From 4c76a56e9b03600e97d7bc489401f4ec39de8c36 Mon Sep 17 00:00:00 2001
From: Guillaume-Duret <guillaume.duret@ec-lyon.fr>
Date: Fri, 5 May 2023 16:49:52 +0200
Subject: [PATCH] compute label for yolo, gt.yml, training.txt, testing.txt for
Densefusion
---
compute_label.py | 78 ++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 78 insertions(+)
create mode 100644 compute_label.py
diff --git a/compute_label.py b/compute_label.py
new file mode 100644
index 0000000..559f303
--- /dev/null
+++ b/compute_label.py
@@ -0,0 +1,78 @@
+import numpy as np
+import yaml
+import os
+import argparse
+
+
+if __name__ == "__main__":
+ # Create the parser
+ parser = argparse.ArgumentParser()
+ # Add an argument
+ parser.add_argument('--path_dataset', type=str, required=True)
+ parser.add_argument('--target_folder', type=str, required=True)
+ # Parse the argument
+ args = parser.parse_args()
+
+ target_folder = args.target_folder
+
+ #objects = ["apple2", "banana1", "pear2"]
+ objects = ["apple2", "apricot", "banana1", "kiwi1", "lemon2", "orange2", "peach1", "pear2"]
+ #obj_ids = {"pear2": 17, "orange2": 18}
+
+ obj_ids = {"apple2": 1, "apricot": 2, "banana1": 3, "kiwi1": 4, "lemon2": 5, "orange2": 6, "peach1": 7, "pear2": 8}
+ #obj_ids = {"apple2": 1, "banana1": 3, "pear2": 8}
+
+ #path_data = "GUIMOD_low/Pose_transformed"
+ #path_data = "/gpfsscratch/rech/uli/ubn15wo/dataset1/s2rg/Fruits_all_medium/GUIMOD_low/Generated_Worlds_Training/orange2/Pose_transformed/"
+ #path_data = f"/gpfsscratch/rech/uli/ubn15wo/dataset1/s2rg/Fruits_all_medium/GUIMOD_low/{target_folder}/"
+ path_data = f"{args.path_dataset}/{target_folder}"
+
+ for obj in objects:
+ file = path_data + "/" + obj
+ #path_gt_obj = f"{obj_ids[obj]}/gt_new.yml"
+ path_gt_obj = f"/gpfsscratch/rech/uli/ubn15wo/DenseFusion/datasets/linemod/Linemod_preprocessed/data/0{obj_ids[obj]}/gt_{target_folder}.yml"
+ #path_bbox = "GUIMOD_low/Bbox/" + obj
+ path_bbox = f"{path_data}/{obj}/Bbox/"
+
+ object_dicts = {}
+ print(file)
+ path_poses = file + "/Pose_transformed"
+ print("poses ", path_poses)
+ print("list : ", os.listdir(path_poses))
+ list_poses = os.listdir(path_poses)
+
+ print("-------------------------------------------------------------------------------------------------------")
+ print("-------------------------------------------------------------------------------------------------------")
+ with open(f"/gpfsscratch/rech/uli/ubn15wo/DenseFusion/datasets/linemod/Linemod_preprocessed/data/0{obj_ids[obj]}/split_file_{target_folder}.txt", 'w') as f:
+ for filename2 in os.listdir(path_poses):
+ id_file2=filename2.split('.')[0]
+ print(id_file2+".png")
+ f.write(f'{id_file2}.png')
+ f.write('\n')
+
+ with open(path_gt_obj, 'w') as yaml_file:
+ for filename in os.listdir(path_poses):
+ #for i in range(num_rgb):
+ data_file = path_poses + "/" + filename
+ print("filename : ", filename)
+ #data_file = file + "/" + str(i) + ".npy"
+ #print(list_RGB[4].split('.')[0])
+ id_file=filename.split('.')[0]
+ print(id_file)
+ print(filename)
+ #final_path_bbox = path_bbox + "/" + str(i) + ".txt"
+ final_path_bbox = path_bbox + "/" + id_file + ".txt"
+ print(final_path_bbox)
+ bbox_file = open(final_path_bbox, "r")
+ line = bbox_file.readline()
+ bbox_string = line.split(" ")
+ bbox = [float(val) for val in bbox_string]
+ bbox2 = [int(bbox[0] * 0.5), int(bbox[1] * (2 / 3)), int(bbox[2] * 0.5), int(bbox[3] * (2 / 3))]
+ # print(bbox2)
+ bbox_file.close()
+
+ data = np.load(data_file)
+ cam_R_m2c = np.concatenate((data[0, 0:3], data[1, 0:3], data[2, 0:3]), axis=0).tolist()
+ cam_t_m2c = np.array((float(data[0, 3])*1000.0, float(data[1, 3])*1000.0, float(data[2, 3]*1000.0))).tolist()
+ object_dicts[id_file] = [{"cam_R_m2c": cam_R_m2c, "cam_t_m2c": cam_t_m2c, "obj_bb": bbox2, "obj_id": obj_ids[obj]}]
+ yaml.dump(object_dicts, yaml_file, default_flow_style=None)
--
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