import os from PIL import Image import numpy as np from pspylib.tiff.reader import TiffReader from pspylib.tiff.writer import Writer from matplotlib.colors import LinearSegmentedColormap class Read_tiff: def __call__(self,tiff_path): self.tiff = TiffReader(tiff_path) dict={} try: dict['HEADER'] = self.__dict_header() except: print("No Data") return -1 try: dict['SPECT_DATA'] = self.__spect_data_to_array() dict['SPECT_HEADER'] = self.__dict_spect_header() dict['SPECT_CH_INFO'] = self.__dict_spect_ch_info() except: print("No spectroscopy data in this TIFF file.") try: dict['IMAGE'] = self.__zdata_to_array() dict['THUMBNAIL'] = self.__thumbnail_to_array() dict['COLORMAP'] = self.__colormap_to_matplot() except: print("No image data in this TIFF file.") return dict, self.tiff @staticmethod def __cvt_uint16(value): word = "" for alphabet in value[0]: if alphabet != 0: word += chr(alphabet) else: pass return word def __thumbnail_to_array(self): thumb = self.tiff.data.metaData.thumbnail header = self.tiff.data.scanHeader.scanHeader dshape = (int(header['height'][0]), int(header['width'][0])) thumb_dshape = [0, 0] if (dshape[0] < 256) | (dshape[1] < 256): if dshape[0] > dshape[1]: thumb_dshape[0] = 256 thumb_dshape[1] = round(dshape[1] * 256 / dshape[0]) if dshape[1] > dshape[0]: thumb_dshape[1] = 256 thumb_dshape[0] = round(dshape[0] * 256 / dshape[1]) else: thumb_dshape = [256,256] return np.reshape(thumb,thumb_dshape) def __zdata_to_array(self): Zdata = self.tiff.data.scanData.ZData header = self.tiff.data.scanHeader.scanHeader dshape = (int(header['height'][0]), int(header['width'][0])) npy = np.reshape(Zdata,dshape) npy = np.flipud(npy) return npy def __colormap_to_matplot(self): ori_cm = self.tiff.data.metaData.colorMap['colorMap'][0] r = np.array(ori_cm)[:256] g = np.array(ori_cm)[256:512] b = np.array(ori_cm)[512:768] rgb_cm = np.dstack([r,g,b])[0] / 65536 cmap_name = 'meta_cmap' meta_cmap = LinearSegmentedColormap.from_list(cmap_name, rgb_cm,N=128) return meta_cmap def __dict_header(self): result_dict={} Header = self.tiff.data.scanHeader.scanHeader for i in Header.items(): if i[-1][-1] == 'uint16': cvt_data = self.__cvt_uint16(i[-1]) result_dict[i[0]] = cvt_data else: result_dict[i[0]] =i[-1][0] return result_dict def __dict_spect_header(self): result_dict = {} SpectHeader = self.tiff.data.spectHeader.spectHeader for i in SpectHeader.items(): if[-1][-1] == 'uint16': cvt_data = self.__cvt_uint16(i[-1]) result_dict[i[0]] = cvt_data else: result_dict[i[0]] = i[-1][0] return result_dict def __dict_spect_ch_info(self): result_dict = {} ChInfo = self.tiff.data.spectHeader.channelInfo for j in ChInfo.items(): ch = 'ch'+str(j[0]) result_dict[ch] ={} for i in j[1].items(): if i[-1][-1] == 'uint16': cvt_data = self.__cvt_uint16(i[-1]) result_dict[ch][i[0]] = cvt_data else: result_dict[ch][i[0]] = i[-1][0] return result_dict def __spect_data_to_array(self): spect_data = np.array(self.tiff.data.spectData.rawData,dtype=np.float64) spect_ch_info = self.tiff.data.spectHeader.channelInfo number_of_ch = spect_data.shape[1] for i in range(number_of_ch): spect_data[:,i,:] *= float(spect_ch_info[i]['gain'][0]) return spect_data class Write_tiff(): def __call__(self, orig_tiff, dict_tiff, file_path): self.copy_tiff = orig_tiff self.dict_tiff = dict_tiff try: self.__copy_paste_image() except: print("No image data to write in this TIFF file.") # TODO: Need Debugging # try: # self.__copy_paste_spectroscopy() # except: # print("No spectroscopy data to write in this TIFF file.") dir_path =os.path.dirname(file_path) if not os.path.exists(dir_path):os.makedirs(dir_path) Writer(data=self.copy_tiff.data, path=file_path) def __copy_paste_image(self): image = self.dict_tiff['IMAGE'] self.dict_tiff['THUMBNAIL'] = self.__cvt_thumbnail(image) thumbnail = self.dict_tiff['THUMBNAIL'].astype(int) self.copy_tiff.data.metaData.thumbnail = np.reshape(thumbnail.copy(), (-1,)) z_array = np.flipud(self.dict_tiff['IMAGE']) self.copy_tiff.data.scanData.ZData = np.reshape(z_array.copy(), (-1,)) def __copy_paste_spectroscopy(self): spect_ch_info = self.copy_tiff.data.spectHeader.channelInfo spect_array = self.dict_tiff['SPECT_DATA'] number_of_ch = spect_array.shape[1] for i in range(number_of_ch): spect_array[:, i, :] /= float(spect_ch_info[i]['gain'][0]) spect_data = spect_array.copy().astype(self.copy_tiff.data.spectData.rawData.dtype) self.copy_tiff.data.spectData.rawData = spect_data def __cvt_thumbnail(self,npy): ori_shape = npy.shape norm_thumbnail = self.__minMax(npy) * 255 norm_thumbnail = norm_thumbnail.astype(int) img = Image.fromarray(norm_thumbnail) if (ori_shape[0] < 256) | (ori_shape[1] < 256): if ori_shape[0] < ori_shape[1]: aspect_ratio = 256/ori_shape[1] resize_height = round(ori_shape[0] * aspect_ratio) thumbnail = img.resize((256,resize_height),Image.NEAREST) else: aspect_ratio = 256/ori_shape[0] resize_width = round(ori_shape[1] * aspect_ratio) thumbnail = img.resize((resize_width,256),Image.NEAREST) else: thumbnail = img.resize((256,256),Image.NEAREST) thumbnail = np.array(thumbnail) return thumbnail @staticmethod def __minMax(npy): # Min = 0, Max = 1 min_npy = np.min(npy) denominator = (np.max(npy) - min_npy) + 1e-16 return (npy - min_npy) / denominator if __name__ == "__main__": samples_path = r"C:\Park Systems\SmartScan\samples" reader = Read_tiff() writer = Write_tiff() # IMAGE Read / Write image_tiff_path = os.path.join(samples_path,"Image","Cheese.tiff") image_dict, image_tiff = reader(image_tiff_path) print(image_dict['HEADER']) print(image_dict['IMAGE'].shape) save_path = os.path.join("output","Cheese.tiff") writer(orig_tiff=image_tiff, dict_tiff=dict, file_path=save_path) # SPECTROSCOPY Read spect_tiff_path = os.path.join(samples_path,"Spectroscopy","Microball.tiff") spect_dict, spect_tiff = reader(spect_tiff_path) print(spect_dict['HEADER']) print(spect_dict['SPECT_HEADER']) print(spect_dict['SPECT_CH_INFO']) print(spect_dict['SPECT_DATA'].shape)