Introductory python tutorials for image processing

https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG

These video tutorials are designed to train absolute beginners on Python coding with a special emphasis on image analysis.

Coding enables researchers to ...

extend the capability of scientific software packages
customize applications for specific jobs to be done
automate rudimentary tasks freeing up valuable research time
improve reproducibility of experiments via automation
add an important skill to their portfolio making them well adjusted to the digital world

It is recommended to follow the videos as a course as we've structured them to progressively cover topics from basics of python to advanced libraries for image analysis.

Relevant links: To download Anaconda: https://www.anaconda.com/distribution/

To download ZEN Lite: https://www.zeiss.com/microscopy/us/products/microscope-software/zen-lite.html (If you're from outside US, please Google search for 'ZEN Lite' to find your local download link)

To sign up for APEER account: www.apeer.com

Name		Name	Last commit message	Last commit date
Latest commit History 71 Commits
generating_labeled_data_for_deep_learning		generating_labeled_data_for_deep_learning
images		images
README.md		README.md
YR1_hough_transform.py		YR1_hough_transform.py
YR2_batch_processing_multiple_images_in_python.py		YR2_batch_processing_multiple_images_in_python.py
smooth_tiled_predictions.py		smooth_tiled_predictions.py
tutorial04_appreciating the simplicity of python.py		tutorial04_appreciating the simplicity of python.py
tutorial09_top5_libraries.py		tutorial09_top5_libraries.py
tutorial101_DL_terminology_training_and_valid_curves.ipynb		tutorial101_DL_terminology_training_and_valid_curves.ipynb
tutorial102_DL_terminology_training_testing_validation_data.ipynb		tutorial102_DL_terminology_training_testing_validation_data.ipynb
tutorial103_DL_terminology_data_augmentation1.ipynb		tutorial103_DL_terminology_data_augmentation1.ipynb
tutorial103_DL_terminology_data_augmentation2.ipynb		tutorial103_DL_terminology_data_augmentation2.ipynb
tutorial104_DL_terminology_callbacks_checkpoints_earlystopping.ipynb		tutorial104_DL_terminology_callbacks_checkpoints_earlystopping.ipynb
tutorial105_DL_terminology_Learning_rate_scheduling.ipynb		tutorial105_DL_terminology_Learning_rate_scheduling.ipynb
tutorial107_7_steps_to_DL.ipynb		tutorial107_7_steps_to_DL.ipynb
tutorial109_regression_housing.ipynb		tutorial109_regression_housing.ipynb
tutorial10_first_lines_of_code.py		tutorial10_first_lines_of_code.py
tutorial110_binary_classification_malaria.ipynb		tutorial110_binary_classification_malaria.ipynb
tutorial111_multiclass_classification_cifar.ipynb		tutorial111_multiclass_classification_cifar.ipynb
tutorial112_top_k_accuracy_multiclass_classif.ipynb		tutorial112_top_k_accuracy_multiclass_classif.ipynb
tutorial113_what_are_autoencoders.ipynb		tutorial113_what_are_autoencoders.ipynb
tutorial114_can_autoencoders_be_used_for_semantic_segm.ipynb		tutorial114_can_autoencoders_be_used_for_semantic_segm.ipynb
tutorial116_upsampling_vs_conv2D_transpose.ipynb		tutorial116_upsampling_vs_conv2D_transpose.ipynb
tutorial117_building_unet_using_encoder_decoder_blocks.ipynb		tutorial117_building_unet_using_encoder_decoder_blocks.ipynb
tutorial118_binary_semantic_segmentation_using_unet.ipynb		tutorial118_binary_semantic_segmentation_using_unet.ipynb
tutorial119_multiclass_semantic_segmentation.ipynb		tutorial119_multiclass_semantic_segmentation.ipynb
tutorial11_operators_basic_math.py		tutorial11_operators_basic_math.py
tutorial120_applying_trained_unet_model_to_large_images.ipynb		tutorial120_applying_trained_unet_model_to_large_images.ipynb
tutorial121a_split_folder_into_train_test_val.ipynb		tutorial121a_split_folder_into_train_test_val.ipynb
tutorial121b_loading_data_from_drive_in_batches_for_unet_training.ipynb		tutorial121b_loading_data_from_drive_in_batches_for_unet_training.ipynb
tutorial122_3D_Unet.ipynb		tutorial122_3D_Unet.ipynb
tutorial123_various_pretrained_DL_models.ipynb		tutorial123_various_pretrained_DL_models.ipynb
tutorial124_multiclass_semantic_segmentation_using_pre_trained_networks.ipynb		tutorial124_multiclass_semantic_segmentation_using_pre_trained_networks.ipynb
tutorial125_semantic_segmentation_using_Xfer_learning_VGG_XGBoost.ipynb		tutorial125_semantic_segmentation_using_Xfer_learning_VGG_XGBoost.ipynb
tutorial126_classification_using_Xfer_learning_VGG_XGBoost.ipynb		tutorial126_classification_using_Xfer_learning_VGG_XGBoost.ipynb
tutorial12_lists.py		tutorial12_lists.py
tutorial13_tuples.py		tutorial13_tuples.py
tutorial14_dictionaries.py		tutorial14_dictionaries.py
tutorial15_numpy_arrays.py		tutorial15_numpy_arrays.py
tutorial16_data_types_in_python.py		tutorial16_data_types_in_python.py
tutorial17_if_else_statements.py		tutorial17_if_else_statements.py
tutorial18_automating_using_while_loops.py		tutorial18_automating_using_while_loops.py
tutorial19_automating_using_for_loops.py		tutorial19_automating_using_for_loops.py
tutorial20_functions.py		tutorial20_functions.py
tutorial21_lambda_functions.py		tutorial21_lambda_functions.py
tutorial22_reading_images_into_python.py		tutorial22_reading_images_into_python.py
tutorial23_reading_proprietary_images_into_python.py		tutorial23_reading_proprietary_images_into_python.py
tutorial24_saving_images_in_python.py		tutorial24_saving_images_in_python.py
tutorial25_viewing_images_in_python.py		tutorial25_viewing_images_in_python.py
tutorial26_plotting_using_pyplots_python.py		tutorial26_plotting_using_pyplots_python.py
tutorial27_using_glob_to_read_multiple_images.py		tutorial27_using_glob_to_read_multiple_images.py
tutorial28_using_os.listdir_to_read_multiple_images.py		tutorial28_using_os.listdir_to_read_multiple_images.py
tutorial29_image_processing_using_scikit-image.py		tutorial29_image_processing_using_scikit-image.py
tutorial30_image_processing_using_opencv.py		tutorial30_image_processing_using_opencv.py
tutorial31_image_sharpening_using_unsharp_mask.py		tutorial31_image_sharpening_using_unsharp_mask.py
tutorial32_denoising_using_gaussian.py		tutorial32_denoising_using_gaussian.py
tutorial33_denoising_using_median.py		tutorial33_denoising_using_median.py
tutorial34_denoising_using_bilateral.py		tutorial34_denoising_using_bilateral.py
tutorial35_denoising_using_NLM.py		tutorial35_denoising_using_NLM.py
tutorial36_denoising_using_total_variance.py		tutorial36_denoising_using_total_variance.py
tutorial37_denoising_using_BM3D.py		tutorial37_denoising_using_BM3D.py
tutorial38_edge_detection_filters.py		tutorial38_edge_detection_filters.py
tutorial39_edge_detection_using_canny.py		tutorial39_edge_detection_using_canny.py
tutorial40_what_is_fourier_transform.py		tutorial40_what_is_fourier_transform.py
tutorial41_image_filters_using_fourier_transform_DFT.py		tutorial41_image_filters_using_fourier_transform_DFT.py
tutorial42_what_is_histogram_equalization_CLAHE.py		tutorial42_what_is_histogram_equalization_CLAHE.py
tutorial43_shading_correction_using_rolling_ball.py		tutorial43_shading_correction_using_rolling_ball.py
tutorial44_a_note_about_color_spaces.py		tutorial44_a_note_about_color_spaces.py
tutorial45_applying_grey_scale_filters_to_color_images.py		tutorial45_applying_grey_scale_filters_to_color_images.py
tutorial46_img_registration_libraries_in_python.py		tutorial46_img_registration_libraries_in_python.py
tutorial47_image_registration_using_pystackreg.py		tutorial47_image_registration_using_pystackreg.py
tutorial48_reference_based_image_quality.py		tutorial48_reference_based_image_quality.py
tutorial49_no_reference_image_quality_sharpness.py		tutorial49_no_reference_image_quality_sharpness.py
tutorial50_no_reference_image_quality_assessment_BRISQUE.py		tutorial50_no_reference_image_quality_assessment_BRISQUE.py
tutorial51_what_is_image_segmentation_and_thresholding.py		tutorial51_what_is_image_segmentation_and_thresholding.py
tutorial52_multi_thresholding_and_morph_operators.py		tutorial52_multi_thresholding_and_morph_operators.py
tutorial53-using_texture_to_segment_images.py		tutorial53-using_texture_to_segment_images.py
tutorial54-scratch_assay_in_python.py		tutorial54-scratch_assay_in_python.py
tutorial55_image_segmentation_followed_by_measurements.py		tutorial55_image_segmentation_followed_by_measurements.py
tutorial56_simple_blob_detector.py		tutorial56_simple_blob_detector.py
tutorial56b_hough_circles.py		tutorial56b_hough_circles.py
tutorial57_Cell Nuclei analysis using watershed.py		tutorial57_Cell Nuclei analysis using watershed.py
tutorial58_object_detection_by_template_matching.py		tutorial58_object_detection_by_template_matching.py
tutorial59_data_analysis_using_Pandas_Intro_data_loading.py		tutorial59_data_analysis_using_Pandas_Intro_data_loading.py
tutorial60_data_analysis_using_Pandas_data_handling.py		tutorial60_data_analysis_using_Pandas_data_handling.py
tutorial61_data_analysis_using_Pandas_data_sorting.py		tutorial61_data_analysis_using_Pandas_data_sorting.py
tutorial62_data_analysis_using_Pandas_data_grouping.py		tutorial62_data_analysis_using_Pandas_data_grouping.py
tutorial63_dealing with null data_in_Pandas.py		tutorial63_dealing with null data_in_Pandas.py
tutorial64_basic_plotting_of_data_from_pandas_dataframe.py		tutorial64_basic_plotting_of_data_from_pandas_dataframe.py
tutorial65_pandas_data_plotting_using_Seaborn.py		tutorial65_pandas_data_plotting_using_Seaborn.py
tutorial66a_need_for_data_normalization.py		tutorial66a_need_for_data_normalization.py
tutorial66b_various_data_normalization_techniques.py		tutorial66b_various_data_normalization_techniques.py
tutorial68-linear_regression.py		tutorial68-linear_regression.py
tutorial69-multi_linear_regression.py		tutorial69-multi_linear_regression.py
tutorial70-Logistic_regression_breast_cancer.py		tutorial70-Logistic_regression_breast_cancer.py
tutorial71-K_means.py		tutorial71-K_means.py
tutorial71a-K_means_demo.py		tutorial71a-K_means_demo.py
tutorial72-GMM_image_segmentation.py		tutorial72-GMM_image_segmentation.py
tutorial74_what is gabor filter.py		tutorial74_what is gabor filter.py
tutorial75-Extracting_features_using_Gabor_Filter.py		tutorial75-Extracting_features_using_Gabor_Filter.py

bnsreenu/python_for_image_processing_APEER

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