def test_track_all(dataset = "test"):
image_dir = "/export/guanghan/Data_2018_lighttrack/posetrack_data/"
visualize_folder = "/export/guanghan/Data_2018_lighttrack/posetrack_results/lighttrack/visualize/"
output_video_folder = "/export/guanghan/cpn/data"
track_json_folder = "/export/guanghan/Data_2018/posetrack_results/{}/track/jsons_submission3".format(dataset)
video_name_list = ["018630_mpii_test", "024156_mpii_test"]#["022699_mpii_test", "023730_mpii_test", "014307_mpii_test", "014313_mpii_test", "015868_mpii_test"]
for video_name in video_name_list:
json_name = os.path.basename(video_name) + ".json"
track_json = os.path.join(track_json_folder, json_name)
print("visualization!")
create_folder(visualize_folder)
if dataset == "val":
image_subfolder = os.path.join(image_dir, "images/val", os.path.basename(video_name))
elif dataset == "test":
image_subfolder = os.path.join(image_dir, "images/test", os.path.basename(video_name))
print(image_subfolder)
visualize_subfolder = os.path.join(visualize_folder, os.path.basename(video_name))
create_folder(visualize_subfolder)
show_all_from_standard_json(track_json, classes, joint_pairs, joint_names, image_subfolder, visualize_subfolder, flag_track = True)
img_paths = get_immediate_childfile_paths(visualize_subfolder)
visualize_video_path = os.path.join(visualize_folder, os.path.basename(video_name)+".mp4")
make_video_from_images(img_paths, visualize_video_path, fps=15, size=None, is_color=True, format="xvid")
return
def test_show_boxes_from_standard_json():
json_file_path = "../standardize/convert/keypoint_to_standard/keypoint_CPN_to_standard.json"
img_folder_path = "/Users/guanghan.ning/Desktop/working/hard_examples/"
output_folder_path = "/Users/guanghan.ning/Desktop/working/hard_examples_detection/"
create_folder(output_folder_path)
show_boxes_from_standard_json(json_file_path, classes, img_folder_path, output_folder_path)
print("Done!")
def show_boxes_from_standard_json(json_file_path,
classes,
img_folder_path=None,
output_folder_path=None,
track_id=-1):
dets = read_json_from_file(json_file_path)
for det in dets:
python_data = det
if img_folder_path is None:
img_path = os.path.join(python_data["image"]["folder"],
python_data["image"]["name"])
else:
img_path = os.path.join(img_folder_path,
python_data["image"]["name"])
if is_image(img_path): img = cv2.imread(img_path)
candidates = python_data["candidates"]
for candidate in candidates:
bbox = np.array(candidate["det_bbox"]).astype(int)
score = candidate["det_score"]
if score >= bbox_thresh:
img = draw_bbox(img, bbox, score, classes, track_id=track_id)
if output_folder_path is not None:
create_folder(output_folder_path)
img_output_path = os.path.join(output_folder_path,
python_data["image"]["name"])
cv2.imwrite(img_output_path, img)
return True
def test_show_all_from_standard_json_track():
json_file_path = "../standardize/convert/keypoint_track_to_standard/json/track_results_standard_format.json"
img_folder_path = "/Users/guanghan.ning/Desktop/working/test_posetrack/"
output_folder_path = "/Users/guanghan.ning/Desktop/working/test_posetrack_visualize/"
create_folder(output_folder_path)
show_all_from_standard_json(json_file_path, classes, joint_pairs, joint_names, img_folder_path, output_folder_path, flag_track = True)
print("Done!")
def test_save_vec_as_numpy():
output_folder_path = '../temp_folder'
create_folder(output_folder_path)
pairs_list_numpy_file_path = '/home/ngh/dev/ROLO-TRACK/training_list/list.npy'
dataset_annotation_folder_path = '/home/ngh/dev/ROLO-dev/benchmark/ILSVRC2015/Annotations/VID/train/ILSVRC2015_VID_train_0000'
batchsize = 8
nsteps = 3
list_batch_pairs = load_list_batch_pairs_from_numpy_file(
pairs_list_numpy_file_path, batchsize)
for batch_pairs in list_batch_pairs[0:10]:
batch_frame_ids = [int(batch_pair[1]) for batch_pair in batch_pairs]
batch_subfolder_names = [batch_pair[0] for batch_pair in batch_pairs]
batch_numpy_folder_paths = [
os.path.join(dataset_annotation_folder_path, subfolder_name,
'VID_loc_gt')
for subfolder_name in batch_subfolder_names
]
attempted_yolo_batch = batchload_yolovecs_from_numpy_folders(
batch_numpy_folder_paths, batch_frame_ids, batchsize, nsteps)
if attempted_yolo_batch is not False:
batch_output_vecs = attempted_yolo_batch
for id in range(batchsize):
frame_id = batch_frame_ids[id]
output_vec = batch_output_vecs[id][nsteps - 1]
save_vec_as_numpy_by_frame_id(output_folder_path, frame_id,
output_vec)
return True
def show_poses_from_standard_json(json_file_path,
joint_pairs,
joint_names,
img_folder_path=None,
output_folder_path=None):
poses = read_json_from_file(json_file_path)
for pose in poses:
python_data = pose
if img_folder_path is None:
img_path = os.path.join(python_data["image"]["folder"],
python_data["image"]["name"])
else:
img_path = os.path.join(img_folder_path,
python_data["image"]["name"])
if is_image(img_path): img = cv2.imread(img_path)
candidates = python_data["candidates"]
for candidate in candidates:
pose_keypoints_2d = candidate["pose_keypoints_2d"]
joints = reshape_keypoints_into_joints(pose_keypoints_2d)
img = show_poses_from_python_data(img, joints, joint_pairs,
joint_names)
if output_folder_path is not None:
create_folder(output_folder_path)
img_output_path = os.path.join(output_folder_path,
python_data["image"]["name"])
cv2.imwrite(img_output_path, img)
return
def test_show_boxes_from_standard_json():
json_file_path = "/Users/guanghan.ning/Desktop/working/POSETRACK_visualize/results/pose/009602_mpii_test.json"
img_folder_path = "/Users/guanghan.ning/Desktop/working/POSETRACK_visualize/images/009602_mpii_test"
output_folder_path = "/Users/guanghan.ning/Desktop/working/POSETRACK_visualize/visualize/detection"
create_folder(output_folder_path)
show_boxes_from_standard_json(json_file_path, classes, img_folder_path,
output_folder_path)
print("Done!")
def test_show_all_from_standard_json():
#json_file_path = "../standardize/convert/keypoint_to_standard/json/keypoint_CPN_to_standard.json"
#img_folder_path = "/Users/guanghan.ning/Desktop/working/hard_examples/"
#output_folder_path = "/Users/guanghan.ning/Desktop/working/hard_examples_visualize/"
json_file_path = "../standardize/convert/keypoint_to_standard/json/temp.json"
img_folder_path = "/Users/guanghan.ning/Desktop/working/test_openSVAI/"
output_folder_path = "/Users/guanghan.ning/Desktop/working/test_openSVAI_visualize/"
create_folder(output_folder_path)
show_all_from_standard_json(json_file_path, classes, joint_pairs, joint_names, img_folder_path, output_folder_path)
print("Done!")
def test_show_all_from_standard_json_track():
json_file_path = "/Users/guanghan.ning/Desktop/working/POSETRACK_visualize/results/track/009602_mpii_test.json"
img_folder_path = "/Users/guanghan.ning/Desktop/working/POSETRACK_visualize/images/009602_mpii_test"
output_folder_path = "/Users/guanghan.ning/Desktop/working/POSETRACK_visualize/visualize/track/"
create_folder(output_folder_path)
show_all_from_standard_json(json_file_path,
classes,
joint_pairs,
joint_names,
img_folder_path,
output_folder_path,
flag_track=True)
print("Done!")
def make_images_from_video(video_path, outimages_path=None):
cap = cv2.VideoCapture(video_path)
isOpened = cap.isOpened()
fps = cap.get(cv2.CAP_PROP_FPS) # 帧率<每秒中展示多少张图片>
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) # 获取宽度
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 获取宽度
i = 0
while (isOpened): # 当视频被打开了
i = i + 1 # i++
(flag, frame) = cap.read() # 读取每一张 flag<读取是否成功> frame<内容>
file_name = "{0:08d}.jpg".format(i)
if outimages_path is not None:
utils_io_folder.create_folder(outimages_path)
file_path = os.path.join(outimages_path, file_name)
else:
utils_io_folder.create_folder("output")
file_path = os.path.join("output", file_name)
if flag: # 读取成功的话
# 写入文件，1 文件名 2 文件内容 3 质量设置
cv2.imwrite(file_path, frame, [cv2.IMWRITE_JPEG_QUALITY, 100])
def test_show_poly_from_standard_json():
#json_file_path = "../standardize/convert/poly_to_standard/json/poly_results_standard_format.json" # generated locally
'''
json_file_path = "/Users/guanghan.ning/Desktop/dev/polyrnn/poly.json" # downloaded from server
img_folder_path = "/Users/guanghan.ning/Desktop/working/douyin_4/images/"
output_folder_path = "/Users/guanghan.ning/Desktop/working/douyin_4/visualize/poly/"
'''
json_file_path = "/Users/guanghan.ning/Desktop/leftImg8bit_demoVideo/leftImg8bit/demoVideo/results/poly/cityscape.json" # downloaded from server
img_folder_path = "/Users/guanghan.ning/Desktop/leftImg8bit_demoVideo/leftImg8bit/demoVideo/images/"
output_folder_path = "/Users/guanghan.ning/Desktop/leftImg8bit_demoVideo/leftImg8bit/demoVideo/visualize/poly/"
create_folder(output_folder_path)
show_poly_from_standard_json(json_file_path,
classes,
joint_pairs,
joint_names,
img_folder_path,
output_folder_path,
flag_track=True)
print("Done!")
def video_to_images(video_file_path, output_img_folder_path=None):
video = file_to_video(video_file_path)
if output_img_folder_path is None:
parent_folder_path, _ = get_parent_folder_from_path(video_file_path)
video_name = os.path.basename(video_file_path)
print(parent_folder_path)
print(video_name)
video_name_no_ext = os.path.splitext(video_name)[0]
output_img_folder_path = os.path.join(parent_folder_path,
video_name_no_ext)
create_folder(output_img_folder_path)
success, image = video.read()
count = 0
while success:
img_name = "frame%05d.jpg" % count
img_path = os.path.join(output_img_folder_path, img_name)
cv2.imwrite(img_path, image) # save frame as JPEG file
success, image = video.read()
#print('Read a new frame: ', success)
count += 1
return True
def test_save_vec_as_numpy():
output_folder_path = '../temp_folder'
create_folder(output_folder_path)
pairs_list_numpy_file_path = '/home/ngh/dev/ROLO-TRACK/training_list/list.npy'
dataset_annotation_folder_path = '/home/ngh/dev/ROLO-dev/benchmark/ILSVRC2015/Annotations/VID/train/ILSVRC2015_VID_train_0000'
batchsize = 8
nsteps = 3
list_batch_pairs = load_list_batch_pairs_from_numpy_file(pairs_list_numpy_file_path, batchsize)
for batch_pairs in list_batch_pairs[0:10]:
batch_frame_ids = [int(batch_pair[1]) for batch_pair in batch_pairs]
batch_subfolder_names = [batch_pair[0] for batch_pair in batch_pairs]
batch_numpy_folder_paths = [os.path.join(dataset_annotation_folder_path, subfolder_name, 'VID_loc_gt') for subfolder_name in batch_subfolder_names]
attempted_yolo_batch = batchload_yolovecs_from_numpy_folders(batch_numpy_folder_paths, batch_frame_ids, batchsize, nsteps)
if attempted_yolo_batch is not False:
batch_output_vecs = attempted_yolo_batch
for id in range(batchsize):
frame_id = batch_frame_ids[id]
output_vec = batch_output_vecs[id][nsteps-1]
save_vec_as_numpy_by_frame_id(output_folder_path, frame_id, output_vec)
return True
