diff --git a/compute_features.py b/compute_features.py
index babf84be459ba8f06bbd26e6f90e0cdbb11fbeac..cee73a35acd1f93f49d67e949c93eb27ee43fbc4 100644
--- a/compute_features.py
+++ b/compute_features.py
@@ -33,7 +33,7 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
for i in range(World_begin, World_begin + Nb_world): # worlds
-
+ print(i)
if i > 4 :
destination_folders["Worlds"] = f"Generated_Worlds_Testing"
elif i > 3 :
@@ -41,7 +41,7 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
else :
destination_folders["Worlds"] = f"Generated_Worlds_Training"
- catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, catergories_label_to_id = compute_categories_id(data_name, i)
+ categories_instance_array_id_to_cat, categories_instance_array_cat_to_id, categories_label_to_id = compute_categories_id(data_name, i)
for j in range(1, Nb_camera+1): # cameras
p = ((i-1)*Nb_camera) + j
@@ -62,7 +62,7 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
else :
destination_folders["Mix_all"] = "dont_save"
- catergories_occ_array = compute_id_good_occ(data_name, p, catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, occ_target)
+ categories_occ_array, categories_array = compute_id_good_occ(data_name, p, categories_instance_array_id_to_cat, categories_instance_array_cat_to_id, occ_target)
### 3D Poses ###
with open(f'{data_name}/Pose/{p}.json', 'r') as f:
@@ -84,9 +84,11 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
with open(f'{data_name}/{destination_folders[scenario_loop]}/Count_{p-1}.json') as f:
list_count_categories[scenario_loop][destination_folders[scenario_loop]] = json.load(f)
+ #res_all = []
for categories in list_categories:
- if categories in catergories_occ_array.keys():
- Nb_instance = len(catergories_occ_array[categories])
+ if categories in categories_occ_array.keys():
+ Nb_instance = len(categories_array[categories])
+ Nb_instance_occ = len(categories_occ_array[categories])
for scenario_loop in scenarios:
@@ -103,14 +105,14 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
meta['id_generated'] = list_count_categories[scenario_loop][destination_folders[scenario_loop]][categories][f"{Nb_instance}_instances"]
meta['id_original'] = p
- meta['id_category'] = catergories_label_to_id[categories]
- meta['id_instance'] = catergories_occ_array[categories]
+ meta['id_category'] = categories_label_to_id[categories]
+ meta['id_instance'] = categories_occ_array[categories]
meta['id_dataset'] = 1
meta['world'] = i
meta['camera'] = f"grabber_{j}"
meta['occlusion'] = occ_target
meta['Nb_instance_category'] = Nb_instance
-
+
if not os.path.isfile(f'{data_name}/{destination_folders[scenario_loop]}/{categories}/Meta_Gen/{categories}.json'):
with open(f'{data_name}/{destination_folders[scenario_loop]}/{categories}/Meta_Gen/{categories}.json', mode='w') as f:
feeds = {}
@@ -122,10 +124,11 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
feeds[meta['id_generated']]=meta
with open(f'{data_name}/{destination_folders[scenario_loop]}/{categories}/Meta_Gen/{categories}.json', mode='w') as f:
f.write(json.dumps(feeds, indent=4))
+
+ if (Nb_instance_occ == 1 and Nb_instance == 0 ): # condition of only one instance of occ >= 0.5 and no other < 0.05
- if (Nb_instance == 1):
for k in range(len(data_3D_pose)):
- if data_3D_pose[k]['id'] == catergories_occ_array[categories][0]:
+ if data_3D_pose[k]['id'] == categories_occ_array[categories][0]:
rpy = data_3D_pose[k]['pose']['rpy']
rot = convert2(rpy)
R_exp = transformation @ rot
@@ -135,11 +138,13 @@ 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
+ for scenario_loop in scenarios:
+ if not destination_folders[scenario_loop] == "dont_save" :
+ np.save(f'{data_name}/{destination_folders[scenario_loop]}/{categories}/Pose_transformed/{p}.npy', num_arr) # save
else:
continue
- if data_Bbox_2d[k]['id'] == catergories_occ_array[categories][0]:
+ if data_Bbox_2d[k]['id'] == categories_occ_array[categories][0]:
bbox = bbox_2d(data_Bbox_2d[k])
for scenario_loop in scenarios:
if not destination_folders[scenario_loop] == "dont_save" :
@@ -147,7 +152,7 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
else:
continue
- if data_Bbox_3d[k]['id'] == catergories_occ_array[categories][0]:
+ if data_Bbox_3d[k]['id'] == categories_occ_array[categories][0]:
bbox3d_size = data_Bbox_3d[k]['bbox']['size']
for scenario_loop in scenarios:
if not destination_folders[scenario_loop] == "dont_save" :
@@ -155,18 +160,38 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
else:
continue
- id = catergories_occ_array[categories][0]
+ id = categories_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)
for scenario_loop in scenarios:
if not destination_folders[scenario_loop] == "dont_save" :
cv2.imwrite(f"{data_name}/{destination_folders[scenario_loop]}/{categories}/Instance_Mask/{p}.png", 255*instance_img)
+
+ id_obj = 0.0
+ res = [id_obj]
+
+ image = cv2.imread(f"{data_name}/{destination_folders[scenario_loop]}/{categories}/Instance_Mask/{p}.png", 0)
+ image = image/255.0
+ contours, _ = cv2.findContours(image.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
+ for l in range(len(contours[0])):
+ x = contours[0][l][0][0]/640.0
+ res.append(x)
+ y = contours[0][l][0][1]/480.0
+ res.append(y)
+ #id_obj += 1.0
+ #res_all.append(res)
+
+ a_file = open(f"{data_name}/{destination_folders[scenario_loop]}/{categories}/Labels/{p}.txt", "w")
+ #for row in res_all:
+ np.savetxt(a_file, np.array(res).reshape(1, len(res)))
+
+ a_file.close()
+
instance_img_resized = cv2.resize(instance_img, new_size)
for scenario_loop in scenarios:
if not destination_folders[scenario_loop] == "dont_save" :
cv2.imwrite(f"{data_name}/{destination_folders[scenario_loop]}/{categories}/Instance_Mask_resized/{p}.png", 255*instance_img_resized)
-
img = cv2.imread(f"{data_name}/RGB/{p}.png")
for scenario_loop in scenarios:
if not destination_folders[scenario_loop] == "dont_save" :
@@ -178,7 +203,9 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
np.set_printoptions(precision=15)
#for scenario_loop in scenarios:
- pose = np.load(f'{data_name}/Generated/{categories}/Pose_transformed/{p}.npy')
+ for scenario_loop in scenarios:
+ if not destination_folders[scenario_loop] == "dont_save" :
+ pose = np.load(f'{data_name}/{destination_folders[scenario_loop]}/{categories}/Pose_transformed/{p}.npy')
R_exp = pose[0:3, 0:3]
tVec = pose[0:3, 3]
@@ -186,7 +213,7 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
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 = [int(catergories_occ_array[categories][0])] #len have to be 1 !!
+ out = [int(categories_occ_array[categories][0])] #len have to be 1 !!
ind = 1
for point in points:
x = point[0][0] / img.shape[1]
@@ -200,7 +227,7 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
np.savetxt(f'{data_name}/{destination_folders[scenario_loop]}/{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 !
+ out_resized = [int(categories_occ_array[categories][0])] #len have to be 1 !
ind_resized = 1
for point_resized in points_resized:
x_resized = point_resized[0][0] / img_resized.shape[1]
diff --git a/main.py b/main.py
index eaf6f3f82598457468c0f57641e53bc9eb5d47d8..a746677cadde9540c6372750d97009852a548c6c 100644
--- a/main.py
+++ b/main.py
@@ -12,11 +12,11 @@ def generate_folders(name, list_categories, scenario):
is_exist = os.path.exists(name)
if not is_exist:
os.mkdir(name)
- folders = ["RGB", "RGB_Gen", "RGB_resized", "Meta_Gen", "Depth", "Mask", "Meta", "Pose", "Bbox_2d", "Bbox_2d_loose", "Bbox_3d", "Bbox_3d_Gen", "Instance_Segmentation", "Semantic_Segmentation", "Instance_Mask", "Instance_Mask_resized", "Occlusion", "Models", "Pose_transformed", "Bbox", "FPS", "FPS_resized"]
+ folders = ["RGB", "RGB_Gen", "RGB_resized", "Meta_Gen", "Depth", "Mask", "Meta", "Pose", "Bbox_2d", "Bbox_2d_loose", "Bbox_3d", "Bbox_3d_Gen", "Instance_Segmentation", "Semantic_Segmentation", "Instance_Mask", "Labels", "Instance_Mask_resized", "Occlusion", "Models", "Pose_transformed", "Bbox", "FPS", "FPS_resized"]
for f in folders:
is_exist = os.path.exists(f"{name}/{f}")
if not is_exist:
- if f not in ["RGB_Gen", "RGB_resized", "Instance_Mask", "Instance_Mask_resized", "Meta_Gen", "Models", "Pose_transformed", "Bbox", "Bbox_3d_Gen", "FPS" , "FPS_resized"]:
+ if f not in ["RGB_Gen", "RGB_resized", "Instance_Mask", "Labels", "Instance_Mask_resized", "Meta_Gen", "Models", "Pose_transformed", "Bbox", "Bbox_3d_Gen", "FPS" , "FPS_resized"]:
os.mkdir(f"{name}/{f}")
else:
for cat in list_categories:
diff --git a/utils.py b/utils.py
index b8f4b334e003e027307742ec8f7f69a73a306112..456fbc856c081d2971253278cf03bcb2783e56a0 100644
--- a/utils.py
+++ b/utils.py
@@ -3,9 +3,7 @@ import json
def compute_categories_id(data_name, world):
- #Category = 'banana1'
- #Category = 'pear2'
- #Category = "orange2"
+
# Opening JSON file
f = open(f'{data_name}/Meta/{world}.json')
@@ -16,49 +14,46 @@ def compute_categories_id(data_name, world):
# 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={}
+ categories_label_to_id={}
+ categories_id_to_label={}
+ categories_instance_array_cat_to_id={}
+ categories_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']]=[]
+ categories_label_to_id[k['label']]=k['id']
+ categories_id_to_label[k['id']]=k['label']
+ categories_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'])
+ categories_instance_array_id_to_cat[k['id']] = categories_id_to_label[k['category_id']]
+ categories_instance_array_cat_to_id[categories_id_to_label[k['category_id']]].append(k['id'])
# Closing file
f.close()
- return catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, catergories_label_to_id
+ return categories_instance_array_id_to_cat, categories_instance_array_cat_to_id, categories_label_to_id
-def compute_id_good_occ(data_name, count, catergories_instance_array_id_to_cat, catergories_instance_array_cat_to_id, Occ_wanted):
+def compute_id_good_occ(data_name, count, categories_instance_array_id_to_cat, categories_instance_array_cat_to_id, Occ_wanted):
f2 = open(f'{data_name}/Occlusion/{count}.json')
data2 = json.load(f2)
- catergories_occ_array = {}
+ categories_occ_array = {}
+ categories_array = {}
- for cat in catergories_instance_array_cat_to_id :
- #print(cat)
- catergories_occ_array[cat] = []
+ for cat in categories_instance_array_cat_to_id :
+ categories_occ_array[cat] = []
+ categories_array[cat] = []
for i in data2:
if i['occlusion_value'] >= Occ_wanted :
- catergories_occ_array[catergories_instance_array_id_to_cat[i['id']]].append(i['id'])
+ categories_occ_array[categories_instance_array_id_to_cat[i['id']]].append(i['id'])
+ elif i['occlusion_value'] >= 0.05 and i['occlusion_value'] < Occ_wanted:
+ categories_array[categories_instance_array_id_to_cat[i['id']]].append(i['id'])
# Closing file
f2.close()
- return catergories_occ_array
\ No newline at end of file
+ return categories_occ_array, categories_array
\ No newline at end of file