diff --git a/compute_features.py b/compute_features.py
index e153c5a90e9652bf70f98f10196bb7d3b3b637fb..babf84be459ba8f06bbd26e6f90e0cdbb11fbeac 100644
--- a/compute_features.py
+++ b/compute_features.py
@@ -17,32 +17,56 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
[0.0000000, 0.0000000, -1.0000000],
[1.0000000, 0.0000000, 0.0000000]])
+ scenarios = ["Worlds", "Cameras", "Mix_all"]
+
+ destination_folders_list = {}
+ for scenario_loop in scenarios:
+ destination_folders_list[scenario_loop] = [f"Generated_{scenario_loop}_Testing", f"Generated_{scenario_loop}_Evaluating", f"Generated_{scenario_loop}_Training" ]
+
list_count_categories = {}
+ for scenario_loop in scenarios :
+ list_count_categories[scenario_loop] = {}
+ for destination_folder_loop in destination_folders_list[scenario_loop] : # [f"Generated_{scenario}_Testing", f"Generated_{scenario}_Evaluating", f"Generated_{scenario}_Training"] :
+ list_count_categories[scenario_loop][destination_folder_loop] = {}
+ destination_folders = {}
for i in range(World_begin, World_begin + Nb_world): # worlds
- scenario = "Worlds"
if i > 4 :
- destination_folder = f"Generated_{scenario}_Testing"
+ destination_folders["Worlds"] = f"Generated_Worlds_Testing"
elif i > 3 :
- destination_folder = f"Generated_{scenario}_Evaluating"
+ destination_folders["Worlds"] = f"Generated_Worlds_Evaluating"
else :
- destination_folder = f"Generated_{scenario}_Training"
+ 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)
for j in range(1, Nb_camera+1): # cameras
p = ((i-1)*Nb_camera) + j
+ if j > 12 :
+ destination_folders["Cameras"] = f"Generated_Cameras_Testing"
+ elif j > 9 :
+ destination_folders["Cameras"] = f"Generated_Cameras_Evaluating"
+ else :
+ destination_folders["Cameras"] = f"Generated_Cameras_Training"
+
+ if i > 4 and j > 12 :
+ destination_folders["Mix_all"] = f"Generated_Mix_all_Testing"
+ elif i > 3 and i <=4 and j > 9 and j >= 12 :
+ destination_folders["Mix_all"] = f"Generated_Mix_all_Evaluating"
+ elif i <= 3 and j <= 9 :
+ destination_folders["Mix_all"] = f"Generated_Mix_all_Training"
+ 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)
### 3D Poses ###
with open(f'{data_name}/Pose/{p}.json', 'r') as f:
data_3D_pose = json.load(f)
- #print(data)
- #print("len(data)", len(data_3D_pose))
### 2D BBox ###
with open(f"{data_name}/Bbox_2d/{p}.json", 'r') as f:
@@ -53,48 +77,51 @@ 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}/{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 scenario_loop in scenarios:
+ if not destination_folders[scenario_loop] == "dont_save" :
+ if os.path.isfile(f'{data_name}/{destination_folders[scenario_loop]}/Count_{p-1}.json'):
+ 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)
for categories in list_categories:
if categories in catergories_occ_array.keys():
Nb_instance = len(catergories_occ_array[categories])
- meta = {}
- if categories in list_count_categories.keys():
- print("hello")
- else:
- #list_count_categories[categories] = {categories}
- list_count_categories[categories] = {}
-
- if Nb_instance in list_count_categories[categories].keys() :
- list_count_categories[categories][Nb_instance] += 1
- else :
- list_count_categories[categories][Nb_instance] = 1
-
- meta['id_generated'] = list_count_categories[categories][Nb_instance]
- meta['id_original'] = p
- meta['id_category'] = catergories_label_to_id[categories]
- meta['id_instance'] = catergories_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_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}/{destination_folder}/{categories}/Meta_Gen/{categories}.json') as feedsjson:
- feeds = json.load(feedsjson)
- feeds[meta['id_generated']]=meta
- with open(f'{data_name}/{destination_folder}/{categories}/Meta_Gen/{categories}.json', mode='w') as f:
- f.write(json.dumps(feeds, indent=4))
+ for scenario_loop in scenarios:
+
+ meta = {}
+ #
+ if not destination_folders[scenario_loop] in list_count_categories[scenario_loop].keys():
+ list_count_categories[scenario_loop][destination_folders[scenario_loop]] = {}
+ if not categories in list_count_categories[scenario_loop][destination_folders[scenario_loop]].keys():
+ list_count_categories[scenario_loop][destination_folders[scenario_loop]][categories] = {}
+ if f"{Nb_instance}_instances" in list_count_categories[scenario_loop][destination_folders[scenario_loop]][categories].keys() :
+ list_count_categories[scenario_loop][destination_folders[scenario_loop]][categories][f"{Nb_instance}_instances"] += 1
+ else :
+ list_count_categories[scenario_loop][destination_folders[scenario_loop]][categories][f"{Nb_instance}_instances"] = 1
+
+ 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_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 = {}
+ feeds[meta['id_generated']]=meta
+ f.write(json.dumps(feeds, indent=2))
+ else:
+ with open(f'{data_name}/{destination_folders[scenario_loop]}/{categories}/Meta_Gen/{categories}.json') as feedsjson:
+ feeds = json.load(feedsjson)
+ 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 == 1):
for k in range(len(data_3D_pose)):
@@ -108,19 +135,23 @@ 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}/{destination_folder}/{categories}/Pose_transformed/{p}.npy', num_arr) # save
+ np.save(f'{data_name}/Generated/{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}/{destination_folder}/{categories}/Bbox/{p}.txt', np.array(bbox).reshape((1, 4))) # save
+ for scenario_loop in scenarios:
+ if not destination_folders[scenario_loop] == "dont_save" :
+ np.savetxt(f'{data_name}/{destination_folders[scenario_loop]}/{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}/{destination_folder}/{categories}/Bbox_3d_Gen/{p}.txt', bbox3d_size) # save
+ for scenario_loop in scenarios:
+ if not destination_folders[scenario_loop] == "dont_save" :
+ np.savetxt(f'{data_name}/{destination_folders[scenario_loop]}/{categories}/Bbox_3d_Gen/{p}.txt', bbox3d_size) # save
else:
continue
@@ -128,17 +159,26 @@ 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}/{destination_folder}/{categories}/Instance_Mask/{p}.png", 255*instance_img)
+ 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)
instance_img_resized = cv2.resize(instance_img, new_size)
- cv2.imwrite(f"{data_name}/{destination_folder}/{categories}/Instance_Mask_resized/{p}.png", 255*instance_img_resized)
+ 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")
- cv2.imwrite(f"{data_name}/{destination_folder}/{categories}/RGB_Gen/{p}.png", img)
+ for scenario_loop in scenarios:
+ if not destination_folders[scenario_loop] == "dont_save" :
+ cv2.imwrite(f"{data_name}/{destination_folders[scenario_loop]}/{categories}/RGB_Gen/{p}.png", img)
img_resized = cv2.resize(img, new_size)
- cv2.imwrite(f"{data_name}/{destination_folder}/{categories}/RGB_resized/{p}.png", img_resized)
+ for scenario_loop in scenarios:
+ if not destination_folders[scenario_loop] == "dont_save" :
+ cv2.imwrite(f"{data_name}/{destination_folders[scenario_loop]}/{categories}/RGB_resized/{p}.png", img_resized)
np.set_printoptions(precision=15)
- pose = np.load(f'{data_name}/{destination_folder}/{categories}/Pose_transformed/{p}.npy')
+ #for scenario_loop in scenarios:
+ pose = np.load(f'{data_name}/Generated/{categories}/Pose_transformed/{p}.npy')
R_exp = pose[0:3, 0:3]
tVec = pose[0:3, 3]
@@ -154,7 +194,10 @@ 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}/{destination_folder}/{categories}/FPS/{p}.txt', np.array(out).reshape(1, len(out)))
+
+ for scenario_loop in scenarios:
+ if not destination_folders[scenario_loop] == "dont_save" :
+ 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 !
@@ -165,10 +208,16 @@ 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}/{destination_folder}/{categories}/FPS_resized/{p}.txt', np.array(out_resized).reshape(1, len(out_resized)))
-
- 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)
+ for scenario_loop in scenarios:
+ if not destination_folders[scenario_loop] == "dont_save" :
+ np.savetxt(f'{data_name}/{destination_folders[scenario_loop]}/{categories}/FPS_resized/{p}.txt', np.array(out_resized).reshape(1, len(out_resized)))
+
+ for scenario_loop in scenarios:
+ for destination_folder_loop in destination_folders_list[scenario_loop] : # [f"Generated_{scenario}_Testing", f"Generated_{scenario}_Evaluating", f"Generated_{scenario}_Training"] :
+ with open(f'{data_name}/{destination_folder_loop}/Count_{p}.json', mode='w') as f:
+ f.write(json.dumps(list_count_categories[scenario_loop][destination_folder_loop], indent=4))
+ with open(f'{data_name}/Count_{p}.json', mode='w') as f:
+ f.write(json.dumps(list_count_categories, indent=4))
+ #print(list_count_categories)
diff --git a/fps_alg.py b/fps_alg.py
index fa2f93f64d93135cbf2cd3086aa82850a6401336..802a7ea4e01f5b052f20d7248443b470d300a4b8 100644
--- a/fps_alg.py
+++ b/fps_alg.py
@@ -132,8 +132,8 @@ def process(pcd_box, pcd2, R_exp, tVec, camera, img):
keypoint_2d = cv2.projectPoints(pcd2_in_numpy, R_exp, tVec, camera, np.zeros(shape=[5, 1], dtype='float64'))
keypoint_2d2 = cv2.projectPoints(pcd2_in_numpy2, R_exp, tVec, camera, np.zeros(shape=[5, 1], dtype='float64'))
- for n in range(len(pcd2_in_numpy)):
- print(pcd2_in_numpy[n], '==>', keypoint_2d[0][n])
+ # for n in range(len(pcd2_in_numpy)):
+ # print(pcd2_in_numpy[n], '==>', keypoint_2d[0][n])
points = []
for n in range(len(pcd2_in_numpy)):
@@ -203,5 +203,5 @@ def generate_fps(data_name, camera, Nb_camera, Nb_world, list_categories, occ_ta
out[0][ind + 1] = point[0][1] / img.shape[0]
ind += 2
np.savetxt(f'{data_name}/Generated/FPS/{categories}/{p}.txt', out)
- print("stop")
+
diff --git a/main.py b/main.py
index d031724b04fce617f5cb677ce263fc5974ac19aa..149e226a57e43762ce47b28d21d38f6d9324645b 100644
--- a/main.py
+++ b/main.py
@@ -23,15 +23,22 @@ def generate_folders(name, list_categories, scenario):
is_exist2 = os.path.exists(f"{name}/Generated/{cat}")
if not is_exist2:
os.makedirs(f"{name}/Generated/{cat}")
- is_exist2 = os.path.exists(f"{name}/Generated_{scenario}_Training/{cat}/{f}")
+ is_exist2 = os.path.exists(f"{name}/Generated/{cat}/Pose_transformed")
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}")
+ os.makedirs(f"{name}/Generated/{cat}/Pose_transformed")
+ for scenario in ["Worlds", "Cameras", "Mix_all"] :
+ 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}")
+ is_exist2 = os.path.exists(f"{name}/dont_save/{cat}/{f}")
+ if not is_exist2:
+ os.makedirs(f"{name}/dont_save/{cat}/{f}")
diff --git a/pose.py b/pose.py
index 6f8e477f506260722826c2b3d63b9c6fcc6379bc..6f29610c95e3042d8db920b78cb3b239ed3d200c 100644
--- a/pose.py
+++ b/pose.py
@@ -38,9 +38,6 @@ def compute_categories_id(data_name, world):
# 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()
@@ -63,8 +60,6 @@ def compute_id_good_occ(data_name, count, catergories_instance_array_id_to_cat,
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()
@@ -99,9 +94,6 @@ def transform_pose(data_name, Nb_camera, Nb_world, list_categories, 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)):
for categories in list_categories:
diff --git a/test_fps.py b/test_fps.py
index 9ed329ec5eb4d8f62ca35cd405e9be0283f43148..0a012a158e560f70568a4930443f957d0bcf1ac0 100644
--- a/test_fps.py
+++ b/test_fps.py
@@ -214,7 +214,7 @@ new = np.matrix([[0.0000000, -1.0000000, 0.0000000],
t_org = pose[0:3, 3]
tVec = new @ t_org
-print(tVec)
+#print(tVec)
img = image.imread('/media/mahmoud/F/GUIMOD/data/1/grabber_1/color/image/0_0.png')
camera = [[1386.4138492513919, 0.0, 960.5], [0.0, 1386.4138492513919, 540.5], [0.0, 0.0, 1.0]]
diff --git a/test_resize.py b/test_resize.py
index c014c5975f1ef22a590f3e70f8476481789afea2..84443b9312f564a0f3255e5d5debf643d5db0617 100644
--- a/test_resize.py
+++ b/test_resize.py
@@ -111,8 +111,8 @@ def process2(pcd, R_exp, tVec, camera, img, vis= True):
pcd_fps_numpy = np.asarray(pcd)
keypoint_2d = cv2.projectPoints(pcd_fps_numpy, R_exp, tVec, camera, np.zeros(shape=[8, 1], dtype='float64'))
- for n in range(len(pcd_fps_numpy)):
- print(pcd_fps_numpy[n], '==>', keypoint_2d[0][n])
+ # for n in range(len(pcd_fps_numpy)):
+ # print(pcd_fps_numpy[n], '==>', keypoint_2d[0][n])
if vis:
out = np.zeros((img.shape[0], img.shape[1], 16))
@@ -139,8 +139,8 @@ def process(pcd_box, pcd2, R_exp, tVec, camera, img):
keypoint_2d = cv2.projectPoints(pcd2_in_numpy, R_exp, tVec, camera, np.zeros(shape=[5, 1], dtype='float64'))
keypoint_2d2 = cv2.projectPoints(pcd2_in_numpy2, R_exp, tVec, camera, np.zeros(shape=[5, 1], dtype='float64'))
- for n in range(len(pcd2_in_numpy)):
- print(pcd2_in_numpy[n], '==>', keypoint_2d[0][n])
+ # for n in range(len(pcd2_in_numpy)):
+ # print(pcd2_in_numpy[n], '==>', keypoint_2d[0][n])
points = []
for n in range(len(pcd2_in_numpy)):
diff --git a/utils.py b/utils.py
index 2e09900181b6c4ba0515ce9fb69945c4b8af7759..b8f4b334e003e027307742ec8f7f69a73a306112 100644
--- a/utils.py
+++ b/utils.py
@@ -36,9 +36,6 @@ def compute_categories_id(data_name, world):
# 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()
@@ -57,15 +54,10 @@ def compute_id_good_occ(data_name, count, catergories_instance_array_id_to_cat,
#print(cat)
catergories_occ_array[cat] = []
- print(data2)
-
for i in data2:
if i['occlusion_value'] >= Occ_wanted :
- print(i['id'])
catergories_occ_array[catergories_instance_array_id_to_cat[i['id']]].append(i['id'])
- print(catergories_occ_array)
-
# Closing file
f2.close()