def poll(self):
path = os.path.join(self.directory, self.getFilterMask())
print(f"Polling: {path}")
files = glob.glob(path)
files.sort()
if not files:
return
if files != self.files:
was_on_last_image = False
if self.video is None:
was_on_last_image = True
self.show()
elif self.state["frame_idx"] == self.video.last_frame_idx:
was_on_last_image = True
self.files = files
self.video = Video.from_image_filenames(filenames=files)
self.load_video(video=self.video)
if was_on_last_image:
self.state["frame_idx"] = self.video.last_frame_idx
elif self.state["frame_idx"]:
self.state["frame_idx"] = min(self.state["frame_idx"],
self.video.last_frame_idx)
def poll(self):
"""Re-scans directory (using current filter) and updates widget."""
path = os.path.join(self.directory, self._current_filter_mask)
print(f"Polling: {path}")
files = glob.glob(path)
files.sort()
if not files:
return
if files != self.files:
was_on_last_image = False
if self.video is None:
was_on_last_image = True
self.show()
elif self.state["frame_idx"] == self.video.last_frame_idx:
was_on_last_image = True
self.files = files
self.video = Video.from_image_filenames(filenames=files)
self.load_video(video=self.video)
if was_on_last_image:
self.state["frame_idx"] = self.video.last_frame_idx
elif self.state["frame_idx"]:
self.state["frame_idx"] = min(self.state["frame_idx"],
self.video.last_frame_idx)
def read(cls, file: FileHandle, *args, **kwargs,) -> Labels:
filename = file.filename
# Load data from the YAML file
project_data = yaml.load(file.text, Loader=yaml.SafeLoader)
# Create skeleton which we'll use for each video
skeleton = Skeleton()
skeleton.add_nodes(project_data["bodyparts"])
# Get subdirectories of videos and labeled data
root_dir = os.path.dirname(filename)
videos_dir = os.path.join(root_dir, "videos")
labeled_data_dir = os.path.join(root_dir, "labeled-data")
with os.scandir(labeled_data_dir) as file_iterator:
data_subdirs = [file.path for file in file_iterator if file.is_dir()]
labeled_frames = []
# Each subdirectory of labeled data corresponds to a video.
# We'll go through each and import the labeled frames.
for data_subdir in data_subdirs:
csv_files = find_files_by_suffix(
data_subdir, prefix="CollectedData", suffix=".csv"
)
if csv_files:
csv_path = csv_files[0]
# Try to find a full video corresponding to this subdir.
# If subdirectory is foo, we look for foo.mp4 in videos dir.
shortname = os.path.split(data_subdir)[-1]
video_path = os.path.join(videos_dir, f"{shortname}.mp4")
if os.path.exists(video_path):
video = Video.from_filename(video_path)
else:
# When no video is found, the individual frame images
# stored in the labeled data subdir will be used.
print(
f"Unable to find {video_path} so using individual frame images."
)
video = None
# Import the labeled fraems
labeled_frames.extend(
LabelsDeepLabCutCsvAdaptor.read_frames(
FileHandle(csv_path), full_video=video, skeleton=skeleton
)
)
else:
print(f"No csv data file found in {data_subdir}")
return Labels(labeled_frames=labeled_frames)
def show_datagen_preview(labels: Labels,
config_info_list: List[ConfigFileInfo]):
"""
Shows window(s) with preview images of training data for model configs.
"""
labels_reader = LabelsReader.from_user_instances(labels)
win_x = 300
def show_win(results: dict,
key: Text,
head_name: Text,
video: Video,
scale_to_height=None):
nonlocal win_x
scale = None
if scale_to_height:
overlay_height = results[key].shape[
1] # frames, height, width, channels
scale = scale_to_height // overlay_height
if key == "confmap":
win = demo_confmaps(results[key], video, scale=scale)
elif key == "paf":
win = demo_pafs(results[key], video, scale=scale, decimation=2)
else:
raise ValueError(f"Cannot show preview window for {key}")
win.activateWindow()
win.setWindowTitle(f"{head_name} {key}")
win.resize(400, 400)
win.move(win_x, 300)
win_x += 420
for cfg_info in config_info_list:
results = make_datagen_results(labels_reader, cfg_info.config)
if "image" in results:
vid = Video.from_numpy(results["image"])
if "confmap" in results:
show_win(
results,
"confmap",
cfg_info.head_name,
vid,
scale_to_height=vid.height,
)
if "paf" in results:
show_win(results,
"paf",
cfg_info.head_name,
vid,
scale_to_height=vid.height)
def demo_receptive_field():
app = QtWidgets.QApplication([])
video = Video.from_filename("tests/data/videos/centered_pair_small.mp4")
win = ReceptiveFieldImageWidget()
win.setImage(video.get_frame(0))
win._set_field_size(50)
win.show()
app.exec_()
def make_video_for_image_list(cls, image_dir, filenames) -> Video:
"""Creates a Video object from frame images."""
# the image filenames in the csv may not match where the user has them
# so we'll change the directory to match where the user has the csv
def fix_img_path(img_dir, img_filename):
img_filename = img_filename.replace("\\", "/")
img_filename = os.path.basename(img_filename)
img_filename = os.path.join(img_dir, img_filename)
return img_filename
filenames = list(map(lambda f: fix_img_path(image_dir, f), filenames))
return Video.from_image_filenames(filenames)
def read(
cls,
file: FileHandle,
video_path: str,
skeleton_path: str,
*args,
**kwargs,
) -> Labels:
f = file.file
video = Video.from_filename(video_path)
skeleton_data = pd.read_csv(skeleton_path, header=0)
skeleton = Skeleton()
skeleton.add_nodes(skeleton_data["name"])
nodes = skeleton.nodes
for name, parent, swap in skeleton_data.itertuples(index=False,
name=None):
if parent is not np.nan:
skeleton.add_edge(parent, name)
lfs = []
pose_matrix = f["pose"][:]
track_count, frame_count, node_count, _ = pose_matrix.shape
tracks = [Track(0, f"Track {i}") for i in range(track_count)]
for frame_idx in range(frame_count):
lf_instances = []
for track_idx in range(track_count):
points_array = pose_matrix[track_idx, frame_idx, :, :]
points = dict()
for p in range(len(points_array)):
x, y, score = points_array[p]
points[nodes[p]] = Point(x, y) # TODO: score
inst = Instance(skeleton=skeleton,
track=tracks[track_idx],
points=points)
lf_instances.append(inst)
lfs.append(
LabeledFrame(video,
frame_idx=frame_idx,
instances=lf_instances))
return Labels(labeled_frames=lfs)
def read(
cls,
file: FileHandle,
video: Union[Video, str],
*args,
**kwargs,
) -> Labels:
connect_adj_nodes = False
if video is None:
raise ValueError(
"Cannot read analysis hdf5 if no video specified.")
if not isinstance(video, Video):
video = Video.from_filename(video)
f = file.file
tracks_matrix = f["tracks"][:].T
track_names_list = f["track_names"][:].T
node_names_list = f["node_names"][:].T
# shape: frames * nodes * 2 * tracks
frame_count, node_count, _, track_count = tracks_matrix.shape
tracks = [
Track(0, track_name.decode()) for track_name in track_names_list
]
skeleton = Skeleton()
last_node_name = None
for node_name in node_names_list:
node_name = node_name.decode()
skeleton.add_node(node_name)
if connect_adj_nodes and last_node_name:
skeleton.add_edge(last_node_name, node_name)
last_node_name = node_name
frames = []
for frame_idx in range(frame_count):
instances = []
for track_idx in range(track_count):
points = tracks_matrix[frame_idx, ..., track_idx]
if not np.all(np.isnan(points)):
point_scores = np.ones(len(points))
# make everything a PredictedInstance since the usual use
# case is to export predictions for analysis
instances.append(
PredictedInstance.from_arrays(
points=points,
point_confidences=point_scores,
skeleton=skeleton,
track=tracks[track_idx],
instance_score=1,
))
if instances:
frames.append(
LabeledFrame(video=video,
frame_idx=frame_idx,
instances=instances))
return Labels(labeled_frames=frames)
def from_json_data(cls,
data: Union[str, dict],
match_to: Optional["Labels"] = None) -> "Labels":
"""
Create instance of class from data in dictionary.
Method is used by other methods that load from JSON.
Args:
data: Dictionary, deserialized from JSON.
match_to: If given, we'll replace particular objects in the
data dictionary with *matching* objects in the match_to
:class:`Labels` object. This ensures that the newly
instantiated :class:`Labels` can be merged without
duplicate matching objects (e.g., :class:`Video` objects ).
Returns:
A new :class:`Labels` object.
"""
# Parse the json string if needed.
if type(data) is str:
dicts = json_loads(data)
else:
dicts = data
dicts["tracks"] = dicts.get(
"tracks", []) # don't break if json doesn't include tracks
# First, deserialize the skeletons, videos, and nodes lists.
# The labels reference these so we will need them while deserializing.
nodes = cattr.structure(dicts["nodes"], List[Node])
idx_to_node = {i: nodes[i] for i in range(len(nodes))}
skeletons = Skeleton.make_cattr(idx_to_node).structure(
dicts["skeletons"], List[Skeleton])
videos = Video.cattr().structure(dicts["videos"], List[Video])
try:
# First try unstructuring tuple (newer format)
track_cattr = cattr.Converter(
unstruct_strat=cattr.UnstructureStrategy.AS_TUPLE)
tracks = track_cattr.structure(dicts["tracks"], List[Track])
except:
# Then try unstructuring dict (older format)
try:
tracks = cattr.structure(dicts["tracks"], List[Track])
except:
raise ValueError("Unable to load tracks as tuple or dict!")
# if we're given a Labels object to match, use its objects when they match
if match_to is not None:
for idx, sk in enumerate(skeletons):
for old_sk in match_to.skeletons:
if sk.matches(old_sk):
# use nodes from matched skeleton
for (node, match_node) in zip(sk.nodes, old_sk.nodes):
node_idx = nodes.index(node)
nodes[node_idx] = match_node
# use skeleton from match
skeletons[idx] = old_sk
break
for idx, vid in enumerate(videos):
for old_vid in match_to.videos:
# Try to match videos using either their current or source filename
# if available.
old_vid_paths = [old_vid.filename]
if getattr(old_vid.backend, "has_embedded_images", False):
old_vid_paths.append(
old_vid.backend._source_video.filename)
new_vid_paths = [vid.filename]
if getattr(vid.backend, "has_embedded_images", False):
new_vid_paths.append(
vid.backend._source_video.filename)
is_match = False
for old_vid_path in old_vid_paths:
for new_vid_path in new_vid_paths:
if old_vid_path == new_vid_path or weak_filename_match(
old_vid_path, new_vid_path):
is_match = True
videos[idx] = old_vid
break
if is_match:
break
if is_match:
break
suggestions = []
if "suggestions" in dicts:
suggestions_cattr = cattr.Converter()
suggestions_cattr.register_structure_hook(
Video, lambda x, type: videos[int(x)])
try:
suggestions = suggestions_cattr.structure(
dicts["suggestions"], List[SuggestionFrame])
except Exception as e:
print("Error while loading suggestions (1)")
print(e)
try:
# Convert old suggestion format to new format.
# Old format: {video: list of frame indices}
# New format: [SuggestionFrames]
old_suggestions = suggestions_cattr.structure(
dicts["suggestions"], Dict[Video, List])
for video in old_suggestions.keys():
suggestions.extend([
SuggestionFrame(video, idx)
for idx in old_suggestions[video]
])
except Exception as e:
print("Error while loading suggestions (2)")
print(e)
pass
if "negative_anchors" in dicts:
negative_anchors_cattr = cattr.Converter()
negative_anchors_cattr.register_structure_hook(
Video, lambda x, type: videos[int(x)])
negative_anchors = negative_anchors_cattr.structure(
dicts["negative_anchors"], Dict[Video, List])
else:
negative_anchors = dict()
if "provenance" in dicts:
provenance = dicts["provenance"]
else:
provenance = dict()
# If there is actual labels data, get it.
if "labels" in dicts:
label_cattr = make_instance_cattr()
label_cattr.register_structure_hook(
Skeleton, lambda x, type: skeletons[int(x)])
label_cattr.register_structure_hook(Video,
lambda x, type: videos[int(x)])
label_cattr.register_structure_hook(
Node, lambda x, type: x
if isinstance(x, Node) else nodes[int(x)])
label_cattr.register_structure_hook(
Track, lambda x, type: None if x is None else tracks[int(x)])
labels = label_cattr.structure(dicts["labels"], List[LabeledFrame])
else:
labels = []
return Labels(
labeled_frames=labels,
videos=videos,
skeletons=skeletons,
nodes=nodes,
suggestions=suggestions,
negative_anchors=negative_anchors,
tracks=tracks,
provenance=provenance,
)
def write(
cls,
filename: str,
source_object: str,
compress: Optional[bool] = None,
save_frame_data: bool = False,
frame_data_format: str = "png",
):
"""
Save a Labels instance to a JSON format.
Args:
filename: The filename to save the data to.
source_object: The labels dataset to save.
compress: Whether the data be zip compressed or not? If True,
the JSON will be compressed using Python's shutil.make_archive
command into a PKZIP zip file. If compress is True then
filename will have a .zip appended to it.
save_frame_data: Whether to save the image data for each frame.
For each video in the dataset, all frames that have labels
will be stored as an imgstore dataset.
If save_frame_data is True then compress will be forced to True
since the archive must contain both the JSON data and image
data stored in ImgStores.
frame_data_format: If save_frame_data is True, then this argument
is used to set the data format to use when writing frame
data to ImgStore objects. Supported formats should be:
* 'pgm',
* 'bmp',
* 'ppm',
* 'tif',
* 'png',
* 'jpg',
* 'npy',
* 'mjpeg/avi',
* 'h264/mkv',
* 'avc1/mp4'
Note: 'h264/mkv' and 'avc1/mp4' require separate installation
of these codecs on your system. They are excluded from SLEAP
because of their GPL license.
Returns:
None
"""
labels = source_object
if compress is None:
compress = filename.endswith(".zip")
# Lets make a temporary directory to store the image frame data or pre-compressed json
# in case we need it.
with tempfile.TemporaryDirectory() as tmp_dir:
# If we are saving frame data along with the datasets. We will replace videos with
# new video object that represent video data from just the labeled frames.
if save_frame_data:
# Create a set of new Video objects with imgstore backends. One for each
# of the videos. We will only include the labeled frames though. We will
# then replace each video with this new video
new_videos = labels.save_frame_data_imgstore(
output_dir=tmp_dir, format=frame_data_format)
# Make video paths relative
for vid in new_videos:
tmp_path = vid.filename
# Get the parent dir of the YAML file.
# Use "/" since this works on Windows and posix
img_store_dir = (
os.path.basename(os.path.split(tmp_path)[0]) + "/" +
os.path.basename(tmp_path))
# Change to relative path
vid.backend.filename = img_store_dir
# Convert to a dict, not JSON yet, because we need to patch up the videos
d = labels.to_dict()
d["videos"] = Video.cattr().unstructure(new_videos)
else:
d = labels.to_dict()
# Set file format version
d["format_id"] = cls.FORMAT_ID
if compress or save_frame_data:
# Ensure that filename ends with .json
# shutil will append .zip
filename = re.sub("(\\.json)?(\\.zip)?$", ".json", filename)
# Write the json to the tmp directory, we will zip it up with the frame data.
full_out_filename = os.path.join(tmp_dir,
os.path.basename(filename))
json_dumps(d, full_out_filename)
# Create the archive
shutil.make_archive(base_name=filename,
root_dir=tmp_dir,
format="zip")
# If the user doesn't want to compress, then just write the json to the filename
else:
json_dumps(d, filename)
def write(
cls,
filename: str,
source_object: object,
append: bool = False,
save_frame_data: bool = False,
frame_data_format: str = "png",
all_labeled: bool = False,
suggested: bool = False,
):
labels = source_object
# Delete the file if it exists, we want to start from scratch since
# h5py truncates the file which seems to not actually delete data
# from the file. Don't if we are appending of course.
if os.path.exists(filename) and not append:
os.unlink(filename)
# Serialize all the meta-data to JSON.
d = labels.to_dict(skip_labels=True)
if save_frame_data:
new_videos = labels.save_frame_data_hdf5(
filename,
format=frame_data_format,
user_labeled=True,
all_labeled=all_labeled,
suggested=suggested,
)
# Replace path to video file with "." (which indicates that the
# video is in the same file as the HDF5 labels dataset).
# Otherwise, the video paths will break if the HDF5 labels
# dataset file is moved.
for vid in new_videos:
vid.backend.filename = "."
d["videos"] = Video.cattr().unstructure(new_videos)
with h5py.File(filename, "a") as f:
# Add all the JSON metadata
meta_group = f.require_group("metadata")
meta_group.attrs["format_id"] = cls.FORMAT_ID
# If we are appending and there already exists JSON metadata
if append and "json" in meta_group.attrs:
# Otherwise, we need to read the JSON and append to the lists
old_labels = labels_json.LabelsJsonAdaptor.from_json_data(
meta_group.attrs["json"].tostring().decode()
)
# A function to join to list but only include new non-dupe entries
# from the right hand list.
def append_unique(old, new):
unique = []
for x in new:
try:
matches = [y.matches(x) for y in old]
except AttributeError:
matches = [x == y for y in old]
# If there were no matches, this is a unique object.
if sum(matches) == 0:
unique.append(x)
else:
# If we have an object that matches, replace the instance
# with the one from the new list. This will will make sure
# objects on the Instances are the same as those in the
# Labels lists.
for i, match in enumerate(matches):
if match:
old[i] = x
return old + unique
# Append the lists
labels.tracks = append_unique(old_labels.tracks, labels.tracks)
labels.skeletons = append_unique(old_labels.skeletons, labels.skeletons)
labels.videos = append_unique(old_labels.videos, labels.videos)
labels.nodes = append_unique(old_labels.nodes, labels.nodes)
# FIXME: Do something for suggestions and negative_anchors
# Get the dict for JSON and save it over the old data
d = labels.to_dict(skip_labels=True)
if not append:
# These items are stored in separate lists because the metadata
# group got to be too big.
for key in ("videos", "tracks", "suggestions"):
# Convert for saving in hdf5 dataset
data = [np.string_(json_dumps(item)) for item in d[key]]
hdf5_key = f"{key}_json"
# Save in its own dataset (e.g., videos_json)
f.create_dataset(hdf5_key, data=data, maxshape=(None,))
# Clear from dict since we don't want to save this in attribute
d[key] = []
# Output the dict to JSON
meta_group.attrs["json"] = np.string_(json_dumps(d))
# FIXME: We can probably construct these from attrs fields
# We will store Instances and PredcitedInstances in the same
# table. instance_type=0 or Instance and instance_type=1 for
# PredictedInstance, score will be ignored for Instances.
instance_dtype = np.dtype(
[
("instance_id", "i8"),
("instance_type", "u1"),
("frame_id", "u8"),
("skeleton", "u4"),
("track", "i4"),
("from_predicted", "i8"),
("score", "f4"),
("point_id_start", "u8"),
("point_id_end", "u8"),
]
)
frame_dtype = np.dtype(
[
("frame_id", "u8"),
("video", "u4"),
("frame_idx", "u8"),
("instance_id_start", "u8"),
("instance_id_end", "u8"),
]
)
num_instances = len(labels.all_instances)
max_skeleton_size = max([len(s.nodes) for s in labels.skeletons], default=0)
# Initialize data arrays for serialization
points = np.zeros(num_instances * max_skeleton_size, dtype=Point.dtype)
pred_points = np.zeros(
num_instances * max_skeleton_size, dtype=PredictedPoint.dtype
)
instances = np.zeros(num_instances, dtype=instance_dtype)
frames = np.zeros(len(labels), dtype=frame_dtype)
# Pre compute some structures to make serialization faster
skeleton_to_idx = {
skeleton: labels.skeletons.index(skeleton)
for skeleton in labels.skeletons
}
track_to_idx = {
track: labels.tracks.index(track) for track in labels.tracks
}
track_to_idx[None] = -1
video_to_idx = {
video: labels.videos.index(video) for video in labels.videos
}
instance_type_to_idx = {Instance: 0, PredictedInstance: 1}
# Each instance we create will have and index in the dataset, keep track of
# these so we can quickly add from_predicted links on a second pass.
instance_to_idx = {}
instances_with_from_predicted = []
instances_from_predicted = []
# If we are appending, we need look inside to see what frame, instance, and
# point ids we need to start from. This gives us offsets to use.
if append and "points" in f:
point_id_offset = f["points"].shape[0]
pred_point_id_offset = f["pred_points"].shape[0]
instance_id_offset = f["instances"][-1]["instance_id"] + 1
frame_id_offset = int(f["frames"][-1]["frame_id"]) + 1
else:
point_id_offset = 0
pred_point_id_offset = 0
instance_id_offset = 0
frame_id_offset = 0
point_id = 0
pred_point_id = 0
instance_id = 0
for frame_id, label in enumerate(labels):
frames[frame_id] = (
frame_id + frame_id_offset,
video_to_idx[label.video],
label.frame_idx,
instance_id + instance_id_offset,
instance_id + instance_id_offset + len(label.instances),
)
for instance in label.instances:
# Add this instance to our lookup structure we will need for
# from_predicted links
instance_to_idx[instance] = instance_id
parray = instance.get_points_array(copy=False, full=True)
instance_type = type(instance)
# Check whether we are working with a PredictedInstance or an
# Instance.
if instance_type is PredictedInstance:
score = instance.score
pid = pred_point_id + pred_point_id_offset
else:
score = np.nan
pid = point_id + point_id_offset
# Keep track of any from_predicted instance links, we will
# insert the correct instance_id in the dataset after we are
# done.
if instance.from_predicted:
instances_with_from_predicted.append(instance_id)
instances_from_predicted.append(instance.from_predicted)
# Copy all the data
instances[instance_id] = (
instance_id + instance_id_offset,
instance_type_to_idx[instance_type],
frame_id,
skeleton_to_idx[instance.skeleton],
track_to_idx[instance.track],
-1,
score,
pid,
pid + len(parray),
)
# If these are predicted points, copy them to the predicted point
# array otherwise, use the normal point array
if type(parray) is PredictedPointArray:
pred_points[
pred_point_id : (pred_point_id + len(parray))
] = parray
pred_point_id = pred_point_id + len(parray)
else:
points[point_id : (point_id + len(parray))] = parray
point_id = point_id + len(parray)
instance_id = instance_id + 1
# Add from_predicted links
for instance_id, from_predicted in zip(
instances_with_from_predicted, instances_from_predicted
):
try:
instances[instance_id]["from_predicted"] = instance_to_idx[
from_predicted
]
except KeyError:
# If we haven't encountered the from_predicted instance yet then
# don't save the link. It's possible for a user to create a regular
# instance from a predicted instance and then delete all predicted
# instances from the file, but in this case I don’t think there's
# any reason to remember which predicted instance the regular
# instance came from.
pass
# We pre-allocated our points array with max possible size considering the
# max skeleton size, drop any unused points.
points = points[0:point_id]
pred_points = pred_points[0:pred_point_id]
# Create datasets if we need to
if append and "points" in f:
f["points"].resize((f["points"].shape[0] + points.shape[0]), axis=0)
f["points"][-points.shape[0] :] = points
f["pred_points"].resize(
(f["pred_points"].shape[0] + pred_points.shape[0]), axis=0
)
f["pred_points"][-pred_points.shape[0] :] = pred_points
f["instances"].resize(
(f["instances"].shape[0] + instances.shape[0]), axis=0
)
f["instances"][-instances.shape[0] :] = instances
f["frames"].resize((f["frames"].shape[0] + frames.shape[0]), axis=0)
f["frames"][-frames.shape[0] :] = frames
else:
f.create_dataset(
"points", data=points, maxshape=(None,), dtype=Point.dtype
)
f.create_dataset(
"pred_points",
data=pred_points,
maxshape=(None,),
dtype=PredictedPoint.dtype,
)
f.create_dataset(
"instances", data=instances, maxshape=(None,), dtype=instance_dtype
)
f.create_dataset(
"frames", data=frames, maxshape=(None,), dtype=frame_dtype
)
def read(
cls,
file: FileHandle,
img_dir: str,
use_missing_gui: bool = False,
*args,
**kwargs,
) -> Labels:
dicts = file.json
# Make skeletons from "categories"
skeleton_map = dict()
for category in dicts["categories"]:
skeleton = Skeleton(name=category["name"])
skeleton_id = category["id"]
node_names = category["keypoints"]
skeleton.add_nodes(node_names)
try:
for src_idx, dst_idx in category["skeleton"]:
skeleton.add_edge(node_names[src_idx], node_names[dst_idx])
except IndexError as e:
# According to the COCO data format specifications[^1], the edges
# are supposed to be 1-indexed. But in some of their own
# dataset the edges are 1-indexed! So we'll try.
# [1]: http://cocodataset.org/#format-data
# Clear any edges we already created using 0-indexing
skeleton.clear_edges()
# Add edges
for src_idx, dst_idx in category["skeleton"]:
skeleton.add_edge(node_names[src_idx - 1], node_names[dst_idx - 1])
skeleton_map[skeleton_id] = skeleton
# Make videos from "images"
# Remove images that aren't referenced in the annotations
img_refs = [annotation["image_id"] for annotation in dicts["annotations"]]
dicts["images"] = list(filter(lambda im: im["id"] in img_refs, dicts["images"]))
# Key in JSON file should be "file_name", but sometimes it's "filename",
# so we have to check both.
img_filename_key = "file_name"
if img_filename_key not in dicts["images"][0].keys():
img_filename_key = "filename"
# First add the img_dir to each image filename
img_paths = [
os.path.join(img_dir, image[img_filename_key]) for image in dicts["images"]
]
# See if there are any missing files
img_missing = [not os.path.exists(path) for path in img_paths]
if sum(img_missing):
if use_missing_gui:
okay = MissingFilesDialog(img_paths, img_missing).exec_()
if not okay:
return None
else:
raise FileNotFoundError(
f"Images for COCO dataset could not be found in {img_dir}."
)
# Update the image paths (with img_dir or user selected path)
for image, path in zip(dicts["images"], img_paths):
image[img_filename_key] = path
# Create the video objects for the image files
image_video_map = dict()
vid_id_video_map = dict()
for image in dicts["images"]:
image_id = image["id"]
image_filename = image[img_filename_key]
# Sometimes images have a vid_id which links multiple images
# together as one video. If so, we'll use that as the video key.
# But if there isn't a vid_id, we'll treat each images as a
# distinct video and use the image id as the video id.
vid_id = image.get("vid_id", image_id)
if vid_id not in vid_id_video_map:
kwargs = dict(filenames=[image_filename])
for key in ("width", "height"):
if key in image:
kwargs[key] = image[key]
video = Video.from_image_filenames(**kwargs)
vid_id_video_map[vid_id] = video
frame_idx = 0
else:
video = vid_id_video_map[vid_id]
frame_idx = video.num_frames
video.backend.filenames.append(image_filename)
image_video_map[image_id] = (video, frame_idx)
# Make instances from "annotations"
lf_map = dict()
track_map = dict()
for annotation in dicts["annotations"]:
skeleton = skeleton_map[annotation["category_id"]]
image_id = annotation["image_id"]
video, frame_idx = image_video_map[image_id]
keypoints = np.array(annotation["keypoints"], dtype="int").reshape(-1, 3)
track = None
if "track_id" in annotation:
track_id = annotation["track_id"]
if track_id not in track_map:
track_map[track_id] = Track(frame_idx, str(track_id))
track = track_map[track_id]
points = dict()
any_visible = False
for i in range(len(keypoints)):
node = skeleton.nodes[i]
x, y, flag = keypoints[i]
if flag == 0:
# node not labeled for this instance
continue
is_visible = flag == 2
any_visible = any_visible or is_visible
points[node] = Point(x, y, is_visible)
if points:
# If none of the points had 2 has the "visible" flag, we'll
# assume this incorrect and just mark all as visible.
if not any_visible:
for point in points.values():
point.visible = True
inst = Instance(skeleton=skeleton, points=points, track=track)
if image_id not in lf_map:
lf_map[image_id] = LabeledFrame(video, frame_idx)
lf_map[image_id].insert(0, inst)
return Labels(labeled_frames=list(lf_map.values()))
def read(
cls, file: FileHandle, gui: bool = True, *args, **kwargs,
):
filename = file.filename
mat_contents = sio.loadmat(filename)
box_path = cls._unwrap_mat_scalar(mat_contents["boxPath"])
# If the video file isn't found, try in the same dir as the mat file
if not os.path.exists(box_path):
file_dir = os.path.dirname(filename)
box_path_name = box_path.split("\\")[-1] # assume windows path
box_path = os.path.join(file_dir, box_path_name)
if not os.path.exists(box_path):
if gui:
video_paths = [box_path]
missing = [True]
okay = MissingFilesDialog(video_paths, missing).exec_()
if not okay or missing[0]:
return
box_path = video_paths[0]
else:
# Ignore missing videos if not loading from gui
box_path = ""
if os.path.exists(box_path):
vid = Video.from_hdf5(
dataset="box", filename=box_path, input_format="channels_first"
)
else:
vid = None
nodes_ = mat_contents["skeleton"]["nodes"]
edges_ = mat_contents["skeleton"]["edges"]
points_ = mat_contents["positions"]
edges_ = edges_ - 1 # convert matlab 1-indexing to python 0-indexing
nodes = cls._unwrap_mat_array(nodes_)
edges = cls._unwrap_mat_array(edges_)
nodes = list(map(str, nodes)) # convert np._str to str
sk = Skeleton(name=filename)
sk.add_nodes(nodes)
for edge in edges:
sk.add_edge(source=nodes[edge[0]], destination=nodes[edge[1]])
labeled_frames = []
node_count, _, frame_count = points_.shape
for i in range(frame_count):
new_inst = Instance(skeleton=sk)
for node_idx, node in enumerate(nodes):
x = points_[node_idx][0][i]
y = points_[node_idx][1][i]
new_inst[node] = Point(x, y)
if len(new_inst.points):
new_frame = LabeledFrame(video=vid, frame_idx=i)
new_frame.instances = (new_inst,)
labeled_frames.append(new_frame)
labels = Labels(labeled_frames=labeled_frames, videos=[vid], skeletons=[sk])
return labels
