def get_roi_im(w, h):
sz = FastImage.Size(w, h)
roi_im = FastImage.FastImage8u(sz)
arr = numpy.asarray(roi_im) # numpy view
arr[:, :] = 0
arr[YT, XT] = 50
return roi_im, result_validator_func
def test_sobel_horiz_1():
a = np.linspace(0,255,40000).astype(np.float32)
a.shape=200,200
imA=fi.asfastimage(a)
result = np.asarray(imA.sobel_horiz(imA.size))
b = np.ones_like(a)
imB=fi.asfastimage(b)
imA.sobel_horiz(imA.size,dest=imB)
assert np.allclose(result,b)
def play_func(loaded_trx, im_pts_segs_q, playing ):
playing.set()
try:
n_frames = loaded_trx['n_frames']
all_vals = loaded_trx['all_vals']
fmf = loaded_trx['fmf']
bg_image = loaded_trx['bg_image']
tracker = loaded_trx['tracker']
cam_id = loaded_trx['cam_id']
format = loaded_trx['format']
fibg = FastImage.asfastimage(bg_image)
smallframe_size = None
fmf.seek(0)
#for fno in range(671,672):#n_frames):
for fno in range(n_frames):
# reconstruct original frame #################
posx, posy, orientation, windowx, windowy, data_timestamp = all_vals[fno][:6]
smallframe,fmf_timestamp = fmf.get_frame(fno)
fismall = FastImage.asfastimage(smallframe)
assert fmf_timestamp == data_timestamp
timestamp = fmf_timestamp
if 0:
fullsize_image = fibg.get_8u_copy(fibg.size)
software_roi = fullsize_image.roi( windowx, windowy, fismall.size )
fismall.get_8u_copy_put( software_roi, fismall.size )
else:
fullsize_image = bg_image.copy()
fullsize_image[ windowy:windowy+smallframe.shape[0], windowx:windowx+smallframe.shape[1]] = smallframe
fullsize_image = FastImage.asfastimage(fullsize_image)
# process with flytrax #################
buf_offset=0,0
framenumber=fno
points,linesegs = tracker.process_frame(cam_id,
fullsize_image,
buf_offset,
timestamp,
framenumber)
tup = fullsize_image, points, linesegs
im_pts_segs_q.put( tup )
#time.sleep(1e-2)
finally:
playing.clear()
def test_get_8u_copy():
a = np.linspace(0,255,40000).astype(np.float32)
a.shape=200,200
imA=fi.asfastimage(a)
imB=imA.get_8u_copy(imA.size)
b_test = np.asarray(imB)
diff = a-b_test
assert np.all(abs(diff)<1.0)
def test_mem(self):
size = fi.Size(20,10)
for (fif,ar_dtype) in zip(self.fastimagefactories,self.ar_dtypes):
imA=fif(size)
imA.set_val(0,size)
arA = np.zeros((size.h, size.w),ar_dtype)
imB=fi.copy(imA)
imB.set_val(2,size)
self.assert_( not np.allclose(np.asarray(imA),np.asarray(imB)) )
def test_from_nx2(self):
A = np.array(32.2,np.float32)
sz = fi.Size(33,323)
arA = np.array(A,np.float32)*np.ones((sz.h, sz.w),np.float32)
imA=fi.copy(arA)
arA[1,3:40] = 3024.03
self.assert_( not np.allclose(arA, np.asarray(imA)))
def test_conversion_step():
sz = fi.Size(660,480)
cls_dtype_nbytes = [ (fi.FastImage8u, np.uint8, 1),
(fi.FastImage32f, np.float32, 4),
]
for cls, dtype, nbytes in cls_dtype_nbytes:
imA=cls(sz)
npB = np.empty( (sz.h,imA._step//nbytes), dtype=dtype )
npB = npB[:,:sz.w]
imB = fi.asfastimage( npB )
assert imA._step == imB._step
assert imA.size == imB.size
def test_mem(self):
size = fi.Size(20,10)
for (fif,ar_dtype) in zip(self.fastimagefactories,self.ar_dtypes):
imA=fif(size)
imA.set_val(0,size)
arA = np.zeros((size.h, size.w),ar_dtype)
imB=fi.copy(imA)
imB.set_val(2,size)
self.assert_( not np.allclose(np.asarray(imA),np.asarray(imB)) )
# check that views work
imA.set_val(0,size)
arrA1 = np.array(imA,copy=False)
arrA2 = np.array(imA,copy=False)
arrA1[2,5] = 12
assert(np.allclose(arrA1,arrA2))
# check that copy isn't view
imA.set_val(0,size)
arrA1 = np.array(imA,copy=True)
arrA2 = np.array(imA,copy=True)
arrA1[2,5] = 12
assert(not np.allclose(arrA1,arrA2))
def retrack_movies(
h5_filename,
output_h5_filename=None,
max_n_frames=None,
start=None,
stop=None,
ufmf_dir=None,
cfg_filename=None,
ufmf_filenames=None,
save_debug_images=False,
):
# 2D data format for PyTables:
Info2D = flydra_core.data_descriptions.Info2D
if ufmf_filenames is None:
ufmf_filenames = auto_discover_ufmfs.find_ufmfs(h5_filename,
ufmf_dir=ufmf_dir,
careful=True)
print("ufmf_filenames: %r" % ufmf_filenames)
if len(ufmf_filenames) == 0:
raise RuntimeError(
"nothing to do (autodetection of .ufmf files failed)")
if ufmf_dir is not None:
if (not ufmf_filenames[0].startswith("/")) and (not os.path.isfile(
ufmf_filenames[0])):
# filenames are not absolute and are not present, convert
ufmf_filenames = [
os.path.join(ufmf_dir, fname) for fname in ufmf_filenames
]
else:
raise RuntimeError(
"ufmf_dir given but ufmf_filenames exist without it")
if os.path.exists(output_h5_filename):
raise RuntimeError("will not overwrite old file '%s'" %
output_h5_filename)
# get name of data
config = get_config_defaults()
if cfg_filename is not None:
loaded_cfg = cherrypy.lib.reprconf.as_dict(cfg_filename)
for section in loaded_cfg:
config[section].update(loaded_cfg.get(section, {}))
default_camcfg = config["default"]
for cam_id in config.keys():
if cam_id == "default":
continue
# ensure default key/value pairs in each cam_id
for key, value in default_camcfg.iteritems():
if key not in config[cam_id]:
config[cam_id][key] = value
datetime_str = os.path.splitext(os.path.split(h5_filename)[-1])[0]
datetime_str = datetime_str[4:19]
retrack_cam_ids = [
ufmf_tools.get_cam_id_from_ufmf_fname(f) for f in ufmf_filenames
]
with open_file_safe(h5_filename, mode="r") as h5:
# Find camns in original data
camn2cam_id, cam_id2camns = result_utils.get_caminfo_dicts(h5)
retrack_camns = []
for cam_id in retrack_cam_ids:
retrack_camns.extend(cam_id2camns[cam_id])
all_camns = camn2cam_id.keys()
# Save results to temporary file. Copy to real location on success.
tmpdir = tempfile.mkdtemp()
tmp_output_h5_filename = os.path.join(tmpdir, "retrack.h5")
with open_file_safe(tmp_output_h5_filename,
mode="w",
delete_on_error=True) as output_h5:
out_data2d = output_h5.create_table(
output_h5.root,
"data2d_distorted",
Info2D,
"2d data",
expectedrows=h5.root.data2d_distorted.nrows,
)
# Are there any camns in original h5 that are not being retracked?
if len(set(all_camns) - set(retrack_camns)):
# Yes.
# OK, exclude all camns to be retracked...
orig_data2d = h5.root.data2d_distorted[:] # read all data
for camn in retrack_camns:
delete_cond = orig_data2d["camn"] == camn
save_cond = ~delete_cond
orig_data2d = orig_data2d[save_cond]
# And save original data for untouched camns
out_data2d.append(orig_data2d)
for input_node in h5.root._f_iter_nodes():
if input_node._v_name not in [
"data2d_distorted",
"kalman_estimates",
"ML_estimates",
"ML_estimates_2d_idxs",
]:
print("copying", input_node._v_name)
# copy everything from source to dest
input_node._f_copy(output_h5.root, recursive=True)
fpc = realtime_image_analysis.FitParamsClass(
) # allocate FitParamsClass
count = 0
iterate_frames = ufmf_tools.iterate_frames # shorten notation
for frame_enum, (frame_dict, frame) in enumerate(
iterate_frames(
h5_filename,
ufmf_filenames,
max_n_frames=max_n_frames,
start=start,
stop=stop,
)):
if (frame_enum % 100) == 0:
print("%s: frame %d" % (datetime_str, frame))
for ufmf_fname in ufmf_filenames:
try:
frame_data = frame_dict[ufmf_fname]
except KeyError:
# no data saved (frame skip on Prosilica camera?)
continue
count += 1
camn = frame_data["camn"]
cam_id = frame_data["cam_id"]
camcfg = config.get(cam_id, default_camcfg)
image = frame_data["image"]
cam_received_timestamp = frame_data[
"cam_received_timestamp"]
timestamp = frame_data["timestamp"]
detected_points = True
obj_slices = None
if len(frame_data["regions"]) == 0:
# no data this frame -- go to next camera or frame
detected_points = False
if detected_points:
# print frame,cam_id,len(frame_data['regions'])
absdiff_im = abs(
frame_data["mean"].astype(np.float32) - image)
thresh_val = (np.max(absdiff_im) *
camcfg["absdiff_max_frac_thresh"])
thresh_val = max(camcfg["min_absdiff"], thresh_val)
thresh_im = absdiff_im > thresh_val
labeled_im, n_labels = scipy.ndimage.label(thresh_im)
if not n_labels:
detected_points = False
else:
obj_slices = scipy.ndimage.find_objects(labeled_im)
detection = out_data2d.row
if detected_points:
height, width = image.shape
if save_debug_images:
xarr = []
yarr = []
frame_pt_idx = 0
detected_points = False # possible not to find any below
for i in range(n_labels):
y_slice, x_slice = obj_slices[i]
# limit pixel operations to covering rectangle
this_labeled_im = labeled_im[y_slice, x_slice]
this_label_im = this_labeled_im == (i + 1)
# calculate area (number of binarized pixels)
xsum = np.sum(this_label_im, axis=0)
pixel_area = np.sum(xsum)
if pixel_area < camcfg["area_minimum_threshold"]:
continue
# calculate center
xpos = np.arange(x_slice.start, x_slice.stop,
x_slice.step)
ypos = np.arange(y_slice.start, y_slice.stop,
y_slice.step)
xmean = np.sum((xsum * xpos)) / np.sum(xsum)
ysum = np.sum(this_label_im, axis=1)
ymean = np.sum((ysum * ypos)) / np.sum(ysum)
if 1:
if camcfg["pixel_aspect"] == 1:
this_fit_im = this_label_im
elif camcfg["pixel_aspect"] == 2:
this_fit_im = np.repeat(this_label_im,
2,
axis=0)
else:
raise ValueError("unknown pixel_aspect")
fast_foreground = FastImage.asfastimage(
this_fit_im.astype(np.uint8))
fail_fit = False
try:
(
x0_roi,
y0_roi,
weighted_area,
slope,
eccentricity,
) = fpc.fit(fast_foreground)
except realtime_image_analysis.FitParamsError as err:
fail_fit = True
print("frame %d, ufmf %s: fit failed" %
(frame, ufmf_fname))
print(err)
else:
if camcfg["pixel_aspect"] == 2:
y0_roi *= 0.5
xmean = x_slice.start + x0_roi
ymean = y_slice.start + y0_roi
del weighted_area # don't leave room for confusion
else:
fail_fit = True
if fail_fit:
slope = np.nan
eccentricity = np.nan
detection["camn"] = camn
detection["frame"] = frame
detection["timestamp"] = timestamp
detection[
"cam_received_timestamp"] = cam_received_timestamp
detection["x"] = xmean
detection["y"] = ymean
detection["area"] = pixel_area
detection["slope"] = slope
detection["eccentricity"] = eccentricity
detection["frame_pt_idx"] = frame_pt_idx
# XXX These are not yet implemented:
detection["cur_val"] = 0
detection["mean_val"] = np.nan
detection["sumsqf_val"] = np.nan
frame_pt_idx += 1
if save_debug_images:
xarr.append(xmean)
yarr.append(ymean)
detection.append()
detected_points = True
if save_debug_images:
save_dir = "debug"
mkdir_p(save_dir)
save_fname = "debug_%s_%d.png" % (cam_id, frame)
save_fname_path = os.path.join(
save_dir, save_fname)
print("saving", save_fname_path)
from . import benu
canv = benu.Canvas(save_fname_path, width, height)
maxlabel = np.max(labeled_im)
fact = int(np.floor(255.0 / maxlabel))
canv.imshow((labeled_im * fact).astype(np.uint8),
0, 0)
canv.scatter(
xarr,
yarr,
color_rgba=(0, 1, 0, 1),
radius=10,
)
canv.save()
if not detected_points:
# If no point was tracked for this frame,
# still save timestamp.
detection["camn"] = camn
detection["frame"] = frame
detection["timestamp"] = timestamp
detection[
"cam_received_timestamp"] = cam_received_timestamp
detection["x"] = np.nan
detection["y"] = np.nan
detection["area"] = np.nan
detection["slope"] = np.nan
detection["eccentricity"] = np.nan
detection["frame_pt_idx"] = 0
detection["cur_val"] = 0
detection["mean_val"] = np.nan
detection["sumsqf_val"] = np.nan
detection.append()
if count == 0:
raise RuntimeError("no frames processed")
# move to correct location
shutil.move(tmp_output_h5_filename, output_h5_filename)
import sys
import os.path
import numpy
from setuptools import setup, Extension, find_packages
from Cython.Build import cythonize
import pkg_resources # make sure FastImage is importable
import motmot.FastImage as fi_mod
import motmot.FastImage.FastImage as FastImage
import motmot.FastImage.util as FastImage_util
# build with same IPP as FastImage
ipp_root = os.environ['IPPROOT']
vals = FastImage_util.get_build_info(ipp_arch=FastImage.get_IPP_arch(),
ipp_static=FastImage.get_IPP_static(),
ipp_root=ipp_root)
ext_modules = []
if 1:
realtime_image_analysis_sources=['motmot/realtime_image_analysis/realtime_image_analysis.pyx',
'src/c_fit_params.cpp',
'src/eigen.c',
'src/c_time_time.c',
]
ext_modules.append(Extension(name='motmot.realtime_image_analysis.realtime_image_analysis',
sources=realtime_image_analysis_sources,
include_dirs=vals['ipp_include_dirs']+['src']+[numpy.get_include(), fi_mod.get_include()],
library_dirs=vals['ipp_library_dirs'],
libraries=vals['ipp_libraries'],
define_macros=vals['ipp_define_macros'],
version = flydra_camnode.version.__version__
import numpy as np
ext_modules = []
if 1:
import motmot.FastImage.FastImage
import motmot.FastImage as fi_mod
FastImage = motmot.FastImage.FastImage
import motmot.FastImage.util as FastImage_util
IPPROOT = os.environ['IPPROOT']
vals = FastImage_util.get_build_info(
ipp_arch=FastImage.get_IPP_arch(),
ipp_root=IPPROOT,
)
ext_modules.append(
Extension(
name='flydra_camnode.camnode_colors',
sources=[
'flydra_camnode/camnode_colors.pyx', 'flydra_camnode/colors.c'
],
include_dirs=vals['ipp_include_dirs'] +
[np.get_include(), fi_mod.get_include()],
library_dirs=vals['ipp_library_dirs'],
libraries=vals['ipp_libraries'],
define_macros=vals['ipp_define_macros'],
extra_link_args=vals['extra_link_args'],
def test_fast_vs_slow(self):
h,w = 3,5
shape = h,w # 3 rows, 5 cols
results = []
for func in [slow.do_bg_maint,
ria.do_bg_maint]:
running_mean_im = 4*numpy.ones( shape, dtype=numpy.float32 )
hw_roi_frame = 5*numpy.ones( shape, dtype=numpy.uint8 )
max_frame_size = FastImage.Size( w,h )
ALPHA = 0.25
running_mean8u_im = numpy.empty( shape, dtype=numpy.uint8 )
fastframef32_tmp = numpy.empty( shape, dtype=numpy.float32 )
running_sumsqf = 16*numpy.ones( shape, dtype=numpy.float32 )
mean2 = numpy.empty( shape, dtype=numpy.float32 )
std2 = numpy.empty( shape, dtype=numpy.float32 )
running_stdframe = numpy.empty( shape, dtype=numpy.float32 )
n_sigma = 2.0
compareframe8u = numpy.empty( shape, dtype=numpy.uint8 )
bright_non_gaussian_cutoff = 255
noisy_pixels_mask = numpy.ones( shape, dtype=numpy.uint8 )
bright_non_gaussian_replacement = 5
bench = 0
func( FastImage.asfastimage(running_mean_im),
FastImage.asfastimage(hw_roi_frame),
max_frame_size,
ALPHA,
FastImage.asfastimage(running_mean8u_im),
FastImage.asfastimage(fastframef32_tmp),
FastImage.asfastimage(running_sumsqf),
FastImage.asfastimage(mean2),
FastImage.asfastimage(std2),
FastImage.asfastimage(running_stdframe),
n_sigma,
FastImage.asfastimage(compareframe8u),
bright_non_gaussian_cutoff,
FastImage.asfastimage(noisy_pixels_mask),
bright_non_gaussian_replacement,
bench=bench)
results_order = ('running_mean8u_im',
'fastframef32_tmp',
'running_sumsqf',
'mean2',
'std2',
'running_stdframe',
)
this_results = [ locals()[name] for name in results_order ]
results.append( this_results )
for i,(slow_result_arr, fast_result_arr) in enumerate(zip(*results)):
name = results_order[i]
if 0:
print name
print slow_result_arr
print fast_result_arr
print
assert slow_result_arr.shape == fast_result_arr.shape
assert numpy.allclose( slow_result_arr, fast_result_arr )
def test_arch(self):
arch = fi.get_IPP_arch()
##im.show() ##print np.__version__ ##nview = np.asarray( im ) ##print ai ##print nview ##nview[0,1] = 240 ##print nview ##im.show() a=np.arange(12).astype(np.uint8) a=np.reshape(a,(3,4)) b=fi.asfastimage(a) print b.stringview() b.set_val(1,b.size) br = b.roi(1,1,fi.Size(1,1)) br.set_val(10,br.size) print a c=fi.copy(a.astype(np.float32)) print c.stringview() print b.stringview() #print 'c.size == b.size',c.size == b.size c.toself_add_weighted( b, c.size, 0.1 ) print c.stringview()
N=stack_N_images,
)
# The variable fno (the first element of the results
# tuple) is guaranteed to be contiguous and to span
# the range from the first to last frames available.
for (fno, av_im, lowerleft,
orig_data2d_rownum, orig_idx,
orig_idxs_in_average) in results:
# Clip image to reduce moment arms.
av_im[av_im <= final_thresh] = 0
fail_fit = False
fast_av_im = FastImage.asfastimage( av_im.astype(np.uint8) )
try:
(x0_roi, y0_roi, area, slope, eccentricity) = fpc.fit(
fast_av_im )
except realtime_image_analysis.FitParamsError, err:
fail_fit = True
this_morph_failures = morph_failures[orig_idxs_in_average]
n_failed_images = np.sum( this_morph_failures)
n_good_images = stack_N_images-n_failed_images
if n_good_images >= stack_N_images_min:
n_images_is_acceptable = True
else:
n_images_is_acceptable = False
if fail_fit:
def test_fast_vs_slow(self):
h, w = 3, 5
shape = h, w # 3 rows, 5 cols
results = []
for func in [slow.do_bg_maint, ria.do_bg_maint]:
running_mean_im = 4 * numpy.ones(shape, dtype=numpy.float32)
hw_roi_frame = 5 * numpy.ones(shape, dtype=numpy.uint8)
max_frame_size = FastImage.Size(w, h)
ALPHA = 0.25
running_mean8u_im = numpy.empty(shape, dtype=numpy.uint8)
fastframef32_tmp = numpy.empty(shape, dtype=numpy.float32)
running_sumsqf = 16 * numpy.ones(shape, dtype=numpy.float32)
mean2 = numpy.empty(shape, dtype=numpy.float32)
std2 = numpy.empty(shape, dtype=numpy.float32)
running_stdframe = numpy.empty(shape, dtype=numpy.float32)
n_sigma = 2.0
compareframe8u = numpy.empty(shape, dtype=numpy.uint8)
bright_non_gaussian_cutoff = 255
noisy_pixels_mask = numpy.ones(shape, dtype=numpy.uint8)
bright_non_gaussian_replacement = 5
bench = 0
func(FastImage.asfastimage(running_mean_im),
FastImage.asfastimage(hw_roi_frame),
max_frame_size,
ALPHA,
FastImage.asfastimage(running_mean8u_im),
FastImage.asfastimage(fastframef32_tmp),
FastImage.asfastimage(running_sumsqf),
FastImage.asfastimage(mean2),
FastImage.asfastimage(std2),
FastImage.asfastimage(running_stdframe),
n_sigma,
FastImage.asfastimage(compareframe8u),
bright_non_gaussian_cutoff,
FastImage.asfastimage(noisy_pixels_mask),
bright_non_gaussian_replacement,
bench=bench)
results_order = (
'running_mean8u_im',
'fastframef32_tmp',
'running_sumsqf',
'mean2',
'std2',
'running_stdframe',
)
this_results = [locals()[name] for name in results_order]
results.append(this_results)
for i, (slow_result_arr, fast_result_arr) in enumerate(zip(*results)):
name = results_order[i]
if 0:
print name
print slow_result_arr
print fast_result_arr
print
assert slow_result_arr.shape == fast_result_arr.shape
assert numpy.allclose(slow_result_arr, fast_result_arr)
def test_static(self):
result = fi.get_IPP_static()
def process_frame(self, cam_id, buf, buf_offset, timestamp, framenumber):
"""do work on each frame
This function gets called on every single frame capture. It is
called within the realtime thread, NOT the wxPython
application mainloop's thread. Therefore, be extremely careful
(use threading locks) when sharing data with the rest of the
class.
"""
assert self.pixel_format == "MONO8"
now = time.time()
if self.pub_image is not None:
if (now - self.pub_last_image) > 30.0:
msg = self.pub_image_class()
msg.header.seq = framenumber
# XXX TODO: once camera trigger is ROS node, get accurate timestamp
msg.header.stamp = self._rospy_time_from_sec(now)
msg.header.frame_id = "0"
npbuf = np.array(buf)
(height, width) = npbuf.shape
msg.height = height
msg.width = width
msg.encoding = "mono8"
msg.step = width
msg.data = npbuf.tostring() # let numpy convert to string
self.pub_image.publish(msg)
self.pub_last_image = now
if self.pub_mean_luminance is not None:
if (now - self.pub_last_mean_luminance) > 0.5:
mean_lum = np.mean(buf)
self.pub_mean_luminance.publish(float(mean_lum))
self.pub_last_mean_luminance = now
buf = FastImage.asfastimage(buf)
ros_list = []
point_list = []
draw_linesegs = [] # [ (x0,y0,x1,y1) ]
if self.enabled.isSet():
light_on_dark = self.light_on_dark.isSet()
offset_x, offset_y = buf_offset
if 1:
# work on a copy of the image so the original is displayed unaltered
copyview = self.copybuf.roi(offset_x, offset_y, buf.size)
buf.get_8u_copy_put(copyview, buf.size)
buf = copyview
buf_view = np.asarray(buf) # get numpy view of data
# step 1. extract points
analysis_radius = self.analysis_radius.get()
luminance_threshold = self.luminance_threshold.get()
if light_on_dark:
clearval = 0
else:
clearval = 255
# outside mask, set values to clearval
mask = self.get_mask()
buf_view[mask] = clearval
for pt_num in range(self.num_points.get()):
if light_on_dark:
# find brightest point
max_val, x, y = buf.max_index(buf.size)
if max_val < luminance_threshold:
break
else:
# find darkest point
min_val, x, y = buf.min_index(buf.size)
if min_val > (255 - luminance_threshold):
break
# calculate region around found point
clearxmin = max(0, x - analysis_radius)
clearxmax = min(buf.size.w - 1, x + analysis_radius)
clearymin = max(0, y - analysis_radius)
clearymax = min(buf.size.h - 1, y + analysis_radius)
# extract a view of this region
this_region = buf_view[clearymin:clearymax, clearxmin:clearxmax]
if light_on_dark:
binary_region = this_region > (max_val / 1.1)
else:
binary_region = this_region < (min_val * 1.1)
num_pixels_classified = np.sum(binary_region.ravel())
# print '%d, (%d, %d)'%(num_pixels_classified,x,y)
if num_pixels_classified > self.max_area.get():
# we don't want this point
pass
else:
# compute luminance center of mass
if not light_on_dark:
# make a light-on-dark image
this_region2 = 255 - this_region
else:
this_region2 = this_region
fibuf = FastImage.asfastimage(this_region2)
try:
results = realtime_image_analysis.py_fit_params(fibuf)
except Exception as err:
print "%s: error extracting image data. ignoring." % (err,)
else:
(x0, y0, area, slope, eccentricity) = results
theta = np.arctan(slope)
x1 = x0 + clearxmin
y1 = y0 + clearymin
# save values
ros_list.append((offset_x + x1, offset_y + y1, theta))
point_list.append((offset_x + x1, offset_y + y1))
# clear the region near the detected point
buf_view[clearymin:clearymax, clearxmin:clearxmax] = clearval
# send data over ROS
if self.num_points.get():
if self.pub_position is not None:
msg = self.pub_position_class()
msg.header.stamp.secs = int(np.floor(timestamp))
msg.header.stamp.nsecs = int((timestamp % 1.0) * 1e9)
msg.header.frame_id = "pixels"
msg.framenumber = framenumber
for (x, y, theta) in ros_list:
pose = self.pub_pose_class()
pose.x = x
pose.y = y
pose.theta = theta
msg.points.append(pose)
self.pub_position.publish(msg)
if self.view_mask_mode.isSet():
w, h = self.image_size
x = self.mask_center_x.get()
y = self.mask_center_y.get()
radius = self.mask_radius.get()
draw_linesegs.extend(lineseg_circle(x, y, radius))
return point_list, draw_linesegs
def process_frame(self,cam_id,buf,buf_offset,timestamp,framenumber):
if self.pixel_format[cam_id]=='YUV422':
buf = imops.yuv422_to_mono8( numpy.asarray(buf) ) # convert
elif not (self.pixel_format[cam_id].startswith('MONO8') or
self.pixel_format[cam_id].startswith('RAW8')):
warnings.warn("flytrax plugin incompatible with data format")
return [], []
bunch = self.bunches[cam_id]
do_bg_maint = False
clear_and_take_bg_image = self.clear_and_take_bg_image[cam_id]
# this is called in realtime thread
fibuf = FastImage.asfastimage(buf) # FastImage view of image data (hardware ROI)
l,b = buf_offset
lbrt = l, b, l+fibuf.size.w-1, b+fibuf.size.h-1
running_mean_im = bunch.running_mean_im_full.roi(l, b, fibuf.size) # set ROI view
running_sumsqf = bunch.running_sumsqf_full.roi(l, b, fibuf.size) # set ROI view
new_clear_threshold = self.new_clear_threshold[cam_id]
new_diff_threshold = self.new_diff_threshold[cam_id]
new_roi2_radius = self.new_roi2_radius[cam_id]
realtime_analyzer = self.realtime_analyzer[cam_id]
realtime_analyzer.roi = lbrt # hardware ROI
max_frame_size = self.max_frame_size[cam_id]
display_active = self.display_active[cam_id]
use_roi2 = True
use_cmp = False # use variance-based background subtraction/analysis
draw_points = []
draw_linesegs = []
running_mean8u_im_full = realtime_analyzer.get_image_view('mean')
running_mean8u_im = running_mean8u_im_full.roi(l, b, fibuf.size)
if (bunch.initial_take_bg_state is not None or
clear_and_take_bg_image.isSet()):
src_fullframe_fi = fibuf.get_8u_copy(max_frame_size)
if bunch.initial_take_bg_state is not None:
assert bunch.initial_take_bg_state == 'gather'
n_initial_take = 5
bunch.initial_take_frames.append( numpy.array(src_fullframe_fi) ) # copied above
if len( bunch.initial_take_frames ) >= n_initial_take:
initial_take_frames = numpy.array( bunch.initial_take_frames, dtype=numpy.float32 )
mean_frame = numpy.mean( initial_take_frames, axis=0)
sumsqf_frame = numpy.sum(initial_take_frames**2, axis=0)/len( initial_take_frames )
numpy.asarray(running_mean_im)[:,:] = mean_frame
numpy.asarray(running_sumsqf)[:,:] = sumsqf_frame
# we're done with initial transient, set stuff
do_bg_maint = True
bunch.initial_take_bg_state = None
bunch.initial_take_frames = []
if clear_and_take_bg_image.isSet():
bunch.initial_take_bg_state = 'gather'
bunch.initial_take_frames = []
with self.bg_update_lock:
bunch.last_running_mean_im = None
clear_and_take_bg_image.clear()
if 1:
self.ticks_since_last_update[cam_id] += 1
update_interval = self.ongoing_bg_image_update_interval[cam_id].get()
if self.ticks_since_last_update[cam_id]%update_interval == 0:
do_bg_maint = True
if do_bg_maint:
hw_roi_frame = fibuf
cur_fisize = hw_roi_frame.size
bg_frame_alpha = 1.0/bunch.inverse_alpha.get_nowait()
n_sigma = bunch.n_sigma.get_nowait()
bright_non_gaussian_cutoff = 255
bright_non_gaussian_replacement = 255
compareframe8u_full = realtime_analyzer.get_image_view('cmp')
compareframe8u = compareframe8u_full.roi(l, b, fibuf.size)
fastframef32_tmp = bunch.fastframef32_tmp_full.roi(l, b, fibuf.size)
mean2 = bunch.mean2_full.roi(l, b, fibuf.size)
std2 = bunch.std2_full.roi(l, b, fibuf.size)
running_stdframe = bunch.running_stdframe_full.roi(l, b, fibuf.size)
noisy_pixels_mask = bunch.noisy_pixels_mask_full.roi(l, b, fibuf.size)
realtime_image_analysis.do_bg_maint(
running_mean_im,#in
hw_roi_frame,#in
cur_fisize,#in
bg_frame_alpha, #in
running_mean8u_im,
fastframef32_tmp,
running_sumsqf, #in
mean2,
std2,
running_stdframe,
n_sigma,#in
compareframe8u,
bright_non_gaussian_cutoff,#in
noisy_pixels_mask,#in
bright_non_gaussian_replacement,#in
bench=0 )
#debug=0)
#chainbuf.real_std_est= tmpresult
bg_changed = True
bg_frame_number = 0
with self.bg_update_lock:
bunch.last_running_mean_im = running_mean_im # XXX should copy?
bunch.last_running_sumsqf_image = running_sumsqf # XXX should copy?
bunch.last_bgcmp_image_timestamp = timestamp
if new_clear_threshold.isSet():
nv = self.clear_threshold_value[cam_id]
realtime_analyzer.clear_threshold = nv
#print 'set clear',nv
new_clear_threshold.clear()
if new_diff_threshold.isSet():
nv = self.diff_threshold_value[cam_id]
realtime_analyzer.diff_threshold = nv
#print 'set diff',nv
new_diff_threshold.clear()
if new_roi2_radius.isSet():
nv = self.roi2_radius_value[cam_id]
realtime_analyzer.roi2_radius = nv
new_roi2_radius.clear()
n_pts = 0
with self.ufmf_writer_lock:
with self.tracking_enabled_lock:
ufmf_writer = self.ufmf_writer.get(cam_id,None)
tracking_enabled = self.tracking_enabled.get(cam_id,False)
if (ufmf_writer is not None) or tracking_enabled:
try:
realtime_analyzer.max_num_points = bunch.max_num_points.get_nowait()
except AttributeError, err:
warnings.warn('old realtime_analyzer does not support dynamic '
'setting of max_num_points')
points = realtime_analyzer.do_work(fibuf,
timestamp, framenumber, use_roi2,
use_cmp=use_cmp)
pts = []
w = h = realtime_analyzer.roi2_radius*2
for pt in points:
pts.append( (pt[0], pt[1], w, h ) )
if ufmf_writer is not None:
saved_points = ufmf_writer.add_frame( fibuf, timestamp, pts )
else:
saved_points = self.dummy_ufmf_writer[cam_id].add_frame( fibuf, timestamp, pts )
lineseg_lists = [ corners2linesegs( *corners ) for corners in saved_points]
for linesegs in lineseg_lists:
draw_linesegs.extend( linesegs )
# save any pending background model updates
with self.bg_update_lock:
if bunch.last_running_mean_im is not None:
if ufmf_writer is not None:
ufmf_writer.add_keyframe('mean',
bunch.last_running_mean_im,
bunch.last_bgcmp_image_timestamp)
ufmf_writer.add_keyframe('sumsq',
bunch.last_running_sumsqf_image,
bunch.last_bgcmp_image_timestamp)
# delete it
bunch.last_running_mean_im = None
def test_version(self):
major,minor,build = fi.get_IPP_version()
def doit(
h5_filename=None,
output_h5_filename=None,
ufmf_filenames=None,
kalman_filename=None,
start=None,
stop=None,
view=None,
erode=0,
save_images=False,
save_image_dir=None,
intermediate_thresh_frac=None,
final_thresh=None,
stack_N_images=None,
stack_N_images_min=None,
old_sync_timestamp_source=False,
do_rts_smoothing=True,
):
"""
Copy all data in .h5 file (specified by h5_filename) to a new .h5
file in which orientations are set based on image analysis of
.ufmf files. Tracking data to associate 2D points from subsequent
frames is read from the .h5 kalman file specified by
kalman_filename.
"""
if view is None:
view = ["orig" for f in ufmf_filenames]
else:
assert len(view) == len(ufmf_filenames)
if intermediate_thresh_frac is None or final_thresh is None:
raise ValueError("intermediate_thresh_frac and final_thresh must be "
"set")
filename2view = dict(zip(ufmf_filenames, view))
ca = core_analysis.get_global_CachingAnalyzer()
obj_ids, use_obj_ids, is_mat_file, data_file, extra = ca.initial_file_load(
kalman_filename)
try:
ML_estimates_2d_idxs = data_file.root.ML_estimates_2d_idxs[:]
except tables.exceptions.NoSuchNodeError as err1:
# backwards compatibility
try:
ML_estimates_2d_idxs = data_file.root.kalman_observations_2d_idxs[:]
except tables.exceptions.NoSuchNodeError as err2:
raise err1
if os.path.exists(output_h5_filename):
raise RuntimeError("will not overwrite old file '%s'" %
output_h5_filename)
with open_file_safe(output_h5_filename, delete_on_error=True,
mode="w") as output_h5:
if save_image_dir is not None:
if not os.path.exists(save_image_dir):
os.mkdir(save_image_dir)
with open_file_safe(h5_filename, mode="r") as h5:
fps = result_utils.get_fps(h5, fail_on_error=True)
for input_node in h5.root._f_iter_nodes():
# copy everything from source to dest
input_node._f_copy(output_h5.root, recursive=True)
print("done copying")
# Clear values in destination table that we may overwrite.
dest_table = output_h5.root.data2d_distorted
for colname in [
"x",
"y",
"area",
"slope",
"eccentricity",
"cur_val",
"mean_val",
"sumsqf_val",
]:
if colname == "cur_val":
fill_value = 0
else:
fill_value = np.nan
clear_col(dest_table, colname, fill_value=fill_value)
dest_table.flush()
print("done clearing")
camn2cam_id, cam_id2camns = result_utils.get_caminfo_dicts(h5)
cam_id2fmfs = collections.defaultdict(list)
cam_id2view = {}
for ufmf_filename in ufmf_filenames:
fmf = ufmf.FlyMovieEmulator(
ufmf_filename,
# darken=-50,
allow_no_such_frame_errors=True,
)
timestamps = fmf.get_all_timestamps()
cam_id = get_cam_id_from_filename(fmf.filename,
cam_id2camns.keys())
cam_id2fmfs[cam_id].append(
(fmf, result_utils.Quick1DIndexer(timestamps)))
cam_id2view[cam_id] = filename2view[fmf.filename]
# associate framenumbers with timestamps using 2d .h5 file
data2d = h5.root.data2d_distorted[:] # load to RAM
data2d_idxs = np.arange(len(data2d))
h5_framenumbers = data2d["frame"]
h5_frame_qfi = result_utils.QuickFrameIndexer(h5_framenumbers)
fpc = realtime_image_analysis.FitParamsClass(
) # allocate FitParamsClass
for obj_id_enum, obj_id in enumerate(use_obj_ids):
print("object %d of %d" % (obj_id_enum, len(use_obj_ids)))
# get all images for this camera and this obj_id
obj_3d_rows = ca.load_dynamics_free_MLE_position(
obj_id, data_file)
this_obj_framenumbers = collections.defaultdict(list)
if save_images:
this_obj_raw_images = collections.defaultdict(list)
this_obj_mean_images = collections.defaultdict(list)
this_obj_absdiff_images = collections.defaultdict(list)
this_obj_morphed_images = collections.defaultdict(list)
this_obj_morph_failures = collections.defaultdict(list)
this_obj_im_coords = collections.defaultdict(list)
this_obj_com_coords = collections.defaultdict(list)
this_obj_camn_pt_no = collections.defaultdict(list)
for this_3d_row in obj_3d_rows:
# iterate over each sample in the current camera
framenumber = this_3d_row["frame"]
if start is not None:
if not framenumber >= start:
continue
if stop is not None:
if not framenumber <= stop:
continue
h5_2d_row_idxs = h5_frame_qfi.get_frame_idxs(framenumber)
frame2d = data2d[h5_2d_row_idxs]
frame2d_idxs = data2d_idxs[h5_2d_row_idxs]
obs_2d_idx = this_3d_row["obs_2d_idx"]
kobs_2d_data = ML_estimates_2d_idxs[int(obs_2d_idx)]
# Parse VLArray.
this_camns = kobs_2d_data[0::2]
this_camn_idxs = kobs_2d_data[1::2]
# Now, for each camera viewing this object at this
# frame, extract images.
for camn, camn_pt_no in zip(this_camns, this_camn_idxs):
# find 2D point corresponding to object
cam_id = camn2cam_id[camn]
movie_tups_for_this_camn = cam_id2fmfs[cam_id]
cond = (frame2d["camn"] == camn) & (
frame2d["frame_pt_idx"] == camn_pt_no)
idxs = np.nonzero(cond)[0]
assert len(idxs) == 1
idx = idxs[0]
orig_data2d_rownum = frame2d_idxs[idx]
if not old_sync_timestamp_source:
# Change the next line to 'timestamp' for old
# data (before May/June 2009 -- the switch to
# fview_ext_trig)
frame_timestamp = frame2d[idx][
"cam_received_timestamp"]
else:
# previous version
frame_timestamp = frame2d[idx]["timestamp"]
found = None
for fmf, fmf_timestamp_qi in movie_tups_for_this_camn:
fmf_fnos = fmf_timestamp_qi.get_idxs(
frame_timestamp)
if not len(fmf_fnos):
continue
assert len(fmf_fnos) == 1
# should only be one .ufmf with this frame and cam_id
assert found is None
fmf_fno = fmf_fnos[0]
found = (fmf, fmf_fno)
if found is None:
print(
"no image data for frame timestamp %s cam_id %s"
% (repr(frame_timestamp), cam_id))
continue
fmf, fmf_fno = found
image, fmf_timestamp = fmf.get_frame(fmf_fno)
mean_image = fmf.get_mean_for_timestamp(fmf_timestamp)
coding = fmf.get_format()
if imops.is_coding_color(coding):
image = imops.to_rgb8(coding, image)
mean_image = imops.to_rgb8(coding, mean_image)
else:
image = imops.to_mono8(coding, image)
mean_image = imops.to_mono8(coding, mean_image)
xy = (
int(round(frame2d[idx]["x"])),
int(round(frame2d[idx]["y"])),
)
maxsize = (fmf.get_width(), fmf.get_height())
# Accumulate cropped images. Note that the region
# of the full image that the cropped image
# occupies changes over time as the tracked object
# moves. Thus, averaging these cropped-and-shifted
# images is not the same as simply averaging the
# full frame.
roiradius = 25
warnings.warn(
"roiradius hard-coded to %d: could be set "
"from 3D tracking" % roiradius)
tmp = clip_and_math(image, mean_image, xy, roiradius,
maxsize)
im_coords, raw_im, mean_im, absdiff_im = tmp
max_absdiff_im = absdiff_im.max()
intermediate_thresh = intermediate_thresh_frac * max_absdiff_im
absdiff_im[absdiff_im <= intermediate_thresh] = 0
if erode > 0:
morphed_im = scipy.ndimage.grey_erosion(absdiff_im,
size=erode)
## morphed_im = scipy.ndimage.binary_erosion(absdiff_im>1).astype(np.float32)*255.0
else:
morphed_im = absdiff_im
y0_roi, x0_roi = scipy.ndimage.center_of_mass(
morphed_im)
x0 = im_coords[0] + x0_roi
y0 = im_coords[1] + y0_roi
if 1:
morphed_im_binary = morphed_im > 0
labels, n_labels = scipy.ndimage.label(
morphed_im_binary)
morph_fail_because_multiple_blobs = False
if n_labels > 1:
x0, y0 = np.nan, np.nan
# More than one blob -- don't allow image.
if 1:
# for min flattening
morphed_im = np.empty(morphed_im.shape,
dtype=np.uint8)
morphed_im.fill(255)
morph_fail_because_multiple_blobs = True
else:
# for mean flattening
morphed_im = np.zeros_like(morphed_im)
morph_fail_because_multiple_blobs = True
this_obj_framenumbers[camn].append(framenumber)
if save_images:
this_obj_raw_images[camn].append(
(raw_im, im_coords))
this_obj_mean_images[camn].append(mean_im)
this_obj_absdiff_images[camn].append(absdiff_im)
this_obj_morphed_images[camn].append(morphed_im)
this_obj_morph_failures[camn].append(
morph_fail_because_multiple_blobs)
this_obj_im_coords[camn].append(im_coords)
this_obj_com_coords[camn].append((x0, y0))
this_obj_camn_pt_no[camn].append(orig_data2d_rownum)
if 0:
fname = "obj%05d_%s_frame%07d_pt%02d.png" % (
obj_id,
cam_id,
framenumber,
camn_pt_no,
)
plot_image_subregion(
raw_im,
mean_im,
absdiff_im,
roiradius,
fname,
im_coords,
view=filename2view[fmf.filename],
)
# Now, all the frames from all cameras for this obj_id
# have been gathered. Do a camera-by-camera analysis.
for camn in this_obj_absdiff_images:
cam_id = camn2cam_id[camn]
image_framenumbers = np.array(this_obj_framenumbers[camn])
if save_images:
raw_images = this_obj_raw_images[camn]
mean_images = this_obj_mean_images[camn]
absdiff_images = this_obj_absdiff_images[camn]
morphed_images = this_obj_morphed_images[camn]
morph_failures = np.array(this_obj_morph_failures[camn])
im_coords = this_obj_im_coords[camn]
com_coords = this_obj_com_coords[camn]
camn_pt_no_array = this_obj_camn_pt_no[camn]
all_framenumbers = np.arange(image_framenumbers[0],
image_framenumbers[-1] + 1)
com_coords = np.array(com_coords)
if do_rts_smoothing:
# Perform RTS smoothing on center-of-mass coordinates.
# Find first good datum.
fgnz = np.nonzero(~np.isnan(com_coords[:, 0]))
com_coords_smooth = np.empty(com_coords.shape,
dtype=np.float)
com_coords_smooth.fill(np.nan)
if len(fgnz[0]):
first_good = fgnz[0][0]
RTS_com_coords = com_coords[first_good:, :]
# Setup parameters for Kalman filter.
dt = 1.0 / fps
A = np.array(
[
[1, 0, dt, 0], # process update
[0, 1, 0, dt],
[0, 0, 1, 0],
[0, 0, 0, 1],
],
dtype=np.float,
)
C = np.array(
[[1, 0, 0, 0], [0, 1, 0, 0]
], # observation matrix
dtype=np.float,
)
Q = 0.1 * np.eye(4) # process noise
R = 1.0 * np.eye(2) # observation noise
initx = np.array(
[
RTS_com_coords[0, 0], RTS_com_coords[0, 1],
0, 0
],
dtype=np.float,
)
initV = 2 * np.eye(4)
initV[0, 0] = 0.1
initV[1, 1] = 0.1
y = RTS_com_coords
xsmooth, Vsmooth = adskalman.adskalman.kalman_smoother(
y, A, C, Q, R, initx, initV)
com_coords_smooth[first_good:] = xsmooth[:, :2]
# Now shift images
image_shift = com_coords_smooth - com_coords
bad_cond = np.isnan(image_shift[:, 0])
# broadcast zeros to places where no good tracking
image_shift[bad_cond, 0] = 0
image_shift[bad_cond, 1] = 0
shifted_morphed_images = [
shift_image(im, xy)
for im, xy in zip(morphed_images, image_shift)
]
results = flatten_image_stack(
image_framenumbers,
shifted_morphed_images,
im_coords,
camn_pt_no_array,
N=stack_N_images,
)
else:
results = flatten_image_stack(
image_framenumbers,
morphed_images,
im_coords,
camn_pt_no_array,
N=stack_N_images,
)
# The variable fno (the first element of the results
# tuple) is guaranteed to be contiguous and to span
# the range from the first to last frames available.
for (
fno,
av_im,
lowerleft,
orig_data2d_rownum,
orig_idx,
orig_idxs_in_average,
) in results:
# Clip image to reduce moment arms.
av_im[av_im <= final_thresh] = 0
fail_fit = False
fast_av_im = FastImage.asfastimage(
av_im.astype(np.uint8))
try:
(x0_roi, y0_roi, area, slope,
eccentricity) = fpc.fit(fast_av_im)
except realtime_image_analysis.FitParamsError as err:
fail_fit = True
this_morph_failures = morph_failures[
orig_idxs_in_average]
n_failed_images = np.sum(this_morph_failures)
n_good_images = stack_N_images - n_failed_images
if n_good_images >= stack_N_images_min:
n_images_is_acceptable = True
else:
n_images_is_acceptable = False
if fail_fit:
x0_roi = np.nan
y0_roi = np.nan
area, slope, eccentricity = np.nan, np.nan, np.nan
if not n_images_is_acceptable:
x0_roi = np.nan
y0_roi = np.nan
area, slope, eccentricity = np.nan, np.nan, np.nan
x0 = x0_roi + lowerleft[0]
y0 = y0_roi + lowerleft[1]
if 1:
for row in dest_table.iterrows(
start=orig_data2d_rownum,
stop=orig_data2d_rownum + 1):
row["x"] = x0
row["y"] = y0
row["area"] = area
row["slope"] = slope
row["eccentricity"] = eccentricity
row.update() # save data
if save_images:
# Display debugging images
fname = "av_obj%05d_%s_frame%07d.png" % (
obj_id,
cam_id,
fno,
)
if save_image_dir is not None:
fname = os.path.join(save_image_dir, fname)
raw_im, raw_coords = raw_images[orig_idx]
mean_im = mean_images[orig_idx]
absdiff_im = absdiff_images[orig_idx]
morphed_im = morphed_images[orig_idx]
raw_l, raw_b = raw_coords[:2]
imh, imw = raw_im.shape[:2]
n_ims = 5
if 1:
# increase contrast
contrast_scale = 2.0
av_im_show = np.clip(av_im * contrast_scale, 0,
255)
margin = 10
scale = 3
# calculate the orientation line
yintercept = y0 - slope * x0
xplt = np.array([
lowerleft[0] - 5,
lowerleft[0] + av_im_show.shape[1] + 5,
])
yplt = slope * xplt + yintercept
if 1:
# only send non-nan values to plot
plt_good = ~np.isnan(xplt) & ~np.isnan(yplt)
xplt = xplt[plt_good]
yplt = yplt[plt_good]
top_row_width = scale * imw * n_ims + (
1 + n_ims) * margin
SHOW_STACK = True
if SHOW_STACK:
n_stack_rows = 4
rw = scale * imw * stack_N_images + (
1 + n_ims) * margin
row_width = max(top_row_width, rw)
col_height = (n_stack_rows * scale * imh +
(n_stack_rows + 1) * margin)
stack_margin = 20
else:
row_width = top_row_width
col_height = scale * imh + 2 * margin
stack_margin = 0
canv = benu.Canvas(
fname,
row_width,
col_height + stack_margin,
color_rgba=(1, 1, 1, 1),
)
if SHOW_STACK:
for (stacki, s_orig_idx
) in enumerate(orig_idxs_in_average):
(s_raw_im,
s_raw_coords) = raw_images[s_orig_idx]
s_raw_l, s_raw_b = s_raw_coords[:2]
s_imh, s_imw = s_raw_im.shape[:2]
user_rect = (s_raw_l, s_raw_b, s_imw,
s_imh)
x_display = (stacki + 1) * margin + (
scale * imw) * stacki
for show in ["raw", "absdiff", "morphed"]:
if show == "raw":
y_display = scale * imh + 2 * margin
elif show == "absdiff":
y_display = 2 * scale * imh + 3 * margin
elif show == "morphed":
y_display = 3 * scale * imh + 4 * margin
display_rect = (
x_display,
y_display + stack_margin,
scale * raw_im.shape[1],
scale * raw_im.shape[0],
)
with canv.set_user_coords(
display_rect,
user_rect,
transform=cam_id2view[cam_id],
):
if show == "raw":
s_im = s_raw_im.astype(
np.uint8)
elif show == "absdiff":
tmp = absdiff_images[
s_orig_idx]
s_im = tmp.astype(np.uint8)
elif show == "morphed":
tmp = morphed_images[
s_orig_idx]
s_im = tmp.astype(np.uint8)
canv.imshow(s_im, s_raw_l, s_raw_b)
sx0, sy0 = com_coords[s_orig_idx]
X = [sx0]
Y = [sy0]
# the raw coords in red
canv.scatter(X,
Y,
color_rgba=(1, 0.5,
0.5, 1))
if do_rts_smoothing:
sx0, sy0 = com_coords_smooth[
s_orig_idx]
X = [sx0]
Y = [sy0]
# the RTS smoothed coords in green
canv.scatter(
X,
Y,
color_rgba=(0.5, 1, 0.5,
1))
if s_orig_idx == orig_idx:
boxx = np.array([
s_raw_l,
s_raw_l,
s_raw_l + s_imw,
s_raw_l + s_imw,
s_raw_l,
])
boxy = np.array([
s_raw_b,
s_raw_b + s_imh,
s_raw_b + s_imh,
s_raw_b,
s_raw_b,
])
canv.plot(
boxx,
boxy,
color_rgba=(0.5, 1, 0.5,
1),
)
if show == "morphed":
canv.text(
"morphed %d" %
(s_orig_idx - orig_idx, ),
display_rect[0],
(display_rect[1] +
display_rect[3] +
stack_margin - 20),
font_size=font_size,
color_rgba=(1, 0, 0, 1),
)
# Display raw_im
display_rect = (
margin,
margin,
scale * raw_im.shape[1],
scale * raw_im.shape[0],
)
user_rect = (raw_l, raw_b, imw, imh)
with canv.set_user_coords(
display_rect,
user_rect,
transform=cam_id2view[cam_id],
):
canv.imshow(raw_im.astype(np.uint8), raw_l,
raw_b)
canv.plot(
xplt, yplt,
color_rgba=(0, 1, 0,
0.5)) # the orientation line
canv.text(
"raw",
display_rect[0],
display_rect[1] + display_rect[3],
font_size=font_size,
color_rgba=(0.5, 0.5, 0.9, 1),
shadow_offset=1,
)
# Display mean_im
display_rect = (
2 * margin + (scale * imw),
margin,
scale * mean_im.shape[1],
scale * mean_im.shape[0],
)
user_rect = (raw_l, raw_b, imw, imh)
with canv.set_user_coords(
display_rect,
user_rect,
transform=cam_id2view[cam_id],
):
canv.imshow(mean_im.astype(np.uint8), raw_l,
raw_b)
canv.text(
"mean",
display_rect[0],
display_rect[1] + display_rect[3],
font_size=font_size,
color_rgba=(0.5, 0.5, 0.9, 1),
shadow_offset=1,
)
# Display absdiff_im
display_rect = (
3 * margin + (scale * imw) * 2,
margin,
scale * absdiff_im.shape[1],
scale * absdiff_im.shape[0],
)
user_rect = (raw_l, raw_b, imw, imh)
absdiff_clip = np.clip(absdiff_im * contrast_scale,
0, 255)
with canv.set_user_coords(
display_rect,
user_rect,
transform=cam_id2view[cam_id],
):
canv.imshow(absdiff_clip.astype(np.uint8),
raw_l, raw_b)
canv.text(
"absdiff",
display_rect[0],
display_rect[1] + display_rect[3],
font_size=font_size,
color_rgba=(0.5, 0.5, 0.9, 1),
shadow_offset=1,
)
# Display morphed_im
display_rect = (
4 * margin + (scale * imw) * 3,
margin,
scale * morphed_im.shape[1],
scale * morphed_im.shape[0],
)
user_rect = (raw_l, raw_b, imw, imh)
morphed_clip = np.clip(morphed_im * contrast_scale,
0, 255)
with canv.set_user_coords(
display_rect,
user_rect,
transform=cam_id2view[cam_id],
):
canv.imshow(morphed_clip.astype(np.uint8),
raw_l, raw_b)
if 0:
canv.text(
"morphed",
display_rect[0],
display_rect[1] + display_rect[3],
font_size=font_size,
color_rgba=(0.5, 0.5, 0.9, 1),
shadow_offset=1,
)
# Display time-averaged absdiff_im
display_rect = (
5 * margin + (scale * imw) * 4,
margin,
scale * av_im_show.shape[1],
scale * av_im_show.shape[0],
)
user_rect = (
lowerleft[0],
lowerleft[1],
av_im_show.shape[1],
av_im_show.shape[0],
)
with canv.set_user_coords(
display_rect,
user_rect,
transform=cam_id2view[cam_id],
):
canv.imshow(
av_im_show.astype(np.uint8),
lowerleft[0],
lowerleft[1],
)
canv.plot(
xplt, yplt,
color_rgba=(0, 1, 0,
0.5)) # the orientation line
canv.text(
"stacked/flattened",
display_rect[0],
display_rect[1] + display_rect[3],
font_size=font_size,
color_rgba=(0.5, 0.5, 0.9, 1),
shadow_offset=1,
)
canv.text(
"%s frame % 7d: eccentricity % 5.1f, min N images %d, actual N images %d"
% (
cam_id,
fno,
eccentricity,
stack_N_images_min,
n_good_images,
),
0,
15,
font_size=font_size,
color_rgba=(0.6, 0.7, 0.9, 1),
shadow_offset=1,
)
canv.save()
# Save results to new table
if 0:
recarray = np.rec.array(list_of_rows_of_data2d,
dtype=Info2DCol_description)
dest_table.append(recarray)
dest_table.flush()
dest_table.attrs.has_ibo_data = True
data_file.close()
