Robodummy

.gitignore

@@ -7,4 +7,5 @@ tiktorch/.idea
 tiktorch/__pycache/
 /#wrapper.py#
 /.#wrapper.py#
-.py~
+.py~
+*.hdf

environment.yml

@@ -13,6 +13,9 @@ dependencies:
   - inferno=v0.4.*
   - pyzmq=18.0.1
   - pyyaml=3.13
+  - seaborn
+  - z5py
+  - scikit-learn
   - pytest=4.3.0
   - black
   - isort

mr_robot/__main__.py

@@ -0,0 +1,23 @@
+import argparse
+
+from mr_robot.mr_robot import MrRobot, strategies
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-c", "--config", help="Path to config file", type=str, required=True)
+parser.add_argument(
+    "-s", "--strategy", help="Annotation strategy to use", type=str, choices=strategies.keys(), required=True
+)
+parser.add_argument("-d", "--device", help="Which device to use", type=str, required=True, default="cpu")
+
+
+def main():
+    args = parser.parse_args()
+    robo = MrRobot(args.config, args.strategy, args.device)
+    robo._load_model()
+    robo._run()
+    return 0
+
+
+if __name__ == "__main__":
+    exit(main())

mr_robot/annotator/annotate.py

@@ -0,0 +1,54 @@
+import numpy as np
+import random
+
+from mr_robot.utils import get_coordinate
+
+
+class Annotater:
+    """ The annotater class prvides methods for different labelling strategies, emulating a user
+    in some way
+
+    Args:
+    annotation_percent (int): percentage of pixels in the patch to annotate
+    """
+
+    def __init__(self, annotation_percent):
+        self.annotation_percent = annotation_percent
+        # self.block_shape = block_shape
+
+    def get_random_index(self, block_shape):
+        random_index = []
+        for i in range(len(block_shape)):
+            random_index.append(np.random.randint(0, block_shape[i]))
+
+        return tuple(random_index)
+
+    def get_random_patch(self, block_shape):
+        rand_index = self.get_random_index(block_shape)
+        patch_dimension = []
+        for i in range(len(block_shape)):
+            patch_dimension.append(np.random.randint(0, block_shape[i] - rand_index[i]))
+
+        block = []
+        for i in range(len(patch_dimension)):
+            block.append(slice(rand_index[i], rand_index[i] + patch_dimension[i]))
+        return tuple(block)
+
+    def dense(self, label):
+        return label
+
+    def random_sparse(self, label):
+        ret_label = np.zeros(label.shape)
+        for i in range(int(self.annotation_percent) * np.product(label.shape)):
+            index = self.get_random_index(label.shape)
+            ret_label[index] = label[index]
+        return ret_label
+
+    def random_blob(self, label):
+        ret_label = np.ones(label.shape)
+        for i in range(int(self.annotation_percent) * np.product(label.shape)):
+            random_block = self.get_random_patch(label.shape)
+            ret_label[random_block] = label[random_block]
+            i += np.product(label[random_block].shape)
+        print("random blob annotated label:", np.unique(ret_label), "labels of actual return patch:", np.unique(label))
+        return ret_label