

  # Find nearest-neighbor matches using a KD tree.
  d1_tree = cKDTree(descriptors_1)
  distances, indices = d1_tree.query(
      descriptors_2, distance_upper_bound=_DISTANCE_THRESHOLD)

  # Select feature locations for putative matches.
  locations_2_to_use = np.array([
      locations_2[i,] for i in range(num_features_2)
      if indices[i] != num_features_1
  ])
  locations_1_to_use = np.array([
      locations_1[indices[i],] for i in range(num_features_2)
      if indices[i] != num_features_1
  ])

  # Perform geometric verification using RANSAC.
  model_robust, inliers = ransac(
      (locations_1_to_use, locations_2_to_use),
      AffineTransform,
      min_samples=3,
      residual_threshold=20,
      max_trials=1000)

  tf.logging.info('Found %d inliers' % sum(inliers))

  # Visualize correspondences, and save to file.
  fig, ax = plt.subplots()
  img_1 = mpimg.imread(cmd_args.image_1_path)
  img_2 = mpimg.imread(cmd_args.image_2_path)
  inlier_idxs = np.nonzero(inliers)[0]
  plot_matches(
      ax,
      img_1,
      img_2,
      locations_1_to_use,
      locations_2_to_use,
      np.column_stack((inlier_idxs, inlier_idxs)),
      matches_color='b')
  ax.axis('off')
  ax.set_title('DELF correspondences')
  plt.savefig(cmd_args.output_image) 

def main(unused_argv):
  tf.logging.set_verbosity(tf.logging.INFO)

  # Read features.
  locations_1, _, descriptors_1, _, _ = feature_io.ReadFromFile(
      cmd_args.features_1_path)
  num_features_1 = locations_1.shape[0]
  tf.logging.info("Loaded image 1's %d features" % num_features_1)
  locations_2, _, descriptors_2, _, _ = feature_io.ReadFromFile(
      cmd_args.features_2_path)
  num_features_2 = locations_2.shape[0]
  tf.logging.info("Loaded image 2's %d features" % num_features_2)

  # Find nearest-neighbor matches using a KD tree.
  d1_tree = cKDTree(descriptors_1)
  _, indices = d1_tree.query(
      descriptors_2, distance_upper_bound=_DISTANCE_THRESHOLD)

  # Select feature locations for putative matches.
  locations_2_to_use = np.array([
      locations_2[i,]
      for i in range(num_features_2)
      if indices[i] != num_features_1
  ])
  locations_1_to_use = np.array([
      locations_1[indices[i],]
      for i in range(num_features_2)
      if indices[i] != num_features_1
  ])

  # Perform geometric verification using RANSAC.
  _, inliers = ransac(
      (locations_1_to_use, locations_2_to_use),
      AffineTransform,
      min_samples=3,
      residual_threshold=20,
      max_trials=1000)

  tf.logging.info('Found %d inliers' % sum(inliers))

  # Visualize correspondences, and save to file.
  _, ax = plt.subplots()
  img_1 = mpimg.imread(cmd_args.image_1_path)
  img_2 = mpimg.imread(cmd_args.image_2_path)
  inlier_idxs = np.nonzero(inliers)[0]
  plot_matches(
      ax,
      img_1,
      img_2,
      locations_1_to_use,
      locations_2_to_use,
      np.column_stack((inlier_idxs, inlier_idxs)),
      matches_color='b')
  ax.axis('off')
  ax.set_title('DELF correspondences')
  plt.savefig(cmd_args.output_image) 

def main(unused_argv):
  # Read features.
  locations_1, _, descriptors_1, _, _ = feature_io.ReadFromFile(
      cmd_args.features_1_path)
  num_features_1 = locations_1.shape[0]
  print(f"Loaded image 1's {num_features_1} features")
  locations_2, _, descriptors_2, _, _ = feature_io.ReadFromFile(
      cmd_args.features_2_path)
  num_features_2 = locations_2.shape[0]
  print(f"Loaded image 2's {num_features_2} features")

  # Find nearest-neighbor matches using a KD tree.
  d1_tree = spatial.cKDTree(descriptors_1)
  _, indices = d1_tree.query(
      descriptors_2, distance_upper_bound=_DISTANCE_THRESHOLD)

  # Select feature locations for putative matches.
  locations_2_to_use = np.array([
      locations_2[i,]
      for i in range(num_features_2)
      if indices[i] != num_features_1
  ])
  locations_1_to_use = np.array([
      locations_1[indices[i],]
      for i in range(num_features_2)
      if indices[i] != num_features_1
  ])

  # Perform geometric verification using RANSAC.
  _, inliers = measure.ransac((locations_1_to_use, locations_2_to_use),
                              transform.AffineTransform,
                              min_samples=3,
                              residual_threshold=20,
                              max_trials=1000)

  print(f'Found {sum(inliers)} inliers')

  # Visualize correspondences, and save to file.
  _, ax = plt.subplots()
  img_1 = mpimg.imread(cmd_args.image_1_path)
  img_2 = mpimg.imread(cmd_args.image_2_path)
  inlier_idxs = np.nonzero(inliers)[0]
  feature.plot_matches(
      ax,
      img_1,
      img_2,
      locations_1_to_use,
      locations_2_to_use,
      np.column_stack((inlier_idxs, inlier_idxs)),
      matches_color='b')
  ax.axis('off')
  ax.set_title('DELF correspondences')
  plt.savefig(cmd_args.output_image) 

def main(unused_argv):
  tf.logging.set_verbosity(tf.logging.INFO)

  # Read features.
  locations_1, _, descriptors_1, _, _ = feature_io.ReadFromFile(
      cmd_args.features_1_path)
  num_features_1 = locations_1.shape[0]
  tf.logging.info("Loaded image 1's %d features" % num_features_1)
  locations_2, _, descriptors_2, _, _ = feature_io.ReadFromFile(
      cmd_args.features_2_path)
  num_features_2 = locations_2.shape[0]
  tf.logging.info("Loaded image 2's %d features" % num_features_2)

  # Find nearest-neighbor matches using a KD tree.
  d1_tree = cKDTree(descriptors_1)
  _, indices = d1_tree.query(
      descriptors_2, distance_upper_bound=_DISTANCE_THRESHOLD)

  # Select feature locations for putative matches.
  locations_2_to_use = np.array([
      locations_2[i,]
      for i in range(num_features_2)
      if indices[i] != num_features_1
  ])
  locations_1_to_use = np.array([
      locations_1[indices[i],]
      for i in range(num_features_2)
      if indices[i] != num_features_1
  ])

  # Perform geometric verification using RANSAC.
  _, inliers = ransac(
      (locations_1_to_use, locations_2_to_use),
      AffineTransform,
      min_samples=3,
      residual_threshold=20,
      max_trials=1000)

  tf.logging.info('Found %d inliers' % sum(inliers))

  # Visualize correspondences, and save to file.
  _, ax = plt.subplots()
  img_1 = mpimg.imread(cmd_args.image_1_path)
  img_2 = mpimg.imread(cmd_args.image_2_path)
  inlier_idxs = np.nonzero(inliers)[0]
  plot_matches(
      ax,
      img_1,
      img_2,
      locations_1_to_use,
      locations_2_to_use,
      np.column_stack((inlier_idxs, inlier_idxs)),
      matches_color='b')
  ax.axis('off')
  ax.set_title('DELF correspondences')
  plt.savefig(cmd_args.output_image)