def average_diode_sep():
clust_eps = 0.2
min_dist = 2.0
min_samples = 3.0
thold = 240
FRAMES = np.arange(4000)*2
dataset = "bukowski_02.C"
cf = pickle.load(open(os.path.join(ddir(dataset), 'config.pickle')))
region = pickle.load(open(os.path.join(ddir(dataset), 'region.pickle')))
env = util.Environmentz(cf['field_dim_m'], cf['frame_dim_pix'])
x_min, y_min = env.gc.real_to_image(region['x_pos_min'], region['y_pos_min'])
x_max, y_max = env.gc.real_to_image(region['x_pos_max'], region['y_pos_max'])
print x_min, x_max
print y_min, y_max
if y_min < 0:
y_min = 0
frame_images = organizedata.get_frames(ddir(dataset), FRAMES)
num_clusters = np.zeros(len(FRAMES))
dists = []
for fi, im in enumerate(frame_images):
im = im[y_min:y_max+1, x_min:x_max+1]
centers = frame_clust_points(im, 240, min_dist,
clust_eps, min_samples)
num_clusters[fi] = len(centers)
if len(centers) == 2:
dists.append(distance.pdist(centers)[0])
dists = np.array(dists)
pylab.hist(dists[dists < 50], bins=20)
pylab.savefig("average_diode_sep.%s.png" % dataset, dpi=300)
def find_params():
FRAMES = np.arange(30)*100
frame_images = organizedata.get_frames(ddir("bukowski_04.W2"), FRAMES)
print "DONE READING DATA"
CLUST_EPS = np.linspace(0, 0.5, 10)
MIN_SAMPLES = [2, 3, 4, 5]
MIN_DISTS = [2, 3, 4, 5, 6]
THOLD = 240
fracs_2 = np.zeros((len(CLUST_EPS), len(MIN_SAMPLES), len(MIN_DISTS)))
for cei, CLUST_EP in enumerate(CLUST_EPS):
for msi, MIN_SAMPLE in enumerate(MIN_SAMPLES):
for mdi, MIN_DIST in enumerate(MIN_DISTS):
print cei, msi, mdi
numclusters = np.zeros(len(FRAMES))
for fi, im in enumerate(frame_images):
centers = frame_clust_points(im, THOLD, MIN_DIST,
CLUST_EP, MIN_SAMPLE)
# cluster centers
numclusters[fi] = len(centers)
fracs_2[cei, msi, mdi] = float(np.sum(numclusters == 2))/len(numclusters)
pylab.figure(figsize=(12, 8))
for mdi, MIN_DIST in enumerate(MIN_DISTS):
pylab.subplot(len(MIN_DISTS), 1, mdi+1)
for msi in range(len(MIN_SAMPLES)):
pylab.plot(CLUST_EPS, fracs_2[:, msi, mdi], label='%d' % MIN_SAMPLES[msi])
pylab.title("min_dist= %3.2f" % MIN_DIST)
pylab.legend()
pylab.savefig('test.png', dpi=300)
def average_diode_sep():
clust_eps = 0.2
min_dist = 2.0
min_samples = 3.0
thold = 240
FRAMES = np.arange(4000) * 2
dataset = "bukowski_02.C"
cf = pickle.load(open(os.path.join(ddir(dataset), 'config.pickle')))
region = pickle.load(open(os.path.join(ddir(dataset), 'region.pickle')))
env = util.Environmentz(cf['field_dim_m'], cf['frame_dim_pix'])
x_min, y_min = env.gc.real_to_image(region['x_pos_min'],
region['y_pos_min'])
x_max, y_max = env.gc.real_to_image(region['x_pos_max'],
region['y_pos_max'])
print x_min, x_max
print y_min, y_max
if y_min < 0:
y_min = 0
frame_images = organizedata.get_frames(ddir(dataset), FRAMES)
num_clusters = np.zeros(len(FRAMES))
dists = []
for fi, im in enumerate(frame_images):
im = im[y_min:y_max + 1, x_min:x_max + 1]
centers = frame_clust_points(im, 240, min_dist, clust_eps, min_samples)
num_clusters[fi] = len(centers)
if len(centers) == 2:
dists.append(distance.pdist(centers)[0])
dists = np.array(dists)
pylab.hist(dists[dists < 50], bins=20)
pylab.savefig("average_diode_sep.%s.png" % dataset, dpi=300)
def find_params():
FRAMES = np.arange(30) * 100
frame_images = organizedata.get_frames(ddir("bukowski_04.W2"), FRAMES)
print "DONE READING DATA"
CLUST_EPS = np.linspace(0, 0.5, 10)
MIN_SAMPLES = [2, 3, 4, 5]
MIN_DISTS = [2, 3, 4, 5, 6]
THOLD = 240
fracs_2 = np.zeros((len(CLUST_EPS), len(MIN_SAMPLES), len(MIN_DISTS)))
for cei, CLUST_EP in enumerate(CLUST_EPS):
for msi, MIN_SAMPLE in enumerate(MIN_SAMPLES):
for mdi, MIN_DIST in enumerate(MIN_DISTS):
print cei, msi, mdi
numclusters = np.zeros(len(FRAMES))
for fi, im in enumerate(frame_images):
centers = frame_clust_points(im, THOLD, MIN_DIST, CLUST_EP,
MIN_SAMPLE)
# cluster centers
numclusters[fi] = len(centers)
fracs_2[cei, msi, mdi] = float(
np.sum(numclusters == 2)) / len(numclusters)
pylab.figure(figsize=(12, 8))
for mdi, MIN_DIST in enumerate(MIN_DISTS):
pylab.subplot(len(MIN_DISTS), 1, mdi + 1)
for msi in range(len(MIN_SAMPLES)):
pylab.plot(CLUST_EPS,
fracs_2[:, msi, mdi],
label='%d' % MIN_SAMPLES[msi])
pylab.title("min_dist= %3.2f" % MIN_DIST)
pylab.legend()
pylab.savefig('test.png', dpi=300)
d = np.zeros(FRAMES_TO_ANALYZE, dtype=DTYPE_POS_CONF)
FRAMES_AT_A_TIME = 10
frames = np.arange(FRAMES_TO_ANALYZE)
for frame_subset in util.chunk(frames, FRAMES_AT_A_TIME):
fs = organizedata.get_frames(basedir, frame_subset)
for fi, frame_no in enumerate(frame_subset):
real_x, real_y, conf = func(fs[fi], env, **config)
d[frame_no]['x'] = real_x
d[frame_no]['y'] = real_y
d[frame_no]['confidence'] = conf
return d
@transform(ddir("*/positions.npy"),
regex(r".+/(.+)/positions.npy$"),
[os.path.join(REPORT_DIR,
r"\1", "truth.npy")],
r"\1"
)
def get_truth(positions_file, (output_file, ), basedir):
truth_data = truth(ddir(basedir))
try:
os.makedirs(rdir(basedir))
except OSError:
pass
np.save(output_file, truth_data)
def algodir(basedir):
try:
d = np.zeros(FRAMES_TO_ANALYZE, dtype=DTYPE_POS_CONF)
FRAMES_AT_A_TIME = 10
frames = np.arange(FRAMES_TO_ANALYZE)
for frame_subset in util.chunk(frames, FRAMES_AT_A_TIME):
fs = organizedata.get_frames(basedir, frame_subset)
for fi, frame_no in enumerate(frame_subset):
real_x, real_y, conf = func(fs[fi], env, **config)
d[frame_no]['x'] = real_x
d[frame_no]['y'] = real_y
d[frame_no]['confidence'] = conf
return d
@transform(ddir("*/positions.npy"), regex(r".+/(.+)/positions.npy$"),
[os.path.join(REPORT_DIR, r"\1", "truth.npy")], r"\1")
def get_truth(positions_file, (output_file, ), basedir):
truth_data = truth(ddir(basedir))
try:
os.makedirs(rdir(basedir))
except OSError:
pass
np.save(output_file, truth_data)
def algodir(basedir):
try:
os.makedirs(rdir(os.path.join(basedir, "algo")))
except OSError:
pass