diff --git a/compute_features.py b/compute_features.py
index 7367d1ed19e7eaecb6746b606fcd32e01c4eabf7..e153c5a90e9652bf70f98f10196bb7d3b3b637fb 100644
--- a/compute_features.py
+++ b/compute_features.py
@@ -18,8 +18,18 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
[1.0000000, 0.0000000, 0.0000000]])
list_count_categories = {}
-
+
+
for i in range(World_begin, World_begin + Nb_world): # worlds
+
+ scenario = "Worlds"
+
+ if i > 4 :
+ destination_folder = f"Generated_{scenario}_Testing"
+ elif i > 3 :
+ destination_folder = f"Generated_{scenario}_Evaluating"
+ else :
+ destination_folder = f"Generated_{scenario}_Training"
catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, catergories_label_to_id = compute_categories_id(data_name, i)
@@ -44,8 +54,8 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
if len(data_Bbox_2d) != len(data_3D_pose) :
raise TypeError("size of datas are differents !!")
- if os.path.isfile(f'{data_name}/Generated/Count_{p-1}.json'):
- with open(f'{data_name}/Generated/Count_{p-1}.json') as f:
+ if os.path.isfile(f'{data_name}/{destination_folder}/Count_{p-1}.json'):
+ with open(f'{data_name}/{destination_folder}/Count_{p-1}.json') as f:
list_count_categories = json.load(f)
for categories in list_categories:
@@ -74,16 +84,16 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
meta['occlusion'] = occ_target
meta['Nb_instance_category'] = Nb_instance
- if not os.path.isfile(f'{data_name}/Generated/{categories}/Meta_Gen/{categories}.json'):
- with open(f'{data_name}/Generated/{categories}/Meta_Gen/{categories}.json', mode='w') as f:
+ if not os.path.isfile(f'{data_name}/{destination_folder}/{categories}/Meta_Gen/{categories}.json'):
+ with open(f'{data_name}/{destination_folder}/{categories}/Meta_Gen/{categories}.json', mode='w') as f:
feeds = {}
feeds[meta['id_generated']]=meta
f.write(json.dumps(feeds, indent=2))
else:
- with open(f'{data_name}/Generated/{categories}/Meta_Gen/{categories}.json') as feedsjson:
+ with open(f'{data_name}/{destination_folder}/{categories}/Meta_Gen/{categories}.json') as feedsjson:
feeds = json.load(feedsjson)
feeds[meta['id_generated']]=meta
- with open(f'{data_name}/Generated/{categories}/Meta_Gen/{categories}.json', mode='w') as f:
+ with open(f'{data_name}/{destination_folder}/{categories}/Meta_Gen/{categories}.json', mode='w') as f:
f.write(json.dumps(feeds, indent=4))
if (Nb_instance == 1):
@@ -98,19 +108,19 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
T_exp = transformation @ xyz
T_exp = np.array(T_exp)
num_arr = np.c_[R_exp, T_exp[0]]
- np.save(f'{data_name}/Generated/{categories}/Pose_transformed/{p}.npy', num_arr) # save
+ np.save(f'{data_name}/{destination_folder}/{categories}/Pose_transformed/{p}.npy', num_arr) # save
else:
continue
if data_Bbox_2d[k]['id'] == catergories_occ_array[categories][0]:
bbox = bbox_2d(data_Bbox_2d[k])
- np.savetxt(f'{data_name}/Generated/{categories}/Bbox/{p}.txt', np.array(bbox).reshape((1, 4))) # save
+ np.savetxt(f'{data_name}/{destination_folder}/{categories}/Bbox/{p}.txt', np.array(bbox).reshape((1, 4))) # save
else:
continue
if data_Bbox_3d[k]['id'] == catergories_occ_array[categories][0]:
bbox3d_size = data_Bbox_3d[k]['bbox']['size']
- np.savetxt(f'{data_name}/Generated/{categories}/Bbox_3d_Gen/{p}.txt', bbox3d_size) # save
+ np.savetxt(f'{data_name}/{destination_folder}/{categories}/Bbox_3d_Gen/{p}.txt', bbox3d_size) # save
else:
continue
@@ -118,17 +128,17 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
img = cv2.imread(f"{data_name}/Instance_Segmentation/{p}.png", cv2.IMREAD_UNCHANGED) # plt.imread(path)
instance_img = instance(img, id)
- cv2.imwrite(f"{data_name}/Generated/{categories}/Instance_Mask/{p}.png", 255*instance_img)
+ cv2.imwrite(f"{data_name}/{destination_folder}/{categories}/Instance_Mask/{p}.png", 255*instance_img)
instance_img_resized = cv2.resize(instance_img, new_size)
- cv2.imwrite(f"{data_name}/Generated/{categories}/Instance_Mask_resized/{p}.png", 255*instance_img_resized)
+ cv2.imwrite(f"{data_name}/{destination_folder}/{categories}/Instance_Mask_resized/{p}.png", 255*instance_img_resized)
img = cv2.imread(f"{data_name}/RGB/{p}.png")
- cv2.imwrite(f"{data_name}/Generated/{categories}/RGB_Gen/{p}.png", img)
+ cv2.imwrite(f"{data_name}/{destination_folder}/{categories}/RGB_Gen/{p}.png", img)
img_resized = cv2.resize(img, new_size)
- cv2.imwrite(f"{data_name}/Generated/{categories}/RGB_resized/{p}.png", img_resized)
+ cv2.imwrite(f"{data_name}/{destination_folder}/{categories}/RGB_resized/{p}.png", img_resized)
np.set_printoptions(precision=15)
- pose = np.load(f'{data_name}/Generated/{categories}/Pose_transformed/{p}.npy')
+ pose = np.load(f'{data_name}/{destination_folder}/{categories}/Pose_transformed/{p}.npy')
R_exp = pose[0:3, 0:3]
tVec = pose[0:3, 3]
@@ -144,7 +154,7 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
out.append(x)
out.append(y)
ind += 2
- np.savetxt(f'{data_name}/Generated/{categories}/FPS/{p}.txt', np.array(out).reshape(1, len(out)))
+ np.savetxt(f'{data_name}/{destination_folder}/{categories}/FPS/{p}.txt', np.array(out).reshape(1, len(out)))
points_resized = process2(fps_points, R_exp, tVec, camera_resized, img_resized, vis)
out_resized = [int(catergories_occ_array[categories][0])] #len have to be 1 !
@@ -155,9 +165,9 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
out_resized.append(x_resized)
out_resized.append(y_resized)
ind_resized += 2
- np.savetxt(f'{data_name}/Generated/{categories}/FPS_resized/{p}.txt', np.array(out_resized).reshape(1, len(out_resized)))
+ np.savetxt(f'{data_name}/{destination_folder}/{categories}/FPS_resized/{p}.txt', np.array(out_resized).reshape(1, len(out_resized)))
- with open(f'{data_name}/Generated/Count_{p}.json', mode='w') as f:
+ with open(f'{data_name}/{destination_folder}/Count_{p}.json', mode='w') as f:
f.write(json.dumps(list_count_categories, indent=4))
print(list_count_categories)
diff --git a/main.py b/main.py
index 70878ada2e390b5dc8a12d993dd1a6f256e33a6e..6541bb50ec91a8c77d0c4be36452c57271e32512 100644
--- a/main.py
+++ b/main.py
@@ -8,7 +8,7 @@ import open3d as o3d
from scipy.spatial import distance
import argparse
-def generate_folders(name, list_categories):
+def generate_folders(name, list_categories, scenario):
is_exist = os.path.exists(name)
if not is_exist:
os.mkdir(name)
@@ -20,9 +20,20 @@ def generate_folders(name, list_categories):
os.mkdir(f"{name}/{f}")
else:
for cat in list_categories:
- is_exist2 = os.path.exists(f"{name}/Generated/{cat}/{f}")
+ is_exist2 = os.path.exists(f"{name}/Generated/{cat}")
if not is_exist2:
- os.makedirs(f"{name}/Generated/{cat}/{f}")
+ os.makedirs(f"{name}/Generated/{cat}")
+ is_exist2 = os.path.exists(f"{name}/Generated_{scenario}_Training/{cat}/{f}")
+ if not is_exist2:
+ os.makedirs(f"{name}/Generated_{scenario}_Training/{cat}/{f}")
+ is_exist2 = os.path.exists(f"{name}/Generated_{scenario}_Evaluating/{cat}/{f}")
+ if not is_exist2:
+ os.makedirs(f"{name}/Generated_{scenario}_Evaluating/{cat}/{f}")
+ is_exist2 = os.path.exists(f"{name}/Generated_{scenario}_Testing/{cat}/{f}")
+ if not is_exist2:
+ os.makedirs(f"{name}/Generated_{scenario}_Testing/{cat}/{f}")
+
+
def calc_pts_diameter2(pts):
"""Calculates the diameter of a set of 3D points (i.e. the maximum distance
@@ -45,12 +56,14 @@ if __name__ == '__main__':
# Parse the argument
args = parser.parse_args()
+ scenario = "Worlds"
+
### parameters ###
Categories = [] # to read
Nb_instance = 1
occ_target = 0.5
dataset_src = "/media/gduret/DATA/dataset/s2rg/Fruits_all_medium/data"
- choice = "high" # depth of rgb resolution datas
+ choice = "low" # depth of rgb resolution datas
data_options = {"high": "ground_truth_rgb",
"low": "ground_truth_depth"}
dataset_type = data_options[choice]
@@ -58,7 +71,7 @@ if __name__ == '__main__':
list_categories = ["banana1", "kiwi1", "pear2", "strawberry1", "apricot", "orange2", "peach1", "lemon2", "apple2" ]
Nb_camera = 15
- generate_folders(dataset_name, list_categories)
+ generate_folders(dataset_name, list_categories, scenario)
if choice == 'high':
camera = np.matrix([[1386.4138492513919, 0.0, 960.5],
@@ -80,7 +93,8 @@ if __name__ == '__main__':
new_size = (640, 480)
new_camera = trans @ camera
- np.savetxt(f'{dataset_name}/Generated/camera_{choice}.txt', camera)
+
+ #np.savetxt(f'{dataset_name}/Generated/camera_{choice}.txt', camera)
reform_data(dataset_src, dataset_name, dataset_type, Nb_camera, args.World_begin, args.Nb_worlds)
@@ -100,6 +114,7 @@ if __name__ == '__main__':
pcd = o3d.io.read_point_cloud(point_cloud)
fps_points = apply_fps(pcd, 8)
+
np.savetxt(f'{dataset_name}/Generated/{categories}/{categories}_fps_3d.txt', fps_points)
point_cloud_in_numpy = np.asarray(pcd.points)
@@ -111,5 +126,5 @@ if __name__ == '__main__':
bbox = get_3D_bbox(ext)
np.savetxt(f'{dataset_name}/Generated/{categories}/{categories}_bbox_3d.txt', bbox) # save
- process_compute(dataset_name, camera, new_camera, new_size, Nb_camera, args.World_begin, args.Nb_worlds, list_categories, occ_target, True)
+ process_compute(dataset_name, camera, new_camera, new_size, Nb_camera, args.World_begin, args.Nb_worlds, list_categories, occ_target, False)