Improved line detection

src/processing/linefinder.py

@@ -108,59 +108,56 @@ def group_empty_boxes(seams):
 
 
 def generate_lines(labeled, ncomponents, gray):
-    plt.imshow(labeled, cmap=rand_cmap(ncomponents, type='hard', first_color_black=True, last_color_black=False, verbose=False))
-    plt.show()
-    plt.imsave("result/groups.png", labeled, cmap=rand_cmap(ncomponents, type='hard', first_color_black=True, last_color_black=False, verbose=False))
     groups = np.copy(labeled)
     group_id = 1
-
     entries = []
     pixelgroup = None
     in_top_mode = True
     indices = np.indices(labeled.shape).T[:, :, [1, 0]]
     indices = np.swapaxes(indices, 0, 1)
-    colors = [[0, 0, 0]]
+
+    def submit_entry():
+        nonlocal pixelgroup, group_id, entries
+        minps = np.min(pixelgroup, axis=0)
+        maxps = np.max(pixelgroup, axis=0)
+        if pixel.shape[0] < 20 or maxps[1] - minps[1] < 5 or maxps[0] - minps[0] < 5:
+            return
+        entry = gray[minps[1]:maxps[1] + 1, minps[0]:maxps[0] + 1]
+        if pixelgroup.shape[0] / entry.shape[0] / entry.shape[1] < 0.10:
+            return
+        pixelgroup = np.subtract(pixelgroup, minps)
+        white = np.ones_like(entry) * np.max(entry)
+        for w in range(entry.shape[1]):
+            ys = pixelgroup[pixelgroup[:, 0] == w][:, 1]
+            if ys.size > 0:
+                ymin = np.min(ys)
+                ymax = np.max(ys)
+                white[ymin:ymax, w] = entry[ymin:ymax, w]
+        #white[pixelgroup[:, 1], pixelgroup[:, 0]] = entry[pixelgroup[:, 1], pixelgroup[:, 0]]
+        entries.append(white)
+        pixelgroup = None
+        group_id += 1
+
     for label in range(1, ncomponents+1):
         pixel = indices[labeled == label]
         minp = np.min(pixel, axis=0)
         maxp = np.max(pixel, axis=0)
         right_pixel = pixel[pixel[:, 0] == maxp[0]][:, 1]
         second_pixel = pixel[pixel[:, 0] == maxp[0]-1][:, 1]
-        color = [0, 0, 1]
         if second_pixel.size > 0 and min(right_pixel) > min(second_pixel):
             # up
-            color[0] = 1
             if not in_top_mode:
-                minps = np.min(pixelgroup, axis=0)
+                submit_entry()
-                maxps = np.max(pixelgroup, axis=0)
-                if pixel.shape[0] < 20 or maxps[1]-minps[1] < 5 or maxps[0]-minps[0] < 5:
-                    continue
-                entry = gray[minps[1]:maxps[1]+1, minps[0]:maxps[0]+1]
-                pixelgroup = np.subtract(pixelgroup, minps)
-                white = np.ones_like(entry) * np.max(entry)
-                white[pixelgroup[:, 1], pixelgroup[:, 0]] = entry[pixelgroup[:, 1], pixelgroup[:, 0]]
-                entries.append(white)
-                pixelgroup = None
-                group_id += 1
             in_top_mode = True
         if second_pixel.size > 0 and max(right_pixel) < max(second_pixel):
             # down
-            color[1] = 1
             in_top_mode = False
+        if pixelgroup is not None and minp[1] - 5 > np.max(pixelgroup, axis=0)[1]:
+            submit_entry()
         groups[labeled == label] = group_id
         if pixelgroup is None:
             pixelgroup = pixel[:, :]
         else:
             pixelgroup = np.concatenate((pixelgroup, pixel))
-        colors.append(color)
+    submit_entry()
-    #plt.imsave("result/groups_types.png", labeled,
-    #           cmap=list_cmap(np.array(colors)))
-    #g = np.array(load_image("result/groups_types.png")[:, :, :3], dtype="float")
-    #b = load_image("result/gray.png")
-    #t = (g[:, :, :3] + np.tile(b[:, :], (3, 1, 1)).swapaxes(2, 0).swapaxes(1, 0) * 1) / 2
-    #t = (g[:, :, :3] + np.tile(b[:, :], (3, 1, 1)).swapaxes(2, 0).swapaxes(1, 0) * 1) / 2
-    #save_image("result/combined_types.png", t/255)
-    #plt.imsave("result/combined_new.png", groups,
-    #           cmap=rand_cmap(500, type='hard', first_color_black=True, last_color_black=False, verbose=False))
-
     return entries