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How to use move_window method in AutoItDriverServer

Best Python code snippet using AutoItDriverServer_python

crop_images.py

Source:crop_images.py

1import os2import SimpleITK as sitk3import numpy as np4import get_data as gd5import Preprocessing as p6import useful_functions as uf7def crop_images(dataframe, new_dimension, destination_folders_list, image_filename, mask_filename, tumor_value):8    """9    The function crops the images with path given under 'imagePaths' in the dataframe to the dimension specified by new_dimension.10    The cropped images and masks are saved to the destination paths, given in destination_folders_list.11    :param dataframe: dataframe containing information of image/mask paths and dimensions12    :param new_dimension: wanted image dimensions after cropping13    :param destination_folders_list: list of destination paths to each patient14    :param image_filename: filename of image15    :param mask_filename: filename of mask16    :param tumor_value: value of tumor voxels in mask17    :return: cropped images and masks are saved to destination paths18    """19    for i in range(len(dataframe['imagePaths'])):20        image_original = sitk.ReadImage(dataframe['imagePaths'][i])21        mask_original = sitk.ReadImage(dataframe['maskPaths'][i])22        image_imsize_original = image_original.GetSize()23        if image_imsize_original[1] != 256:24            crop_start = int((image_imsize_original[1] - new_dimension) / 2)25            print(crop_start)26            crop_stop = int(image_imsize_original[1] - crop_start)27            print(crop_stop)28            image_cropped = image_original[crop_start:crop_stop, (crop_start + 10):(crop_stop + 10), :]29            mask_cropped = mask_original[crop_start:crop_stop, (crop_start + 10):(crop_stop + 10), :]30            print('Original imagesize:', dataframe['imagePaths'][i], image_original.GetSize())31            print('Original masksize:', dataframe['maskPaths'][i], mask_original.GetSize())32            print('Cropped imagesize:', image_cropped.GetSize())33            print('Cropped masksize:', mask_cropped.GetSize())34            mask_array_original = sitk.GetArrayFromImage(mask_original)35            mask_array_cropped = sitk.GetArrayFromImage(mask_cropped)36            tumor_originally = np.count_nonzero(mask_array_original.flatten() == tumor_value)37            tumor_cropped = np.count_nonzero(mask_array_cropped.flatten() == tumor_value)38            move_window = 039            while tumor_originally != tumor_cropped:40                print('THE AMOUNT OF TUMOR IS REDUCED AFTER CROPPING! PATH:', dataframe['imagePaths'][i])41                move_window += 542                if image_filename=='T2.nii':43                    image_cropped = image_original[(crop_start - move_window):(crop_stop - move_window),44                                    (crop_start + 10 - move_window):(crop_stop + 10 - move_window), :]45                    mask_cropped = mask_original[(crop_start - move_window):(crop_stop - move_window),46                                   (crop_start + 10 - move_window):(crop_stop + 10 - move_window), :]47                else:48                    image_cropped = image_original[(crop_start + move_window):(crop_stop + move_window),49                                    (crop_start + 10 + move_window):(crop_stop + 10 + move_window), :]50                    mask_cropped = mask_original[(crop_start + move_window):(crop_stop + move_window),51                                   (crop_start + 10 + move_window):(crop_stop + 10 + move_window), :]52                mask_array_cropped = sitk.GetArrayFromImage(mask_cropped)53                tumor_cropped = np.count_nonzero(mask_array_cropped.flatten() == tumor_value)54                print('Tumor before cropping:', tumor_originally)55                print('Tumor after moving cropping window:', tumor_cropped)56        else:57            image_cropped = image_original58            mask_cropped = mask_original59        sitk.WriteImage(image_cropped, os.path.join(destination_folders_list[i], image_filename))60        sitk.WriteImage(mask_cropped, os.path.join(destination_folders_list[i], mask_filename))61def crop_masks(dataframe, new_dimension, destination_folders_list, mask_filename, tumor_value):62    """63    The function crops the masks with path given under 'maskPaths' in the dataframe to the dimension specified by new_dimension.64    The cropped images and masks are saved to the destination paths, given in destination_folders_list.65    :param dataframe: dataframe containing information of image/mask paths and dimensions66    :param new_dimension: wanted image dimensions after cropping67    :param destination_folders_list: list of destination paths to each patient68    :param mask_filename: filename of mask69    :param tumor_value: value of tumor voxels in mask70    :return: cropped masks are saved to destination paths71    """72    for i in range(len(dataframe['maskPaths'])):73        mask_original = sitk.ReadImage(dataframe['maskPaths'][i])74        mask_imsize_original = mask_original.GetSize()75        if mask_imsize_original[1] != 256:76            crop_start = int((mask_imsize_original[1] - new_dimension) / 2)77            print(crop_start)78            crop_stop = int(mask_imsize_original[1] - crop_start)79            print(crop_stop)80            mask_cropped = mask_original[crop_start:crop_stop, (crop_start + 10):(crop_stop + 10), :]81            print('Original masksize:', dataframe['maskPaths'][i], mask_original.GetSize())82            print('Cropped masksize:', mask_cropped.GetSize())83            mask_array_original = sitk.GetArrayFromImage(mask_original)84            mask_array_cropped = sitk.GetArrayFromImage(mask_cropped)85            tumor_originally = np.count_nonzero(mask_array_original.flatten() == tumor_value)86            tumor_cropped = np.count_nonzero(mask_array_cropped.flatten() == tumor_value)87            move_window = 088            while tumor_originally != tumor_cropped:89                print('THE AMOUNT OF TUMOR IS REDUCED AFTER CROPPING! PATH:', dataframe['maskPaths'][i])90                move_window += 591                if mask_filename=='Manual_shh.nii' or mask_filename=='Manual_an.nii':92                    mask_cropped = mask_original[(crop_start - move_window):(crop_stop - move_window),93                                   (crop_start + 10 - move_window):(crop_stop + 10 - move_window), :]94                else:95                    mask_cropped = mask_original[(crop_start + move_window):(crop_stop + move_window),96                                   (crop_start + 10 + move_window):(crop_stop + 10 + move_window), :]97                mask_array_cropped = sitk.GetArrayFromImage(mask_cropped)98                tumor_cropped = np.count_nonzero(mask_array_cropped.flatten() == tumor_value)99                print('Tumor before cropping:', tumor_originally)100                print('Tumor after moving cropping window:', tumor_cropped)101        else:102            mask_cropped = mask_original103        sitk.WriteImage(mask_cropped, os.path.join(destination_folders_list[i], mask_filename))104def remove_nonTumor_slices(dataframe):105    """106    :param dataframe: dataframe with image paths and mask paths107    :return: saves images and masks which only contains tumor for each patient108    """109    #Iterate through each patient110    for i in range(len(dataframe['maskPaths'])):111        #Create image and mask object from file path112        image = sitk.ReadImage(dataframe['imagePaths'][i])113        mask_an = sitk.ReadImage(dataframe['maskPaths'][i])114        path_shh = dataframe['patientPaths'][i] + '/Manual_shh.nii'115        print(path_shh)116        mask_shh = sitk.ReadImage(path_shh)117        print(dataframe['imagePaths'][i])118        print(dataframe['maskPaths'][i])119        #Create image and mask as arrays120        image_array = sitk.GetArrayFromImage(image)121        mask_array_an = sitk.GetArrayFromImage(mask_an)122        mask_array_shh = sitk.GetArrayFromImage(mask_shh)123        slices = image.GetSize()[2] #Number of slices for the given patient124        tumor_slices = []125        #Iterate through all the image slices for a patient126        for j in range(slices):127            #If the slice contains tumor then save the slice index in the tumor_slices list128            if 1 in mask_array_an[j][:][:]:129                if dataframe['ID'][i] == 'LARC-RRP-033' and j == 10: #Do not include this slice (noise from radiologist)130                    break131                else:132                    tumor_slices.append(j)133        #Get the x- and y-dimensions of the images and masks134        x_dim = int(mask_array_an.shape[1])135        y_dim = int(mask_array_an.shape[2])136        #Create new image and mask arrays with the shape of the original image and mask objects, and with137        #the same number of slices as saved in tumor_slices list138        new_mask_array_an = np.zeros((len(tumor_slices), x_dim, y_dim))139        new_mask_array_shh = np.zeros((len(tumor_slices), x_dim, y_dim))140        new_image_array = np.zeros((len(tumor_slices), x_dim, y_dim))141        #Save the new images and masks as the original images and masks with tumor142        for k in range(len(tumor_slices)):143            new_mask_array_an[k][:][:] = mask_array_an[tumor_slices[k]][:][:]144            new_mask_array_shh[k][:][:] = mask_array_shh[tumor_slices[k]][:][:]145            new_image_array[k][:][:] = image_array[tumor_slices[k]][:][:]146        #Create image and mask from new arrays147        new_mask_an = sitk.GetImageFromArray(new_mask_array_an)148        new_mask_shh = sitk.GetImageFromArray(new_mask_array_shh)149        new_image = sitk.GetImageFromArray(new_image_array)150        #Save the new images and masks (only containing tumor) in the destination paths151        sitk.WriteImage(new_image, dataframe['imagePaths'][i])152        sitk.WriteImage(new_mask_an, dataframe['maskPaths'][i])153        sitk.WriteImage(new_mask_shh, path_shh)154def crop_t2_dwi_mask(dataframe, new_dimension, tumor_value, destination_folders_list, image_filename, mask_filename):155    """156    The function crops the images with path given under 'imagePaths' in the dataframe to the dimension specified by new_dimension.157    The cropped images and masks are saved to the destination paths, given in destination_folders_list.158    :param dataframe: dataframe containing information of image/mask paths and dimensions159    :param new_dimension: wanted image dimensions after cropping160    :param destination_folders_list: list of destination paths to each patient161    :param image_filename: filename of image162    :param mask_filename: filename of mask163    :param tumor_value: value of tumor voxels in mask164    :return: cropped images and masks are saved to destination paths165    """166    for row in range(dataframe.shape[0]):167        print(dataframe['mask'][row])168        mask_original = sitk.ReadImage(dataframe['mask'][row])169        mask_size_original = mask_original.GetSize()170        if mask_size_original[1] != 256:171            crop_start = int((mask_size_original[1] - new_dimension) / 2)172            print(crop_start)173            crop_stop = int(mask_size_original[1] - crop_start)174            print(crop_stop)175            mask_cropped = mask_original[crop_start:crop_stop, (crop_start + 10):(crop_stop + 10), :]176            images = {'b0': [], 'b1': [], 'b2': [], 'b3': [], 'b4': [], 'b5': [], 'b6': [], 't2':[]}177            for column in dataframe.columns[:-1]:178                if os.path.isfile(dataframe[column][row]):179                    print(dataframe[column][row])180                    image_original = sitk.ReadImage(dataframe[column][row])181                    print('Original DWI size:', image_original.GetSize())182                    image_cropped = image_original[crop_start:crop_stop, (crop_start + 10):(crop_stop + 10), :]183                    images[column] = image_cropped184                    print('New DWI size:', images[column].GetSize())185                else:186                    print(dataframe[column][row], 'does not exist')187                    images[column] = None188            print('Original masksize:', dataframe['mask'][row], mask_original.GetSize())189            print('Cropped masksize:', mask_cropped.GetSize())190            mask_array_original = sitk.GetArrayFromImage(mask_original)191            mask_array_cropped = sitk.GetArrayFromImage(mask_cropped)192            tumor_originally = np.count_nonzero(mask_array_original.flatten() == tumor_value)193            tumor_cropped = np.count_nonzero(mask_array_cropped.flatten() == tumor_value)194            move_window = 0195            while tumor_originally != tumor_cropped:196                print('THE AMOUNT OF TUMOR IS REDUCED AFTER CROPPING! PATH:', dataframe['mask'][row])197                move_window += 5198                if image_filename == 'T2.nii':199                    mask_cropped = mask_original[(crop_start - move_window):(crop_stop - move_window),200                                   (crop_start + 10 - move_window):(crop_stop + 10 - move_window), :]201                    for column in dataframe.columns[:-1]:202                        if os.path.isfile(dataframe[column][row]):203                            print(dataframe[column][row])204                            image_original = sitk.ReadImage(dataframe[column][row])205                            print('Original DWI size:', image_original.GetSize())206                            image_cropped = image_original[(crop_start - move_window):(crop_stop - move_window),207                                   (crop_start + 10 - move_window):(crop_stop + 10 - move_window), :]208                            images[column] = image_cropped209                            print('New DWI size:', images[column].GetSize())210                        else:211                            print(dataframe[column][row], 'does not exist')212                            images[column] = None213                else:214                    mask_cropped = mask_original[(crop_start + move_window):(crop_stop + move_window),215                                   (crop_start + 10 + move_window):(crop_stop + 10 + move_window), :]216                    for column in dataframe.columns[:-1]:217                        if os.path.isfile(dataframe[column][row]):218                            print(dataframe[column][row])219                            image_original = sitk.ReadImage(dataframe[column][row])220                            print('Original DWI size:', image_original.GetSize())221                            image_cropped = image_original[(crop_start + move_window):(crop_stop + move_window),222                                   (crop_start + 10 + move_window):(crop_stop + 10 + move_window), :]223                            images[column] = image_cropped224                            print('New DWI size:', images[column].GetSize())225                        else:226                            print(dataframe[column][row], 'does not exist')227                            images[column] = None228                mask_array_cropped = sitk.GetArrayFromImage(mask_cropped)229                tumor_cropped = np.count_nonzero(mask_array_cropped.flatten() == tumor_value)230                print('Tumor before cropping:', tumor_originally)231                print('Tumor after moving cropping window:', tumor_cropped)232        sitk.WriteImage(mask_cropped, os.path.join(destination_folders_list[row], mask_filename))233        for key in images:234            if images[key] == None:235                print('None')236            else:237                im_filename = key + '.nii'238                sitk.WriteImage(images[key], os.path.join(destination_folders_list[row], im_filename))239def remove_nonTumor_slices_dwi(dataframe, dst_folders):240    """241    :param dataframe: dataframe with image paths and mask paths242    :return: saves images and masks which only contains tumor for each patient243    """244    #Iterate through each patient245    for i in range(len(dataframe['mask'])):246        #Create mask object from file path247        mask = sitk.ReadImage(dataframe['mask'][i])248        print(dataframe['mask'][i])249        #Create image and mask as arrays250        mask_array = sitk.GetArrayFromImage(mask)251        slices = mask.GetSize()[2] #Number of slices for the given patient252        tumor_slices = []253        #Iterate through all the image slices for a patient254        for j in range(slices):255            #If the slice contains tumor then save the slice index in the tumor_slices list256            if 1 in mask_array[j][:][:]:257                #if dataframe['ID'][i] == 'LARC-RRP-033' and j == 10: #Do not include this slice (noise from radiologist)258                #    break259                #else:260                tumor_slices.append(j)261        #Get the x- and y-dimensions of the images and masks262        x_dim = int(mask_array.shape[1])263        y_dim = int(mask_array.shape[2])264        #Create new image and mask arrays with the shape of the original image and mask objects, and with265        #the same number of slices as saved in tumor_slices list266        new_mask_array = np.zeros((len(tumor_slices), x_dim, y_dim))267        new_image_array = np.zeros((len(tumor_slices), x_dim, y_dim))268        for k in range(len(tumor_slices)):269            new_mask_array[k][:][:] = mask_array[tumor_slices[k]][:][:]270        # Create mask from new arrays271        new_mask = sitk.GetImageFromArray(new_mask_array)272        # Save the masks (only containing tumor) in the destination paths273        sitk.WriteImage(new_mask, dataframe['mask'][i])274        for column in dataframe.columns[:-1]:275            print(dataframe[column][i])276            if os.path.isfile(dataframe[column][i]):277                image = sitk.ReadImage(dataframe[column][i])278                print('Original image size:', image.GetSize())279                image_array = sitk.GetArrayFromImage(image)280                for k in range(len(tumor_slices)):281                    new_mask_array[k][:][:] = mask_array[tumor_slices[k]][:][:]282                    new_image_array[k][:][:] = image_array[tumor_slices[k]][:][:]283                new_image = sitk.GetImageFromArray(new_image_array)284                print('New image size:', new_image.GetSize())285                im_filename = column + '.nii'286                sitk.WriteImage(new_image, os.path.join(dst_folders[i], im_filename))287            else:288                print(dataframe[column][i], 'does not exist')289def remove_rotated_dwi(dataframe, dst_folders):290    """291    The function removes the slices where the DW images does not cover the tumor (due to slightly different orientations292    of DWI and T2).293    :param dataframe: dataframe of paths to all DW images, T2 image and manual delineation (mask) for each patient294    :param dst_folders: list of paths to folders of patients where the new images will be saved295    :return: DW images, T2 image and manual delineation where the slices that the DW images do not cover the entire296    tumor is removed297    """298    #Iterate through each patient299    for row in range(dataframe.shape[0]):300        #Create mask array301        mask = sitk.ReadImage(dataframe['mask'][row])302        mask_array = sitk.GetArrayFromImage(mask)303        #Iterate over all image files for the patient304        for column in dataframe.columns[:-2]:305            print(dataframe[column][row])306            #Check if the image file exists307            if os.path.isfile(dataframe[column][row]):308                #Create image array309                image = sitk.ReadImage(dataframe[column][row])310                image_array = sitk.GetArrayFromImage(image)311                #Go through each image slice and check if the tumor is outside of the DW image.312                #If the tumor is outside of the DW image then the slice is not added to the slices_to_keep list.313                #If the tumor is inside of the DW image then the slice is added to the slices_to_keep list.314                slices_to_keep = []315                keep = 0316                for slice in range(image_array.shape[0]):317                    for i in range(image_array.shape[1]):318                        for j in range(image_array.shape[2]):319                            pixel_im = image_array[slice][i][j]320                            pixel_mask = mask_array[slice][i][j]321                            if pixel_im == 0 and pixel_mask == 1:322                                print('Tumor outside of DWI', slice)323                                keep = 1324                                break325                        else:326                            continue327                        break328                    if keep == 1:329                        print('Not adding slice number', slice)330                    else:331                        slices_to_keep.append(slice)332                    keep = 0333            else:334                print(dataframe[column][row], 'does not exist')335        #Print which slices we want to keep336        print(slices_to_keep)337        #Get the x- and y-dimensions of the images and masks338        x_dim = int(mask_array.shape[1])339        y_dim = int(mask_array.shape[2])340        #Create new image and mask arrays with the shape of the original image and mask objects, and with341        #the same number of slices as saved in tumor_slices list342        new_mask_array = np.zeros((len(slices_to_keep), x_dim, y_dim))343        new_image_array = np.zeros((len(slices_to_keep), x_dim, y_dim))344        for k in range(len(slices_to_keep)):345            new_mask_array[k][:][:] = mask_array[slices_to_keep[k]][:][:]346        #Create mask from new arrays347        new_mask = sitk.GetImageFromArray(new_mask_array)348        #Save the masks (only containing tumor) in the destination paths349        sitk.WriteImage(new_mask, dataframe['mask'][row])350        #Create new images for all of the image files, and save them351        for column in dataframe.columns[:-1]:352            print(dataframe[column][row])353            if os.path.isfile(dataframe[column][row]):354                image = sitk.ReadImage(dataframe[column][row])355                print('Original image size:', image.GetSize())356                image_array = sitk.GetArrayFromImage(image)357                for k in range(len(slices_to_keep)):358                    new_image_array[k][:][:] = image_array[slices_to_keep[k]][:][:]359                new_image = sitk.GetImageFromArray(new_image_array)360                print('New image size:', new_image.GetSize())361                im_filename = column + '.nii'362                sitk.WriteImage(new_image, os.path.join(dst_folders[row], im_filename))363            else:364                print(dataframe[column][row], 'does not exist')365if __name__ == '__main__':366    #Oxy_patientPaths, Oxy_patientNames, Oxy_imagePaths, Oxy_maskPaths = gd.get_paths('/Volumes/LaCie/MasterThesis_Ingvild/Data/LARC/TumorSlices/LARC_cropped_TS_new', 'image.nii', '1 RTSTRUCT LARC_MRS1-label.nii')367    Oxy_patientPaths, Oxy_PatientNames, Oxy_imagePaths, Oxy_maskPaths = gd.get_paths('/Volumes/LaCie/MasterThesis_Ingvild/Data/dwi/Oxy_all_cropped', 'b4', 'an.nii')368    Oxy_df = p.dataframe(Oxy_patientPaths, Oxy_PatientNames, Oxy_imagePaths, Oxy_maskPaths)369    Oxy_df = p.dimensions(Oxy_df)370    #remove_nonTumor_slices(Oxy_df)371#Oxy_patientPaths, Oxy_PatientNames, Oxy_imagePaths, Oxy_maskPaths = gd.get_paths('/Volumes/Untitled 1/Ingvild_Oxytarget', 'T2', 'an.nii')372#Create dataframe with information373#Oxy_df = p.dataframe(Oxy_patientPaths, Oxy_PatientNames, Oxy_imagePaths, Oxy_maskPaths)374#Oxy_df = p.dimensions(Oxy_df)375#Create list of destination paths376#dst_paths = uf.create_dst_paths('/Volumes/LaCie/MasterThesis_Ingvild/Oxy_cropped_corrected')377#crop_images(Oxy_df, 352, dst_paths, 'T2.nii', 'Manual_an.nii', 1000)...

main.py

Source:main.py

1# Others2import time3import keyboard4# Pet5from monitor import init_monitors6from pet import Pet7from pet import PetState8# Systems9from idle import Idle10from stroll import Stroll11from headpat import Headpat12from got_mouse import GotMouse13from chase_mouse import ChaseMouse14from scream import Scream15from open_window import *16from move_window import *17def quit_combination():18    return keyboard.is_pressed("ctrl") and keyboard.is_pressed('alt') and keyboard.is_pressed('2') and keyboard.is_pressed('9') and keyboard.is_pressed('Y')19if __name__ == "__main__":20    monitor.init_monitors()21    # Pet22    pet = Pet()23    24    # Systems25    idle = Idle(0, PetState.IDLE)26    stroll = Stroll(0, PetState.STROLL)27    headpat = Headpat(6, PetState.HEADPAT)28    chase_mouse = ChaseMouse(0, PetState.CHASE_MOUSE)29    got_mouse = GotMouse(0, PetState.GOT_MOUSE)30    open_window = OpenWindow(0, PetState.OPEN_WINDOW)31    move_window = MoveWindow(0, PetState.MOVE_WINDOW)32    scream = Scream(0, PetState.SCREAM)33    34    # Main Loop35    prev_time = time.time()36    while True:37        # Calculate delta time.38        curr_time = time.time()39        delta_time = curr_time - prev_time40        prev_time = curr_time41        # System Update42        idle.update(pet, delta_time)43        stroll.update(pet, delta_time)44        headpat.update(pet, delta_time)45        chase_mouse.update(pet, delta_time)46        got_mouse.update(pet, delta_time)47        open_window.update(pet, delta_time)48        move_window.update(pet, delta_time)49        scream.update(pet, delta_time)50        # Pet Update51        pet.update(delta_time)52        pet.track_footprints()53        # Exit the application, can be changed.54        if quit_combination():55            break56        # if keyboard.is_pressed("1"):57        #     pet.change_state(PetState.IDLE)58        # elif keyboard.is_pressed("2"):59        #     pet.change_state(PetState.STROLL)60        # elif keyboard.is_pressed("3"):61        #     pet.change_state(PetState.CHASE_MOUSE)62        # elif keyboard.is_pressed("4"):63        #     pet.change_state(PetState.OPEN_WINDOW)64        # elif keyboard.is_pressed("5"):65        #     pet.change_state(PetState.MOVE_WINDOW)66        # elif keyboard.is_pressed("6"):67        #     pet.change_state(PetState.HEADPAT)68        # elif keyboard.is_pressed("7"):...

rabin_karp.py

Source:rabin_karp.py

...12        #start index of current window13        self.window_start = 014        #end of index window15        self.window_end = size_word16    def move_window(self):17        if self.window_end <= len(self.text) - 1:18            #remove left letter from hash value19            self.hash -= (ord(self.text[self.window_start]) - ord("a")+1)*26**(self.size_word-1)20            self.hash *= 2621            self.hash += ord(self.text[self.window_end])- ord("a")+122            self.window_start += 123            self.window_end += 124    def window_text(self):25        return self.text[self.window_start:self.window_end]26def rabin_karp(word, text):27    if word == "" or text == "":28        return None29    if len(word) > len(text):30        return None31    rolling_hash = RollingHash(text, len(word))32    word_hash = RollingHash(word, len(word))33    #word_hash.move_window()34    for i in range(len(text) - len(word) + 1):35        if rolling_hash.hash == word_hash.hash:36            if rolling_hash.window_text() == word:37                return i38        rolling_hash.move_window()...

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