added in label visualization to auto annotation runner

CHANGELOG.md

@@ -19,6 +19,7 @@ https://github.com/opencv/cvat/issues/750).
 - Datumaro is an experimental framework to build, analyze, debug and visualize datasets for DL algorithms
 - Text Detection Auto Annoation Script in OpenVINO format for version 4
 - Added in OpenVINO Semantic Segmentation for roads
+- Ability to visualize labels when using Auto Annotation runner
 
 ### Changed
 - page_size parameter for all REST API methods

utils/auto_annotation/README.md

@@ -37,8 +37,21 @@ $ python cvat/utils/auto_annotation/run_model.py --py /path/to/python/interp.py
                                                  --show-images
 ```
 
+If you'd like to see the labels printed on the image, use the `--show-labels` flag
+
+```shell
+$ python cvat/utils/auto_annotation/run_model.py --py /path/to/python/interp.py \
+                                                 --xml /path/to/xml/file.xml \
+                                                 --bin /path/to/bin/file.bin \
+                                                 --json /path/to/json/mapping/mapping.json \
+                                                 --image-files /path/to/img.jpg /path2/to/img2.png /path/to/img3.jpg \ 
+                                                 --show-images \
+						 --show-labels
+```
+
 There's a command that let's you scan quickly by setting the length of time (in milliseconds) to display each image. 
 Use the `--show-image-delay` flag and set the appropriate time.
+In this example, 2000 milliseconds is 2 seconds for each image.
 
 ```shell
 # Display each image in a window for 2 seconds

utils/auto_annotation/run_model.py

@@ -29,6 +29,7 @@ def _get_kwargs():
     parser.add_argument('--show-images', action='store_true', help='Show the results of the annotation in a window')
     parser.add_argument('--show-image-delay', default=0, type=int, help='Displays the images for a set duration in milliseconds, default is until a key is pressed')
     parser.add_argument('--serialize', default=False, action='store_true', help='Try to serialize the result')
+    parser.add_argument('--show-labels', action='store_true', help='Show the labels on the window')
 
     return vars(parser.parse_args())
 
@@ -45,6 +46,16 @@ def pairwise(iterable):
         result.append((iterable[i], iterable[i+1]))
     return np.array(result, dtype=np.int32)
 
+def find_min_y(array):
+    min_ = sys.maxsize
+    index = None
+    for i, pair in enumerate(array):
+        if pair[1] < min_:
+            min_ = pair[1]
+            index = i
+
+    return array[index]
+
 
 def main():
     kwargs = _get_kwargs()
@@ -104,34 +115,8 @@ def main():
                                               py_file,
                                               restricted=restricted)
 
-    if kwargs['serialize']:
-        os.environ['DJANGO_SETTINGS_MODULE'] = 'cvat.settings.production'
-        import django
-        django.setup()
-
-        from cvat.apps.engine.serializers import LabeledDataSerializer
 
-        # NOTE: We're actually using `run_inference_engine_annotation`
-        # incorrectly here. The `mapping` dict is supposed to be a mapping
-        # of integers -> integers and represents the transition from model
-        # integers to the labels in the database. We're using a mapping of
-        # integers -> strings. For testing purposes, this shortcut is fine.
-        # We just want to make sure everything works. Until, that is....
-        # we want to test using the label serializer. Then we have to transition
-        # back to integers, otherwise the serializer complains about have a string
-        # where an integer is expected. We'll just brute force that.
-
-        for shape in results['shapes']:
-            # Change the english label to an integer for serialization validation
-            shape['label_id'] = 1
-
-        serializer = LabeledDataSerializer(data=results)
-
-        if not serializer.is_valid():
-            logging.critical('Data unable to be serialized correctly!')
-            serializer.is_valid(raise_exception=True)
-
-    logging.warning('Program didn\'t have any errors.')
+    logging.warning('Inference didn\'t have any errors.')
     show_images = kwargs.get('show_images', False)
 
     if show_images:
@@ -146,23 +131,64 @@ def main():
             return
 
         show_image_delay = kwargs['show_image_delay']
+        show_labels = kwargs.get('show_labels')
+
         for index, data in enumerate(image_data):
             for detection in results['shapes']:
                 if not detection['frame'] == index:
                     continue
                 points = detection['points']
+                label_str = detection['label_id']
+
                 # Cv2 doesn't like floats for drawing
                 points = [int(p) for p in points]
                 color = random_color()
+
                 if detection['type'] == 'rectangle':
                     cv2.rectangle(data, (points[0], points[1]), (points[2], points[3]), color, 3)
+
+                    if show_labels:
+                        cv2.putText(data, label_str, (points[0], points[1] - 7), cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 1)
+
                 elif detection['type'] in ('polygon', 'polyline'):
                     # polylines is picky about datatypes
                     points = pairwise(points)
                     cv2.polylines(data, [points], 1, color)
+
+                    if show_labels:
+                        min_point = find_min_y(points)
+                        cv2.putText(data, label_str, (min_point[0], min_point[1] - 7), cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 1)
+
             cv2.imshow(str(index), data)
             cv2.waitKey(show_image_delay)
             cv2.destroyWindow(str(index))
 
+    if kwargs['serialize']:
+        os.environ['DJANGO_SETTINGS_MODULE'] = 'cvat.settings.production'
+        import django
+        django.setup()
+
+        from cvat.apps.engine.serializers import LabeledDataSerializer
+
+        # NOTE: We're actually using `run_inference_engine_annotation`
+        # incorrectly here. The `mapping` dict is supposed to be a mapping
+        # of integers -> integers and represents the transition from model
+        # integers to the labels in the database. We're using a mapping of
+        # integers -> strings. For testing purposes, this shortcut is fine.
+        # We just want to make sure everything works. Until, that is....
+        # we want to test using the label serializer. Then we have to transition
+        # back to integers, otherwise the serializer complains about have a string
+        # where an integer is expected. We'll just brute force that.
+
+        for shape in results['shapes']:
+            # Change the english label to an integer for serialization validation
+            shape['label_id'] = 1
+
+        serializer = LabeledDataSerializer(data=results)
+
+        if not serializer.is_valid():
+            logging.critical('Data unable to be serialized correctly!')
+            serializer.is_valid(raise_exception=True)
+
 if __name__ == '__main__':
     main()