Augmentation visualisation

cpp/visualmesh/engine/cpu/engine.hpp

@@ -269,7 +269,7 @@ namespace engine {
                 return ClassifiedMesh<Scalar, N_NEIGHBOURS>{std::move(projected.pixel_coordinates),
                                                             std::move(projected.neighbourhood),
                                                             std::move(projected.global_indices),
-                                                            input};
+                                                            std::move(input)};
             }
 
             /**

cpp/visualmesh/visualmesh.hpp

@@ -148,7 +148,7 @@ class VisualMesh {
         // z height from the transformation matrix
         const Scalar& h = Hoc[2][3];
         auto mesh       = height(h);
-        return mesh->lookup(Hoc, lens);
+        return std::make_pair(mesh, mesh.lookup(Hoc, lens));
     }
 
 private:

example/augmentation_visualisation.py

@@ -0,0 +1,40 @@
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import yaml
+
+image_path = "images/image0000002.jpg"
+config_path = "augmentations_example.yaml"
+
+# Decode image into tensor
+image = open(image_path, 'rb').read()
+image_tensor = tf.image.decode_jpeg(image)
+
+# Get augmentations
+with open(config_path, "r") as file:
+    data = yaml.safe_load(file)
+augmentations = data["example"]["config"]["augmentations"]
+
+# Apply the augmentations that were listed
+if "brightness" in augmentations:
+    v = augmentations["brightness"]
+    image_tensor = tf.image.adjust_brightness(image_tensor, tf.random.truncated_normal(shape=(), **v))
+if "contrast" in augmentations:
+    v = augmentations["contrast"]
+    image_tensor = tf.image.adjust_contrast(image_tensor, tf.random.truncated_normal(shape=(), **v))
+if "hue" in augmentations:
+    v = augmentations["hue"]
+    image_tensor = tf.image.adjust_hue(image_tensor, tf.random.truncated_normal(shape=(), **v))
+if "saturation" in augmentations:
+    v = augmentations["saturation"]
+    image_tensor = tf.image.adjust_saturation(image_tensor, tf.random.truncated_normal(shape=(), **v))
+if "gamma" in augmentations:
+    v_gamma = augmentations["gamma"]["gamma"]
+    v_gain = augmentations["gamma"]["gain"]
+    image_tensor = tf.image.adjust_gamma(
+        image_tensor, tf.random.truncated_normal(shape=(), **v_gamma), tf.random.truncated_normal(shape=(), **v_gain)
+    )
+
+# Display image using matplotlib
+plt.imshow(image_tensor)
+plt.axis('off')
+plt.show()

example/augmentations_example.yaml

@@ -0,0 +1,17 @@
+
+example:
+  type: Image
+  config:
+    augmentations:
+      # Adjust the brightness `x + delta`
+      brightness:  { mean: 0, stddev: 0.05 }
+      # Adjust the contrast `(x - mean) * delta + mean`
+      contrast: { mean: 1, stddev: 0.05 }
+      # Convert to hsv, adjust the hue by a value from [-1 -> 1] and back to rgb
+      hue: { mean: 0, stddev: 0.05 }
+      # Convert to hsv, multiply saturation by value and convert back to rgb
+      saturation: { mean: 1, stddev: 0.05 }
+      # Adjust the gamma `gain * x**gamma`
+      gamma:
+        gamma: { mean: 1, stddev: 0.05 }
+        gain: { mean: 1, stddev: 0.05 }