diff --git a/compute_features.py b/compute_features.py
index a252f82ef1d07d909ac3145ec4a48bff9420d239..bbd857d040ce849311da3b3b3557990f6ddaf61d 100644
--- a/compute_features.py
+++ b/compute_features.py
@@ -1,7 +1,4 @@
-
-
-import math
import numpy as np
import json
from utils import compute_categories_id, compute_id_good_occ
@@ -51,7 +48,6 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
with open(f'{data_name}/Generated/Count_{p-1}.json') as f:
list_count_categories = json.load(f)
-
for categories in list_categories:
if categories in catergories_occ_array.keys():
if len(catergories_occ_array[categories]) == 1 :
@@ -73,7 +69,6 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
meta['occlusion'] = occ_target
meta['Nb_instance_category'] = 1
-
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:
feeds = {}
@@ -86,9 +81,6 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
with open(f'{data_name}/Generated/{categories}/Meta_Gen/{categories}.json', mode='w') as f:
f.write(json.dumps(feeds, indent=4))
- # with open(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json', "a") as meta_file:
- # json.dump(meta, meta_file, indent=4)
-
for k in range(len(data_3D_pose)):
if data_3D_pose[k]['id'] == catergories_occ_array[categories][0]:
@@ -122,73 +114,44 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
instance_img = instance(img, id)
cv2.imwrite(f"{data_name}/Generated/{categories}/Instance_Mask/{p}.png", 255*instance_img)
- cat_mask = cv2.resize(instance_img, new_size)
- cv2.imwrite(f"{data_name}/Generated/{categories}/Instance_Mask_resized/{p}.png", 255*cat_mask)
+ 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)
img = cv2.imread(f"{data_name}/RGB/{p}.png")
cv2.imwrite(f"{data_name}/Generated/{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)
np.set_printoptions(precision=15)
pose = np.load(f'{data_name}/Generated/{categories}/Pose_transformed/{p}.npy')
- #print(pose)
R_exp = pose[0:3, 0:3]
tVec = pose[0:3, 3]
- #print(tVec)
- # camera = np.matrix([[1386.4138492513919, 0.0, 960.5],
- # [0.0, 1386.4138492513919, 540.5],
- # [0.0, 0.0, 1.0]])
-
-
fps_points = np.loadtxt(f'{data_name}/Generated/{categories}/{categories}_fps_3d.txt')
- # process(pcd_bbox, pcd, R_exp, tVec, camera, img)
center = fps_points.mean(0)
fps_points = np.append(fps_points, [center], axis=0)
-
-
points = process2(fps_points, R_exp, tVec, camera, img, vis)
- #out = np.zeros((1, ))
-
- out = [int(catergories_occ_array[categories][0])]# [catergories_occ_array[categories][0]] #obj_id #len have to be 1 !!
- print(out)
-
-
+ out = [int(catergories_occ_array[categories][0])] #len have to be 1 !!
ind = 1
for point in points:
- #out[0][ind] = point[0][0] / img.shape[1]
- #[0][ind + 1] = point[0][1] / img.shape[0]
x = point[0][0] / img.shape[1]
y = point[0][1] / img.shape[0]
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)))
- #print("stop")
points_resized = process2(fps_points, R_exp, tVec, camera_resized, img_resized, vis)
- #out = np.zeros((1, ))
-
- out_resized = [int(catergories_occ_array[categories][0])]# [catergories_occ_array[categories][0]] #obj_id #len have to be 1 !!
- print(out)
-
-
+ out_resized = [int(catergories_occ_array[categories][0])] #len have to be 1 !
ind_resized = 1
for point_resized in points_resized:
- #out[0][ind] = point[0][0] / img.shape[1]
- #[0][ind + 1] = point[0][1] / img.shape[0]
x_resized = point_resized[0][0] / img_resized.shape[1]
y_resized = point_resized[0][1] / img_resized.shape[0]
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)))
- #print("stop")
-
+
with open(f'{data_name}/Generated/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 1642cbfbcb03eed209a229c0372ecf3cd8b18b0c..5b3349a2dd4d229ae36a407252d331b6cb3cdc21 100644
--- a/main.py
+++ b/main.py
@@ -1,19 +1,12 @@
import os
import numpy as np
-import json
from prepare_data import reform_data
-#from pose import transform_pose
-#from bbox_2d import generate_2d_bbox
-#from instance_mask import generate_instance_mask
from fps_alg import apply_fps
from bbox_3d import get_3D_bbox
from compute_features import process_compute
-from utils import compute_categories_id, compute_id_good_occ
-import shutil
import open3d as o3d
-# Import the library
-import argparse
from scipy.spatial import distance
+import argparse
def generate_folders(name, list_categories):
is_exist = os.path.exists(name)
@@ -52,31 +45,20 @@ if __name__ == '__main__':
# Parse the argument
args = parser.parse_args()
-
### parameters ###
Categories = [] # to read
Nb_instance = 1
occ_target = 0.5
dataset_src = "/media/gduret/DATA/dataset/s2rg/Fruits_all_medium/data"
- #dataset_src = "/media/mahmoud/E/Fruits_easy/data"
choice = "low" # depth of rgb resolution datas
data_options = {"high": "ground_truth_rgb",
"low": "ground_truth_depth"}
dataset_type = data_options[choice]
dataset_name = f"GUIMOD_{choice}"
list_categories = ["banana1", "kiwi1", "pear2", "strawberry1", "apricot", "orange2", "peach1", "lemon2", "apple2" ]
- # frame = "1_600000000"
- #frame = "1_926000000"
Nb_camera = 15
- #Nb_world = 2
generate_folders(dataset_name, list_categories)
- # for cat in list_categories:
- # src_bbox = f"Models/{cat}/{cat.lower()}.ply"
- # dst_bbox = f"{dataset_name}/Generated/{cat}/{cat.lower()}.ply"
- # shutil.copy(src_bbox, dst_bbox)
-
-
if choice == 'high':
camera = np.matrix([[1386.4138492513919, 0.0, 960.5],
@@ -94,13 +76,11 @@ if __name__ == '__main__':
[0.0, 0.0, 1.0]])
new_camera = trans @ 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)
list_categories = ["banana1", "kiwi1", "pear2", "strawberry1", "apricot", "orange2", "peach1", "lemon2", "apple2" ]
-
objs = {"banana1": [ 0.02949700132012367249, 0.1511049866676330566, 0.06059300713241100311 ],
"kiwi1": [ 0.04908600077033042908, 0.07206099480390548706, 0.04909799993038177490 ],
"pear2": [ 0.06601099669933319092, 0.1287339925765991211, 0.06739201396703720093 ],
@@ -115,49 +95,17 @@ if __name__ == '__main__':
point_cloud = f"Models/{categories}/{categories.lower()}.ply"
pcd = o3d.io.read_point_cloud(point_cloud)
- #print("pcd", pcd)
-
fps_points = apply_fps(pcd, 8)
- #print(fps_points)
np.savetxt(f'{dataset_name}/Generated/{categories}/{categories}_fps_3d.txt', fps_points)
- #point_cloud = f'/home/mahmoud/PycharmProjects/data/GUIMOD_low/Models/{obj}/{obj.lower()}.ply'
- #pcd = o3d.io.read_point_cloud(point_cloud)
point_cloud_in_numpy = np.asarray(pcd.points)
dim = calc_pts_diameter2(point_cloud_in_numpy) * 100
-
- print(dim)
-
np.savetxt(f'{dataset_name}/Generated/{categories}/{categories}_diameter.txt', np.array([dim]))
size_bb = objs[categories]
-
ext = [x / 2 for x in size_bb]
bbox = get_3D_bbox(ext)
np.savetxt(f'{dataset_name}/Generated/{categories}/{categories}_bbox_3d.txt', bbox) # save
- # json_num = 2
- # catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, catergories_label_to_id = compute_categories_id(dataset_name, json_num)
- # print(f'{dataset_name}/Bbox_3d/{json_num}.json')
- # with open(f"{dataset_name}/Bbox_3d/{json_num}.json", 'r') as f:
- # data_Bbox_3d = json.load(f)
-
- # print("catergories_instance_array_cat_to_id : ", catergories_instance_array_cat_to_id)
- # print("data_Bbox_3d : ", data_Bbox_3d)
-
- # for k in range(len(data_Bbox_3d)):
- # if data_Bbox_3d[k]['id'] in catergories_instance_array_cat_to_id[categories]:
- # print(data_Bbox_3d)
- # size_bb = data_Bbox_3d[k]["bbox"]["size"]
- # ext = [x / 2 for x in size_bb]
- # bbox = get_3D_bbox(ext)
- # np.savetxt(f'{dataset_name}/Generated/{categories}/{categories}_bbox_3d.txt', bbox) # save
- # break
-
-
process_compute(dataset_name, camera, new_camera, new_size, Nb_camera, args.World_begin, args.Nb_worlds, list_categories, occ_target, False)
- #transform_pose(dataset_name, Nb_camera, Nb_world, list_categories, occ_target)
- #generate_2d_bbox(dataset_name, Nb_camera, Nb_world, list_categories, occ_target)
- #generate_instance_mask(dataset_name, Nb_camera, Nb_world, list_categories, occ_target)
- #generate_fps(dataset_name, camera, Nb_camera, Nb_world, list_categories, occ_target, True)
- #generate_3d_bbox(dataset_name)
+