forked from ericmjonas/franktrack
/
pftestreal.py
659 lines (535 loc) · 24.3 KB
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pftestreal.py
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import numpy as np
import glob
import scipy.stats
import pandas
import cPickle as pickle
import likelihood
import util2 as util
import model
import template
import time
from matplotlib import pylab
from matplotlib.collections import LineCollection
import cloud
import plotparticles
import os
import organizedata
import videotools
import measure
import video
from ssm import particlefilter as pf
import ssm
import proposals
import filters
import skimage.feature
import datasets
import dettrack
from ruffus import *
T_DELTA = 1/30.
MODEL_CONFIGS = [
# ('le1', {'power' : 1, 'log' : False, 'normalize' : False}),
# ('le2', {'power' : 2, 'log' : False, 'normalize' : False}),
# ('le3', {'power' : 10, 'log' : False, 'normalize' : False}),
# ('le4', {'power' : 1, 'log' : False, 'normalize' : True}),
# ('le5', {'power' : 2, 'log' : False, 'normalize' : True}),
# ('le6', {'power' : 10, 'log' : False, 'normalize' : True}),
# ('le7', {'power' : 1, 'log' : True, 'normalize' : False}),
# ('le1', {'power' : 1.0, 'log' : False,
# 'exp' : False, 'normalize' : True,
# 'dist-thold' : 30}),
# ('le8', {'power' : 0.5, 'log' : True,
# 'exp' : False, 'normalize' : True,
# 'dist-thold' : None,
# 'closest-n' : 5}),
('lc0', {'power' : 2.0,
'mark-min' : 120,
'mark-max' : 240,
'delta_scale' : 255
}),
]
FL_DATA = "data/fl"
TemplateObj = template.TemplateRenderCircleBorder2
def enlarge_sep(eo_params, amount=1.0, front_amount = 1.0, back_amount=1.0):
b = (eo_params[0]*amount, eo_params[1]*front_amount, eo_params[2]*back_amount)
return b
def params():
PARTICLEN = 200
np.random.seed(0)
for posnoise in [0.01]:
for velnoise in [0.05]: # , 0.02, 0.05, 0.10]:
for diode_scale in [1.0, 1.2]:
for epoch, frame_start in datasets.all():
frame_end = frame_start + 100
for pix_threshold in [230]:
for config_name, config_params in MODEL_CONFIGS:
infile = [os.path.join(FL_DATA, epoch),
os.path.join(FL_DATA, epoch, 'config.pickle'),
os.path.join(FL_DATA, epoch, 'region.pickle'),
os.path.join(FL_DATA, epoch, 'led.params.pickle'),
]
outfile = 'particles.%s.%s.%3.3f.%3.3f.%3.3f.%d.%d.%d-%d.npz' % (epoch, config_name, posnoise,
velnoise, diode_scale, pix_threshold,
PARTICLEN, frame_start, frame_end)
yield (infile, outfile, epoch,
(config_name, config_params),
posnoise, velnoise, pix_threshold,
PARTICLEN, frame_start, frame_end, diode_scale)
class CombinedLE(object):
def __init__(self, LEs, weights):
self.LEs = LEs
self.weights = weights
def score_state(self, state, img):
score = 0.0
for le, weight in zip(self.LEs, self.weights):
score += weight * le.score_state(state, img)
return score
@files(params)
def pf_run((epoch_dir, epoch_config_filename,
region_filename, led_params_filename), outfile,
epoch, (config_name, config_params),
posnoise,
velnoise, pix_threshold, PARTICLEN,
frame_start, frame_end, diode_scale):
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'))
eoparams = enlarge_sep(measure.led_params_to_EO(cf, led_params),
amount = diode_scale,
front_amount = diode_scale, back_amount = diode_scale)
#print "EO PARAMS ARE", eoparams
tr = TemplateObj(0.8, 0.4)
tr.set_params(*eoparams)
le1 = likelihood.LikelihoodEvaluator2(env, tr, similarity='dist',
likeli_params = config_params)
model_inst = model.CustomModel(env, le1,
POS_NOISE_STD=posnoise,
VELOCITY_NOISE_STD=velnoise)
frame_pos = np.arange(frame_start, frame_end)
# load frames
frames = organizedata.get_frames(epoch_dir, frame_pos)
y = frames
prop2 = proposals.HigherIsotropic()
def img_to_points(img):
return dettrack.point_est_track2(img, env, eoparams)
prop3 = proposals.MultimodalData(env, img_to_points, prop2)
mpk = ssm.proposal.MixtureProposalKernel([prop2, prop3],
[0.5, 0.5])
unnormed_weights, particles, ancestors = pf.arbitrary_prop(y, model_inst,
mpk,
PARTICLEN)
np.savez_compressed(outfile,
frame_pos = frame_pos,
unnormed_weights=unnormed_weights,
particles=particles,
ancestors=ancestors)
def params_rendered():
for p in params():
yield ((p[0][0], p[0][1], p[1]), (p[1] + ".png",
p[1] + ".xy.pdf",
p[1] + ".examples.png",
p[1] + ".stats.pickle"),
p[2:])
@follows(pf_run)
@files(params_rendered)
def pf_plot((epoch_dir, epoch_config_filename, particles_file),
(all_plot_filename, just_xy_filename, examples_filename,
results_pickle), other_params):
a = np.load(particles_file)
frame_pos = a['frame_pos']
weights = ssm.util.norm_weights(a['unnormed_weights'])
particles = a['particles']
N = len(particles)
led_params_filename = os.path.join(epoch_dir, "led.params.pickle")
cf = pickle.load(open(epoch_config_filename))
truth = np.load(os.path.join(epoch_dir, 'positions.npy'))
env = util.Environmentz(cf['field_dim_m'],
cf['frame_dim_pix'])
led_params = pickle.load(open(led_params_filename, 'r'))
eoparams = enlarge_sep(measure.led_params_to_EO(cf, led_params))
tr = TemplateObj()
tr.set_params(*eoparams)
led_sep_pix = eoparams[0]
led_sep_m = led_sep_pix / env.gc.pix_per_meter[0]
truth_interp, missing = measure.interpolate(truth)
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)
vals_dict[v] = v_bar
v_truth_interp = truth_interp_dict[v][frame_pos]
x = frame_pos
cred = np.zeros((len(x), 2), dtype=np.float)
v_range = np.zeros((len(x), 2), dtype=np.float)
if v == "theta":
for ci, (p, w) in enumerate(zip(vals[v], weights)):
p_n = np.abs(p -np.pi/2)
cred[ci] = util.credible_interval(p_n, w)
v_range[ci] = util.range(p_n)
else:
for ci, (p, w) in enumerate(zip(vals[v], weights)):
cred[ci] = util.credible_interval(p, w)
v_range[ci] = util.range(p)
ax = pylab.subplot(len(STATEVARS) + 1,1, 1+vi)
# plot the estimate and cred interval
ax.plot(x, v_bar, color='b')
ax.fill_between(x, cred[:, 0],
cred[:, 1], facecolor='b',
alpha=0.4)
ax.plot(x, v_range[:, 0],
c = 'r')
ax.plot(x, v_range[:, 1],
c = 'r')
# plot truth
ax.plot(x, v_truth_interp,
linewidth=1, c='k')
if v != 'theta':
ax.set_ylim(np.min(v_truth_interp)-0.1,
np.max(v_truth_interp) + 0.1)
results[v] = {'pfmean' : v_bar,
'pfcred' : cred,
'pfrange' : v_range,
'truth' : v_truth_interp}
ax.grid(1)
# this should probably be a separate chunk of the pipeline,
# but whatever
pickle.dump({'params' : other_params,
'variables' : results},
open(results_pickle, 'w'))
pylab.subplot(len(STATEVARS) + 1, 1, len(STATEVARS)+1)
# now plot the # of particles consuming 95% of the prob mass
real_particle_num = np.zeros((len(weights),
2), dtype=np.float32)
for wi, wgts in enumerate(weights):
wgts_idx = np.argsort(wgts)[::-1] # sort, reverse order
wgts_sorted = wgts[wgts_idx]
wcs = np.cumsum(wgts_sorted)
wcsi = np.searchsorted(wcs, 0.95)+1
# then for each of these particles, count their origin
for i in range(wcsi):
particle_proposal_src = particles[wi][i]['meta']
real_particle_num[wi, particle_proposal_src] += 1
pylab.fill_between(frame_pos, np.zeros(len(frame_pos)),
real_particle_num[:, 0], color='b')
pylab.fill_between(frame_pos, real_particle_num[:, 0],
real_particle_num[:, 0] + real_particle_num[:, 1], color='r')
pylab.savefig(all_plot_filename, dpi=400)
f2 = pylab.figure(figsize=(16, 8))
for vi, v in enumerate(['x', 'y']):
v_bar = np.average(vals[v], axis=1, weights=weights)
truth_interp = truth_interp_dict[v][frame_pos]
x = frame_pos
cred = np.zeros((len(x), 2), dtype=np.float)
for ci, (p, w) in enumerate(zip(vals[v], weights)):
cred[ci] = util.credible_interval(p, w)
ax = pylab.subplot(2, 1, vi+1)
ax.plot(x, v_bar, color='b')
ax.fill_between(x, cred[:, 0],
cred[:, 1], facecolor='b',
alpha=0.4)
ax.plot(x, truth_interp,
linewidth=1, c='k')
for i in np.argwhere(np.isnan(truth[v][frame_pos])):
ax.axvline(i, c='r', linewidth=0.1, alpha=0.5)
ax.grid(1)
ax.set_xlim((np.min(x), np.max(x)))
ax.set_ylim((np.min(truth_interp),
np.max(truth_interp)))
ax.set_xlabel("time (frames)")
ax.set_ylabel("position (m)")
f2.suptitle(just_xy_filename)
pylab.savefig(just_xy_filename, dpi=300)
# find the errors per point
PLOT_ERRORS = 12
errors = np.zeros(len(vals), dtype=np.float32)
deltas = np.sqrt((vals_dict['x'] - truth['x'][frame_pos])**2 + \
(vals_dict['y'] - truth['y'][frame_pos])**2)
deltas[np.isnan(deltas)] = -1 # only to find the Nans and make sorting work
# find index of errors in decreasing order
error_i = np.argsort(deltas)[::-1]
errs = error_i[:PLOT_ERRORS]
framepos_errs = frame_pos[errs]
error_frames = organizedata.get_frames(epoch_dir,
framepos_errs)
f = pylab.figure()
WINDOW_PIX = 30
for plot_error_i, error_frame_i in enumerate(errs):
abs_frame = framepos_errs[plot_error_i]
ax = pylab.subplot(3, 4, plot_error_i + 1)
ax.imshow(error_frames[plot_error_i],
interpolation='nearest', cmap=pylab.cm.gray)
true_x = truth['x'][abs_frame]
true_y = truth['y'][abs_frame]
true_x_pix, true_y_pix = env.gc.real_to_image(true_x, true_y)
est_x = vals_dict['x'][error_frame_i]
est_y = vals_dict['y'][error_frame_i]
est_phi = vals_dict['phi'][error_frame_i]
est_theta = vals_dict['theta'][error_frame_i]
est_x_pix, est_y_pix = env.gc.real_to_image(est_x, est_y)
rendered_img = tr.render(est_phi, est_theta)
# now compute position of diodes
front_pos, back_pos = util.compute_pos(tr.length, est_x_pix,
est_y_pix,
est_phi, est_theta)
cir = pylab.Circle(front_pos, radius=eoparams[1],
ec='g', fill=False,
linewidth=2)
ax.add_patch(cir)
cir = pylab.Circle(back_pos, radius=eoparams[2],
ec='r', fill=False,
linewidth=2)
ax.add_patch(cir)
# true
ax.axhline(true_y_pix, c='b')
ax.axvline(true_x_pix, c='b')
# extimated
ax.axhline(est_y_pix, c='y')
ax.axvline(est_x_pix, c='y')
# LED points in ground truth
for field_name, color in [('led_front', 'g'), ('led_back', 'r')]:
lpx, lpy = env.gc.real_to_image(*truth[field_name][abs_frame])
ax.scatter([lpx], [lpy], c=color)
ax.set_xlim((true_x_pix - WINDOW_PIX, true_x_pix + WINDOW_PIX))
ax.set_ylim((true_y_pix - WINDOW_PIX, true_y_pix + WINDOW_PIX))
ax.set_xticks([])
ax.set_yticks([])
ax.set_title(framepos_errs[plot_error_i])
pylab.savefig(examples_filename, dpi=300)
def params_render_vid():
for p in params():
yield ((p[0][0], p[0][1], p[1]), (p[1] + ".avi",), p[3], p[10])
@follows(pf_run)
@files(params_render_vid)
def pf_render_vid((epoch_dir, epoch_config_filename, particles_file),
(vid_filename,), (config_name, config_params),
diode_scale):
a = np.load(particles_file)
frame_pos = a['frame_pos']
weights = ssm.util.norm_weights(a['unnormed_weights'])
particles = a['particles']
N = len(particles)
led_params_filename = os.path.join(epoch_dir, "led.params.pickle")
cf = pickle.load(open(epoch_config_filename))
truth = np.load(os.path.join(epoch_dir, 'positions.npy'))
env = util.Environmentz(cf['field_dim_m'],
cf['frame_dim_pix'])
led_params = pickle.load(open(led_params_filename, 'r'))
eoparams = enlarge_sep(measure.led_params_to_EO(cf, led_params),
front_amount = diode_scale, back_amount = diode_scale)
tr = TemplateObj()
tr.set_params(*eoparams)
region_size_thold = (tr.front_size + tr.back_size ) * 2.5
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:
vals[v] = np.array(vals[v])
vals_dict = {}
for vi, v in enumerate(STATEVARS):
v_bar = np.average(vals[v], axis=1, weights=weights)
vals_dict[v] = v_bar
frames = organizedata.get_frames(epoch_dir,
frame_pos)
truth, missing = measure.interpolate(truth)
derived_truth = measure.compute_derived(truth,
T_DELTA)
WINDOW_PIX = 40
f = pylab.figure()
ax_est = pylab.subplot(2,3, 2)
ax_particles = pylab.subplot(2, 3, 3)
ax_particles_global = pylab.subplot(2, 3, 4)
ax_rawvid = pylab.subplot(2, 3, 5)
ax_filt = pylab.subplot(2, 3, 6)
ax_scale_bars = pylab.subplot(2,3, 1)
plot_temp_filenames = []
for fi in range(N):
abs_frame = frame_pos[fi] # absolute frame position
ax_est.clear()
ax_particles.clear()
ax_rawvid.clear()
ax_filt.clear()
ax_particles_global.clear()
ax_scale_bars.clear()
true_x = truth['x'][abs_frame]
true_y = truth['y'][abs_frame]
true_phi = derived_truth['phi'][abs_frame]
true_x_pix, true_y_pix = env.gc.real_to_image(true_x, true_y)
est_x = vals_dict['x'][fi]
est_y = vals_dict['y'][fi]
est_phi = vals_dict['phi'][fi]
est_theta = vals_dict['theta'][fi]
est_x_pix, est_y_pix = env.gc.real_to_image(est_x, est_y)
# now compute position of diodes
front_pos, back_pos = util.compute_pos(tr.length, est_x_pix,
est_y_pix,
est_phi, est_theta)
regions = filters.extract_region_filter(frames[fi], region_size_thold,
config_params['mark-min'],
config_params['mark-max'])
ax_est.imshow(regions > 0,
interpolation='nearest', cmap=pylab.cm.gray)
ax_scale_bars.imshow(frames[fi].copy(),
interpolation='nearest', cmap=pylab.cm.gray)
ax_rawvid.imshow(frames[fi].copy(),
interpolation='nearest', cmap=pylab.cm.gray)
# filtered image
ax_filt.imshow(regions,
interpolation='nearest') # , cmap=pylab.cm.gray)
#ax_filt.plot([p[1] for p in coordinates], [p[0] for p in coordinates], 'r.')
ax_filt.set_title('ax_filt')
ax_particles.imshow(frames[fi],
interpolation='nearest', cmap=pylab.cm.gray)
ax_particles_global.imshow(frames[fi].copy(),
interpolation='nearest', cmap=pylab.cm.gray)
# plot the circles
cir = pylab.Circle(front_pos[:2], radius=eoparams[1],
ec='g', fill=False,
linewidth=2)
ax_est.add_patch(cir)
cir_back = pylab.Circle(back_pos[:2], radius=eoparams[2],
ec='r', fill=False,
linewidth=2)
ax_est.add_patch(cir_back)
# true
ax_est.axhline(true_y_pix, c='b')
ax_est.axvline(true_x_pix, c='b')
# extimated
ax_est.axhline(est_y_pix, c='y')
ax_est.axvline(est_x_pix, c='y')
# true
ax_rawvid.axhline(true_y_pix, c='b')
ax_rawvid.axvline(true_x_pix, c='b')
# extimated
ax_rawvid.axhline(est_y_pix, c='y')
ax_rawvid.axvline(est_x_pix, c='y')
# LED points in ground truth
for field_name, color in [('led_front', 'g'), ('led_back', 'r')]:
lpx, lpy = env.gc.real_to_image(*truth[field_name][abs_frame])
ax_est.scatter([lpx], [lpy], c=color)
# now all particles
particle_pix_pts = np.zeros((len(particles[fi]), 2, 3), dtype=np.float)
particle_sources = particles[fi]['meta']
for pi in range(len(particles[fi])):
lpx, lpy = env.gc.real_to_image(particles[fi, pi]['x'],
particles[fi, pi]['y'])
front_pos, back_pos = util.compute_pos(tr.length, lpx, lpy,
particles[fi, pi]['phi'],
particles[fi, pi]['theta'])
particle_pix_pts[pi, 0] = front_pos
particle_pix_pts[pi, 1] = back_pos
# ax_particles.scatter(particle_pix_pts[:, 0, 0],
# particle_pix_pts[:, 0, 1],
# linewidth=0,
# alpha = 1.0, c='g', s=1)
# ax_particles.scatter(particle_pix_pts[:, 1, 0],
# particle_pix_pts[:, 1, 1],
# linewidth=0,
# alpha = 1.0, c='r', s=1)
lcp = np.array((zip(particle_pix_pts[:, 0, :2], particle_pix_pts[:, 1, :2])))
lct = LineCollection(lcp[particle_sources == 0], alpha=0.1, color='b')
ax_particles.add_collection(lct)
lcd = LineCollection(lcp[particle_sources == 1], alpha=0.1, color='r')
ax_particles.add_collection(lcd)
# ax_particles.scatter(particle_pix_pts[:, 0, 0],
# particle_pix_pts[:, 0, 1],
# linewidth=0,
# alpha = 1.0, c='g', s=1)
# ax_particles.scatter(particle_pix_pts[:, 1, 0],
# particle_pix_pts[:, 1, 1],
# linewidth=0,
# alpha = 1.0, c='r', s=1)
ax_particles_global.scatter(particle_pix_pts[:, 0, 0],
particle_pix_pts[:, 0, 1],
linewidth=0,
alpha = 1.0, c='g', s=2)
ax_particles_global.scatter(particle_pix_pts[:, 1, 0],
particle_pix_pts[:, 1, 1],
linewidth=0,
alpha = 1.0, c='r', s=2)
for ax in [ax_est, ax_particles, ax_scale_bars]:
ax.set_xlim((true_x_pix - WINDOW_PIX, true_x_pix + WINDOW_PIX))
ax.set_ylim((true_y_pix - WINDOW_PIX, true_y_pix + WINDOW_PIX))
ax.set_xticks([])
ax.set_yticks([])
ax.set_title(frame_pos[fi])
bar_offset_x = 5 + true_x_pix - WINDOW_PIX
bar_offset_y = true_y_pix - WINDOW_PIX
ax_scale_bars.plot([bar_offset_x,
eoparams[0] + bar_offset_x],
[bar_offset_y + 5, bar_offset_y + 5],
c='b', linewidth=5)
ax_scale_bars.plot([bar_offset_x,
eoparams[1]*2 + bar_offset_x],
[bar_offset_y + 10, bar_offset_y + 10],
c='g', linewidth=3)
ax_scale_bars.plot([bar_offset_x,
eoparams[2]*2 + bar_offset_x],
[bar_offset_y + 15, bar_offset_y + 15],
c='r', linewidth=3)
ax_rawvid.set_xticks([])
ax_rawvid.set_yticks([])
ax_filt.set_xticks([])
ax_filt.set_yticks([])
ax_particles_global.set_xticks([])
ax_particles_global.set_yticks([])
plot_filename = "%s.%08d.png" % (vid_filename, fi)
f.savefig(plot_filename, dpi=200)
plot_temp_filenames.append(plot_filename)
video.frames_to_mpng("%s.*.png" % vid_filename, vid_filename, fps = float(len(plot_temp_filenames))/10.0)
# delete extras
for f in plot_temp_filenames:
os.remove(f)
@merge(pf_plot, 'particles.summary.pickle')
def results_summarize(infiles, summary_file):
df_rows = []
for infile in infiles:
stats_filename = infile[-1]
data = pickle.load(open(stats_filename, 'r'))
for var_name, var_data in data['variables'].iteritems():
dp = data['params']
d = {'epoch' : dp[0],
'likelihood_name' : dp[1][0],
'posnoise' : dp[2],
'velnoise' : dp[3],
'pix_threshold' : dp[4],
'particlen' : dp[5],
'frame_start' : dp[6],
'frame_end' : dp[7],
'variable' : var_name,
'pfmean' : var_data['pfmean'],
'pfcred' : var_data['pfcred'],
'truth' : var_data['truth']}
df_rows.append(d)
df = pandas.DataFrame(df_rows)
pickle.dump(df, open(summary_file, 'w'))
pipeline_run([pf_run, pf_plot, pf_render_vid,
results_summarize], multiprocess=4)