def vis(result_sha, data_root, result_root):
def show_image_with_boxes(img, objects_res, object_gt, calib, save_path, height_threshold=0):
img2 = np.copy(img)
for obj in objects_res:
box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
color_tmp = tuple([int(tmp * 255) for tmp in colors[obj.id % max_color]])
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=color_tmp)
text = 'ID: %d' % obj.id
if box3d_pts_2d is not None:
img2 = cv2.putText(img2, text, (int(box3d_pts_2d[4, 0]), int(box3d_pts_2d[4, 1]) - 8), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color=color_tmp)
img = Image.fromarray(img2)
img = img.resize((width, height))
img.save(save_path)
for seq in seq_list:
image_dir = os.path.join(data_root, 'image_02/%s' % seq)
calib_file = os.path.join(data_root, 'calib/%s.txt' % seq)
result_dir = os.path.join(result_root, '%s/trk_withid/%s' % (result_sha, seq))
save_3d_bbox_dir = os.path.join(result_dir, '../../trk_image_vis/%s' % seq); mkdir_if_missing(save_3d_bbox_dir)
# load the list
images_list, num_images = load_list_from_folder(image_dir)
print('number of images to visualize is %d' % num_images)
start_count = 0
for count in range(start_count, num_images):
image_tmp = images_list[count]
if not is_path_exists(image_tmp):
count += 1
continue
image_index = int(fileparts(image_tmp)[1])
image_tmp = np.array(Image.open(image_tmp))
img_height, img_width, img_channel = image_tmp.shape
result_tmp = os.path.join(result_dir, '%06d.txt'%image_index) # load the result
if not is_path_exists(result_tmp): object_res = []
else: object_res = read_label(result_tmp)
print('processing index: %d, %d/%d, results from %s' % (image_index, count+1, num_images, result_tmp))
calib_tmp = Calibration(calib_file) # load the calibration
object_res_filtered = []
for object_tmp in object_res:
if object_tmp.type not in type_whitelist: continue
if hasattr(object_tmp, 'score'):
if object_tmp.score < score_threshold: continue
center = object_tmp.t
object_res_filtered.append(object_tmp)
num_instances = len(object_res_filtered)
save_image_with_3dbbox_gt_path = os.path.join(save_3d_bbox_dir, '%06d.jpg' % (image_index))
show_image_with_boxes(image_tmp, object_res_filtered, [], calib_tmp, save_path=save_image_with_3dbbox_gt_path)
print('number of objects to plot is %d' % (num_instances))
count += 1
def ask_overwrite_if_file_exists(filename: str, target: str) -> bool:
file_url = utils.join_paths(target, filename)
if utils.is_path_exists(file_url):
do_overwrite = None
while do_overwrite not in ('y', 'n'):
Logger.logPrint(
f'\nFile {filename} already exists, overwrite it ? (y/n)')
do_overwrite = input().lower()
return do_overwrite == 'y'
else:
return True
def login_weibo():
"""
微博登录
:return:
"""
is_exists_cookies_json = is_path_exists(COOKIES_JSON)
if is_exists_cookies_json:
print("cookies json 已存在")
is_expiry = is_expiry_sub()
if is_expiry:
print("Cookies 即将过期 重新获取")
print("帐号密码登录")
driver_initial()
driver = get_browser()
try:
print('准备登陆Weibo.cn网站...')
driver.get(WEIBO_LOGIN_URL)
# WebDriverWait(driver, 10).until(ec.presence_of_element_located((By.ID, "loginAction"))) 该句相较于下句不起作用
WebDriverWait(driver, 10).until(
ec.visibility_of_element_located((By.ID, "loginAction")))
elem_user = driver.find_element_by_id("loginName")
elem_user.send_keys(Mine().username) # 用户名
elem_pwd = driver.find_element_by_id("loginPassword")
elem_pwd.send_keys(Mine().password) # 密码
elem_sub = driver.find_element_by_id("loginAction")
elem_sub.click()
# 点击登陆，登录多次或异地登录可能会有验证码
WebDriverWait(driver, 20).until(ec.url_contains('m.weibo.cn'))
sina_cookies = driver.get_cookies() # 包含多个 cookie 的字典列表
# for cookie in sina_cookies:
# cookie['table'] = 'weibo_cookies'
# with open(COOKIES_JSON, 'w', encoding="utf-8") as f: # 保存Cookies
# f.write(json.dumps(sina_cookies, indent=4))
dump_dict_to_json(sina_cookies, COOKIES_JSON)
print('<登陆成功>')
driver.close()
except Exception as e:
print("Error: <登录失败> {}".format(e))
else:
print("Cookies 登录")
weibo_cookies = loads_json(COOKIES_JSON)
driver_initial()
driver = get_browser()
driver.delete_all_cookies()
driver.get("https://weibo.cn/")
for wc in weibo_cookies:
wc.pop('domain')
driver.add_cookie(wc)
driver.get("https://weibo.cn/")
def initial_configure() -> None:
if not is_path_exists(COOKIES_JSON):
login_weibo()
"title AstroSaveConverter 2.0 - Convert your Astroneer saves between Microsoft and Steam"
)
except:
pass
args = get_args()
conversion_type = Scenario.ask_conversion_type()
try:
if not args.savesPath:
original_save_path = Scenario.ask_for_save_folder(
conversion_type)
else:
original_save_path = args.savesPath
if not utils.is_path_exists(original_save_path):
raise FileNotFoundError
except FileNotFoundError as e:
Logger.logPrint(
'\nSave folder or container not found, press any key to exit')
Logger.logPrint(e, 'exception')
utils.wait_and_exit(1)
if conversion_type == AstroConvType.WIN2STEAM:
windows_to_steam_conversion(original_save_path)
elif conversion_type == AstroConvType.STEAM2WIN:
steam_to_windows_conversion(original_save_path)
Logger.logPrint(f'\nTask completed, press any key to exit')
utils.wait_and_exit(0)
except Exception as e:
def export_save_to_xbox(save: AstroSave, from_file: str, to_path: str) -> None:
chunk_uuids, converted_chunks = save.convert_to_xbox(from_file)
chunk_count = len(chunk_uuids)
if chunk_count >= 10:
Logger.logPrint(
f'The selected save contains {chunk_count} which is over the 9 chunks limit AstroSaveconverter can handle yet'
)
Logger.logPrint(
f'Congrats for having such a huge save, please open an issue on the GitHub :D'
)
for i in range(chunk_count):
# The file name is the HEX upper form of the uuid
chunk_name = save.chunks_names[i]
Logger.logPrint(f'UUID as file name: {chunk_name}', "debug")
target_full_path = utils.join_paths(to_path, chunk_name)
Logger.logPrint(f'Chunk file written to: {target_full_path}', "debug")
# Regenerating chunk name if it already exists. Very, very unlikely
while utils.is_path_exists(target_full_path):
Logger.logPrint(f'UUID: {chunk_name} already exists ! (omg)',
"debug")
chunk_uuids[i] = save.regenerate_uuid(i)
chunk_name = save.chunks_names[i]
Logger.logPrint(f'Regenerated UUID: {chunk_name}', "debug")
target_full_path = utils.join_paths(to_path, chunk_name)
# TODO [enhance] raise exception if can't write, catch it then delete all the chunks already written and exit
utils.write_buffer_to_file(target_full_path, converted_chunks[i])
# Container is updated only after all the chunks of the save have been written successfully
container_file_name = Container.get_containers_list(to_path)[0]
container_full_path = utils.join_paths(to_path, container_file_name)
with open(container_full_path, "r+b") as container:
container.read(4)
current_container_chunk_count = int.from_bytes(container.read(4),
byteorder='little')
new_container_chunk_count = current_container_chunk_count + chunk_count
container.seek(-4, 1)
container.write(
new_container_chunk_count.to_bytes(4, byteorder='little'))
chunks_buffer = BytesIO()
for i in range(chunk_count):
total_written_len = 0
encoded_save_name = save.name.encode('utf-16le', errors='ignore')
total_written_len += chunks_buffer.write(encoded_save_name)
if chunk_count > 1:
# Multi-chunks save. Adding metadata, format: '$${i}${chunk_count}$1'
chunk_metadata = f'$${i}${chunk_count}$1'
encoded_metadata = chunk_metadata.encode('utf-16le',
errors='ignore')
total_written_len += chunks_buffer.write(encoded_metadata)
chunks_buffer.write(b"\00" * (144 - total_written_len))
chunks_buffer.write(chunk_uuids[i].bytes_le)
Logger.logPrint(f'Editing container: {container_full_path}', "debug")
utils.append_buffer_to_file(container_full_path, chunks_buffer)
def vis(result_sha, data_root, result_root):
def show_image_with_boxes(img,
velo,
objects_res,
objects_res_det,
objects_res_raw,
labeldata,
object_gt,
calib,
save_path,
height_threshold=0,
show_lidar=True,
save_image=False):
img2 = np.copy(img)
for obj in objects_res:
box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
color_tmp = tuple(
[int(tmp * 255) for tmp in colors[obj.id % max_color]])
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=(0, 0, 255))
text = 'Tracked ID: %d, Type: %s' % (obj.id, obj.type)
if box3d_pts_2d is not None:
img2 = cv2.putText(
img2,
text,
(int(box3d_pts_2d[4, 0]), int(box3d_pts_2d[4, 1]) - 8),
cv2.FONT_HERSHEY_TRIPLEX,
0.5,
color=(0, 0, 255))
for obj in objects_res_det:
box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
color_tmp = tuple(
[int(tmp * 255) for tmp in colors[obj.id % max_color]])
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=(0, 255, 0))
text = 'Detection ID: %d, Type: %s' % (obj.id, obj.type)
if box3d_pts_2d is not None:
img2 = cv2.putText(
img2,
text,
(int(box3d_pts_2d[3, 0]), int(box3d_pts_2d[3, 1]) - 8),
cv2.FONT_HERSHEY_TRIPLEX,
0.5,
color=(0, 255, 0))
import itertools
labeldata, labeldata2 = itertools.tee(labeldata)
for obj in labeldata:
# print("here")
box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=(255, 0, 0))
text = 'GT, Type: %s' % (obj.type)
if box3d_pts_2d is not None:
# print("also")
print(text)
img2 = cv2.putText(
img2,
text,
(int(box3d_pts_2d[4, 0]), int(box3d_pts_2d[4, 1]) - 8),
cv2.FONT_HERSHEY_TRIPLEX,
0.5,
color=(255, 0, 0))
# for obj in objects_res_raw:
# box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
# color_tmp = tuple([int(tmp * 255)
# for tmp in colors[obj.id % max_color]])
# img2 = draw_projected_box3d(img2, box3d_pts_2d, color=(255,0,0))
# text = 'Estimate ID: %d' % obj.id
# if box3d_pts_2d is not None:
# img2 = cv2.putText(img2, text, (int(box3d_pts_2d[2, 0]), int(
# box3d_pts_2d[2, 1]) - 8), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color=(255,0,0))
if show_lidar:
show_lidar_with_boxes(velo,
objects=labeldata2,
calib=calib,
objects_pred=objects_res)
img = Image.fromarray(img2)
img = img.resize((width, height))
cv2.imshow("Image", img2)
cv2.waitKey()
if save_image:
print("Saving Image at", save_path)
img.save(save_path)
return img2
for seq in seq_list:
image_dir = os.path.join(data_root, 'image_02/%s' % seq)
calib_file = os.path.join(data_root, 'calib/%s.txt' % seq)
label_file = os.path.join(data_root, 'label_02/%s.txt' % seq)
velo_dir = os.path.join(data_root, 'velodyne/%s' % seq)
result_dir = [
os.path.join(result_root,
'%s/trk_withid/%s' % (result_sha[0], seq)),
os.path.join(result_root,
'%s/trk_withid/%s' % (result_sha[1], seq)),
os.path.join(result_root,
'%s/trk_withid/%s' % (result_sha[2], seq))
]
save_3d_bbox_dir = os.path.join(
result_root,
'%s/trk_image_vis/%s' % ("Combined_Final_WithLabel", seq))
mkdir_if_missing(save_3d_bbox_dir)
# load the list
images_list, num_images = load_list_from_folder(image_dir)
velo_list, num_velo = load_list_from_folder(velo_dir)
print('number of images to visualize is %d' % num_images)
start_count = 0
filecontent = np.array([f.split() for f in open(label_file, 'r')])
# alllabels = np.unique(filecontent[:,2])
# labels = ['Car', 'Pedestrian', 'Cyclist']
# finallabelset = [x for x in alllabels if x not in labels]
# print(alllabels)
# print(finallabelset)
# for val in finallabelset:
# filecontent = filecontent[filecontent[:,2]!=val,:]
# print(np.unique(filecontent[:,2]))
size = (width, height)
out = cv2.VideoWriter(f'{result_root}/{seq}.avi',
cv2.VideoWriter_fourcc(*'DIVX'), 15, size)
num_images = 1
for count in range(start_count, num_images):
image_tmp = images_list[count]
velo_tmp = velo_list[count]
if not is_path_exists(image_tmp):
count += 1
continue
image_index = int(fileparts(image_tmp)[1])
image_tmp = np.array(Image.open(image_tmp))
img_height, img_width, img_channel = image_tmp.shape
filecontentframe = filecontent[filecontent[:, 0] ==
str(image_index), :]
print(len(filecontentframe))
print(f"Labels for frame {image_index}",
np.unique(filecontentframe[:, 2]))
labeldata = (Object3d(getstringfromarray(line[2:]))
for line in filecontentframe)
object_res = []
object_res_det = []
object_res_raw = []
for dirt in result_dir:
result_tmp = os.path.join(dirt, '%06d.txt' %
image_index) # load the result
if is_path_exists(result_tmp):
object_res = object_res + read_label(result_tmp)
result_tmp_det = os.path.join(dirt, 'det%06d.txt' %
image_index) # load the result
if is_path_exists(result_tmp_det):
object_res_det = object_res_det + \
read_label(result_tmp_det)
result_tmp_raw = os.path.join(dirt, 'raw%06d.txt' %
image_index) # load the result
if is_path_exists(result_tmp_raw):
object_res_raw = object_res_raw + \
read_label(result_tmp_raw)
print('processing index: %d, %d/%d, results from %s' %
(image_index, count + 1, num_images, result_tmp))
calib_tmp = Calibration(calib_file) # load the calibration
object_res_filtered = []
for object_tmp in object_res:
if object_tmp.type not in type_whitelist:
continue
if hasattr(object_tmp, 'score'):
if object_tmp.score < score_threshold:
continue
center = object_tmp.t
object_res_filtered.append(object_tmp)
object_res_filtered_det = []
for object_tmp in object_res_det:
if object_tmp.type not in type_whitelist:
continue
if hasattr(object_tmp, 'score'):
if object_tmp.score < score_threshold:
continue
center = object_tmp.t
object_res_filtered_det.append(object_tmp)
object_res_filtered_raw = []
for object_tmp in object_res_raw:
if object_tmp.type not in type_whitelist:
continue
# if hasattr(object_tmp, 'score'):
# if object_tmp.score < score_threshold:
# continue
center = object_tmp.t
object_res_filtered_raw.append(object_tmp)
num_instances = len(object_res_filtered)
save_image_with_3dbbox_gt_path = os.path.join(
save_3d_bbox_dir, '%06d.jpg' % (image_index))
velodyne_scan = load_velo_scan(velo_tmp, np.float32, n_vec=4)[:,
0:4]
img = show_image_with_boxes(
image_tmp,
velodyne_scan,
object_res_filtered,
object_res_filtered_det,
object_res_filtered_raw,
labeldata, [],
calib_tmp,
save_path=save_image_with_3dbbox_gt_path)
print('number of objects to plot is %d, %d, %d' %
(num_instances, len(object_res_filtered_det),
len(object_res_filtered_raw)))
count += 1
out.write(img)
out.release()