diff --git a/bbox_2d.py b/bbox_2d.py
index f039ae370c38ba61b32b6da0f5bf534bd48dcd2b..4da8cb51db3ab9ae2d50ad3e1941548a45b25398 100644
--- a/bbox_2d.py
+++ b/bbox_2d.py
@@ -19,11 +19,11 @@ def bbox_2d(data_sample):
return bbox_res
-def generate_2d_bbox(data_name):
+def generate_2d_bbox(data_name, Nb_camera, Nb_world):
cont1, cont2, cont3 = 0, 0, 0
num_arr = 0
- for p in range(4995):
+ for p in range(1, Nb_camera*Nb_world +1 ):
with open(f"{data_name}/Bbox_2d/{p}.json", 'r') as f:
data = json.load(f)
@@ -31,17 +31,17 @@ def generate_2d_bbox(data_name):
if data[i]['id'] == 4:
cont1 += 1
bbox = bbox_2d(data[i])
- np.savetxt(f'{data_name}/Bbox/Banana/{p}.txt', np.array(bbox).reshape((1, 4))) # save
+ np.savetxt(f'{data_name}/Bbox/banana1/{p}.txt', np.array(bbox).reshape((1, 4))) # save
elif data[i]['id'] == 5:
cont2 += 1
bbox = bbox_2d(data[i])
- np.savetxt(f'{data_name}/Bbox/Orange/{p}.txt', np.array(bbox).reshape((1, 4))) # save
+ np.savetxt(f'{data_name}/Bbox/orange2/{p}.txt', np.array(bbox).reshape((1, 4))) # save
elif data[i]['id'] == 6:
cont3 += 1
bbox = bbox_2d(data[i])
- np.savetxt(f'{data_name}/Bbox/Pear/{p}.txt', np.array(bbox).reshape((1, 4))) # save
+ np.savetxt(f'{data_name}/Bbox/pear2/{p}.txt', np.array(bbox).reshape((1, 4))) # save
else:
continue
diff --git a/data_label.py b/data_label.py
index 02cf8367cb8bfd629aa39de861aa5ac576cce527..919376f2c51012a7a5145827120aee0c856ee5ad 100644
--- a/data_label.py
+++ b/data_label.py
@@ -5,7 +5,7 @@ import numpy as np
for im in range(4995):
id_obj = 0.0
res_all = []
- for obj in ['banana_mask', 'orange_mask', 'pear_mask']:
+ for obj in ['banana1_mask', 'orange2_mask', 'pear2_mask']:
res = [id_obj]
image = cv2.imread(f"{obj}/{im}.png", 0)
image = image/255.0
diff --git a/fps_alg.py b/fps_alg.py
index 68632a80b2eff2e8553d936f0a41198e886a3fd3..1ba47529e55094f0d36c438685ec96831946b4e5 100644
--- a/fps_alg.py
+++ b/fps_alg.py
@@ -4,6 +4,7 @@ from matplotlib import pyplot as plt
from matplotlib import image
import cv2
from skimage.io import imshow
+from pathlib import Path
def fps(points, centroid, n_samples):
@@ -162,37 +163,42 @@ def process(pcd_box, pcd2, R_exp, tVec, camera, img):
# ==============================================================================
-def generate_fps(data_name, camera, vis=False):
+def generate_fps(data_name, camera, Nb_camera, Nb_world, list_objects, vis=False):
# Read the point cloud
- for obj in ["Banana", "Orange", "Pear"]:
+ for obj in list_objects:
obj_id = 1
point_cloud = f'{data_name}/Models/{obj}/{obj.lower()}.ply'
pcd = o3d.io.read_point_cloud(point_cloud)
- fps_points = apply_fps(pcd, 8)
+ print("pcd", pcd)
+
+
+ fps_points = apply_fps(pcd, 200)
np.savetxt(f'{data_name}/FPS/{obj}_fps_3d.txt', fps_points)
- for i in range(4995):
- img = image.imread(f"{data_name}/RGB/{i}.png")
-
- pose = np.load(f'{data_name}/Pose_transformed/{obj}/{i}.npy')
- R_exp = pose[0:3, 0:3]
- tVec = pose[0:3, 3]
-
- # camera = np.matrix([[1386.4138492513919, 0.0, 960.5],
- # [0.0, 1386.4138492513919, 540.5],
- # [0.0, 0.0, 1.0]])
-
- # process(pcd_bbox, pcd, R_exp, tVec, camera, img)
- points = process2(fps_points, R_exp, tVec, camera, img, vis)
- out = np.zeros((1, 17))
-
- out[0] = obj_id
- ind = 1
- for point in points:
- out[0][ind] = point[0][0] / img.shape[1]
- out[0][ind + 1] = point[0][1] / img.shape[0]
- ind += 2
- np.savetxt(f'{data_name}/FPS/{obj}/{i}.txt', out)
- print("stop")
+ for i in range(1, Nb_camera*Nb_world +1):
+ path = Path(f'{data_name}/Pose_transformed/{obj}/{i}.npy')
+ if path.is_file():
+ img = image.imread(f"{data_name}/RGB/{i}.png")
+
+ pose = np.load(f'{data_name}/Pose_transformed/{obj}/{i}.npy')
+ R_exp = pose[0:3, 0:3]
+ tVec = pose[0:3, 3]
+
+ # camera = np.matrix([[1386.4138492513919, 0.0, 960.5],
+ # [0.0, 1386.4138492513919, 540.5],
+ # [0.0, 0.0, 1.0]])
+
+ # process(pcd_bbox, pcd, R_exp, tVec, camera, img)
+ points = process2(fps_points, R_exp, tVec, camera, img, vis)
+ out = np.zeros((1, 401))
+
+ out[0] = obj_id
+ ind = 1
+ for point in points:
+ out[0][ind] = point[0][0] / img.shape[1]
+ out[0][ind + 1] = point[0][1] / img.shape[0]
+ ind += 2
+ np.savetxt(f'{data_name}/FPS/{obj}/{i}.txt', out)
+ print("stop")
obj_id += 1
diff --git a/instance_mask.py b/instance_mask.py
index ead299034d3419bbdd6b77cd3162c25cc3f3c5f4..f4429bc87a9dedd5f35be5aa89727982f803afa2 100644
--- a/instance_mask.py
+++ b/instance_mask.py
@@ -1,24 +1,135 @@
from matplotlib import pyplot as plt
import cv2
+import json
+from pathlib import Path
+
+
+
+def compute_categories_id(data_name, world):
+ #Category = 'banana1'
+ #Category = 'pear2'
+ #Category = "orange2"
+ # Opening JSON file
+ f = open(f'{data_name}/Meta/{world}.json')
+
+ # returns JSON object as
+ # a dictionary
+ data = json.load(f)
+
+ # Iterating through the json
+ # list
+
+ catergories_label_to_id={}
+ catergories_id_to_label={}
+ catergories_instance_array_cat_to_id={}
+ catergories_instance_array_id_to_cat={}
+
+ for k in data['categories']:
+ catergories_label_to_id[k['label']]=k['id']
+ catergories_id_to_label[k['id']]=k['label']
+ catergories_instance_array_cat_to_id[k['label']]=[]
+
+ for k in data['objects']:
+ #print(k)
+ #catergories_instance_array[catergories_id_to_label[i['category_id']]]
+ catergories_instance_array_id_to_cat[k['id']] = catergories_id_to_label[k['category_id']]
+ catergories_instance_array_cat_to_id[catergories_id_to_label[k['category_id']]].append(k['id'])
+ # if i['category_id'] == id_category :
+ # print("Hello fruits instance")
+ # id_instances.append(i['id'])
+ # print(i['id'])
+
+ # print("catergories_instance_array_cat_to_id : ", catergories_instance_array_cat_to_id)
+ # print("catergories_instance_array_id_to_cat : ", catergories_instance_array_id_to_cat)
+
+
+ # Closing file
+ f.close()
+
+
+ return catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id
+
+
+
+def compute_id_good_occ(data_name, count, catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, Occ_wanted):
+
+ f2 = open(f'{data_name}/Occlusion/{count}.json')
+
+ data2 = json.load(f2)
+
+
+ catergories_occ_array = {}
+
+ for cat in catergories_instance_array_cat_to_id :
+ #print(cat)
+
+ catergories_occ_array[cat] = []
+
+ for i in data2:
+ #print('i : ',i)
+ #print(i['id'])
+ #print(id_instances)
+ if i['occlusion_value'] > 0.5 :
+ catergories_occ_array[catergories_instance_array_id_to_cat[i['id']]].append(i['id'])
+
+
+
+ # if i['id'] in id_instances :
+ # print("Hello banana instance occ")
+ # if i['occlusion_value'] > 0.5 :
+ # id_instances_good.append(i['id'])
+ # print(i['id'])
+ # print(i['occlusion_value'])
+
+ print(catergories_occ_array)
+
+
+
+
+ # Closing file
+ f2.close()
+
+ return catergories_occ_array
+
+
def instance(im, id):
- im = im * 255
+ #im = im * 255
im[im == id] = 255
im[im < 255] = 0
im = im / 255
- # plt.imshow(im, cmap='gray')
- # plt.show()
+ #plt.imshow(im, cmap='gray')
+ #plt.show()
return im
-def generate_instance_mask(data_name):
- ids = {"Banana": 108, "Orange": 117, "Pear": 121}
+def generate_instance_mask(data_name, Nb_camera, Nb_world):
+ ids = {"banana1": 25, "orange2": 36, "pear2": 121}
+ for i in range(1, Nb_world + 1): # worlds
+
+ #catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id = compute_categories_id(data_name, i)
+
+
+
+ for j in range(1, Nb_camera+1): # cameras
+ k = ((i-1)*Nb_camera) + j
+
+ #catergories_occ_array = compute_id_good_occ(data_name, p, catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, occ_target)
+
+ for key in ids.keys():
+ id = ids[key]
+ path = Path(f"{data_name}/Instance_Segmentation/{k}.png")
+ if path.is_file():
+ print("iddd : ",id)
+ img = cv2.imread(f"{data_name}/Instance_Segmentation/{k}.png", cv2.IMREAD_UNCHANGED) # plt.imread(path)
+
+ #print("img[817][308] : ", img[817][308])
+ print("img[308][817] : ", img[308][817])
+
+
+ instance_img = instance(img, id)
+ print("instance_img[308][817] : ", instance_img[308][817])
- for i in range(4995):
- for key in ids.keys():
- id = ids[key]
- img = plt.imread(f"{data_name}/Mask/{i}.png")
- instance_img = instance(img, id)
- cv2.imwrite(f"{data_name}/Instance_Mask/{key}/{i}.png", instance_img)
+ cv2.imwrite(f"{data_name}/Instance_Mask/{key}/{k}.png", 255*instance_img)
diff --git a/main.py b/main.py
index 90c471b4d07237ef0dd9c8aca06a528d74d2b2c1..4e8a37f6801c7c6e2033524001c85f6022bb373f 100644
--- a/main.py
+++ b/main.py
@@ -1,5 +1,6 @@
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
@@ -14,31 +15,213 @@ def generate_folders(name):
if not is_exist:
os.mkdir(name)
- folders = ["RGB", "Depth", "Mask", "Pose", "Bbox_2d", "Instance_Mask", "Models", "Pose_transformed", "Bbox", "FPS"]
+ folders = ["RGB", "Depth", "Mask", "Meta", "Pose", "Bbox_2d", "Bbox_2d_loose", "Instance_Segmentation", "Semantic_Segmentation", "Instance_Mask", "Occlusion", "Models", "Pose_transformed", "Bbox", "FPS"]
for f in folders:
is_exist = os.path.exists(f"{name}/{f}")
if not is_exist:
if f not in ["Instance_Mask", "Models", "Pose_transformed", "Bbox", "FPS"]:
os.mkdir(f"{name}/{f}")
else:
- os.makedirs(f"{name}/{f}/Banana")
- os.makedirs(f"{name}/{f}/Orange")
- os.makedirs(f"{name}/{f}/Pear")
+ os.makedirs(f"{name}/{f}/banana1")
+ os.makedirs(f"{name}/{f}/orange2")
+ os.makedirs(f"{name}/{f}/pear2")
+
+
+
+def compute_categories_id(dataset_src, world):
+ #Category = 'banana1'
+ #Category = 'pear2'
+ #Category = "orange2"
+ # Opening JSON file
+ f = open(f'{dataset_src}/{world}/meta.json')
+
+ # returns JSON object as
+ # a dictionary
+ data = json.load(f)
+
+ # Iterating through the json
+ # list
+
+ id_category = 0
+ catergories_label_to_id={}
+ catergories_id_to_label={}
+ catergories_instance_array_cat_to_id={}
+ catergories_instance_array_id_to_cat={}
+
+ for i in data['categories']:
+ catergories_label_to_id[i['label']]=i['id']
+ catergories_id_to_label[i['id']]=i['label']
+ catergories_instance_array_cat_to_id[i['label']]=[]
+
+ for i in data['objects']:
+ print(i)
+ #catergories_instance_array[catergories_id_to_label[i['category_id']]]
+ catergories_instance_array_id_to_cat[i['id']] = catergories_id_to_label[i['category_id']]
+ catergories_instance_array_cat_to_id[catergories_id_to_label[i['category_id']]].append(i['id'])
+ # if i['category_id'] == id_category :
+ # print("Hello fruits instance")
+ # id_instances.append(i['id'])
+ # print(i['id'])
+
+ print("catergories_instance_array_cat_to_id : ", catergories_instance_array_cat_to_id)
+ print("catergories_instance_array_id_to_cat : ", catergories_instance_array_id_to_cat)
+
+
+ # Closing file
+ f.close()
+
+
+ return catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id
+
+
+def get_id_wanted(wanted_category):
+ #Category = 'banana1'
+ #Category = 'pear2'
+ #Category = "orange2"
+ # Opening JSON file
+ f = open('/media/gduret/DATA/dataset/s2rg/Fruits_all_medium/data/1/meta.json')
+
+ # returns JSON object as
+ # a dictionary
+ data = json.load(f)
+
+ # Iterating through the json
+ # list
+
+ id_category = 0
+
+ for i in data['categories']:
+ print(i)
+ if i['label'] == wanted_category :
+ print("Hello fruits")
+ id_category = i['id']
+ print(id_category)
+
+ id_instances = []
+
+ for i in data['objects']:
+ print(i)
+ if i['category_id'] == id_category :
+ print("Hello fruits instance")
+ id_instances.append(i['id'])
+ print(i['id'])
+
+ print(id_instances)
+
+ # Closing file
+ f.close()
+
+ return id_instances
+
+
+def compute_id_good_occ(dataset_src, world, camera, type_data, frame, catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, Occ_wanted):
+
+ f2 = open(f'{dataset_src}/{world}/grabber_{camera}/{type_data}/occlusion/{frame}.json')
+
+ data2 = json.load(f2)
+
+
+ catergories_occ_array = {}
+
+ for cat in catergories_instance_array_cat_to_id :
+ #print(cat)
+
+ catergories_occ_array[cat] = []
+
+ for i in data2:
+ #print('i : ',i)
+ #print(i['id'])
+ #print(id_instances)
+ if i['occlusion_value'] > 0.5 :
+ catergories_occ_array[catergories_instance_array_id_to_cat[i['id']]].append(i['id'])
+
+
+
+ # if i['id'] in id_instances :
+ # print("Hello banana instance occ")
+ # if i['occlusion_value'] > 0.5 :
+ # id_instances_good.append(i['id'])
+ # print(i['id'])
+ # print(i['occlusion_value'])
+
+ print(catergories_occ_array)
+
+
+
+
+ # Closing file
+ f2.close()
+
+ return catergories_occ_array
+
+
+def get_id_good_occ(Occ_wanted):
+
+ f2 = open('/media/gduret/DATA/dataset/s2rg/Fruits_all_medium/data/1/grabber_1/ground_truth_rgb/occlusion/4_158000000.json')
+
+ data2 = json.load(f2)
+
+ id_instances_good = []
+
+ for i in data2:
+ print('i : ',i)
+ print(i['id'])
+ print(id_instances)
+
+ if i['id'] in id_instances :
+ print("Hello banana instance occ")
+ if i['occlusion_value'] > 0.5 :
+ id_instances_good.append(i['id'])
+ print(i['id'])
+ print(i['occlusion_value'])
+
+ print(id_instances_good)
+
+
+
+
+ # Closing file
+ f2.close()
if __name__ == '__main__':
- dataset_src = "/media/mahmoud/E/Fruits_easy/data"
+ #dataset_src = "/media/mahmoud/E/Fruits_easy/data"
+
+ # futur parameters :
+
+
+
+ Categories = [] # to read
+ Nb_instance = 1
+ Occ_target = 0.5
+
+
+ #Category = 'banana1'
+ Category = 'pear2'
+ #Category = "orange2"
+
+ dataset_src = "/media/gduret/DATA/dataset/s2rg/Fruits_all_medium/data"
+
+
+ catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id = compute_categories_id(dataset_src, 1)
+
+
+
choice = "low"
data_options = {"high": "ground_truth_rgb",
"low": "ground_truth_depth"}
+
+
dataset_type = data_options[choice]
+ compute_id_good_occ(dataset_src, 1, 1, dataset_type, "1_926000000", catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, Occ_target)
+
dataset_name = f"GUIMOD_{choice}"
generate_folders(dataset_name)
- list_obj = ["Banana", "Orange", "Pear"]
+ list_obj = ["banana1", "orange2", "pear2"]
for obj in list_obj:
src_bbox = f"Models/{obj}/{obj.lower()}.ply"
dst_bbox = f"{dataset_name}/Models/{obj}/{obj.lower()}.ply"
@@ -55,9 +238,16 @@ if __name__ == '__main__':
np.savetxt(f'{dataset_name}/camera_{choice}.txt', camera)
- reform_data(dataset_src, dataset_name, dataset_type)
- transform_pose(dataset_name)
- generate_2d_bbox(dataset_name)
- generate_instance_mask(dataset_name)
- generate_fps(dataset_name, camera, False)
- generate_3d_bbox(dataset_name)
+ # frame = "1_600000000"
+ frame = "1_926000000"
+ Nb_camera = 15
+ Nb_world = 2
+ reform_data(dataset_src, dataset_name, dataset_type, frame, Nb_camera, Nb_world)
+
+
+
+ transform_pose(dataset_name, Nb_camera, Nb_world, Occ_target)
+ generate_2d_bbox(dataset_name, Nb_camera, Nb_world)
+ generate_instance_mask(dataset_name, Nb_camera, Nb_world)
+ generate_fps(dataset_name, camera, Nb_camera, Nb_world, list_obj, True)
+ #generate_3d_bbox(dataset_name)
diff --git a/pose.py b/pose.py
index ee0fd8bef453aba5508fc95e07dedf31047cbc49..0e4b6c258543bf3a22d44e3b25a7e6726fd5a13a 100644
--- a/pose.py
+++ b/pose.py
@@ -4,6 +4,73 @@ import json
from scipy.spatial.transform import Rotation
+def compute_categories_id(data_name, world):
+ #Category = 'banana1'
+ #Category = 'pear2'
+ #Category = "orange2"
+ # Opening JSON file
+ f = open(f'{data_name}/Meta/{world}.json')
+
+ # returns JSON object as
+ # a dictionary
+ data = json.load(f)
+
+ # Iterating through the json
+ # list
+
+ catergories_label_to_id={}
+ catergories_id_to_label={}
+ catergories_instance_array_cat_to_id={}
+ catergories_instance_array_id_to_cat={}
+
+ for k in data['categories']:
+ catergories_label_to_id[k['label']]=k['id']
+ catergories_id_to_label[k['id']]=k['label']
+ catergories_instance_array_cat_to_id[k['label']]=[]
+
+ for k in data['objects']:
+ #print(k)
+ #catergories_instance_array[catergories_id_to_label[i['category_id']]]
+ catergories_instance_array_id_to_cat[k['id']] = catergories_id_to_label[k['category_id']]
+ catergories_instance_array_cat_to_id[catergories_id_to_label[k['category_id']]].append(k['id'])
+ # if i['category_id'] == id_category :
+ # print("Hello fruits instance")
+ # id_instances.append(i['id'])
+ # print(i['id'])
+
+ print("catergories_instance_array_cat_to_id : ", catergories_instance_array_cat_to_id)
+ print("catergories_instance_array_id_to_cat : ", catergories_instance_array_id_to_cat)
+
+ # Closing file
+ f.close()
+
+ return catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id
+
+
+
+def compute_id_good_occ(data_name, count, catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, Occ_wanted):
+
+ f2 = open(f'{data_name}/Occlusion/{count}.json')
+
+ data2 = json.load(f2)
+ catergories_occ_array = {}
+
+ for cat in catergories_instance_array_cat_to_id :
+ #print(cat)
+ catergories_occ_array[cat] = []
+
+ for i in data2:
+ if i['occlusion_value'] > 0.5 :
+ catergories_occ_array[catergories_instance_array_id_to_cat[i['id']]].append(i['id'])
+
+ print(catergories_occ_array)
+
+ # Closing file
+ f2.close()
+
+ return catergories_occ_array
+
+
def convert2(xyz):
(R, P, Y) = (xyz[0], xyz[1], xyz[2])
Q = Rotation.from_euler(seq='xyz', angles=[R, P, Y], degrees=False).as_quat()
@@ -13,54 +80,67 @@ def convert2(xyz):
return rotation
-def transform_pose(data_name):
+def transform_pose(data_name, Nb_camera, Nb_world, occ_target):
transformation = np.matrix([[0.0000000, -1.0000000, 0.0000000],
[0.0000000, 0.0000000, -1.0000000],
[1.0000000, 0.0000000, 0.0000000]])
cont1, cont2, cont3 = 0, 0, 0
- for p in range(4995):
- with open(f'{data_name}/Pose/{p}.json', 'r') as f:
- data = json.load(f)
-
- for i in range(len(data)):
- if data[i]['id'] == 4:
- cont1 += 1
- rpy = data[i]['pose']['rpy']
- rot = convert2(rpy)
- R_exp = transformation @ rot
- R_exp = np.array(R_exp)
-
- xyz = data[i]['pose']['xyz']
- T_exp = transformation @ xyz
- T_exp = np.array(T_exp)
- num_arr = np.c_[R_exp, T_exp[0]]
- np.save(f'{data_name}/Pose_transformed/Banana/{p}.npy', num_arr) # save
-
- elif data[i]['id'] == 5:
- cont2 += 1
- rpy = data[i]['pose']['rpy']
- rot = convert2(rpy)
- R_exp = transformation @ rot
- R_exp = np.array(R_exp)
-
- xyz = data[i]['pose']['xyz']
- T_exp = transformation @ xyz
- T_exp = np.array(T_exp)
- num_arr = np.c_[R_exp, T_exp[0]]
- np.save(f'{data_name}/Pose_transformed/Orange/{p}.npy', num_arr) # save
- elif data[i]['id'] == 6:
- cont3 += 1
- rpy = data[i]['pose']['rpy']
- rot = convert2(rpy)
- R_exp = transformation @ rot
- R_exp = np.array(R_exp)
-
- xyz = data[i]['pose']['xyz']
- T_exp = transformation @ xyz
- T_exp = np.array(T_exp)
- num_arr = np.c_[R_exp, T_exp[0]]
- np.save(f'{data_name}/Pose_transformed/Pear/{p}.npy', num_arr) # save
- else:
- continue
+ for i in range(1, Nb_world + 1): # worlds
+
+ catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id = compute_categories_id(data_name, i)
+
+ for j in range(1, Nb_camera+1): # cameras
+ p = ((i-1)*Nb_camera) + j
+
+ catergories_occ_array = compute_id_good_occ(data_name, p, catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, occ_target)
+
+ with open(f'{data_name}/Pose/{p}.json', 'r') as f:
+ data = json.load(f)
+ #print(data)
+ print("len(data)", len(data))
+
+ for k in range(len(data)):
+
+
+ if len(catergories_occ_array['banana1']) == 1 and data[k]['id'] == catergories_occ_array['banana1'][0]:
+ cont1 += 1
+ rpy = data[k]['pose']['rpy']
+ rot = convert2(rpy)
+ R_exp = transformation @ rot
+ R_exp = np.array(R_exp)
+
+ xyz = data[k]['pose']['xyz']
+ T_exp = transformation @ xyz
+ T_exp = np.array(T_exp)
+ num_arr = np.c_[R_exp, T_exp[0]]
+ np.save(f'{data_name}/Pose_transformed/banana1/{p}.npy', num_arr) # save
+
+ elif len(catergories_occ_array['orange2']) == 1 and data[k]['id'] == catergories_occ_array['orange2'][0]:
+ cont2 += 1
+ rpy = data[k]['pose']['rpy']
+ rot = convert2(rpy)
+ R_exp = transformation @ rot
+ R_exp = np.array(R_exp)
+
+ xyz = data[k]['pose']['xyz']
+ T_exp = transformation @ xyz
+ T_exp = np.array(T_exp)
+ num_arr = np.c_[R_exp, T_exp[0]]
+ np.save(f'{data_name}/Pose_transformed/orange2/{p}.npy', num_arr) # save
+
+ elif len(catergories_occ_array['pear2']) == 1 and data[k]['id'] == catergories_occ_array['pear2'][0]:
+ cont3 += 1
+ rpy = data[k]['pose']['rpy']
+ rot = convert2(rpy)
+ R_exp = transformation @ rot
+ R_exp = np.array(R_exp)
+
+ xyz = data[k]['pose']['xyz']
+ T_exp = transformation @ xyz
+ T_exp = np.array(T_exp)
+ num_arr = np.c_[R_exp, T_exp[0]]
+ np.save(f'{data_name}/Pose_transformed/pear2/{p}.npy', num_arr) # save
+ else:
+ continue
print(cont1, cont2, cont3)
diff --git a/prepare_data.py b/prepare_data.py
index 394d8bf83c939a7adcefc60b91ff857c101d871d..42ec5802d0cf65b28162f07ae9ea6682aea98c48 100644
--- a/prepare_data.py
+++ b/prepare_data.py
@@ -1,39 +1,48 @@
import shutil
-def reform_data(src, data_name, data_option):
- k = 0
- for i in range(1, 1000):
- g = 0
- for j in range(1, 6):
- count = (k*5) + g
-
+def reform_data(src, data_name, data_option, frame, Nb_camera, Nb_world):
+ for i in range(1, Nb_world + 1): # worlds
+ src_meta = f"{src}/{i}/meta.json"
+ dst_meta = f"{data_name}/Meta/{i}.json"
+ shutil.copy(src_meta, dst_meta)
+ for j in range(1, Nb_camera+1): # cameras
+ count = ((i-1)*Nb_camera) + j
if data_option == "ground_truth_rgb":
- src_img = f"{src}/{i}/grabber_{j}/color/image/0_0.png"
+ src_img = f"{src}/{i}/grabber_{j}/color/image/{frame}.png"
dst_img = f"{data_name}/RGB/{count}.png"
else:
- src_img = f"{src}/{i}/grabber_{j}/depth/image/0_0.png"
+ src_img = f"{src}/{i}/grabber_{j}/depth/image/{frame}.png"
dst_img = f"{data_name}/RGB/{count}.png"
shutil.copy(src_img, dst_img)
- src_mask = f"{src}/{i}/grabber_{j}/{data_option}/semantic_map/0_0.png"
- dst_mask = f"{data_name}/Mask/{count}.png"
- shutil.copy(src_mask, dst_mask)
+ src_id = f"{src}/{i}/grabber_{j}/{data_option}/id_map/{frame}.png"
+ dst_id = f"{data_name}/Instance_Segmentation/{count}.png"
+ shutil.copy(src_id, dst_id)
+
+ src_semantic = f"{src}/{i}/grabber_{j}/{data_option}/semantic_map/{frame}.png"
+ dst_semantic = f"{data_name}/Semantic_Segmentation/{count}.png"
+ shutil.copy(src_semantic, dst_semantic)
- src_path_pose = f"{src}/{i}/grabber_{j}/{data_option}/3d_pose/0_0.json"
+ src_path_pose = f"{src}/{i}/grabber_{j}/{data_option}/3d_pose/{frame}.json"
dst_path_pose = f"{data_name}/Pose/{count}.json"
shutil.copy(src_path_pose, dst_path_pose)
- src_depth = f"{src}/{i}/grabber_{j}/depth/depth_map/0_0.tiff"
+ src_path_occlusion = f"{src}/{i}/grabber_{j}/{data_option}/occlusion/{frame}.json"
+ dst_path_occlusion = f"{data_name}/Occlusion/{count}.json"
+ shutil.copy(src_path_occlusion, dst_path_occlusion)
+
+ src_depth = f"{src}/{i}/grabber_{j}/depth/depth_map/{frame}.tiff"
dst_depth = f"{data_name}/Depth/{count}.tiff"
shutil.copy(src_depth, dst_depth)
- src_bbox = f"{src}/{i}/grabber_{j}/{data_option}/2d_detection/0_0.json"
+ src_bbox = f"{src}/{i}/grabber_{j}/{data_option}/2d_detection/{frame}.json"
dst_bbox = f"{data_name}/Bbox_2d/{count}.json"
shutil.copy(src_bbox, dst_bbox)
- g += 1
- k += 1
+ src_bbox_loose = f"{src}/{i}/grabber_{j}/{data_option}/2d_detection_loose/{frame}.json"
+ dst_bbox_loose = f"{data_name}/Bbox_2d_loose/{count}.json"
+ shutil.copy(src_bbox_loose, dst_bbox_loose)
diff --git a/read_dataset.py b/read_dataset.py
index a2057cb224cd33a321a0799afc194c27fd16e3e9..a9da4c8469afa32680453747a3e682cc0b8360c4 100644
--- a/read_dataset.py
+++ b/read_dataset.py
@@ -26,9 +26,9 @@ import json
#Category = 'banana1'
Category = 'pear2'
-
+#Category = "orange2"
# Opening JSON file
-f = open('/media/gduret/DATA/dataset/s2rg/Fruits_all_medium/data/6/meta.json')
+f = open('/media/gduret/DATA/dataset/s2rg/Fruits_all_medium/data/1/meta.json')
# returns JSON object as
# a dictionary
@@ -60,7 +60,7 @@ print(id_instances)
# Closing file
f.close()
-f2 = open('/media/gduret/DATA/dataset/s2rg/Fruits_all_medium/data/6/grabber_1/ground_truth_rgb/occlusion/4_158000000.json')
+f2 = open('/media/gduret/DATA/dataset/s2rg/Fruits_all_medium/data/1/grabber_1/ground_truth_rgb/occlusion/4_158000000.json')
data2 = json.load(f2)
diff --git a/resize.py b/resize.py
index 7060013971359c71f2cc04630ddaaf33e16b7f16..70cc608640bcb86bab7e2941ba3b7e98659a96c6 100644
--- a/resize.py
+++ b/resize.py
@@ -38,14 +38,14 @@ if __name__ == '__main__':
depth = cv2.resize(cv2.imread(f"{dataset_name}/Depth/{i}.tiff"), new_size)
- banana_mask = cv2.resize(cv2.imread(f"{dataset_name}/Instance_Mask/Banana/{i}.png"), new_size)
- cv2.imwrite(f"banana_mask/{i}.png", banana_mask*255)
+ banana_mask = cv2.resize(cv2.imread(f"{dataset_name}/Instance_Mask/banana1/{i}.png"), new_size)
+ cv2.imwrite(f"banana1_mask/{i}.png", banana_mask*255)
- orange_mask = cv2.resize(cv2.imread(f"{dataset_name}/Instance_Mask/Orange/{i}.png"), new_size)
- cv2.imwrite(f"orange_mask/{i}.png", orange_mask*255)
+ orange_mask = cv2.resize(cv2.imread(f"{dataset_name}/Instance_Mask/orange2/{i}.png"), new_size)
+ cv2.imwrite(f"orange2_mask/{i}.png", orange_mask*255)
- Pear_mask = cv2.resize(cv2.imread(f"{dataset_name}/Instance_Mask/Pear/{i}.png"), new_size)
- cv2.imwrite(f"pear_mask/{i}.png", Pear_mask*255)
+ Pear_mask = cv2.resize(cv2.imread(f"{dataset_name}/Instance_Mask/pear2/{i}.png"), new_size)
+ cv2.imwrite(f"pear2_mask/{i}.png", Pear_mask*255)
print("Done")
diff --git a/test_resize.py b/test_resize.py
index c014c5975f1ef22a590f3e70f8476481789afea2..58177e3b28c9f070922cbad2000c4f8b2bf8d505 100644
--- a/test_resize.py
+++ b/test_resize.py
@@ -166,6 +166,7 @@ def generate_fps(data_name, camera, vis=False, resize=False):
# Read the point cloud
for obj in ["Banana"]:
+ sfefcij
obj_id = 0
point_cloud = f'{data_name}/Models/{obj}/{obj.lower()}.ply'
pcd = o3d.io.read_point_cloud(point_cloud)