Add image shape and scaling factor to image_meta

The image_meta tensor stores meta data about the
image. This update adds the scale by which the
image was resized and it’s final shape.

model.py

@@ -789,7 +789,7 @@ class DetectionLayer(KE.Layer):
         image_meta = inputs[3]
 
         # Run detection refinement graph on each item in the batch
-        _, _, window, _ = parse_image_meta_graph(image_meta)
+        window = parse_image_meta_graph(image_meta)['window']
         window = norm_boxes_graph(window, self.config.IMAGE_SHAPE[:2])
         detections_batch = utils.batch_slice(
             [rois, mrcnn_class, mrcnn_bbox, window],
@@ -1193,7 +1193,7 @@ def load_image_gt(dataset, config, image_id, augment=False, augmentation=None,
     # Load image and mask
     image = dataset.load_image(image_id)
     mask, class_ids = dataset.load_mask(image_id)
-    shape = image.shape
+    original_shape = image.shape
     image, window, scale, padding = utils.resize_image(
         image,
         min_dim=config.IMAGE_MIN_DIM,
@@ -1262,7 +1262,8 @@ def load_image_gt(dataset, config, image_id, augment=False, augmentation=None,
         mask = utils.minimize_mask(bbox, mask, config.MINI_MASK_SHAPE)
 
     # Image meta data
-    image_meta = compose_image_meta(image_id, shape, window, active_class_ids)
+    image_meta = compose_image_meta(image_id, original_shape, image.shape,
+                                    window, scale, active_class_ids)
 
     return image, image_meta, class_ids, bbox, mask
 
@@ -1926,8 +1927,9 @@ class MaskRCNN():
         if mode == "training":
             # Class ID mask to mark class IDs supported by the dataset the image
             # came from.
-            _, _, _, active_class_ids = KL.Lambda(lambda x: parse_image_meta_graph(x),
-                                                  mask=[None, None, None, None])(input_image_meta)
+            active_class_ids = KL.Lambda(
+                lambda x: parse_image_meta_graph(x)["active_class_ids"]
+                )(input_image_meta)
 
             if not config.USE_RPN_ROIS:
                 # Ignore predicted ROIs and use ROIs provided as an input.
@@ -2321,7 +2323,7 @@ class MaskRCNN():
             molded_image = mold_image(molded_image, self.config)
             # Build image_meta
             image_meta = compose_image_meta(
-                0, image.shape, window,
+                0, image.shape, molded_image.shape, window, scale,
                 np.zeros([self.config.NUM_CLASSES], dtype=np.int32))
             # Append
             molded_images.append(molded_image)
@@ -2544,22 +2546,27 @@ class MaskRCNN():
 #  Data Formatting
 ############################################################
 
-def compose_image_meta(image_id, image_shape, window, active_class_ids):
+def compose_image_meta(image_id, original_image_shape, image_shape,
+                       window, scale, active_class_ids):
     """Takes attributes of an image and puts them in one 1D array.
 
     image_id: An int ID of the image. Useful for debugging.
-    image_shape: [height, width, channels]
+    original_image_shape: [H, W, C] before resizing or padding.
+    image_shape: [H, W, C] after resizing and padding
     window: (y1, x1, y2, x2) in pixels. The area of the image where the real
             image is (excluding the padding)
+    scale: The scaling factor applied to the original image (float32)
     active_class_ids: List of class_ids available in the dataset from which
         the image came. Useful if training on images from multiple datasets
         where not all classes are present in all datasets.
     """
     meta = np.array(
-        [image_id] +            # size=1
-        list(image_shape) +     # size=3
-        list(window) +          # size=4 (y1, x1, y2, x2) in image cooredinates
-        list(active_class_ids)  # size=num_classes
+        [image_id] +                  # size=1
+        list(original_image_shape) +  # size=3
+        list(image_shape) +           # size=3
+        list(window) +                # size=4 (y1, x1, y2, x2) in image cooredinates
+        [scale] +                     # size=1
+        list(active_class_ids)        # size=num_classes
     )
     return meta
 
@@ -2569,12 +2576,23 @@ def parse_image_meta_graph(meta):
     See compose_image_meta() for more details.
 
     meta: [batch, meta length] where meta length depends on NUM_CLASSES
+
+    Returns a dict of the parsed tensors.
     """
     image_id = meta[:, 0]
-    image_shape = meta[:, 1:4]
-    window = meta[:, 4:8]   # (y1, x1, y2, x2) window of image in in pixels
-    active_class_ids = meta[:, 8:]
-    return [image_id, image_shape, window, active_class_ids]
+    original_image_shape = meta[:, 1:4]
+    image_shape = meta[:, 4:7]
+    window = meta[:, 7:11]  # (y1, x1, y2, x2) window of image in in pixels
+    scale = meta[:, 11]
+    active_class_ids = meta[:, 12:]
+    return {
+        "image_id": image_id,
+        "original_image_shape": original_image_shape,
+        "image_shape": image_shape,
+        "window": window,
+        "scale": scale,
+        "active_class_ids": active_class_ids,
+    }
 
 
 def mold_image(images, config):