def calibrate_mag1_from_image_fn(center_fn, other_fn):
"""Calibrate pixel->stageposition coordinates from a set of images.
center_fn: `str`
Reference image at the center of the grid (with the clover in the middle)
other_fn: `tuple` of `str`
Set of images to cross correlate to the first reference image
return:
instance of Calibration class with conversion methods
"""
img_cent, h_cent = read_image(center_fn)
# binsize = h_cent["ImageBinsize"]
cam_dimensions = h_cent['ImageCameraDimensions']
bin_x, bin_y = cam_dimensions / np.array(img_cent.shape)
assert bin_x == bin_y, 'Binsizes do not match {bin_x} != {bin_y}'
binsize = int(bin_x)
img_cent, scale = autoscale(img_cent, maxdim=512)
x_cent, y_cent, _, _, _ = h_cent['StagePosition']
xy_cent = np.array([x_cent, y_cent])
print('Center:', center_fn)
print('Stageposition: x={:.0f} | y={:.0f}'.format(*xy_cent))
print()
shifts = []
stagepos = []
for i, fn in enumerate(other_fn):
img, h = read_image(fn)
img = imgscale(img, scale)
x_xobs, yobs, _, _, _ = h_cent['StagePosition']
print('Image:', fn)
print(f'Stageposition: x={xobs:.0f} | y={yobs:.0f}')
shift, error, phasediff = phase_cross_correlation(img_cent, img, upsample_factor=10)
print('Shift:', shift)
print()
stagepos.append((xobs, yobs))
shifts.append(shift)
# correct for binsize, store as binsize=1
shifts = np.array(shifts) * binsize / scale
stagepos = np.array(stagepos) - xy_cent
c = CalibStage.from_data(shifts, stagepos, reference_position=xy_cent, camera_dimensions=cam_dimensions)
c.plot()
c.to_file()
return c
def calibrate_stage_lowmag_from_image_fn(center_fn, other_fn):
"""Calibrate pixel->stageposition coordinates from a set of images.
center_fn: `str`
Reference image at the center of the grid (with the clover in the middle)
other_fn: `tuple` of `str`
Set of images to cross correlate to the first reference image
return:
instance of Calibration class with conversion methods
"""
img_cent, h_cent = read_image(center_fn)
img_cent, scale = autoscale(img_cent, maxdim=512)
x_cent, y_cent, _, _, _ = h_cent['StagePosition']
xy_cent = np.array([x_cent, y_cent])
print('Center:', center_fn)
print('Stageposition: x={:.0f} | y={:.0f}'.format(*xy_cent))
print()
binsize = h_cent['ImageBinsize']
shifts = []
stagepos = []
for fn in other_fn:
img, h = read_image(fn)
img = imgscale(img, scale)
xobs, yobs, _, _, _ = h['StagePosition']
print('Image:', fn)
print(f'Stageposition: x={xobs:.0f} | y={yobs:.0f}')
print()
shift, error, phasediff = phase_cross_correlation(img_cent,
img,
upsample_factor=10)
stagepos.append((xobs, yobs))
shifts.append(shift)
# correct for binsize, store as binsize=1
shifts = np.array(shifts) * binsize / scale
stagepos = np.array(stagepos) - xy_cent
c = CalibStage.from_data(shifts,
stagepos,
reference_position=xy_cent,
header=h_cent)
c.plot()
return c
def show_image(self):
row = self.tv.item(self.tv.focus())
try:
frame, number, prediction, size, stage_x, stage_y = row["values"]
except ValueError: # no row selected
print("No row selected")
return
fn = Path(row["text"])
root = fn.parents[1]
name = fn.stem.rsplit("_", 1)[0] # strip crystal number
image_fn = root / "images" / f"{name}{fn.suffix}"
if not fn.exists():
print(f"No such file: {fn}")
return
if not image_fn.exists():
print(f"No such file: {image_fn}")
return
data, data_h = read_image(fn)
img, img_h = read_image(image_fn)
cryst_x, cryst_y = img_h["exp_crystal_coords"][number]
fig = plt.figure()
ax1 = plt.subplot(
121,
title=
f"Image\nframe={frame} | number={number}\nsize={size} | x,y=({stage_x}, {stage_y})",
aspect="equal")
ax2 = plt.subplot(
122,
title=f"Diffraction pattern\nprediction={prediction}",
aspect="equal")
# img = np.rot90(img, k=3) # img needs to be rotated to match display
cryst_y, cryst_x = img.shape[0] - cryst_x, cryst_y # rotate coordinates
coords = img_h["exp_crystal_coords"]
for c_x, c_y in coords:
c_y, c_x = img.shape[0] - c_x, c_y
ax1.plot(c_y, c_x, marker="+", color="blue", mew=2)
ax1.imshow(img, vmax=np.percentile(img, 99.5))
ax1.plot(cryst_y, cryst_x, marker="+", color="red", mew=2)
ax2.imshow(data, vmax=np.percentile(data, 99.5))
ShowMatplotlibFig(self, fig, title=fn)
def main():
try:
fn = sys.argv[1]
except:
print("Usage: instamatic.viewer IMG.tiff")
exit()
img, h = read_image(fn)
print("""Loading data: {}
size: {} kB
shape: {}
range: {}-{}
dtype: {}
""".format(fn, img.nbytes/1024, img.shape, img.min(), img.max(), img.dtype))
max_len = max([len(s) for s in h.keys()])
fmt = "{{:{}s}} = {{}}".format(max_len)
for key in sorted(h.keys()):
print(fmt.format(key, h[key]))
plt.imshow(img, cmap="gray")
plt.title(fn)
plt.show()
def main_entry():
import argparse
description = """Find crystals in images."""
parser = argparse.ArgumentParser(
description=description,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('args',
type=str,
nargs='*',
metavar='IMG',
help='Images to find crystals in.')
options = parser.parse_args()
args = options.args
from instamatic.formats import read_image
import warnings
warnings.simplefilter('ignore')
for fn in args:
img, h = read_image(fn)
crystals = find_crystals_timepix(img,
h['exp_magnification'],
plot=True)
for crystal in crystals:
print(crystal)
def test_cbf(data, header):
out = 'out.cbf'
formats.write_cbf(out, data, header)
assert os.path.exists(out)
# Reader Not implemented:
with pytest.raises(NotImplementedError):
img, h = formats.read_image(out)
def test_hdf5(data, header):
out = 'out.h5'
formats.write_hdf5(out, data, header)
assert os.path.exists(out)
img, h = formats.read_image(out)
assert np.allclose(img, data)
assert header == h
def test_smv(data, header):
out = 'out.smv'
formats.write_adsc(out, data, header)
assert os.path.exists(out)
img, h = formats.read_image(out)
assert np.allclose(img, data)
assert 'value' in h # changes type to str
assert h['string'] == header['string']
def test_mrc(data, header):
out = 'out.mrc'
# Header not supported
formats.write_mrc(out, data)
assert os.path.exists(out)
img, h = formats.read_image(out)
assert np.allclose(img, data)
assert isinstance(header, dict)
def main_entry():
from instamatic.formats import read_image
import warnings
warnings.simplefilter('ignore')
for fn in sys.argv[1:]:
img, h = read_image(fn)
crystals = find_crystals_timepix(img,
h["exp_magnification"],
plot=True)
for crystal in crystals:
print(crystal)
def main():
import argparse
description = """
Simple image viewer to open any image collected collected using instamatic. Supported formats include `TIFF`, `MRC`, [`HDF5`](http://www.h5py.org/), and [`SMV`](https://strucbio.biologie.uni-konstanz.de/ccp4wiki/index.php/SMV_file_format).
"""
parser = argparse.ArgumentParser(
description=description,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('args',
type=str,
nargs=1,
metavar='IMG',
help='Image to display (TIFF, HDF5, MRC, SMV).')
options = parser.parse_args()
args = options.args
fn = args[0]
img, h = read_image(fn)
print(f"""Loading data: {fn}
size: {img.nbytes / 1024} kB
shape: {img.shape}
range: {img.min()}-{img.max()}
dtype: {img.dtype}
""")
max_len = max(len(s) for s in h.keys())
fmt = f'{{:{max_len}s}} = {{}}'
for key in sorted(h.keys()):
print(fmt.format(key, h[key]))
plt.imshow(img, cmap='gray')
plt.title(fn)
plt.show()
fontdict = {'fontsize': 30}
vmax_im = 500
vmax_diff = 1500
vmin_diff = 0
save = True
number = 0
if not os.path.isdir('movie'):
os.mkdir('movie')
for i, fn in enumerate(tqdm(fns)):
dps = glob.glob(fn.replace('images', 'data').replace('.h5', '_*.h5'))
im, h_im = read_image(fn)
crystal_coords = np.array(h_im['exp_crystal_coords'])
for j, dp in enumerate(dps):
try:
diff, h_diff = read_image(dp)
except BaseException:
print('fail')
continue
x, y = crystal_coords[j]
fig, (ax1, ax2) = plt.subplots(1,
2,
figsize=(21.5, 10),
def calibrate_mag1_from_image_fn(center_fn, other_fn):
"""
Calibrate pixel->stageposition coordinates from a set of images
center_fn: `str`
Reference image at the center of the grid (with the clover in the middle)
other_fn: `tuple` of `str`
Set of images to cross correlate to the first reference image
return:
instance of Calibration class with conversion methods
"""
img_cent, h_cent = read_image(center_fn)
# binsize = h_cent["ImageBinSize"]
cam_dimensions = h_cent["ImageCameraDimensions"]
bin_x, bin_y = cam_dimensions / np.array(img_cent.shape)
assert bin_x == bin_y, "Binsizes do not match {bin_x} != {bin_y}"
binsize = int(bin_x)
img_cent, scale = autoscale(img_cent, maxdim=512)
x_cent, y_cent, _, _, _ = h_cent["StagePosition"]
# x_cent=40943.0
# y_cent=-6258.4
xy_cent = np.array([x_cent, y_cent])
print("Center:", center_fn)
print("Stageposition: x={:.0f} | y={:.0f}".format(*xy_cent))
print()
shifts = []
stagepos = []
# stage = ((30943.5, -16255.5),
# (30874.701171875, -11258.2998046875),
# (31009.701171875, -6256.89990234375),
# (30876.0, -1256.9000244140625),
# (31011.0, 3744.400146484375),
# (35943.5, -16256.900390625),
# (35874.6015625, -11256.900390625),
# (36011.0, -6256.89990234375),
# (35874.6015625, -1256.9000244140625),
# (36012.30078125, 3743.0),
# (40943.40234375, -16252.7001953125),
# (40943.40234375, -11256.900390625),
# (40943.40234375, -6258.30029296875),
# (40943.40234375, -1256.9000244140625),
# (40943.40234375, 3744.400146484375),
# (45943.40234375, -16255.5),
# (45943.40234375, -11258.2998046875),
# (45943.40234375, -6255.5),
# (45943.40234375, -1259.7000732421875),
# (45943.40234375, 3745.800048828125),
# (50943.40234375, -16255.5),
# (50943.40234375, -11258.2998046875),
# (50943.40234375, -6256.89990234375),
# (50943.40234375, -1256.9000244140625),
# (50943.40234375, 3743.0))
for i, fn in enumerate(other_fn):
img, h = read_image(fn)
img = imgscale(img, scale)
x_xobs, yobs, _, _, _ = h_cent["StagePosition"]
# xobs, yobs = stage[i]
print("Image:", fn)
print(f"Stageposition: x={xobs:.0f} | y={yobs:.0f}")
shift = cross_correlate(img_cent,
img,
upsample_factor=10,
verbose=False)
print("Shift:", shift)
print()
stagepos.append((xobs, yobs))
shifts.append(shift)
# correct for binsize, store as binsize=1
shifts = np.array(shifts) * binsize / scale
stagepos = np.array(stagepos) - xy_cent
c = CalibStage.from_data(shifts,
stagepos,
reference_position=xy_cent,
camera_dimensions=cam_dimensions)
c.plot()
c.to_file()
return c
