From efade89a578a73453350c4d472dc6611b9527089 Mon Sep 17 00:00:00 2001
From: Guillaume Raffy <guillaume.raffy@univ-rennes1.fr>
Date: Wed, 30 Nov 2022 18:44:46 +0100
Subject: [PATCH] cleanup: now the debug images are only outputted in debug
 mode

also removed debugging traces
---
 grassloper/main.py | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/grassloper/main.py b/grassloper/main.py
index 9b2a1e9..6f2580e 100755
--- a/grassloper/main.py
+++ b/grassloper/main.py
@@ -71,7 +71,7 @@ def hdf5_to_trac_data(hdf5_file_path: Path):
     date = f.attrs['date']  # noqa
     trac_data.num_frames = f.attrs['nFrames']
     trac_data.image_size = f.attrs['size']
-    print(type(trac_data.image_size))
+    # print(type(trac_data.image_size))
     # f.attrs['size'] = I0f.shape
     # for items in th[0].keys():
     #     f.attrs['th:' + items] = th[0][items]
@@ -97,11 +97,10 @@ class SlopeFinder():
         isbead_image = SlopeFinder.create_isbead_image(trac_data, frame_index, self.beads_radius)
         mo_pro_listener.onImage(isbead_image, 'isbead')
         mo_pro_listener.onBaseImage(isbead_image, 'isbead')
-        cv2.imwrite('isbead_%04d.tif' % frame_index, isbead_image)
         if False:
             kernel = np.ones((15, 15), np.uint8)
             closing = cv2.morphologyEx(isbead_image, cv2.MORPH_CLOSE, kernel)
-            cv2.imwrite('closing_%04d.tif' % frame_index, closing)
+            mo_pro_listener.onImage(closing, 'closing')
         # remove the jumping beads
         # apply connected component analysis to the thresholded image
         # thresh = cv2.threshold(isbead_image, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
@@ -109,35 +108,36 @@ class SlopeFinder():
         connectivity = 4
         output = cv2.connectedComponentsWithStats(thresh, connectivity, cv2.CV_32S)
         (num_labels, labels_image, stats, centroids) = output
-        print('num_labels: ', num_labels)
-        print('labels_image: ', labels_image)
-        print('stats: ', stats)
-        print('centroids: ', centroids)
-        cv2.imwrite('labels_%04d.tif' % frame_index, labels_image)
+        # print('num_labels: ', num_labels)
+        # print('labels_image: ', labels_image)
+        # print('stats: ', stats)
+        # print('centroids: ', centroids)
+        mo_pro_listener.onImage(labels_image, 'labels')
         is_non_jumping_bead_image = np.zeros(shape=labels_image.shape, dtype=np.uint8)
         bead_area = math.pi * self.beads_radius * self.beads_radius
         for label in range(1, num_labels):  # ignore the first label, as it's the background
             area = stats[label][4]
             if area > bead_area * 1.1:
                 is_non_jumping_bead_image[labels_image == label] = 255
-        cv2.imwrite('non_jumping_beads_%04d.tif' % frame_index, is_non_jumping_bead_image)
+        mo_pro_listener.onImage(is_non_jumping_bead_image, 'non_jumping_beads')
+
         # extract the surface points
         surface_y = np.ndarray(shape=(is_non_jumping_bead_image.shape[1],), dtype=int)
         surface_y.fill(is_non_jumping_bead_image.shape[0])
         contours, hierarchy = cv2.findContours(is_non_jumping_bead_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
-        print('num contours: ', len(contours))
+        # print('num contours: ', len(contours))
         assert(len(contours) == 1)
         contour = contours[0]
         for point in contour:
             x, y = point[0]
             surface_y[x] = min(surface_y[x], y)
-        print(surface_y)
+        # print(surface_y)
         surface_pts = []
         for x in range(len(surface_y)):
             y = surface_y[x]
             if y != is_non_jumping_bead_image.shape[0]:
                 surface_pts.append((x, y))
-        print(surface_pts)
+        # print(surface_pts)
         x = [pt[0] for pt in surface_pts]
         y = [pt[1] for pt in surface_pts]
         lin_regress_result = scipy.stats.linregress(x, y)
@@ -151,12 +151,12 @@ class SlopeFinder():
     @staticmethod
     def create_isbead_image(trac_data: TracData, frame_index: int, particle_radius: float):
         image = np.zeros(shape=trac_data.image_size, dtype=float)
-        print(trac_data.pts)
+        # print(trac_data.pts)
         frame_pts = trac_data.pts[trac_data.pts[:, 0] == frame_index]
         for x in frame_pts[:, 2:4]:
             center_coordinates = (int(x[0]), int(x[1]))
             # center_coordinates = (10,10)
-            print(center_coordinates)
+            # print(center_coordinates)
             cv2.circle(image, center_coordinates, int(particle_radius), color=1.0, thickness=-1)
         return image