def test_is_missing(): vid = Video.from_media(TEST_SMALL_ROBOT_MP4_FILE) assert not vid.is_missing vid = Video.from_media("non-existent-filename.mp4") assert vid.is_missing vid = Video.from_numpy( Video.from_media(TEST_SMALL_ROBOT_MP4_FILE).get_frames((3, 7, 9))) assert not vid.is_missing
def test_mp4_file_not_found(): with pytest.raises(FileNotFoundError): vid = Video.from_media("non-existent-filename.mp4") vid.channels
def load_predicted_labels_json_old( data_path: str, parsed_json: dict = None, adjust_matlab_indexing: bool = True, fix_rel_paths: bool = True, ) -> List[LabeledFrame]: """ Load predicted instances from Talmo's old JSON format. Args: data_path: The path to the JSON file. parsed_json: The parsed json if already loaded, so we can save some time if already parsed. adjust_matlab_indexing: Whether to adjust indexing from MATLAB. fix_rel_paths: Whether to fix paths to videos to absolute paths. Returns: List of :class:`LabeledFrame` objects. """ if parsed_json is None: data = json.loads(open(data_path).read()) else: data = parsed_json videos = pd.DataFrame(data["videos"]) predicted_instances = pd.DataFrame(data["predicted_instances"]) predicted_points = pd.DataFrame(data["predicted_points"]) if adjust_matlab_indexing: predicted_instances.frameIdx -= 1 predicted_points.frameIdx -= 1 predicted_points.node -= 1 predicted_points.x -= 1 predicted_points.y -= 1 skeleton = Skeleton() skeleton.add_nodes(data["skeleton"]["nodeNames"]) edges = data["skeleton"]["edges"] if adjust_matlab_indexing: edges = np.array(edges) - 1 for (src_idx, dst_idx) in edges: skeleton.add_edge( data["skeleton"]["nodeNames"][src_idx], data["skeleton"]["nodeNames"][dst_idx], ) if fix_rel_paths: for i, row in videos.iterrows(): p = row.filepath if not os.path.exists(p): p = os.path.join(os.path.dirname(data_path), p) if os.path.exists(p): videos.at[i, "filepath"] = p # Make the video objects video_objects = {} for i, row in videos.iterrows(): if videos.at[i, "format"] == "media": vid = Video.from_media(videos.at[i, "filepath"]) else: vid = Video.from_hdf5( filename=videos.at[i, "filepath"], dataset=videos.at[i, "dataset"] ) video_objects[videos.at[i, "id"]] = vid track_ids = predicted_instances["trackId"].values unique_track_ids = np.unique(track_ids) spawned_on = { track_id: predicted_instances.loc[predicted_instances["trackId"] == track_id][ "frameIdx" ].values[0] for track_id in unique_track_ids } tracks = { i: Track(name=str(i), spawned_on=spawned_on[i]) for i in np.unique(predicted_instances["trackId"].values).tolist() } # A function to get all the instances for a particular video frame def get_frame_predicted_instances(video_id, frame_idx): points = predicted_points is_in_frame = (points["videoId"] == video_id) & ( points["frameIdx"] == frame_idx ) if not is_in_frame.any(): return [] instances = [] frame_instance_ids = np.unique(points["instanceId"][is_in_frame]) for i, instance_id in enumerate(frame_instance_ids): is_instance = is_in_frame & (points["instanceId"] == instance_id) track_id = predicted_instances.loc[ predicted_instances["id"] == instance_id ]["trackId"].values[0] match_score = predicted_instances.loc[ predicted_instances["id"] == instance_id ]["matching_score"].values[0] track_score = predicted_instances.loc[ predicted_instances["id"] == instance_id ]["tracking_score"].values[0] instance_points = { data["skeleton"]["nodeNames"][n]: PredictedPoint( x, y, visible=v, score=confidence ) for x, y, n, v, confidence in zip( *[ points[k][is_instance] for k in ["x", "y", "node", "visible", "confidence"] ] ) } instance = PredictedInstance( skeleton=skeleton, points=instance_points, track=tracks[track_id], score=match_score, ) instances.append(instance) return instances # Get the unique labeled frames and construct a list of LabeledFrame objects for them. frame_keys = list( { (videoId, frameIdx) for videoId, frameIdx in zip( predicted_points["videoId"], predicted_points["frameIdx"] ) } ) frame_keys.sort() labels = [] for videoId, frameIdx in frame_keys: label = LabeledFrame( video=video_objects[videoId], frame_idx=frameIdx, instances=get_frame_predicted_instances(videoId, frameIdx), ) labels.append(label) return labels