os.path.join(SYNTH_DIR, "fl", r"\1.avi")] ) def fl_to_sim(positions_file, (out_pickle, out_avi)): print "THIS IS", positions_file positions = np.load(positions_file) # frames thing directory = positions_file[:-len('positions.npy')] cf = pickle.load(open(os.path.join(directory, "config.pickle"))) start_f = cf['start_f'] # open the frame tarball tf = tarfile.open(os.path.join(directory, "%08d.tar.gz" % start_f), "r:gz") positions_interp, missing = measure.interpolate(positions) pos_derived = measure.compute_derived(positions_interp) N = len(positions) state = np.zeros(N, dtype=util.DTYPE_STATE) state['x'] = positions_interp['x'] state['y'] = positions_interp['y'] state['phi'] = pos_derived['phi'] state['theta'] = np.pi/2.0 env = util.Environmentz((1.5, 2), (240, 320)) images = simulate.render(env, state[:100]) NOISE = 0
regex(r".+/(.+)/positions.npy$"), [ os.path.join(SYNTH_DIR, "fl", r"\1.pickle"), os.path.join(SYNTH_DIR, "fl", r"\1.avi") ]) def fl_to_sim(positions_file, (out_pickle, out_avi)): print "THIS IS", positions_file positions = np.load(positions_file) # frames thing directory = positions_file[:-len('positions.npy')] cf = pickle.load(open(os.path.join(directory, "config.pickle"))) start_f = cf['start_f'] # open the frame tarball tf = tarfile.open(os.path.join(directory, "%08d.tar.gz" % start_f), "r:gz") positions_interp, missing = measure.interpolate(positions) pos_derived = measure.compute_derived(positions_interp) N = len(positions) state = np.zeros(N, dtype=util.DTYPE_STATE) state['x'] = positions_interp['x'] state['y'] = positions_interp['y'] state['phi'] = pos_derived['phi'] state['theta'] = np.pi / 2.0 env = util.Environmentz((1.5, 2), (240, 320)) images = simulate.render(env, state[:100]) NOISE = 0 new_images = simulate.add_noise_background(images, NOISE, NOISE, [])
np.random.seed(0) cf = pickle.load(open(epoch_config_filename, 'r')) region = pickle.load(open(region_filename, 'r')) env = util.Environmentz(cf['field_dim_m'], cf['frame_dim_pix']) led_params = pickle.load(open(led_params_filename, 'r')) eo_params = measure.led_params_to_EO(cf, led_params) if frame_end > cf['end_f']: frame_end = cf['end_f'] truth = np.load(os.path.join(epoch_dir, 'positions.npy')) truth_interp, missing = measure.interpolate(truth) derived_truth = measure.compute_derived(truth_interp, T_DELTA) frame_pos = np.arange(frame_start, frame_end) # load frames frames = organizedata.get_frames(epoch_dir, frame_pos) FRAMEN = len(frames) coordinates = [] regions = np.zeros((FRAMEN, frames[0].shape[0], frames[0].shape[1]), dtype=np.uint8) point_est_track_data = [] for fi, frame in enumerate(frames):
# convert types vals = dict([(x, []) for x in STATEVARS]) for p in particles: for v in STATEVARS: vals[v].append([s[v] for s in p]) for v in STATEVARS: if v == 'phi': vals[v] = np.array(vals[v]) % (2*np.pi) else: vals[v] = np.array(vals[v]) vals_dict = {} # build up the dictionary of true values derived_truth = measure.compute_derived(truth_interp, T_DELTA, led_sep_m) truth_interp_dict = {'x' : truth_interp['x'], 'y' : truth_interp['y'], 'xdot' : derived_truth['xdot'], 'ydot' : derived_truth['ydot'], 'phi' : derived_truth['phi'] % (2*np.pi), 'theta' : np.abs(derived_truth['theta'] - np.pi/2)} results = {} pylab.figure(figsize=(8, 10)) for vi, v in enumerate(STATEVARS): v_bar = np.average(vals[v], axis=1, weights=weights) if v == "theta" : v_bar = np.abs(v_bar - np.pi / 2)
STATEVARS = ['x', 'y', 'xdot', 'ydot', 'phi', 'theta'] # convert types vals = dict([(x, []) for x in STATEVARS]) for p in particles: for v in STATEVARS: vals[v].append([s[v] for s in p]) for v in STATEVARS: if v == 'phi': vals[v] = np.array(vals[v]) % (2 * np.pi) else: vals[v] = np.array(vals[v]) vals_dict = {} # build up the dictionary of true values derived_truth = measure.compute_derived(truth_interp, T_DELTA, led_sep_m) truth_interp_dict = { 'x': truth_interp['x'], 'y': truth_interp['y'], 'xdot': derived_truth['xdot'], 'ydot': derived_truth['ydot'], 'phi': derived_truth['phi'] % (2 * np.pi), 'theta': np.abs(derived_truth['theta'] - np.pi / 2) } results = {} pylab.figure(figsize=(8, 10)) for vi, v in enumerate(STATEVARS): v_bar = np.average(vals[v], axis=1, weights=weights) if v == "theta": v_bar = np.abs(v_bar - np.pi / 2)
led_params_filename), outfile, epoch, frame_start, frame_end): np.random.seed(0) cf = pickle.load(open(epoch_config_filename, 'r')) region = pickle.load(open(region_filename, 'r')) env = util.Environmentz(cf['field_dim_m'], cf['frame_dim_pix']) led_params = pickle.load(open(led_params_filename, 'r')) eo_params = measure.led_params_to_EO(cf, led_params) if frame_end > cf['end_f']: frame_end = cf['end_f'] truth = np.load(os.path.join(epoch_dir, 'positions.npy')) truth_interp, missing = measure.interpolate(truth) derived_truth = measure.compute_derived(truth_interp, T_DELTA) frame_pos = np.arange(frame_start, frame_end) # load frames frames = organizedata.get_frames(epoch_dir, frame_pos) FRAMEN = len(frames) coordinates = [] regions = np.zeros((FRAMEN, frames[0].shape[0], frames[0].shape[1]), dtype=np.uint8) point_est_track_data = [] for fi, frame in enumerate(frames): abs_frame_index = frame_pos[fi]