Skip to content

rayanirban/python_for_image_processing_APEER

BranchesTags
 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

68 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
 
 

Repository files navigation

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

About

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

Resources

Readme
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 96.7%
  • Python 3.3%