better visualization for detected labels

minibot_vision/scripts/SegmentSign.py

@@ -210,7 +210,9 @@ def visualize_patches(keypoints, patches, text, img_rgb):
         # replace the patch of the zoomed sign so that the patch that is fed to t flow can be directly seen
         img_rgb[y[0]:y[1], x[0]:x[1]] = patch
         cv2.rectangle(img_rgb, (x[0], y[0]), (x[1], y[1]), (255, 255, 255), thickness=1)
-        cv2.putText(img_rgb, text[i], (x[0], y[0] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), thickness=1)
+        # draw a text within a rectangle of uniform color
+        cv2.rectangle(img_rgb, (x[0] - 10, y[0] - 25), (x[0] + TENSOR_RES[0] + 10, y[0]), (255, 255, 255), -1)
+        cv2.putText(img_rgb, text[i], (x[0] - 10, y[0] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 0, 0), thickness=2)
 
     return img_rgb
 

minibot_vision/scripts/SignDetector.py

@@ -154,7 +154,7 @@ def detect_sign(img_rgb_stream, image_timestamp):
 
         # publish results
         publish_results(center, r, d, label, precision, image_timestamp)
-        text.append("c: {} p: {:1.3f} d:{:1.3f}".format(sign_classifier.labelOfClass(label), precision, d))
+        text.append("c: {} p: {}% d: {:1.3f}".format(sign_classifier.labelOfClass(label), int(precision * 100), d))
 
     if visualize:
         if len(text) > 0: