def read_mrc_data(path, show_progress=False):
#return read_mrc(path=path, show_progress=show_progress)['value']
return TIM.read_data(path)
def read_mrc_data(path, show_progress=False):
return TIM.read_data(path)
@jit(nopython=True)
def generate_feature_vector(b, label, cluster_center_number):
result = np.array([0] * cluster_center_number)
# sum_f = np.array((cluster_center_number, 2), 0)
sum_f = np.array([[0 for i in range(2)] for j in range(cluster_center_number)])
for i in range(0, b.shape[0]):
for j in range(0, b.shape[1]):
for k in range(0, b.shape[2]):
sum_f[label[i][j][k]][1] = sum_f[label[i][j][k]][1] + 1
sum_f[label[i][j][k]][0] = sum_f[label[i][j][k]][0] + b[i][j][k]
for i in range(cluster_center_number):
assert sum_f[i][1] > 0
result[i] = sum_f[i][0] / sum_f[i][1]
return result
if __name__ == "__main__":
# file path
path = input("Enter data path: ")
from aitom.io import mrcfile_proxy
a = mrcfile_proxy.read_data(path)
print("file has been read, shape is", a.shape)
start_time = time.time()
saliency_detection(a=a, gaussian_sigma=2.5, gabor_sigma=14.0, gabor_lambda=13.0, cluster_center_number=10000,
multiprocessing_num=0, pick_num=1000, save_flag=True)
end_time = time.time()
print('saliency detection takes', end_time - start_time, 's')
dmax = np.amax(dimg)
dimg = (dimg / dmax) * 255
# TODO: Change the image saving path
img_path = '/Users/apple/Desktop/Lab/Zach_Project/Denoising_Result/Difference_' + str(
type) + '.png'
plt.imsave(img_path, dimg, cmap='gray')
if __name__ == "__main__":
# TODO: Change the data path, name and Gussian denoise type
path = '/Users/apple/Desktop/Lab/Zach_Project/Sample_Data/aitom_demo_single_particle_tomogram.mrc'
name = 'aitom_demo_single_particle_tomogram'
G_type = 1
# read the volume data as numpy array
original = mrcfile_proxy.read_data(path)
# save a slice of original tomogram
oimg = (original[:, :, int(original.shape[2] / 2)]).copy()
# TODO: Change the orginal image saving directory
img_path = '/Users/apple/Desktop/Lab/Zach_Project/Denoising_Result/Original.png'
plt.imsave(img_path, oimg, cmap='gray')
# perform three diffrent kind of denoising
# The difference comparison between original tomogram and filtered tomogram is commented out
g_fimg = g_denoising(G_type,
a=original,
name=name,
gaussian_sigma=2.5,
save_flag=True)
# diff_compare(oimg, g_fimg, "Gaussian")