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Commit 190c75b7 authored by Guillaume Duret's avatar Guillaume Duret
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flexible whole process script for densefusion

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compute_label.py
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......@@ -2,6 +2,7 @@ import numpy as np
import yaml
import os
import argparse
import shutil
if __name__ == "__main__":
......@@ -10,10 +11,12 @@ if __name__ == "__main__":
# Add an argument
parser.add_argument('--path_dataset', type=str, required=True)
parser.add_argument('--target_folder', type=str, required=True)
parser.add_argument('--path_DF_data', type=str, required=True)
# Parse the argument
args = parser.parse_args()
target_folder = args.target_folder
path_data_df = args.path_DF_data
#objects = ["apple2", "banana1", "pear2"]
objects = ["apple2", "apricot", "banana1", "kiwi1", "lemon2", "orange2", "peach1", "pear2"]
......@@ -25,54 +28,113 @@ if __name__ == "__main__":
#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 split in ["_Training","_Evaluating"]:
path_data = f"{args.path_dataset}/{target_folder}{split}"
if not os.path.exists(path_data_df) :
os.makedirs(path_data_df)
for obj in objects:
print(f"move {path_data} to {path_data_df}")
if not os.path.exists(f"{path_data_df}/0{obj_ids[obj]}") :
os.makedirs(f"{path_data_df}/0{obj_ids[obj]}")
if not os.path.exists(f"{path_data_df}/0{obj_ids[obj]}/depth/") :
os.makedirs(f"{path_data_df}/0{obj_ids[obj]}/depth/")
if not os.path.exists(f"{path_data_df}/0{obj_ids[obj]}/rgb/") :
os.makedirs(f"{path_data_df}/0{obj_ids[obj]}/rgb/")
if not os.path.exists(f"{path_data_df}/0{obj_ids[obj]}/mask/") :
os.makedirs(f"{path_data_df}/0{obj_ids[obj]}/mask/")
os.system(f"cp -r {path_data}/{obj}/Depth_resized/* {path_data_df}/0{obj_ids[obj]}/depth/")
os.system(f"cp -r {path_data}/{obj}/RGB_resized/* {path_data_df}/0{obj_ids[obj]}/rgb/")
os.system(f"cp -r {path_data}/{obj}/Instance_Mask_resized/* {path_data_df}/0{obj_ids[obj]}/mask/")
#shutil.copytree(f"{path_data}/{obj}/Depth_resized", f"{path_data_df}/0{obj_ids[obj]}/depth", dirs_exist_ok = True,)
#shutil.copytree(f"{path_data}/{obj}/RGB_resized", f"{path_data_df}/0{obj_ids[obj]}/rgb", dirs_exist_ok = True)
#shutil.copytree(f"{path_data}/{obj}/Instance_Mask_resized", f"{path_data_df}/0{obj_ids[obj]}/mask", dirs_exist_ok = True)
for obj in objects:
file = path_data + "/" + obj
#path_gt_obj = f"{obj_ids[obj]}/gt_new.yml"
path_gt_obj = f"{path_data_df}/0{obj_ids[obj]}/gt_{target_folder}{split}.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("-------------------------------------------------------------------------------------------------------")
if split=="_Training" :
with open(f"{path_data_df}/0{obj_ids[obj]}/train.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')
elif split=="_Evaluating" :
with open(f"{path_data_df}/0{obj_ids[obj]}/test.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')
else :
raise("not in traing or evaluating")
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)
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)
# Reading data from file1
with open(f"{path_data_df}/0{obj_ids[obj]}/gt_{target_folder}_Training.yml") as fp:
data_train = fp.read()
# Reading data from file2
with open(f"{path_data_df}/0{obj_ids[obj]}/gt_{target_folder}_Evaluating.yml") as fp:
data_eval = fp.read()
# Merging 2 files
# To add the data of file2
# from next line
data_gt = data_train
data_gt += data_eval
with open (f"{path_data_df}/0{obj_ids[obj]}/gt.yml", 'w') as fp:
fp.write(data_gt)
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