successfully returned target points and scene points
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@ -19,7 +19,7 @@ class ActivePerceptionPolicy(MultiViewPolicy):
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super().activate(bbox, view_sphere)
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def update(self, img, seg, target_id, x, q):
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self.depth_image_to_ap_input(img, seg, target_id)
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target_points, scene_points = self.depth_image_to_ap_input(img, seg, target_id)
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# if len(self.views) > self.max_views or self.best_grasp_prediction_is_stable():
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# self.done = True
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# else:
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@ -42,6 +42,9 @@ class ActivePerceptionPolicy(MultiViewPolicy):
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# self.x_d = nbv
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def depth_image_to_ap_input(self, depth_img, seg_img, target_id):
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target_points = []
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scene_points = []
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K = self.intrinsic.K
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depth_shape = depth_img.shape
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seg_shape = seg_img.shape
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@ -51,17 +54,32 @@ class ActivePerceptionPolicy(MultiViewPolicy):
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print("Depth image shape and segmentation image shape are not the same")
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return None
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# Depth image to PCD
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u_indices , v_indices = np.meshgrid(np.arange(img_shape[0]), np.arange(img_shape[1]))
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# Convert depth image to 3D points
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u_indices , v_indices = np.meshgrid(np.arange(img_shape[1]), np.arange(img_shape[0]))
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x_factors = (u_indices - K[0, 2]) / K[0, 0]
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y_factors = (v_indices - K[1, 2]) / K[1, 1]
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z_mat = depth_img
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x_mat = x_factors * z_mat
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y_mat = y_factors * z_mat
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for i in range(img_shape[0]):
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for j in range(img_shape[1]):
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seg_id = seg_img[i, j]
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# print(seg_id)
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x = x_mat[i][j]
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y = y_mat[i][j]
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z = z_mat[i][j]
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if(int(seg_id) == int(target_id)):
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print("woahhhhhhhhhhhhhhhhh!")
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# This pixel belongs to the target object to be grasped
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target_points.append([x,y,z])
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else:
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# This pixel belongs to the scene
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scene_points.append([x,y,z])
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target_points = np.asarray(target_points)
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target_points = target_points.reshape(1, target_points.shape[0], 3)
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scene_points = np.asarray(scene_points)
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scene_points = scene_points.reshape(1, scene_points.shape[0], 3)
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return target_points, scene_points
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def best_grasp_prediction_is_stable(self):
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