Tidied code

slider/chart_reg.py

@@ -16,9 +16,6 @@ import matplotlib.pyplot as plt
 
 
 def register_chart_to_slide(chart, slide, out, rlevel=3, boundary_key='outline'):
-    print(chart)
-    print(slide)
-    print(out)
 
     # load chart
     contour, cells = neurolucida.read(chart)
@@ -35,12 +32,46 @@ def register_chart_to_slide(chart, slide, out, rlevel=3, boundary_key='outline')
         f'Rotation: {init.rotation}, Translation: {init.translation}, Scale: {init.scale}, Shear: {init.shear}')
 
     # calculate normal line to boundary points
-    # TODO: move to function
     xfm_edge_coords = _apply_xfm(init, edge_crds)
     init_nrmls = normal(xfm_edge_coords, plot=False, distance=1, verbose=True)
 
-    edge_x, edge_y = np.round(xfm_edge_coords.T).astype(int)
-    nrml_x, nrml_y = init_nrmls.T
+    # refine edge_coords (to image)
+    refined_edge_coords = _refine_edge_coord(img, xfm_edge_coords, init_nrmls)
+
+    # estimate opimised affine transform
+    opt = transform.AffineTransform()
+    opt.estimate(edge_crds, refined_edge_coords)
+    print(opt)
+    print(
+        f'Rotation:\t{opt.rotation}\nTranslation:\t{opt.translation}\nScale:\t\t{list(opt.scale)}\nShear:\t\t{opt.shear}')
+
+    # save opt transform
+    if not op.exists(out):
+        os.makedirs(out)
+
+    np.savetxt(f'{out}/chart-to-image.xfm', opt.params)
+
+    # apply opt-xfm to contours and cells and save
+
+    contour_xfm = {k: _apply_xfm(
+        opt, v[:, :2] * [1, -1]).tolist() for k, v in contour.items()}
+    with open(f'{out}/contour.json', 'w') as fp:
+        json.dump(contour_xfm, fp, indent=4)
+
+    if cells.size > 0:
+        cells_xfm = _apply_xfm(cells[:, :2] * [1, -1])
+        with open(f'{out}/cells.json', 'w') as fp:
+            json.dump(cells_xfm, fp, indent=4)
+
+
+def _refine_edge_coord(img, edge_coords, normals):
+    """
+    Refine edge_coord by sampling binarised image along normal (to edge) and looking for big step change.
+    """
+
+    # calculate normal line (to edge_coords)
+    edge_x, edge_y = np.round(edge_coords.T).astype(int)
+    nrml_x, nrml_y = normals.T
 
     line_smpl = np.arange(-20, 20)
 
@@ -52,8 +83,6 @@ def register_chart_to_slide(chart, slide, out, rlevel=3, boundary_key='outline')
         edge_y[:, np.newaxis] + nrml_y[:, np.newaxis] * line_smpl
     ).astype(int)
 
-    # --- refine edge coord by sampling along normal and looking for big change
-
     # find threshold for background
     image = rgb2gray(img)
     threshold_value = filters.threshold_otsu(image)
@@ -84,30 +113,8 @@ def register_chart_to_slide(chart, slide, out, rlevel=3, boundary_key='outline')
         line_y[np.arange(min_idx.size), min_idx],
     ], axis=-1)
 
-    # estimate opimised affine transform
-    opt = transform.AffineTransform()
-    opt.estimate(edge_crds, refined_edge_coords)
-    print(opt)
-    print(
-        f'Rotation:\t{opt.rotation}\nTranslation:\t{opt.translation}\nScale:\t\t{list(opt.scale)}\nShear:\t\t{opt.shear}')
-
-    # save opt transform
-    if not op.exists(out):
-        os.makedirs(out)
-
-    np.savetxt(f'{out}/chart-to-image.xfm', opt.params)
-
-    # apply opt-xfm to contours and cells and save
+    return refined_edge_coords
 
-    contour_xfm = {k: _apply_xfm(
-        opt, v[:, :2] * [1, -1]).tolist() for k, v in contour.items()}
-    with open(f'{out}/contour.json', 'w') as fp:
-        json.dump(contour_xfm, fp, indent=4)
-
-    if cells.size > 0:
-        cells_xfm = _apply_xfm(cells[:, :2] * [1, -1])
-        with open(f'{out}/cells.json', 'w') as fp:
-            json.dump(cells_xfm, fp, indent=4)
 
 
 def _img_props(img, verbose=False, plots=False):

slider_app.py

@@ -25,9 +25,6 @@ def add_slide_cli(subparsers):
     parser.add_argument("--config", metavar="<default.json>",
                         help="configuration file", default='default.json', type=str,
                         required=False)
-    # parser.add_argument("-v", "--verbose", default=False, action="store_true",
-    #                     help="Print status messages to the command line",
-    #                     required=False)
     parser.set_defaults(method='slide')