def test_basic(self):
path = '/path/to/image'
loc2 = emc.ImageLocation('/path/to/image')
self.assertEqual(loc2.index, emc.ImageLocation.FIRST)
self.assertEqual(loc2.path, path)
loc3 = emc.ImageLocation(path, 2)
self.assertEqual(loc3.path, path)
self.assertNotEqual(loc2, loc3)
loc3.index = emc.ImageLocation.FIRST
self.assertEqual(loc2, loc3)
def createImageModel(cls, path):
""" Create an ImageModel reading path as an emc.Image. """
image = emc.Image()
loc = emc.ImageLocation(path)
image.read(loc)
return models.ImageModel(data=np.array(image, copy=False),
location=(loc.index, loc.path))
def test_readMrc(self):
mrcIO = emc.ImageFile()
testDataPath = os.environ.get("EM_TEST_DATA", None)
if testDataPath:
# 2D image (a big one): micrograph
micName = "xmipp_tutorial/micrographs/BPV_1386.mrc"
micDim = emc.ArrayDim(9216, 9441, 1, 1)
# 2D stack of particles
stackName = "emx/alignment/Test2/stack2D.mrc"
stackDim = emc.ArrayDim(128, 128, 1, 100)
# 3D volumes
vol1Name = "resmap/t20s_proteasome_full.map"
vol1Dim = emc.ArrayDim(300, 300, 300, 1)
fileDims = {
micName: (micDim, 87008256),
stackName: (stackDim, 65536), # 128 * 128 * 4
vol1Name: (vol1Dim, 108000000)
}
for fn, (dim, size) in fileDims.items():
img = emc.Image()
loc = emc.ImageLocation(os.path.join(testDataPath, fn), 1)
print(">>> Reading image: ", loc.path)
img.read(loc)
dim.n = 1
self.assertEqual(img.getDim(), dim)
self.assertEqual(img.getDataSize(), size)
# Test that we can read also from string
# that will be converted to ImageLocation
img = emc.Image()
try:
img.read(emc.ImageLocation(os.path.join(testDataPath,
micName)))
import numpy as np
a = np.array(img, copy=False)
except Exception as ex:
raise ex
self.assertEqual(img.getDim(), micDim)
def test_VolumeModel(self):
volName = self.getDataPaths()[2]
print("Checking %s" % volName)
volModel = dv.models.VolumeModel(
data=emv.utils.ImageManager().getData(volName))
minValue, maxValue = volModel.getMinMax()
def _check(model):
self.assertEqual(model.getDim(), (300, 300, 300))
self.assertAlmostEqual(minValue, -0.011036, places=5)
self.assertAlmostEqual(maxValue, 0.027878, places=5)
self.assertIsNotNone(model.getData())
_check(volModel)
sliceModel = volModel.getSlicesModel(dv.models.AXIS_Z)
_check(sliceModel)
imageModel = volModel.getSliceImageModel(dv.models.AXIS_Z, 0)
self.assertEqual(imageModel.getDim(), (300, 300))
volModel.setData(None)
self.assertIsNone(volModel.getData())
self.assertIsNone(volModel.getDim())
return
# 2D stack of particles
stackName = "emx/alignment/Test2/stack2D.mrc"
stackDim = emc.ArrayDim(128, 128, 1, 100)
# 3D volumes
vol1Name = "resmap/t20s_proteasome_full.map"
vol1Dim = emc.ArrayDim(300, 300, 300, 1)
fileDims = {
volName: (micDim, 87008256),
stackName: (stackDim, 65536), # 128 * 128 * 4
vol1Name: (vol1Dim, 108000000)
}
for fn, (dim, size) in fileDims.items():
img = emc.Image()
loc = emc.ImageLocation(os.path.join(testDataPath, fn), 1)
print(">>> Reading image: ", loc.path)
img.read(loc)
dim.n = 1
self.assertEqual(img.getDim(), dim)
self.assertEqual(img.getDataSize(), size)
#!/usr/bin/env python
from __future__ import print_function
import emcore as emc
import numpy as np
import sys
if __name__ == '__main__':
if len(sys.argv) < 2:
print("Provide input file(s). ")
sys.exit(1)
img = emc.Image()
print("Computing stats with numpy: ")
for fn in sys.argv[1:]:
loc = emc.ImageLocation(fn)
img.read(loc)
data = np.array(img, copy=False)
print("\nFile: ", fn, "\nmin:", np.amin(data), "max:", np.amax(data),
"mean: %0.5f" % np.mean(data), "std: %0.5f" % np.std(data))
mrcIO = emc.ImageFile('mrc')
dataPath = os.environ.get("EM_TEST_DATA", None)
if dataPath:
micName = "xmipp_tutorial/micrographs/BPV_1386.mrc"
micDim = emc.ArrayDim(9216, 9441, 1, 1)
stackName = "emx/alignment/Test2/stack2D.mrc"
stackDim = emc.ArrayDim(128, 128, 1, 100)
fileDims = {micName: micDim, stackName: stackDim}
n = 5
m = 5
img = emc.Image()
try:
loc = emc.ImageLocation(os.path.join(dataPath, stackName))
# Read many images from the particles stack and put them
# together using a numpy array and showing in matplotlib
for i in range(n * m):
loc.index = i + 1
img.read(loc)
a = np.array(img, copy=False)
dim = img.getDim()
iy = i / m
ix = i % m
print("dim: ", dim, "type: ", img.getType())
print("i: ", i, "iy: ", iy, "ix: ", ix)
if i == 0: # only first time
aa = np.zeros((dim.y * n, dim.x * m), dtype=a.dtype)
if __name__ == '__main__':
mrcIO = emc.ImageFile('mrc')
dataPath = os.environ.get("EM_TEST_DATA", None)
if dataPath:
vol1Name = "resmap/t20s_proteasome_full.map"
vol1Dim = emc.ArrayDim(300, 300, 300, 1)
n = 5
m = 5
img = emc.Image()
try:
loc = emc.ImageLocation(os.path.join(dataPath, vol1Name), 1)
img.read(loc)
a = np.array(img, copy=False)
dim = img.getDim()
# show central slice on each axis
aa = np.zeros((dim.y, dim.x * 3), dtype=a.dtype)
sliceZ = dim.z / 2
aa[:, 0:dim.x] = a[sliceZ, :, :]
sliceY = dim.y / 2
aa[:, dim.x:2*dim.x] = a[:, sliceY, :]
sliceX = dim.x / 2
aa[:, 2*dim.x:3*dim.x] = a[:, :, sliceX]