successfully added ap policy class

README.md

@@ -1,6 +1,7 @@
 # Updated installation steps fo my PC environment
 
 ```sh
+# Install Active Grasp
 sudo apt install liborocos-kdl-dev
 mkdir -p ws/src && cd ws/src
 git clone https://github.com/0nhc/active_grasp.git
@@ -18,6 +19,10 @@ git clone https://github.com/0nhc/robot_helpers.git
 cd ..
 rosdep install --from-paths src --ignore-src -r -y
 catkin build
+
+# Install Active Perception
+cd <path-to-your-ws>/src/active_grasp/src/active_grasp/active_perception/modules/module_lib/pointnet2_utils/pointnet2
+pip install -e .
 ```
 
 # Closed-Loop Next-Best-View Planning for Target-Driven Grasping

cfg/active_grasp.yaml

@@ -34,3 +34,7 @@ nbv_grasp:
   max_views: 80
   min_gain: 10
   downsample: 10  # 10/20 for sim/hw respectively
+
+ap_grasp:
+  max_views: 80
+  ap_config_path: $(find active_grasp)/src/active_grasp/active_perception/configs/local_inference_config.yaml

src/active_grasp/__init__.py

@@ -1,9 +1,11 @@
 from .policy import register
 from .baselines import *
 from .nbv import NextBestView
+from.active_perception_policy import ActivePerceptionPolicy
 
 register("initial-view", InitialView)
 register("top-view", TopView)
 register("top-trajectory", TopTrajectory)
 register("fixed-trajectory", FixedTrajectory)
 register("nbv", NextBestView)
+register("ap", ActivePerceptionPolicy)

src/active_grasp/active_perception_demo.py

@@ -13,10 +13,10 @@ for i in range(2):
 PROJECT_ROOT = path
 sys.path.append(PROJECT_ROOT)
 
-from active_perception.configs.config import ConfigManager
-from active_perception.modules.pipeline import Pipeline
+from .active_perception.configs.config import ConfigManager
+from .active_perception.modules.pipeline import Pipeline
 
-class InferenceEngine():
+class APInferenceEngine():
     RESULTS_DIR_NAME: str = 'results'
     LOG_DIR_NAME: str = 'log'
 
@@ -58,7 +58,6 @@ class InferenceEngine():
         self.pipeline.eval()
         with torch.no_grad():
             output = self.pipeline(data, Pipeline.TEST_MODE)
-        
         return output
 
 
@@ -84,7 +83,7 @@ if __name__ == "__main__":
     }
 
     ''' Inference '''
-    infenrence_engine = InferenceEngine(args.config)
+    infenrence_engine = APInferenceEngine(args.config)
     output = infenrence_engine.inference(test_data)
     print(output.keys())
     print(output['estimated_delta_rot_6d'])

src/active_grasp/active_perception_policy.py

@@ -0,0 +1,55 @@
+import itertools
+from numba import jit
+import numpy as np
+import rospy
+from .policy import MultiViewPolicy
+from .timer import Timer
+
+from .active_perception_demo import APInferenceEngine
+
+
+class ActivePerceptionPolicy(MultiViewPolicy):
+    def __init__(self):
+        super().__init__()
+        self.max_views = rospy.get_param("ap_grasp/max_views")
+        self.ap_config_path = rospy.get_param("ap_grasp/ap_config_path")
+        self.ap_inference_engine = APInferenceEngine(self.ap_config_path)
+
+    def activate(self, bbox, view_sphere):
+        super().activate(bbox, view_sphere)
+
+    def update(self, img, x, q):
+        self.depth_image_to_ap_input(img)
+        # if len(self.views) > self.max_views or self.best_grasp_prediction_is_stable():
+        #     self.done = True
+        # else:
+        #     with Timer("state_update"):
+        #         self.integrate(img, x, q)
+        #     with Timer("view_generation"):
+        #         views = self.generate_views(q)
+        #     with Timer("ig_computation"):
+        #         gains = [self.ig_fn(v, self.downsample) for v in views]
+        #     with Timer("cost_computation"):
+        #         costs = [self.cost_fn(v) for v in views]
+        #     utilities = gains / np.sum(gains) - costs / np.sum(costs)
+        #     self.vis.ig_views(self.base_frame, self.intrinsic, views, utilities)
+        #     i = np.argmax(utilities)
+        #     nbv, gain = views[i], gains[i]
+
+        #     if gain < self.min_gain and len(self.views) > self.T:
+        #         self.done = True
+
+        #     self.x_d = nbv
+    
+    def depth_image_to_ap_input(self, depth_img):
+        print(self.intrinsic.K)
+
+    def best_grasp_prediction_is_stable(self):
+        if self.best_grasp:
+            t = (self.T_task_base * self.best_grasp.pose).translation
+            i, j, k = (t / self.tsdf.voxel_size).astype(int)
+            qs = self.qual_hist[:, i, j, k]
+            if np.count_nonzero(qs) == self.T and np.mean(qs) > 0.9:
+                return True
+        return False
+