Details of patch matching about dinov2 HOT 4 OPEN

def scaled_click_to_flattened_index(sc, Ph, Pw):
  dummy = np.zeros((Ph, Pw))
  row, col = sc
  dummy[row, col] = 1
  dummy = dummy.flatten()
  return np.where(dummy)

def flattened_index_to_scaled_click(find, Ph, Pw):
  dummy = np.zeros((Ph, Pw))
  dummy = dummy.flatten()
  dummy[find] = 1
  dummy = dummy.reshape((Ph, Pw))
  return np.where(dummy)



from scipy.optimize import linear_sum_assignment
queryfeats = feats[0]  # Ph x Pw x F, number of patches on the height and weight x number of features
for feat in feats[1:]:  # for all other feature maps, feats[0] is the source or query image
  # compute dissimilarity

  # # ram intensive way (too much for colab)
  # dsim = (queryfeats.reshape(Ph*Pw, 1, F) - feat.reshape(1, Ph*Pw, F)).norm(dim=2)

  # using a for loop + vectorization
  dsim = torch.zeros(Ph*Pw, Ph*Pw)
  vectorfeat = feat.reshape(Ph*Pw, F)
  for qind, queryfeat in enumerate(queryfeats.reshape(Ph*Pw, F)):
    print(f"{qind}/{Ph*Pw}", end='\r')
    dsim[qind] = (queryfeat.reshape(1, F) - vectorfeat).norm(dim=1)

  row_ind, col_ind = linear_sum_assignment(dsim)

  plt.figure()

  plt.subplot(2,2,1)  # plot mapping
  plt.scatter(row_ind, col_ind)

  plt.subplot(2,2,2)  # plot query image and clicks
  rgbquery = PCA(3).fit_transform(queryfeats.reshape(Ph*Pw, F)).reshape(Ph, Pw, 3)
  plt.imshow(norm_fn(rgbquery))
  sc = scaled_clicks[0]
  plt.scatter(sc[1],  Ph-sc[0], marker='x', s=50, c='g')
  sc = scaled_clicks[1]
  plt.scatter(sc[1],  Ph-sc[0], marker='x', s=50, c='r')

  plt.subplot(2,2,3)  # plot key image and mapped clicks
  rgbfeat = PCA(3).fit_transform(vectorfeat).reshape(Ph, Pw, 3)
  plt.imshow(norm_fn(rgbfeat))

  sc = scaled_clicks[0]
  sc_mapping_row = scaled_click_to_flattened_index(sc, Ph, Pw)
  mapped_col = col_ind[sc_mapping_row]
  mapped_sc = flattened_index_to_scaled_click(mapped_col, Ph, Pw)
  plt.scatter(mapped_sc[1],  Ph-mapped_sc[0], marker='x', s=50, c='g')

  sc = scaled_clicks[1]
  sc_mapping_row = scaled_click_to_flattened_index(sc, Ph, Pw)
  mapped_col = col_ind[sc_mapping_row]
  mapped_sc = flattened_index_to_scaled_click(mapped_col, Ph, Pw)
  plt.scatter(mapped_sc[1],  Ph-mapped_sc[0], marker='x', s=50, c='r')