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deterministic_analysis.py
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deterministic_analysis.py
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import numpy as np
import glob
import scipy.stats
import pandas
import cPickle as pickle
import util2 as util
import organizedata
import measure
import time
from matplotlib import pylab
import os
import skimage.feature
import datasets
import dettrack
import filters
import model
from ruffus import *
T_DELTA = 1/30.
FL_DATA = "data/fl"
def clear_ticks(ax):
ax.set_xticks([])
ax.set_yticks([])
def params():
# EPOCHS = ['bukowski_05.W1',
# 'bukowski_02.W1',
# 'bukowski_02.C',
# 'bukowski_04.W1',
# 'bukowski_04.W2',
# 'bukowski_01.linear',
# 'bukowski_01.W1',
# 'bukowski_01.C',
# 'bukowski_05.linear',
# 'Cummings_03.w2',
# 'Cummings_03.linear',
# 'Dickinson_02.c',
# 'H206.1',
# 'H206.2'
# ]
EPOCHS = datasets.CURRENT_EPOCHS
np.random.seed(0)
FRAMES = datasets.CURRENT_FRAMES
# for epoch in EPOCHS:
# for frame_start, frame_end in FRAMES:
MAX = 500
pos = 0
for epoch, frame_start in datasets.current():
frame_end = frame_start + 100
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 = 'deterministic.%s.%d-%d.pickle' % (epoch, frame_start, frame_end)
yield (infile, outfile, epoch,
frame_start, frame_end)
pos += 1
if pos > MAX:
return
@files(params)
def det_run((epoch_dir, epoch_config_filename,
region_filename, 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]
coordinates.append(skimage.feature.peak_local_max(frame,
min_distance=30,
threshold_abs=220))
frame_regions = filters.label_regions(frame, mark_min=120, mark_max=220)
regions[fi] = frame_regions
point_est_track_data.append(dettrack.point_est_track2(frame, env, eo_params))
pickle.dump({'frame_pos' : frame_pos,
'coordinates' : coordinates,
'regions' : regions,
'eo_params' : eo_params,
'point_est_track' : point_est_track_data,
'truth' : truth,
'missing' : missing,
'truth_interp' : truth_interp,
'derived_truth' : derived_truth,
'epoch' : epoch,
'frame_start' : frame_start},
open(outfile, 'w'))
def params_rendered():
for p in params():
yield ((p[0][0], p[0][1], p[1]), (p[1] + ".png",
),
p[2:])
@follows(det_run)
@files(params_rendered)
def det_plot((epoch_dir, epoch_config_filename, results),
(all_plot_filename,), other_params):
data = np.load(results)
frame_pos = data['frame_pos']
FRAMEN = len(frame_pos)
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'])
frames = organizedata.get_frames(epoch_dir, frame_pos)
truth_interp, missing = measure.interpolate(truth)
derived_truth = measure.compute_derived(truth_interp,
T_DELTA)
f1 = pylab.figure(figsize=(32, 8))
ROWN = 7
FRAME_SUBSAMPLE = int(np.ceil(FRAMEN / 30.0))
ax_frames = f1.add_subplot(ROWN, 1, 1)
ax_truth = f1.add_subplot(ROWN, 1, 2)
ax_point_cnt = f1.add_subplot(ROWN, 1, 3)
ax_phi_theta = f1.add_subplot(ROWN, 1, 4)
ax_coord_filt_mean = f1.add_subplot(ROWN, 1, 5)
ax_coord_cnt = f1.add_subplot(ROWN, 1, 6)
ax_img_pos = f1.add_subplot(ROWN, 1, 7)
a = np.hstack(frames[::FRAME_SUBSAMPLE])
ax_frames.imshow(a, interpolation = 'nearest',
vmin=0, vmax=255, cmap=pylab.cm.gray)
clear_ticks(ax_frames)
ax_truth.plot(frame_pos, truth_interp[frame_pos]['x'], label='x')
ax_truth.plot(frame_pos, truth_interp[frame_pos]['y'], label='y')
coordinates = data['coordinates']
regions = data['regions']
eo_params = data['eo_params']
filtered_regions = np.zeros_like(data['regions'])
point_est_track = data['point_est_track']
region_props = []
max_dim = (eo_params[1] + eo_params[2])*2*1.2
for fi, f in enumerate(frame_pos):
filtered_regions[fi] = filters.filter_regions(regions[fi],
max_width = max_dim,
max_height = max_dim)
fc = filters.points_in_mask(filtered_regions[fi] > 0,
coordinates[fi])
region_props.append(skimage.measure.regionprops((filtered_regions[fi]>0).astype(int)))
### how many coordinate points
#ax_point_cnt.plot(frame_pos, [len(x) for x in filtered_coordinates])
ax_point_cnt.plot(frame_pos, [len(x) for x in coordinates])
ax_phi_theta.plot(frame_pos, derived_truth['phi'][frame_pos] % (2*np.pi))
coord = point_est_track
cand_pts_per_frame = np.array([p.shape[0] for p in coord])
have_est = cand_pts_per_frame > 0
def expand_index(inarray, cnt):
assert len(inarray) == len(cnt)
N_idx = np.sum(cnt)
idx_out = np.zeros(N_idx, dtype=np.uint32)
pos = 0
for i in range(len(inarray)):
for j in range(cnt[i]):
idx_out[pos] = inarray[i]
pos += 1
return idx_out
frame_pos_pres = frame_pos[have_est]
frame_pos_abs = frame_pos[np.logical_not(have_est)]
frame_pos_expanded = expand_index(frame_pos, cand_pts_per_frame)
coord_x = np.hstack([c['x'] for c in coord])
coord_y = np.hstack([c['y'] for c in coord])
ax_coord_cnt.plot(frame_pos, [len(p) for p in coord])
# plot x/y
ax_coord_filt_mean.plot(frame_pos, truth_interp[frame_pos]['x'], c='b')
ax_coord_filt_mean.plot(frame_pos, truth_interp[frame_pos]['y'], c='r')
ax_coord_filt_mean.scatter(frame_pos_expanded, coord_x, c='b', linewidth=1, s=7,
facecolors=None)
ax_coord_filt_mean.scatter(frame_pos_expanded, coord_y, c='r', linewidth=1, s=7,
facecolors=None)
for f in frame_pos_abs:
ax_coord_filt_mean.axvline(f, c='k', alpha=0.5)
ax_coord_filt_mean.set_xlim(np.min(frame_pos), np.max(frame_pos))
#plot phi
ax_phi_theta.scatter(frame_pos_expanded,
np.hstack([c['phi'] for c in coord]) % (2*np.pi))
ax_phi_theta.set_xlim(np.min(frame_pos), np.max(frame_pos))
# plot the detected points
a = np.hstack(filtered_regions[::FRAME_SUBSAMPLE])
FRAME_W = frames[0].shape[1]
a_th = (a > 0 ).astype(np.uint8)*255
a[:, ::FRAME_W] = 200
ax_img_pos.imshow(a>0, interpolation='nearest', vmin=0, vmax=1, cmap=pylab.cm.gray)
for pi, point in enumerate(point_est_track[::FRAME_SUBSAMPLE]):
# convert back to pix
for x, y in zip(point['x'], point['y']):
pix_x, pix_y = env.gc.real_to_image(x, y)
ax_img_pos.plot([FRAME_W * pi + pix_x],
[pix_y], 'r.', ms=1.0)
ax_img_pos.set_xlim([0, a.shape[1]])
ax_img_pos.set_ylim([0, a.shape[0]])
ax_img_pos.set_xticks([])
ax_img_pos.set_yticks([])
pylab.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
pylab.savefig(all_plot_filename, dpi=300)
@merge(det_run, 'deterministic.results.pickle')
def aggregate_results(det_run, outfilename):
summary = {}
for infile in det_run:
print "opening", infile
d = pickle.load(open(infile))
epoch = d['epoch']
point_est_track = d['point_est_track']
frame_pos = d['frame_pos']
have_est = np.array([p.shape[0] > 0 for p in point_est_track])
frame_pos_pres = frame_pos[have_est]
frame_pos_abs = frame_pos[np.logical_not(have_est)]
frame_idx_pres = np.argwhere(have_est).flatten()
coord_x = np.array([point_est_track[i][0]['x'] for i in frame_idx_pres])
true_x = d['truth_interp'][frame_pos_pres]['x']
coord_y = np.array([point_est_track[i][0]['y'] for i in frame_idx_pres])
true_y = d['truth_interp'][frame_pos_pres]['y']
error_xy = np.sqrt((coord_x-true_x)**2 + (coord_y-true_y)**2)
# print error_xy.shape, error_xy.dtype, np.mean(error_xy), np.max(error_xy)
# pylab.subplot(3, 1, 1)
# pylab.plot(frame_pos_pres, error_xy)
# pylab.subplot(3, 1, 2)
# pylab.plot(frame_pos_pres, coord_x)
# pylab.plot(frame_pos_pres, true_x)
# pylab.subplot(3, 1, 3)
# pylab.plot(frame_pos_pres, coord_y)
# pylab.plot(frame_pos_pres, true_y)
# pylab.show()
error_xy_mean = np.mean(error_xy)
error_xy_median = np.median(error_xy)
if len(error_xy) > 0:
error_xy_max = np.max(error_xy)
else:
error_xy_max = 0.0
summary["%s.%d" % (epoch, frame_pos[0])] = {'frame_pos_pres' : len(frame_pos_pres),
'frame_n' : len(frame_pos),
'error_xy_mean' : error_xy_mean,
'error_xy_median' : error_xy_median,
'error_xy_max' : error_xy_max}
pickle.dump(summary, open(outfilename, 'w'))
@files(aggregate_results, ('deterministic.fractions.png',
'deterministic.frac_vs_err.png',
'deterministic.report.txt'))
def plot_agg(infile, (fractions_filename, frac_vs_err_filename,
report_filename)):
aggdata = pickle.load(open(infile, 'r'))
df = pandas.DataFrame.from_dict(aggdata, orient='index')
df['frac'] = df['frame_pos_pres'] / df['frame_n'].astype(float)
print df['frac']
pylab.figure()
df2 = df.sort('frac', ascending=False)
pylab.plot(df2['frac'])
pylab.savefig(fractions_filename)
fig = pylab.figure(figsize=(12, 6))
ax = fig.add_subplot(1, 3, 1)
ax.scatter(df['frac'], df['error_xy_mean'])
ax.set_yscale('log')
ax = fig.add_subplot(1, 3, 2)
ax.scatter(df['frac'], df['error_xy_median'])
ax.set_yscale('log')
ax = fig.add_subplot(1, 3, 3)
ax.scatter(df['frac'], df['error_xy_max'])
ax.set_yscale('log')
pylab.savefig(frac_vs_err_filename, dpi=300)
df2 = df.sort('error_xy_mean')
open(report_filename, 'w').write(df2.to_string())
pipeline_run([det_run, det_plot,
aggregate_results,
plot_agg])# , multiprocess=4)