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Commit 91527d70 authored by Guillaume Duret's avatar Guillaume Duret
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add labels for yolo, add stonger occlusion filter, minor issues fixed

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compute_features.py
+ 45
− 18
View file @ 91527d70
......@@ -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]
......
main.py
+ 2
− 2
View file @ 91527d70
......@@ -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:
......
utils.py
+ 21
− 26
View file @ 91527d70
......@@ -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
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