cleaned usused comments

common/vision/lasr_vision_feature_extraction/nodes/service

@@ -29,73 +29,9 @@ def detect(request: TorchFaceFeatureDetectionDescriptionRequest) -> TorchFaceFea
 
     response = TorchFaceFeatureDetectionDescriptionResponse()
     response.description = rst_str
-    # response.detected_features = str(class_pred) + str(colour_pred)
-    # response.detected_features = []
-    # for c in ['hair', 'hat', 'glasses', 'cloth',]:
-    #     # colour_pred[c] = {k: v[0] for k, v in colour_pred[c].items()}
-    #     sorted_list = sorted(colour_pred[c].items(), key=lambda item: item[1], reverse=True)
-    #     # rospy.loginfo(str(sorted_list))
-    #     if len(sorted_list) > 3:
-    #         sorted_list = sorted_list[0:3]
-    #     sorted_list = [k for k, v in sorted_list]
-    #     # rospy.loginfo(str(colour_pred[c]))
-    #     response.detected_features.append(FeatureWithColour(c, class_pred[c], sorted_list))
     return response
 
 
-# def detect(request: TorchFaceFeatureDetectionRequest) -> TorchFaceFeatureDetectionResponse:
-#     # decode the image
-#     rospy.loginfo('Decoding')
-#     frame = msg_to_cv2_img(request.image_raw)
-
-#     # 'hair', 'hat', 'glasses', 'face'
-#     input_image = torch.from_numpy(frame).permute(2, 0, 1).unsqueeze(0).float()
-#     input_image /= 255.0
-#     masks_batch_pred, pred_classes = lasr_vision_feature_extraction.model(input_image)
-
-#     thresholds_mask = [
-#         0.5, 0.75, 0.25, 0.5,  # 0.5, 0.5, 0.5, 0.5,
-#     ]
-#     thresholds_pred = [
-#         0.6, 0.8, 0.1, 0.5,
-#     ]
-#     erosion_iterations = 1
-#     dilation_iterations = 1
-#     categories = ['hair', 'hat', 'glasses', 'face',]
-
-#     masks_batch_pred = binary_erosion_dilation(
-#         masks_batch_pred, thresholds=thresholds_mask,
-#         erosion_iterations=erosion_iterations, dilation_iterations=dilation_iterations
-#     )
-
-#     median_colours = (median_color_float(
-#         input_image, masks_batch_pred).detach().squeeze(0)*255).numpy().astype(np.uint8)
-
-#     # discarded: masks = masks_batch_pred.detach().squeeze(0).numpy().astype(np.uint8)
-#     # discarded: mask_list = [masks[i,:,:] for i in range(masks.shape[0])]
-
-#     pred_classes = pred_classes.detach().squeeze(0).numpy()
-#     # discarded: class_list = [categories[i] for i in range(
-#     #     pred_classes.shape[0]) if pred_classes[i].item() > thresholds_pred[i]]
-#     colour_list = [median_colours[i, :]
-#                    for i in range(median_colours.shape[0])]
-
-#     response = TorchFaceFeatureDetectionResponse()
-#     # response.detected_features = [
-#     #     FeatureWithColour(categories[i], [
-#     #         ColourPrediction(colour, distance)
-#     #         for colour, distance
-#     #         in closest_colours(colour_list[i], HAIR_COLOURS if categories[i] == 'hair' else COLOURS)
-#     #     ])
-#     #     for i
-#     #     in range(pred_classes.shape[0])
-#     #     if pred_classes[i].item() > thresholds_pred[i]
-#     # ]
-#     response.detected_features = "feature"
-
-#     return response
-# test test
-
 rospy.init_node('torch_service')
 rospy.Service('/torch/detect/face_features', TorchFaceFeatureDetectionDescription, detect)
 rospy.loginfo('Torch service started')