Skip to content
Snippets Groups Projects
Commit 4eff969e authored by Guillaume Duret's avatar Guillaume Duret
Browse files

add if in the case no category instance in the world

parent e987bc9a
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
Showing
with 88 additions and 86 deletions
compute_features.py
+ 88
− 86
View file @ 4eff969e
...@@ -62,97 +62,99 @@ def process_compute(data_name, camera, Nb_camera, Nb_world, list_categories, occ ...@@ -62,97 +62,99 @@ def process_compute(data_name, camera, Nb_camera, Nb_world, list_categories, occ
for categories in list_categories: for categories in list_categories:
if len(catergories_occ_array[categories]) == 1 : if categories in catergories_occ_array.keys():
meta = {} if len(catergories_occ_array[categories]) == 1 :
if categories in list_count_categories.keys(): meta = {}
list_count_categories[categories] += 1
else:
list_count_categories[categories] = 1
meta['id_generated'] = list_count_categories[categories] if categories in list_count_categories.keys():
meta['id_original'] = p list_count_categories[categories] += 1
meta['id_category'] = catergories_label_to_id[categories]
meta['id_instance'] = catergories_occ_array[categories][0]
meta['id_dataset'] = 1
meta['world'] = i
meta['camera'] = f"grabber_{j}"
meta['occlusion'] = occ_target
meta['Nb_instance_category'] = 1
if not os.path.isfile(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json'):
with open(f'{data_name}/Generated/Meta_Gen/{categories}/{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}/Generated/Meta_Gen/{categories}/{categories}.json') as feedsjson:
feeds = json.load(feedsjson)
feeds[meta['id_generated']]=meta
with open(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json', mode='w') as f:
f.write(json.dumps(feeds, indent=4))
# with open(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json', "a") as meta_file:
# json.dump(meta, meta_file, indent=4)
for k in range(len(data_3D_pose)):
if data_3D_pose[k]['id'] == catergories_occ_array[categories][0]:
rpy = data_3D_pose[k]['pose']['rpy']
rot = convert2(rpy)
R_exp = transformation @ rot
R_exp = np.array(R_exp)
xyz = data_3D_pose[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}/Generated/Pose_transformed/{categories}/{p}.npy', num_arr) # save
else: else:
continue list_count_categories[categories] = 1
if data_Bbox_2d[k]['id'] == catergories_occ_array[categories][0]: meta['id_generated'] = list_count_categories[categories]
bbox = bbox_2d(data_Bbox_2d[k]) meta['id_original'] = p
np.savetxt(f'{data_name}/Generated/Bbox/{categories}/{p}.txt', np.array(bbox).reshape((1, 4))) # save meta['id_category'] = catergories_label_to_id[categories]
meta['id_instance'] = catergories_occ_array[categories][0]
meta['id_dataset'] = 1
meta['world'] = i
meta['camera'] = f"grabber_{j}"
meta['occlusion'] = occ_target
meta['Nb_instance_category'] = 1
if not os.path.isfile(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json'):
with open(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json', mode='w') as f:
feeds = {}
feeds[meta['id_generated']]=meta
f.write(json.dumps(feeds, indent=2))
else: else:
continue with open(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json') as feedsjson:
feeds = json.load(feedsjson)
id = catergories_occ_array[categories][0] feeds[meta['id_generated']]=meta
img = cv2.imread(f"{data_name}/Instance_Segmentation/{p}.png", cv2.IMREAD_UNCHANGED) # plt.imread(path) with open(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json', mode='w') as f:
f.write(json.dumps(feeds, indent=4))
instance_img = instance(img, id)
cv2.imwrite(f"{data_name}/Generated/Instance_Mask/{categories}/{p}.png", 255*instance_img) # with open(f'{data_name}/Generated/Meta_Gen/{categories}/{categories}.json', "a") as meta_file:
# json.dump(meta, meta_file, indent=4)
img = image.imread(f"{data_name}/RGB/{p}.png") for k in range(len(data_3D_pose)):
np.set_printoptions(precision=15) if data_3D_pose[k]['id'] == catergories_occ_array[categories][0]:
pose = np.load(f'{data_name}/Generated/Pose_transformed/{categories}/{p}.npy') rpy = data_3D_pose[k]['pose']['rpy']
#print(pose) rot = convert2(rpy)
R_exp = pose[0:3, 0:3] R_exp = transformation @ rot
tVec = pose[0:3, 3] R_exp = np.array(R_exp)
#print(tVec) xyz = data_3D_pose[k]['pose']['xyz']
# camera = np.matrix([[1386.4138492513919, 0.0, 960.5], T_exp = transformation @ xyz
# [0.0, 1386.4138492513919, 540.5], T_exp = np.array(T_exp)
# [0.0, 0.0, 1.0]]) num_arr = np.c_[R_exp, T_exp[0]]
np.save(f'{data_name}/Generated/Pose_transformed/{categories}/{p}.npy', num_arr) # save
else:
fps_points = np.loadtxt(f'{data_name}/Generated/FPS/{categories}_fps_3d.txt') continue
# process(pcd_bbox, pcd, R_exp, tVec, camera, img)
points = process2(fps_points, R_exp, tVec, camera, img, vis) if data_Bbox_2d[k]['id'] == catergories_occ_array[categories][0]:
out = np.zeros((1, 401)) bbox = bbox_2d(data_Bbox_2d[k])
np.savetxt(f'{data_name}/Generated/Bbox/{categories}/{p}.txt', np.array(bbox).reshape((1, 4))) # save
out[0] = catergories_occ_array[categories] #obj_id #len have to be 1 !! else:
ind = 1 continue
for point in points:
out[0][ind] = point[0][0] / img.shape[1] id = catergories_occ_array[categories][0]
out[0][ind + 1] = point[0][1] / img.shape[0] img = cv2.imread(f"{data_name}/Instance_Segmentation/{p}.png", cv2.IMREAD_UNCHANGED) # plt.imread(path)
ind += 2
np.savetxt(f'{data_name}/Generated/FPS/{categories}/{p}.txt', out) instance_img = instance(img, id)
#print("stop") cv2.imwrite(f"{data_name}/Generated/Instance_Mask/{categories}/{p}.png", 255*instance_img)
img = image.imread(f"{data_name}/RGB/{p}.png")
np.set_printoptions(precision=15)
pose = np.load(f'{data_name}/Generated/Pose_transformed/{categories}/{p}.npy')
#print(pose)
R_exp = pose[0:3, 0:3]
tVec = pose[0:3, 3]
#print(tVec)
# camera = np.matrix([[1386.4138492513919, 0.0, 960.5],
# [0.0, 1386.4138492513919, 540.5],
# [0.0, 0.0, 1.0]])
fps_points = np.loadtxt(f'{data_name}/Generated/FPS/{categories}_fps_3d.txt')
# 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] = catergories_occ_array[categories] #obj_id #len have to be 1 !!
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}/Generated/FPS/{categories}/{p}.txt', out)
#print("stop")
print(list_count_categories) print(list_count_categories)
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment