def test_int_id_to_binary(): """Test conversion of integer id representation to binary array representation.""" bit_arr = int_id_to_binary(8) assert np.all(bit_arr == np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], dtype=np.uint8)) bit_arr = int_id_to_binary(4095) assert np.all(bit_arr == np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.uint8)) beeid_digits = 12 # test setting of single bits expected_result = np.zeros((beeid_digits, beeid_digits), int) np.fill_diagonal(expected_result, 1) expected_result = np.fliplr(expected_result) result = np.zeros_like(expected_result) for i in range(0, beeid_digits): result[i] = int_id_to_binary(2**i, nb_bits=beeid_digits) assert np.array_equal(result, expected_result) # correct conversion of all integers in range for i in range(0, 2**beeid_digits): bit_array = int_id_to_binary(i, nb_bits=beeid_digits) assert beeid_digits == len(bit_array) assert i == sum([m * 2**n for (n, m) in enumerate(bit_array[::-1])]) with pytest.raises(Exception) as exception_information: # to big value bit_arr = int_id_to_binary(8096) assert 'overflows' in str(exception_information.value)
def score_ids_best_fit(ids1, ids2, length=12): """Compares two lists of ids by choosing the pair with the best score. .. deprecated:: September 2016 This scoring is used with data from the *old* Computer Vision Pipeline. Arguments: ids1 (:obj:`list` of int): Iterable with ids as integers as base ids ids2 (:obj:`list` of int): Iterable with ids as integers to compare base to Keyword Arguments: length (Optional int): number of bits in the bit array used to compare ids Returns: float: Uses Hamming distance of best matching pair """ assert len(ids1) > 0 assert len(ids2) > 0 ids1, ids2 = set(ids1), set(ids2) # best case: we already have the same ids in both sets if ids1 & ids2: return 0 best_score = float("inf") for id1 in ids1: id1 = int_id_to_binary(id1, nb_bits=length) for id2 in ids2: id2 = int_id_to_binary(id2, nb_bits=length) test_score = hamming(id1, id2) if test_score < best_score: best_score = test_score return best_score
def test_int_id_to_binary(int_bin_mapping): """Test conversion of integer id representation to binary array representation.""" for int_repr, bin_repr in int_bin_mapping: assert np.all(int_id_to_binary(int_repr) == bin_repr) with pytest.raises(Exception) as exception_information: # to big value int_id_to_binary(8096) assert 'overflows' in str(exception_information.value)
def test_int_id_to_binary(int_bin_mapping): """Test conversion of integer id representation to binary array representation.""" for int_repr, bin_repr in int_bin_mapping: assert np.all(int_id_to_binary(int_repr) == bin_repr) with pytest.raises(Exception) as exception_information: # to big value int_id_to_binary(8096) assert 'overflows' in str(exception_information.value)
def score_ids_and_orientation(id_orientation_tuple1, id_orientation_tuple2, length=12, range_bonus_orientation=30): """Compares lists of ids by choosing the pair with the best score and considering orientation. The bonus is equal to the negative score of one non matching bit. .. deprecated:: September 2016 This scoring is used with data from the *old* Computer Vision Pipeline. Arguments: id_orientation_tuple1 (tuple): (Iterable with ids, Iterable with orientations) id_orientation_tuple2 (tuple): (Iterable with ids, Iterable with orientations) Keyword Arguments: length (int): number of bits in the bit array used to compare ids range_bonus_orientation (float): range in degrees, so that two orientations get a bonus Returns: float: Uses Hamming distance of best matching pair with bonus for same orientation """ ids1, orientations1 = id_orientation_tuple1 ids2, orientations2 = id_orientation_tuple2 assert len(ids1) > 0 assert len(ids2) > 0 assert len(ids1) == len(orientations1) assert len(ids2) == len(orientations2) # best case: we already have the same ids in both sets if set(ids1) & set(ids2): return 0 best_score = float("inf") for id1, or1 in zip(ids1, orientations1): id1 = int_id_to_binary(id1, nb_bits=length) for id2, or2 in zip(ids2, orientations2): id2 = int_id_to_binary(id2, nb_bits=length) # bonus for orientation orientation_score = 0.0 if distance_orientations(or1, or2) <= range_bonus_orientation: orientation_score = -1. / length test_score = hamming(id1, id2) + orientation_score if test_score < best_score: best_score = test_score if best_score <= 0: return best_score return best_score
def run(gt_file, videos, images, visualize_debug, output, fix_utc_2014, nb_bits=12): """ Converts bb_binary ground truth Cap'n Proto files to hdf5 files and extracts the corresponding rois from videos or images. """ def get_filenames(f): if f is None: return [] else: return [line.rstrip('\n') for line in f.readlines()] gen_factory = FrameGeneratorFactory(get_filenames(videos), get_filenames(images)) if os.path.exists(output): os.remove(output) distribution = DistributionCollection([('bits', Bernoulli(), nb_bits)]) dset = DistributionHDF5Dataset(output, distribution) camIdxs = [] periods = [] for fname in gt_file: fc = load_frame_container(fname) camIdx, start_dt, end_dt = parse_video_fname(fname) if fix_utc_2014 and start_dt.year == 2014: start_dt -= timedelta(hours=2) gt_frames = [] gen = gen_factory.get_generator(camIdx, start_dt) for frame, (video_frame, video_filename) in zip(fc.frames, gen): gt = {} np_frame = convert_frame_to_numpy(frame) rois, mask, positions = extract_gt_rois(np_frame, video_frame, start_dt) for name in np_frame.dtype.names: gt[name] = np_frame[name][mask] bits = [int_id_to_binary(id)[::-1] for id in gt["decodedId"]] gt["bits"] = 2 * np.array(bits, dtype=np.float) - 1 gt["tags"] = 2 * (rois / 255.).astype(np.float16) - 1 gt['filename'] = os.path.basename(video_filename) gt['camIdx'] = camIdx gt_frames.append(gt) print('.', end='', flush=True) print() gt_period = GTPeriod(camIdx, start_dt, end_dt, fname, gt_frames) periods.append( [int(gt_period.start.timestamp()), int(gt_period.end.timestamp())]) camIdxs.append(gt_period.camIdx) append_gt_to_hdf5(gt_period, dset) dset.attrs['periods'] = np.array(periods) dset.attrs['camIdxs'] = np.array(camIdxs) visualize_detection_tiles(dset, os.path.splitext(output)[0]) dset.close()
def score_ids_best_fit_rotating(ids1, ids2, rotation_penalty=1, length=12): """Compares two lists of ids by choosing the pair best score by rotating the bits. .. deprecated:: September 2016 This scoring is used with data from the *old* Computer Vision Pipeline. Arguments: ids1 (:obj:`list` of int): Iterable with ids as integers as base ids ids2 (:obj:`list` of int): Iterable with ids as integers to compare base to Keyword Arguments: rotation_penalty (Optional float): the penalty that is added for a rotation of 1 to the left or right length (Optional int): number of bits in the bit array used to compare ids Returns: float: Uses Hamming distance of best matching (rotated) pair """ assert len(ids1) > 0 assert len(ids2) > 0 # best case: we already have the same ids in both sets if set(ids1) & set(ids2): return 0 rotation_penalty = float(rotation_penalty) / length best_score = float("inf") for id1 in ids1: id1 = int_id_to_binary(id1, nb_bits=length) for id2 in ids2: id2 = int_id_to_binary(id2, nb_bits=length) # rotate left and right for direction in [-1, 1]: for i in range(int(math.ceil(float(length / 2))) + 1): if i * rotation_penalty >= best_score: break test_score = hamming(id1, np.roll( id2, direction * i)) + i * rotation_penalty if test_score < best_score: best_score = test_score if best_score <= 0: return best_score return best_score
def test_binary_id_to_int(int_bin_mapping): """Test conversion of binary array representation to interger id.""" for int_repr, bin_repr in int_bin_mapping: assert binary_id_to_int(bin_repr) == int_repr # test with floats for int_repr, bin_repr in int_bin_mapping: assert binary_id_to_int(bin_repr / 2) == int_repr # change endianess for int_repr, bin_repr in int_bin_mapping: bin_repr = bin_repr[::-1] assert binary_id_to_int(bin_repr / 2, endian='little') == int_repr # different threshold bit_array = np.arange(0, 1.2, 0.1) assert len(bit_array) == 12 assert np.sum(int_id_to_binary(binary_id_to_int(bit_array))) == 12 - 5 assert np.sum(int_id_to_binary(binary_id_to_int(bit_array, threshold=0))) == 12 assert np.sum(int_id_to_binary(binary_id_to_int(bit_array, threshold=1))) == 2
def test_bit_array_to_int_v(): """Tests the conversion of bit arrays to integers (vectorized).""" expected_ids, detections = [], [] for i in range(2**12): expected_ids.append(i) detections.append(make_detection(beeid=int_id_to_binary(i)[::-1] / 2)) calculated_ids = bit_array_to_int_v(detections) assert len(expected_ids) == len(calculated_ids) assert set(expected_ids) == set(calculated_ids) with pytest.raises(AssertionError): bit_array_to_int_v([make_detection(beeid=[1] * 13)])
def test_binary_id_to_int(int_bin_mapping): """Test conversion of binary array representation to interger id.""" for int_repr, bin_repr in int_bin_mapping: assert binary_id_to_int(bin_repr) == int_repr # test with floats for int_repr, bin_repr in int_bin_mapping: assert binary_id_to_int(bin_repr / 2) == int_repr # change endianess for int_repr, bin_repr in int_bin_mapping: bin_repr = bin_repr[::-1] assert binary_id_to_int(bin_repr / 2, endian='little') == int_repr # different threshold bit_array = np.arange(0, 1.2, 0.1) assert len(bit_array) == 12 assert np.sum(int_id_to_binary(binary_id_to_int(bit_array))) == 12 - 5 assert np.sum(int_id_to_binary(binary_id_to_int(bit_array, threshold=0))) == 12 assert np.sum(int_id_to_binary(binary_id_to_int(bit_array, threshold=1))) == 2
def run(bb_gt_files, video_dir, image_dir, visualize_debug, force, output): """ Converts bb_binary ground truth Cap'n Proto files to hdf5 files and extracts the corresponding rois from videos or images. """ gen_factory = FrameGeneratorFactory(video_dir, image_dir) if force and os.path.exists(output): os.remove(output) dset = HDF5Dataset(output) camIdxs = [] periods = [] for fname in bb_gt_files: fc = load_frame_container(fname) camIdx, start_dt, end_dt = parse_video_fname(fname) basename = os.path.basename(fname) gt_frames = [] print(basename) gen = gen_factory.get_generator(camIdx, start_dt) first = True for frame, (video_frame, video_filename) in zip(fc.frames, gen): gt = {} np_frame = convert_frame_to_numpy(frame) rois, mask, positions = extract_gt_rois(np_frame, video_frame, start_dt) for name in np_frame.dtype.names: gt[name] = np_frame[name][mask] gt["bits"] = np.array( [int_id_to_binary(id)[::-1] for id in gt["decodedId"]]) gt["tags"] = rois gt['filename'] = os.path.basename(video_filename) gt_frames.append(gt) if first and visualize_debug: visualize_detections(gt, positions, video_frame) first = False print('.', end='') gt_period = GTPeriod(camIdx, start_dt, end_dt, fname, gt_frames) periods.append( [int(gt_period.start.timestamp()), int(gt_period.end.timestamp())]) camIdxs.append(gt_period.camIdx) append_gt_to_hdf5(gt_period, dset) dset.attrs['periods'] = np.array(periods) dset.attrs['camIdxs'] = np.array(camIdxs) dset.close()
def run(bb_gt_files, video_dir, image_dir, visualize_debug, force, output): """ Converts bb_binary ground truth Cap'n Proto files to hdf5 files and extracts the corresponding rois from videos or images. """ gen_factory = FrameGeneratorFactory(video_dir, image_dir) if force and os.path.exists(output): os.remove(output) dset = HDF5Dataset(output) camIdxs = [] periods = [] for fname in bb_gt_files: fc = load_frame_container(fname) camIdx, start_dt, end_dt = parse_video_fname(fname) basename = os.path.basename(fname) gt_frames = [] print(basename) gen = gen_factory.get_generator(camIdx, start_dt) first = True for frame, (video_frame, video_filename) in zip(fc.frames, gen): gt = {} np_frame = convert_frame_to_numpy(frame) rois, mask, positions = extract_gt_rois(np_frame, video_frame, start_dt) for name in np_frame.dtype.names: gt[name] = np_frame[name][mask] gt["bits"] = np.array([int_id_to_binary(id)[::-1] for id in gt["decodedId"]]) gt["tags"] = rois gt['filename'] = os.path.basename(video_filename) gt_frames.append(gt) if first and visualize_debug: visualize_detections(gt, positions, video_frame) first = False print('.', end='') gt_period = GTPeriod(camIdx, start_dt, end_dt, fname, gt_frames) periods.append([int(gt_period.start.timestamp()), int(gt_period.end.timestamp())]) camIdxs.append(gt_period.camIdx) append_gt_to_hdf5(gt_period, dset) dset.attrs['periods'] = np.array(periods) dset.attrs['camIdxs'] = np.array(camIdxs) dset.close()