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Commit 4eff969e authored by Guillaume Duret's avatar Guillaume Duret
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add if in the case no category instance in the world

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with 88 additions and 86 deletions
compute_features.py
+ 88
− 86
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......@@ -62,97 +62,99 @@ def process_compute(data_name, camera, Nb_camera, Nb_world, list_categories, occ
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():
list_count_categories[categories] += 1
else:
list_count_categories[categories] = 1
meta = {}
meta['id_generated'] = list_count_categories[categories]
meta['id_original'] = p
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
if categories in list_count_categories.keys():
list_count_categories[categories] += 1
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}/Generated/Bbox/{categories}/{p}.txt', np.array(bbox).reshape((1, 4))) # save
list_count_categories[categories] = 1
meta['id_generated'] = list_count_categories[categories]
meta['id_original'] = p
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:
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)
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")
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:
continue
if data_Bbox_2d[k]['id'] == catergories_occ_array[categories][0]:
bbox = bbox_2d(data_Bbox_2d[k])
np.savetxt(f'{data_name}/Generated/Bbox/{categories}/{p}.txt', np.array(bbox).reshape((1, 4))) # save
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)
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)
......
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