Python for Microscopists and other image processing enthusiasts

Author: Dr. Sreenivas Bhattiprolu
Twitter: @digitalsreeni

Python for Microscopists and other image processing enthusiasts

https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

The YouTube channel associated with this code walks you through the entire process of learning to code in Python; all the way from basics to advanced machine learning and deep learning. The primary emphasis will be on image processing and other relevant functionality.

Why did I create this channel? To help you (students and researchers) gain a new skill and succeed in your respective fields.

You may think coding is hard and that it's not your cup of tea, but Python made it easy to code even advanced algorithms. In addition, coding will make you self sufficient, it will teach you how to think, it improves your collaborative skills and it can take your career to new heights. Therefore, if you want to stay ahead of your peers and relevant in your field, overcome your fears and start coding!

Also, checkout WWW.APEER.COM if you want free image processing in the cloud! Free for non-profits / academics / personal use.

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Python for Microscopists and other image processing enthusiasts

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Name		Name	Last commit message	Last commit date
Latest commit History 267 Commits
222_working_with_large_data_that_does_not_fit_memory_semantic_segm		222_working_with_large_data_that_does_not_fit_memory_semantic_segm
228_semantic_segmentation_of_aerial_imagery_using_unet		228_semantic_segmentation_of_aerial_imagery_using_unet
229_smooth_predictions_by_blending_patches		229_smooth_predictions_by_blending_patches
230_landcover_dataset_segmentation		230_landcover_dataset_segmentation
231_234_BraTa2020_Unet_segmentation		231_234_BraTa2020_Unet_segmentation
235-236_pre-training_unet_using_autoencoders		235-236_pre-training_unet_using_autoencoders
237_tflite_using_malaria_binary_classification		237_tflite_using_malaria_binary_classification
238_face_eye_detection_using_opencv		238_face_eye_detection_using_opencv
239_train_emotion_detection		239_train_emotion_detection
240_train_age_gender_detection		240_train_age_gender_detection
241_live_age_gender_emotion_detection		241_live_age_gender_emotion_detection
242 - Real time detection of facial emotion, age, and gender using TensorFlow Lite		242 - Real time detection of facial emotion, age, and gender using TensorFlow Lite
243 - Real time detection of facial emotion, age, and gender using TensorFlow Lite on RaspberryPi		243 - Real time detection of facial emotion, age, and gender using TensorFlow Lite on RaspberryPi
244-what_are_embedding_layers		244-what_are_embedding_layers
245 - Advantages of keras functional API in defining complex models		245 - Advantages of keras functional API in defining complex models
246 - Training a keras model by enumerating epochs and batches		246 - Training a keras model by enumerating epochs and batches
248_keras_implementation_of_GAN		248_keras_implementation_of_GAN
249_keras_implementation-of_conditional_GAN		249_keras_implementation-of_conditional_GAN
251_satellite_image_to_maps_translation		251_satellite_image_to_maps_translation
252_generating_realistic_scientific_images_using_pix2pix		252_generating_realistic_scientific_images_using_pix2pix
253_254_cycleGAN_monet2photo		253_254_cycleGAN_monet2photo
255_256_SRGAN		255_256_SRGAN
257 - Exploring GAN latent space to generate images with desired features		257 - Exploring GAN latent space to generate images with desired features
259_semi_supervised_GAN		259_semi_supervised_GAN
260_image_anomaly_detection_using_autoencoders		260_image_anomaly_detection_using_autoencoders
261_global_average_pooling		261_global_average_pooling
262_localizing_anomalies_in_images		262_localizing_anomalies_in_images
263_Object localization in images_using_GAP_layer		263_Object localization in images_using_GAP_layer
264 - Image outlier detection using alibi-detect		264 - Image outlier detection using alibi-detect
265_feature_engineering_or_deep_learning		265_feature_engineering_or_deep_learning
266_openslide_for_whole_slide_images		266_openslide_for_whole_slide_images
267_processing_whole_slide_images		267_processing_whole_slide_images
268-How to deploy your trained machine learning model into a local web application		268-How to deploy your trained machine learning model into a local web application
269_How to deploy your trained machine learning model as an app on Heroku		269_How to deploy your trained machine learning model as an app on Heroku
270-How to deploy your trained machine learning model as an app on Heroku-HAM10000-no docker		270-How to deploy your trained machine learning model as an app on Heroku-HAM10000-no docker
AMT01_extracting_information_from_outlook_emails		AMT01_extracting_information_from_outlook_emails
images		images
other_files		other_files
tips_tricks_36_pyscript_python_in_the_browser		tips_tricks_36_pyscript_python_in_the_browser
00_A review of COVID19 situation in India using Python.py		00_A review of COVID19 situation in India using Python.py
017-Reading_Images_in_Python.py		017-Reading_Images_in_Python.py
018-image_processing_in_pillow.py		018-image_processing_in_pillow.py
019-image_processing_in_scipy.py		019-image_processing_in_scipy.py
020-image_processing_in_scikit-image.py		020-image_processing_in_scikit-image.py
021-scratch_assay_using_scikit_image.py		021-scratch_assay_using_scikit_image.py
022-denoising.py		022-denoising.py
023-histogram_segmentation_using_scikit_image.py		023-histogram_segmentation_using_scikit_image.py
024-random_walker_segmentation_scikit-image.py		024-random_walker_segmentation_scikit-image.py
025-image_processing_in_openCV_intro1-preprocessing.py		025-image_processing_in_openCV_intro1-preprocessing.py
026-image_processing_in_openCV_intro1-preprocessing.py		026-image_processing_in_openCV_intro1-preprocessing.py
027-image_processing_in_openCV_intro2-Thresholding.py		027-image_processing_in_openCV_intro2-Thresholding.py
028-image_processing_in_openCV_intro2-Thresholding.py		028-image_processing_in_openCV_intro2-Thresholding.py
029-keypoint detectors and descriptors in opencv.py		029-keypoint detectors and descriptors in opencv.py
02_Tips_Tricks_python_tips_and_tricks_2_google_image_download.py		02_Tips_Tricks_python_tips_and_tricks_2_google_image_download.py
030-keypoints_homography_for_registration in opencv.py		030-keypoints_homography_for_registration in opencv.py
031-reading_and_writing_csv.py		031-reading_and_writing_csv.py
032-grain_analysis_saving_to_csv.py		032-grain_analysis_saving_to_csv.py
033-grain_size_analysis_using_wateshed_segmentation.py		033-grain_size_analysis_using_wateshed_segmentation.py
034a-grain_size_analysis_using_wateshed_segmentation_multiple_files.py		034a-grain_size_analysis_using_wateshed_segmentation_multiple_files.py
034b-grain_size_analysis_using_wateshed_segmentation_multiple_files_functions.py		034b-grain_size_analysis_using_wateshed_segmentation_multiple_files_functions.py
035-Cell Nuclei analysis using watershed.py		035-Cell Nuclei analysis using watershed.py
036-data_analysis_using_Pandas_Intro_data_loading.py		036-data_analysis_using_Pandas_Intro_data_loading.py
037-data_analysis_using_Pandas_data_handling.py		037-data_analysis_using_Pandas_data_handling.py
038-data_sorting_using_Pandas.py		038-data_sorting_using_Pandas.py
039-data_grouping_using_Pandas.py		039-data_grouping_using_Pandas.py
040-dealing with null data_using_Pandas.py		040-dealing with null data_using_Pandas.py
041_data_analysis_using_Pandas_Plotting.py		041_data_analysis_using_Pandas_Plotting.py
042_data_analysis_using_Seaborn_Plotting.py		042_data_analysis_using_Seaborn_Plotting.py
045-linear_regression_cells.py		045-linear_regression_cells.py
046-linear_regression_cells_train_test.py		046-linear_regression_cells_train_test.py
047-multi_linear_regression.py		047-multi_linear_regression.py
049-Logistic_regression.py		049-Logistic_regression.py
050-K_means_intro.py		050-K_means_intro.py
051-Kmeans_using_opencv.py		051-Kmeans_using_opencv.py
052-GMM_image_segmentation.py		052-GMM_image_segmentation.py
053-How to pick optimal number of parameters.py		053-How to pick optimal number of parameters.py
055-ML_06_01_how to open proprietary images.py		055-ML_06_01_how to open proprietary images.py
057-ML_06_02_what are features.py		057-ML_06_02_what are features.py
058-ML_06_03_what is gabor filter.py		058-ML_06_03_what is gabor filter.py
060-RandomForest1.py		060-RandomForest1.py
061-Gabor_Filter_Banks.py		061-Gabor_Filter_Banks.py
062-066-ML_06_04_TRAIN_ML_segmentation_All_filters_RForest.py		062-066-ML_06_04_TRAIN_ML_segmentation_All_filters_RForest.py
067-ML_06_05_PREDICT_ML_segmentation_All_filters_RForest.py		067-ML_06_05_PREDICT_ML_segmentation_All_filters_RForest.py
068b-ML_06_04_TRAIN_ML_segmentation_All_filters_RForest_SVM.py		068b-ML_06_04_TRAIN_ML_segmentation_All_filters_RForest_SVM.py
069a-Train_BOVW_V1.0.py		069a-Train_BOVW_V1.0.py
069b-Validate_BOVW_V1.0.py		069b-Validate_BOVW_V1.0.py
071-Malaria_cell_CNN_V5.0_for video.py		071-Malaria_cell_CNN_V5.0_for video.py
074-Defining U-net in Python using Keras.py		074-Defining U-net in Python using Keras.py
076-077-078-Unet_nuclei_tutorial.py		076-077-078-Unet_nuclei_tutorial.py
082-docker_test.zip		082-docker_test.zip
085-auto_encode_single_image_V3.0.py		085-auto_encode_single_image_V3.0.py
086--auto_denoise_mnist.py		086--auto_denoise_mnist.py
087-auto_denoise_custom_file_V3.0.py		087-auto_denoise_custom_file_V3.0.py
088-autoencoder_anomaly_V0.1.py		088-autoencoder_anomaly_V0.1.py
089a-auto_encode_single_image_to_different_image_V1.0.py		089a-auto_encode_single_image_to_different_image_V1.0.py
089b-auto_encode_single_image_to_different_image_multi_file_V1.0.py		089b-auto_encode_single_image_to_different_image_multi_file_V1.0.py
090a-autoencoder_colorize_V0.2.py		090a-autoencoder_colorize_V0.2.py
090b-autoencoder_colorize_V0.1_predict.py		090b-autoencoder_colorize_V0.1_predict.py
091_intro_to_transfer_learning_VGG16.py		091_intro_to_transfer_learning_VGG16.py
092-autoencoder_colorize_transfer_learning_VGG16_V0.1.py		092-autoencoder_colorize_transfer_learning_VGG16_V0.1.py

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