def test_auto_config(): config = get_auto_config() assert 'Decoder' in config assert os.path.exists(config['Decoder']['model_path']) assert 'Localizer' in config assert os.path.exists(config['Localizer']['model_path']) assert 'TagSimilarityEncoder' in config assert os.path.exists(config['TagSimilarityEncoder']['model_path'])
def init_pipeline(output, no_localizer): """Helper function to initialize a new pipeline that generates the desired output. Args: output (list): pipeline objects that the pipeline will generate as the result no_localizer (boolean): whether or not the localizer should be skipped to decode a single tag in the center of a 100x100 image Returns: pipeline object """ if no_localizer: pipeline = Pipeline([objects.Regions, objects.LocalizerPositions], output, **get_auto_config()) else: pipeline = Pipeline([objects.Image], output, **get_auto_config()) return pipeline
def init_pipeline(output): """Helper function to initialize a new pipeline that generates the desired output. Args: output (list): pipeline objects that the pipeline will generate as the result Returns: pipeline object """ pipeline = Pipeline([objects.Image], output, **get_auto_config()) return pipeline
def __init__(self, site_builder, camera_rotations={0: 1, 1: -1, 2:1, 3:-1}, detections_path='detections.pkl'): self.builder = site_builder self.rotations = camera_rotations self.pipeline = Pipeline([Image], [LocalizerInputImage, FinalResultOverlay, CrownOverlay, IDs, SaliencyOverlay], **get_auto_config()) self.crown = ResultCrownVisualizer() self.detections_path = detections_path if os.path.isfile(self.detections_path): self.detections = pickle.load(open(self.detections_path, 'rb')) else: self.detections = []
def init_pipeline(output, no_localizer): """Helper function to initialize a new pipeline that generates the desired output. Args: output (list): pipeline objects that the pipeline will generate as the result no_localizer (boolean): whether or not the localizer should be skipped to decode a single tag in the center of a 100x100 image Returns: pipeline object """ if no_localizer: pipeline = Pipeline([objects.Regions, objects.LocalizerPositions], output, **get_auto_config()) else: pipeline = Pipeline([objects.Image], output, **get_auto_config()) return pipeline
def process_video(args): config = get_auto_config() logger.info('Initializing {} pipeline(s)'.format(args.num_threads)) plines = [Pipeline([Image, Timestamp], [PipelineResult], **config) for _ in range(args.num_threads)] logger.info('Loading bb_binary repository {}'.format(args.repo_output_path)) repo = Repository(args.repo_output_path) camId, _, _ = parse_video_fname(args.video_path) logger.info('Parsed camId = {}'.format(camId)) gen_processor = GeneratorProcessor(plines, lambda: BBBinaryRepoSink(repo, camId=camId)) logger.info('Processing video frames from {}'.format(args.video_path)) gen_processor(video_generator(args.video_path, args.timestamp_format, args.text_root_path))
def run(image_path_file, out, force): cache = {} if force and os.path.exists(out): print("Removing {}.".format(out)) os.remove(out) def add_to_cache(**kwargs): for name, arr in kwargs.items(): if name not in cache: cache[name] = [arr] else: cache[name].append(arr) def flush_cache(): cache_concat = {n: np.concatenate(arrs) for n, arrs in cache.items()} nb_samples = len(next(iter(cache_concat.values()))) permutation = np.random.permutation(nb_samples) cache_shuffled = {n: arrs[permutation] for n, arrs in cache_concat.items()} dset.append(**cache_shuffled) cache.clear() roi_size = 96 image_fnames = [n.rstrip('\n') for n in image_path_file.readlines()] pipeline = Pipeline([Filename], [Image, LocalizerPositions], **get_auto_config()) dset = HDF5Dataset(out) bar = progressbar.ProgressBar(max_value=len(image_fnames)) for i, image_fname in enumerate(bar(image_fnames)): try: results = pipeline([image_fname]) rois, mask = Localizer.extract_rois(results[LocalizerPositions], results[Image], roi_size) except Exception as e: print(e) continue nb_detections = np.sum(mask) camIdx, dt = parse_image_fname(image_fname) season = np.array([dt.year] * nb_detections, dtype=np.uint16) timestamp = np.array([dt.timestamp()] * nb_detections, dtype=np.float64) add_to_cache(rois=rois, season=season, timestamp=timestamp) if i % 50 == 0 and i != 0: flush_cache() flush_cache()
def process_video(video_path, repo_output_path, ts_format, text_root_path, rank): info = lambda msg: logger.info('Process {}: {}'.format(rank, msg)) import theano from pipeline import Pipeline from pipeline.cmdline import logger from pipeline.pipeline import GeneratorProcessor, get_auto_config from pipeline.io import BBBinaryRepoSink, video_generator from pipeline.objects import PipelineResult, Image, Timestamp from bb_binary import Repository, parse_video_fname repo_output_path = os.path.join(repo_output_path, 'process_{}'.format(rank)) info('Theano compile dir: {}'.format(theano.config.base_compiledir)) info('Output dir: {}'.format(repo_output_path)) config = get_auto_config() info('Initializing pipeline') pipeline = Pipeline([Image, Timestamp], [PipelineResult], **config) info('Loading bb_binary repository {}'.format(repo_output_path)) repo = Repository(repo_output_path) camId, _, _ = parse_video_fname(video_path) info('Parsed camId = {}'.format(camId)) gen_processor = GeneratorProcessor( pipeline, lambda: BBBinaryRepoSink(repo, camId=camId)) log_callback = lambda frame_idx: info('Processing frame {} from {}'.format( frame_idx, video_path)) ffmpeg_stderr_fd = open('process_{}_ffmpeg_stderr.log'.format(rank), 'w') info('Processing video frames from {}'.format(video_path)) gen_processor( video_generator(video_path, ts_format, text_root_path, log_callback, ffmpeg_stderr_fd))
def process_video(args): config = get_auto_config() logger.info('Initializing {} pipeline(s)'.format(args.num_threads)) plines = [ Pipeline([Image, Timestamp], [PipelineResult], **config) for _ in range(args.num_threads) ] logger.info('Loading bb_binary repository {}'.format( args.repo_output_path)) repo = Repository(args.repo_output_path) camId, _, _ = parse_video_fname(args.video_path) logger.info('Parsed camId = {}'.format(camId)) gen_processor = GeneratorProcessor( plines, lambda: BBBinaryRepoSink(repo, camId=camId)) logger.info('Processing video frames from {}'.format(args.video_path)) gen_processor( video_generator(args.video_path, args.timestamp_format, args.text_root_path))
def process_video(video_path, repo_output_path, ts_format, text_root_path, rank): info = lambda msg: logger.info(f"Process {rank}: {msg}") import theano from pipeline import Pipeline from pipeline.cmdline import logger from pipeline.pipeline import GeneratorProcessor, get_auto_config from pipeline.io import BBBinaryRepoSink, video_generator from pipeline.objects import PipelineResult, Image, Timestamp from bb_binary import Repository, parse_video_fname repo_output_path = os.path.join(repo_output_path, f"process_{rank}") info(f"Theano compile dir: {theano.config.base_compiledir}") info(f"Output dir: {repo_output_path}") config = get_auto_config() info("Initializing pipeline") pipeline = Pipeline([Image, Timestamp], [PipelineResult], **config) info(f"Loading bb_binary repository {repo_output_path}") repo = Repository(repo_output_path) camId, _, _ = parse_video_fname(video_path) info(f"Parsed camId = {camId}") gen_processor = GeneratorProcessor( pipeline, lambda: BBBinaryRepoSink(repo, camId=camId)) log_callback = lambda frame_idx: info( f"Processing frame {frame_idx} from {video_path}") ffmpeg_stderr_fd = open(f"process_{rank}_ffmpeg_stderr.log", "w") info(f"Processing video frames from {video_path}") gen_processor( video_generator(video_path, ts_format, text_root_path, log_callback, ffmpeg_stderr_fd))
def process_video(video_path, repo_output_path, ts_format, text_root_path, rank): info = lambda msg: logger.info('Process {}: {}'.format(rank, msg)) import theano from pipeline import Pipeline from pipeline.cmdline import logger from pipeline.pipeline import GeneratorProcessor, get_auto_config from pipeline.io import BBBinaryRepoSink, video_generator from pipeline.objects import PipelineResult, Image, Timestamp from bb_binary import Repository, parse_video_fname repo_output_path = os.path.join(repo_output_path, 'process_{}'.format(rank)) info('Theano compile dir: {}'.format(theano.config.base_compiledir)) info('Output dir: {}'.format(repo_output_path)) config = get_auto_config() info('Initializing pipeline') pipeline = Pipeline([Image, Timestamp], [PipelineResult], **config) info('Loading bb_binary repository {}'.format(repo_output_path)) repo = Repository(repo_output_path) camId, _, _ = parse_video_fname(video_path) info('Parsed camId = {}'.format(camId)) gen_processor = GeneratorProcessor(pipeline, lambda: BBBinaryRepoSink(repo, camId=camId)) log_callback = lambda frame_idx: info('Processing frame {} from {}'.format(frame_idx, video_path)) ffmpeg_stderr_fd = open('process_{}_ffmpeg_stderr.log'.format(rank), 'w') info('Processing video frames from {}'.format(video_path)) gen_processor(video_generator(video_path, ts_format, text_root_path, log_callback, ffmpeg_stderr_fd))
# return previously stored events def getEvents(self): return (self.old_events, self.old_event_candidates, self.last_videotime) config = configparser.ConfigParser() config.read('server.cfg') feeders = {} ids = config['Feeders']['feeder_ids'].split(',') addresses = config['Feeders']['feeder_addresses'].split(',') for i in range(len(ids)): feeders[ids[i]] = FileLoader(ids[i], addresses[i]) pipeline = Pipeline([Image], [Positions, Orientations, Saliencies, IDs], **get_auto_config()) print("Pipeline initialized") framenum = 0 vis = ResultCrownVisualizer() event_num = int(config['General']['last_event_id']) previous = None last_time = time.time() csvfile = open(config['General']['csvfile'], 'a') csvwriter = csv.writer(csvfile) running = True while (running): for feeder in feeders.keys(): print("Downloading videos from feeder with ID: " + feeder) feeders[feeder].getFiles() for file in os.listdir(config['General']['videodir']):
def pipeline_config(): return get_auto_config()
def test_auto_config(): config = get_auto_config() assert "Decoder" in config assert os.path.exists(config["Decoder"]["model_path"]) assert "Localizer" in config assert os.path.exists(config["Localizer"]["model_path"])
def pipeline_config(): return get_auto_config()