diff --git a/compute_features.py b/compute_features.py
index 3630f124bb515764d843069779d6587274cf657c..b9e10630625dd419c59952afd99714e1fe03f535 100644
--- a/compute_features.py
+++ b/compute_features.py
@@ -7,15 +7,9 @@ import json
from utils import compute_categories_id, compute_id_good_occ
from scipy.spatial.transform import Rotation
from bbox_2d import bbox_2d
-
-
-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()
- r = Rotation.from_quat(Q)
- rotation = r.as_matrix()
-
- return rotation
+import cv2
+from instance_mask import instance
+from pose import convert2
def process_compute(data_name, Nb_camera, Nb_world, list_categories, occ_target):
@@ -46,11 +40,11 @@ def process_compute(data_name, Nb_camera, Nb_world, list_categories, occ_target)
if len(data_Bbox_2d) != len(data_3D_pose) :
raise TypeError("size of datas are differents !!")
- for k in range(len(data_3D_pose)):
+ for categories in list_categories:
- for categories in list_categories:
+ if len(catergories_occ_array[categories]) == 1 :
- if len(catergories_occ_array[categories]) == 1 :
+ for k in range(len(data_3D_pose)):
if data_3D_pose[k]['id'] == catergories_occ_array[categories][0]:
cont1 += 1
@@ -75,6 +69,16 @@ def process_compute(data_name, Nb_camera, Nb_world, list_categories, occ_target)
else:
continue
+
+ id = catergories_occ_array[categories][0]
+ 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/Instance_Mask/{categories}/{p}.png", 255*instance_img)
+
+
+
+
print(cont1, cont2, cont3)
diff --git a/instance_mask.py b/instance_mask.py
index 28c9072ef92e1101ba3cffeaffa5f5e52872636b..30da40403d648b88b079ba88698d14fa82c3f61a 100644
--- a/instance_mask.py
+++ b/instance_mask.py
@@ -5,95 +5,6 @@ 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 == id] = 255
@@ -104,36 +15,36 @@ def instance(im, id):
return im
-def generate_instance_mask(data_name, Nb_camera, Nb_world,list_categories, occ_target):
+# def generate_instance_mask(data_name, Nb_camera, Nb_world,list_categories, occ_target):
- for i in range(1, Nb_world + 1): # worlds
+# 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)
+# 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
+# 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)
+# 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 categories in list_categories:
+# for categories in list_categories:
- if len(catergories_occ_array[categories]) == 1 :
+# if len(catergories_occ_array[categories]) == 1 :
- id = catergories_occ_array[categories][0]
- print("iddd : ",id)
- img = cv2.imread(f"{data_name}/Instance_Segmentation/{p}.png", cv2.IMREAD_UNCHANGED) # plt.imread(path)
+# id = catergories_occ_array[categories][0]
+# print("iddd : ",id)
+# img = cv2.imread(f"{data_name}/Instance_Segmentation/{p}.png", cv2.IMREAD_UNCHANGED) # plt.imread(path)
- #print("img[817][308] : ", img[817][308])
- print("img[308][817] : ", img[308][817])
+# #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])
+# instance_img = instance(img, id)
+# print("instance_img[308][817] : ", instance_img[308][817])
- cv2.imwrite(f"{data_name}/Generated/Instance_Mask/{categories}/{p}.png", 255*instance_img)
+# cv2.imwrite(f"{data_name}/Generated/Instance_Mask/{categories}/{p}.png", 255*instance_img)
diff --git a/main.py b/main.py
index 5b84ac86ad0455be2cff9afeb5c70c5e5d39a35e..08cc388519ce7824b75b24522bff65091509e2a1 100644
--- a/main.py
+++ b/main.py
@@ -4,13 +4,12 @@ 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 instance_mask import generate_instance_mask
from fps_alg import generate_fps
from bbox_3d import generate_3d_bbox
from compute_features import process_compute
import shutil
-
def generate_folders(name, list_categories):
is_exist = os.path.exists(name)
if not is_exist:
@@ -66,6 +65,6 @@ if __name__ == '__main__':
process_compute(dataset_name, Nb_camera, Nb_world, list_categories, occ_target)
#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_instance_mask(dataset_name, Nb_camera, Nb_world, list_categories, occ_target)
generate_fps(dataset_name, camera, Nb_camera, Nb_world, list_categories, True)
#generate_3d_bbox(dataset_name)