Merge pull request #184 from yfukai/update_example

updated example

.gitignore

@@ -10,3 +10,4 @@ __pycache__/
 tests/data/testimages_lowncc.npy
 tests/data/testimages_lowncc2.npy
 tests/data/testimages_lowncc2.csv
+examples/stitched_image.npy

examples/stitching_example.py

@@ -12,40 +12,42 @@
 props_file_path = path.join(script_path, "../tests/data/testimages_props.csv")
 images = np.load(image_file_path)
 props = pd.read_csv(props_file_path, index_col=0)
-rows = props["col"].to_list()
-cols = props["row"].to_list()
+
+rows = props["row"].to_list()
+cols = props["col"].to_list()
 
 print(images.shape)
 # must be 3-dim, with each dimension meaning (tile_index,x,y)
 print(rows)
-# the row indices for each tile index. for example, [1,1,2,2,2,...]
+# the row (second-last dim.) indices for each tile index. for example, [1,1,2,2,2,...]
 print(cols)
-# the column indices for each tile index. for example, [2,3,1,2,3,...]
+# the column (last dim.) indices for each tile index. for example, [2,3,1,2,3,...]
 
-result_df, _ = m2stitch.stitch_images(images, rows, cols)
+# Note : the row_col_transpose=True is kept only for the sake of version compatibility.
+# In the mejor version, the row_col_transpose=False will be the default.
+result_df, _ = m2stitch.stitch_images(images, rows, cols, row_col_transpose=False)
 
-print(result_df["x_pos"])
-# the absolute x positions of the tiles
 print(result_df["y_pos"])
-# the absolute y positions of the tiles
-
+# the absolute y (second last dim.) positions of the tiles
+print(result_df["x_pos"])
+# the absolute x (last dim.) positions of the tiles
 
 # stitching example
-result_df["x_pos2"] = result_df["x_pos"] - result_df["x_pos"].min()
 result_df["y_pos2"] = result_df["y_pos"] - result_df["y_pos"].min()
+result_df["x_pos2"] = result_df["x_pos"] - result_df["x_pos"].min()
 
-size_x = images.shape[1]
-size_y = images.shape[2]
+size_y = images.shape[1]
+size_x = images.shape[2]
 
 stitched_image_size = (
-    result_df["x_pos2"].max() + size_x,
     result_df["y_pos2"].max() + size_y,
+    result_df["x_pos2"].max() + size_x,
 )
 stitched_image = np.zeros_like(images, shape=stitched_image_size)
 for i, row in result_df.iterrows():
     stitched_image[
-        row["x_pos2"] : row["x_pos2"] + size_x,
         row["y_pos2"] : row["y_pos2"] + size_y,
+        row["x_pos2"] : row["x_pos2"] + size_x,
     ] = images[i]
 
 result_image_file_path = path.join(script_path, "stitched_image.npy")