def Get_feat_test(ind,h,w, M_mean,nb_features):
img=np.empty([h,w,3])
GRAY_IM=np.empty([h,w])
img=M_mean[:,:,:][ind]
gray_img = rgb2gray(img)
gray_img = (gray_img * 255 ).astype( np.uint8 )
GRAY_IM[:,:]=gray_img-np.mean(gray_img)
GRAY_IM
FV=np.empty([nb_features])
matrix = PyImageMatrix()
matrix.allocate(h, w)
numpy_matrix = matrix.as_ndarray()
numpy_matrix[:] = GRAY_IM[:,:]
fv = FeatureVector( name='stufff', long=True, original_px_plane=matrix )
t1 = time.time()
fv.GenerateFeatures(quiet=True, write_to_disk=False)
t2 = time.time()
FV[:]=fv.values
Names=fv.feature_names
FV
FV=FV.astype(float)
return(FV)
def test_LoadSubsetFromFile(self):
"""Calculate one feature family, store to sig, load sig, and use to create larger fs"""
img_filename = "lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E.tif"
orig_img_filepath = pychrm_test_dir + sep + img_filename
full_list = list((
'Pixel Intensity Statistics () [3]',
'Pixel Intensity Statistics (Fourier ()) [3]',
))
tempdir = mkdtemp()
from shutil import copy
try:
# copy the tiff to the tempdir so the .sig files end up there too
copy(orig_img_filepath, tempdir)
input_img_path = tempdir + sep + img_filename
kwargs = {}
kwargs['source_filepath'] = input_img_path
kwargs['tile_num_cols'] = 6
kwargs['tile_num_rows'] = 5
kwargs['tiling_scheme'] = '5x6'
kwargs['tile_col_index'] = 0
kwargs['tile_row_index'] = 0
kwargs['feature_names'] = full_list[1:]
fv1 = FeatureVector(**kwargs).GenerateFeatures(quiet=False)
# modify the sig value and write to sig file to make sure subsequent loading
# used the value from disk and not recalculated it:
fv1.values[0] = -9999
fv1.ToSigFile(quiet=False)
# Now, ask for more features:
kwargs['feature_names'] = full_list
fv2 = FeatureVector(**kwargs)
with self.assertRaises(IncompleteFeatureSetError):
fv2.LoadSigFile()
#import pdb; pdb.set_trace()
fv2.GenerateFeatures()
#self.assertEqual( fv1.values[0], fv2.values[0] )
finally:
rmtree(tempdir)
def calc_features(img_array,
tag,
long=False,
w=None,
h=None,
dx=None,
dy=None):
if len(img_array.shape) != 2:
raise ValueError("array must be two-dimensional")
for i, j, tile in gen_tiles(img_array, w=w, h=h, dx=dx, dy=dy):
signatures = FeatureVector(basename=tag, long=long)
signatures.original_px_plane = get_image_matrix(tile)
signatures.GenerateFeatures(write_to_disk=False)
signatures.x, signatures.y = j, i
signatures.h, signatures.w = tile.shape
yield signatures
def Features_calcul_np_GrayscaleIm_WND(ind, nb_features, w, h, GRAY):
" calculate WND Charm Features from grayscale images (2919 features)"
FV = np.empty([nb_features])
matrix = PyImageMatrix()
matrix.allocate(h, w)
numpy_matrix = matrix.as_ndarray()
numpy_matrix[:] = GRAY[:, :][ind]
fv = FeatureVector(name='stufff', long=True, original_px_plane=matrix)
t1 = time.time()
fv.GenerateFeatures(quiet=True, write_to_disk=False)
t2 = time.time()
FV[:] = fv.values
Names = fv.feature_names
FV
return (FV, Names)
def __get_exp_features(self, a, long=False):
fn = os.path.join(self.wd, "img.tiff")
dump_to_tiff(a, fn)
fv = FeatureVector(source_filepath=fn, long=long)
return fv.GenerateFeatures(write_to_disk=False)
def Get_proba(ind, h, w, M_mean, nb_features):
img = np.empty([h, w, 3])
GRAY_IM = np.empty([h, w])
img = M_mean[:, :, :][ind]
gray_img = rgb2gray(img)
gray_img = (gray_img * 255).astype(np.uint8)
GRAY_IM[:, :] = gray_img - np.mean(gray_img)
GRAY_IM
FV = np.empty([nb_features])
matrix = PyImageMatrix()
matrix.allocate(h, w)
numpy_matrix = matrix.as_ndarray()
numpy_matrix[:] = GRAY_IM[:, :]
fv = FeatureVector(name='stufff', long=True, original_px_plane=matrix)
t1 = time.time()
fv.GenerateFeatures(quiet=True, write_to_disk=False)
t2 = time.time()
FV[:] = fv.values
Names = fv.feature_names
FV
FV = FV.astype(float)
pca = decomposition.PCA()
RFC = RandomForestClassifier()
estimators = [('reduce_dim', pca), ('Random_Forest', RFC)]
pipe = Pipeline(estimators)
params = dict(reduce_dim__n_components=90,
Random_Forest__n_estimators=200,
Random_Forest__random_state=0)
filename_Features_two_blocs = projectpath + 'io/Output/Features_two_blocs.npy'
FV_N = np.load(filename_Features_two_blocs)
X = FV_N
Data_FRAMES = pd.read_pickle(projectpath + 'io/Output/Dataframe_.pkl')
yr = Get_true_y(Data_FRAMES)
filename_yr = projectpath + 'io/Output/yr.npy'
np.save(filename_yr, yr)
yr = np.load(filename_yr)
RFC = RandomForestClassifier(n_estimators=200, random_state=0)
predictedVAL = cross_val_predict(RFC, X, yr, n_jobs=-1)
metrics.accuracy_score(yr, predictedVAL)
Conf_Mat = confusion_matrix(yr, predictedVAL)
RFC.fit(X, yr)
predict_probab = np.ones([M_mean.shape[0], 3])
predict_proba = RFC.predict_proba(FV)
predict_probab[ind, 0] = predict_proba[:, 0]
predict_probab[ind, 1] = predict_proba[:, 1]
predict_probab[ind, 2] = predict_proba[:, 2]
return (predict_probab)
