def analyze_images_in_folder(self,
folder,
generate_zmax=False,
show_result=True,
save_mask=True,
save_excel=True):
"""
Given the folder, perform general analysis over the images in it.
Parameters
----------
folder : str
Path to the folder.
generate_zmax : bool, optional
Whether to calcaulate the z-max projection first. The default is False.
show_result : bool, optional
If show the machine learning segmentation results. The default is True.
save_mask : bool, optional
DESCRIPTION. The default is True.
save_excel : bool, optional
DESCRIPTION. The default is True.
Returns
-------
cell_Data : pd.dataframe
DESCRIPTION.
"""
flat_cell_counted_in_folder = 0
total_cells_counted_in_folder = 0
# If need to do zmax projection first
if generate_zmax == True:
ProcessImage.cam_screening_post_processing(folder)
# Here a new folder for maxProjection is generated inside, change the path
folder = os.path.join(folder, 'maxProjection')
# If background images are taken
if os.path.exists(os.path.join(folder, 'background')):
# If the background image is taken to substract out
background_substraction = True
# Get all the background files names
background_fileNameList = []
for file in os.listdir(os.path.join(folder, 'background')):
if "tif" in file:
background_fileNameList.append(
os.path.join(folder, 'background', file))
background_image = ProcessImage.image_stack_calculation(
background_fileNameList, operation="mean")
# Get a list of file names
fileNameList = []
for file in os.listdir(folder):
if "tif" in file and "LED" not in file:
fileNameList.append(file)
print(fileNameList)
# Analyse each image
for image_file_name in fileNameList:
print(image_file_name)
Rawimage = imread(os.path.join(folder, image_file_name))
if background_substraction == True:
Rawimage = np.abs(Rawimage - background_image)
# Analyze each image
# Run the detection on input image.
MLresults = self.DetectionOnImage(Rawimage,
axis=None,
show_result=show_result)
if save_mask == True:
if not os.path.exists(os.path.join(folder, 'ML_masks')):
# If the folder is not there, create the folder
os.mkdir(os.path.join(folder, 'ML_masks'))
fig, ax = plt.subplots()
# Set class_names = [None,None,None,None] to mute class name display.
visualize.display_instances(
Rawimage,
MLresults['rois'],
MLresults['masks'],
MLresults['class_ids'],
class_names=[None, None, None, None],
ax=ax,
centre_coors=MLresults['Centre_coor'],
Centre_coor_radius=2,
WhiteSpace=(
0, 0)) #MLresults['class_ids'],MLresults['scores'],
# ax.imshow(fig)
fig.tight_layout()
# Save the detection Rawimage
fig_name = os.path.join(
folder, 'ML_masks', 'ML_mask_{}.png'.format(
image_file_name[0:len(image_file_name) - 4]))
plt.savefig(fname=fig_name,
dpi=200,
pad_inches=0.0,
bbox_inches='tight')
if flat_cell_counted_in_folder == 0:
cell_Data, flat_cell_counted_in_folder, total_cells_counted_in_coord = \
ProcessImage.retrieveDataFromML(Rawimage, MLresults, image_file_name, flat_cell_counted_in_folder)
else:
Cell_Data_new, flat_cell_counted_in_folder, total_cells_counted_in_coord = \
ProcessImage.retrieveDataFromML(Rawimage, MLresults, image_file_name, flat_cell_counted_in_folder)
if len(Cell_Data_new) > 0:
cell_Data = cell_Data.append(Cell_Data_new)
total_cells_counted_in_folder += total_cells_counted_in_coord
if save_excel == True:
# Save to excel
cell_Data.to_excel(
os.path.join(
folder, 'CellsProperties_{}flat_outof_{}cells.xlsx'.format(
flat_cell_counted_in_folder,
total_cells_counted_in_folder)))
return cell_Data
def analyze_images_in_folder(
self,
folder,
generate_zmax=False,
show_result=True,
save_mask=True,
save_excel=True,
):
"""
Given the folder, perform general analysis over the images in it.
Parameters
----------
folder : str
Path to the folder.
generate_zmax : bool, optional
Whether to calcaulate the z-max projection first. The default is False.
show_result : bool, optional
If show the machine learning segmentation results. The default is True.
save_mask : bool, optional
DESCRIPTION. The default is True.
save_excel : bool, optional
DESCRIPTION. The default is True.
Returns
-------
cell_Data : pd.dataframe
DESCRIPTION.
"""
flat_cell_counted_in_folder = 0
total_cells_counted_in_folder = 0
background_substraction = False
root_folder = folder
# If need to do zmax projection first
if generate_zmax == True:
ProcessImage.cam_screening_post_processing(root_folder)
# Here a new folder for maxProjection is generated inside, change the path
folder = os.path.join(root_folder, "maxProjection")
# If background images are taken
if os.path.exists(os.path.join(root_folder, "background")):
# If the background image is taken to substract out
background_substraction = True
print("Run background substraction.")
# Get all the background files names
background_fileNameList = []
for file in os.listdir(os.path.join(root_folder, "background")):
if "calculated background" not in file:
if "tif" in file or "TIF" in file:
background_fileNameList.append(
os.path.join(root_folder, "background", file))
# Average over multiple images
background_image = ProcessImage.image_stack_calculation(
background_fileNameList, operation="mean")
# # Smooth the image
# background_image = ProcessImage.average_filtering(
# background_image, filter_side_length = 25)
# Save the individual file.
with skimtiff.TiffWriter(
os.path.join(root_folder, "background",
"calculated background.tif"),
imagej=True,
) as tif:
tif.save(background_image.astype(np.uint16), compress=0)
# Get a list of file names
fileNameList = []
for file in os.listdir(folder):
if "tif" in file and "LED" not in file:
fileNameList.append(file)
print(fileNameList)
# Analyse each image
for image_file_name in fileNameList:
print(image_file_name)
Rawimage = imread(os.path.join(folder, image_file_name))
if background_substraction == True:
Rawimage = np.abs(Rawimage - background_image).astype(
np.uint16)
camera_dark_level = 100
# # Normalize to the illumination intensity
# Rawimage = np.uint16(Rawimage \
# / ((background_image - camera_dark_level)\
# /(np.amin(background_image) - camera_dark_level)))
# Analyze each image
# Run the detection on input image.
MLresults = self.DetectionOnImage(Rawimage,
axis=None,
show_result=show_result)
if save_mask == True:
if not os.path.exists(os.path.join(folder, "ML_masks")):
# If the folder is not there, create the folder
os.mkdir(os.path.join(folder, "ML_masks"))
fig, ax = plt.subplots()
# Set class_names = [None,None,None,None] to mute class name display.
visualize.display_instances(
Rawimage,
MLresults["rois"],
MLresults["masks"],
MLresults["class_ids"],
class_names=[None, None, None, None],
ax=ax,
centre_coors=MLresults["Centre_coor"],
Centre_coor_radius=2,
WhiteSpace=(0, 0),
) # MLresults['class_ids'],MLresults['scores'],
# ax.imshow(fig)
fig.tight_layout()
# Save the detection Rawimage
fig_name = os.path.join(
folder,
"ML_masks",
"ML_mask_{}.png".format(
image_file_name[0:len(image_file_name) - 4]),
)
plt.savefig(fname=fig_name,
dpi=200,
pad_inches=0.0,
bbox_inches="tight")
if flat_cell_counted_in_folder == 0:
(
cell_Data,
flat_cell_counted_in_folder,
total_cells_counted_in_coord,
) = ProcessImage.retrieveDataFromML(
Rawimage, MLresults, image_file_name,
flat_cell_counted_in_folder)
else:
(
Cell_Data_new,
flat_cell_counted_in_folder,
total_cells_counted_in_coord,
) = ProcessImage.retrieveDataFromML(
Rawimage, MLresults, image_file_name,
flat_cell_counted_in_folder)
if len(Cell_Data_new) > 0:
cell_Data = cell_Data.append(Cell_Data_new)
total_cells_counted_in_folder += total_cells_counted_in_coord
if save_excel == True:
# Save to excel
cell_Data.to_excel(
os.path.join(
folder,
"CellsProperties_{}flat_outof_{}cells.xlsx".format(
flat_cell_counted_in_folder,
total_cells_counted_in_folder),
))
return cell_Data