refactor: small refactor fot detect function

calibrators/intrinsic_camera_calibrator/intrinsic_camera_calibrator/intrinsic_camera_calibrator/board_detectors/chessboard_detector.py

@@ -73,6 +73,38 @@ def get_roi(corners, frame_shape, padding=local_padding):
             x_min, y_min = max(0, x_min), max(0, y_min)
             x_max, y_max = min(frame_shape[1], x_max), min(frame_shape[0], y_max)
             return (x_min, y_min, x_max, y_max)
+
+        def calculate_new_size(w,h):
+            # Find the resized dimensions
+            ratio = float(w) / float(h)
+
+            if w > h:
+                resized_w = int(resized_max_resolution)
+                resized_h = int(resized_max_resolution / ratio)
+            else:
+                resized_w = int(resized_max_resolution * ratio)
+                resized_h = int(resized_max_resolution)
+
+            return resized_w, resized_h
+
+        def do_corner_sub_pix(corners):
+            dist_matrix = np.linalg.norm(
+                corners.reshape(-1, 1, 2) - corners.reshape(1, -1, 2), axis=-1
+            )
+            np.fill_diagonal(dist_matrix, np.inf)
+            min_distance = dist_matrix.min()
+            radius = max(
+                1, int(np.ceil(min_distance * 0.5))
+            )  # ensuring radius has a value of at least 1
+
+            criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
+
+            try:
+                corners = cv2.cornerSubPix(grayscale, corners, (radius, radius), (-1, -1), criteria)
+            except Exception as e:
+                logging.error(e)
+
+            return corners
 
         h, w = img.shape[0:2]
         grayscale = to_grayscale(img)
@@ -103,18 +135,10 @@ def get_roi(corners, frame_shape, padding=local_padding):
                     self.detection_results_signal.emit(img, None, stamp)
                     return
         else:
-            # Find the resized dimensions
-            ratio = float(w) / float(h)
-
-            if w > h:
-                resized_w = int(resized_max_resolution)
-                resized_h = int(resized_max_resolution / ratio)
-            else:
-                resized_w = int(resized_max_resolution * ratio)
-                resized_h = int(resized_max_resolution)
+            resized_w, resized_h = calculate_new_size(w,h)
 
             # Resize
-            resized = cv2.resize(img, (resized_w, resized_h), interpolation=cv2.INTER_NEAREST)
+            resized = cv2.resize(grayscale, (resized_w, resized_h), interpolation=cv2.INTER_NEAREST)
 
             # Run the detector on the resized image
             (ok, resized_corners) = cv2.findChessboardCorners(resized, (cols, rows), flags=flags)
@@ -129,10 +153,7 @@ def get_roi(corners, frame_shape, padding=local_padding):
             )
 
             # Estimate the ROI in the original image
-            roi_min_j = int(max(0, corners[:, 0, 1].min() - 10))
-            roi_min_i = int(max(0, corners[:, 0, 0].min() - 10))
-            roi_max_j = int(min(w, corners[:, 0, 1].max() + 10))
-            roi_max_i = int(min(w, corners[:, 0, 0].max() + 10))
+            roi_min_i, roi_min_j, roi_max_i, roi_max_j = get_roi(corners, img.shape[:2])
 
             # Extract the ROI of the original image
             roi = grayscale[roi_min_j:roi_max_j, roi_min_i:roi_max_i]
@@ -148,21 +169,7 @@ def get_roi(corners, frame_shape, padding=local_padding):
             corners = roi_corners + np.array([roi_min_i, roi_min_j], dtype=np.float32)
 
         if sub_pixel_refinement:
-            dist_matrix = np.linalg.norm(
-                corners.reshape(-1, 1, 2) - corners.reshape(1, -1, 2), axis=-1
-            )
-            np.fill_diagonal(dist_matrix, np.inf)
-            min_distance = dist_matrix.min()
-            radius = max(
-                1, int(np.ceil(min_distance * 0.5))
-            )  # ensuring radius has a value of at least 1
-
-            criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
-
-            try:
-                corners = cv2.cornerSubPix(grayscale, corners, (radius, radius), (-1, -1), criteria)
-            except Exception as e:
-                logging.error(e)
+            corners = do_corner_sub_pix(corners=corners)
 
         image_points = corners.reshape((rows, cols, 2))
         x_array = cell_size * (np.array(range(cols)) - 0.5 * cols)