Supporting code for the tutorials on DigitalSreeni YouTube channel

Author: Dr. Sreenivas Bhattiprolu
Twitter: @digitalsreeni

Supporting code for the tutorials on DigitalSreeni YouTube channel

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.

How to cite my work?

1. YouTube video: The general format for citing a YouTube video in APA (American Psychological Association) style is: Author’s Last Name, First Initial. (Year, Month Day Published). Title of video [Video]. YouTube. URL

So, here is an example:

Bhattiprolu, S. (2023, August 23). 330 - Fine tuning Detectron2 for instance segmentation using custom data [Video]. YouTube. https://youtu.be/cEgF0YknpZw

2. GitHub code: There isn't a universally agreed-upon format for citing GitHub repositories, but here's a commonly used one: Author’s Last Name, First Initial. (Year). Title of Repository. GitHub. URL

Example:
Bhattiprolu, S. (2023). python_for_microscopists. GitHub. https://github.com/bnsreenu/python_for_microscopists/blob/master/330_Detectron2_Instance_3D_EM_Platelet.ipynb

Name		Name	Last commit message	Last commit date
Latest commit History 355 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
271-How to deploy your trained machine learning model as an app on Heroku-HAM10000-with docker		271-How to deploy your trained machine learning model as an app on Heroku-HAM10000-with docker
272-Instance segmentation via semantic segmentation by using border class		272-Instance segmentation via semantic segmentation by using border class
273-what_is_voronoi		273-what_is_voronoi
274-object_segmentation_using_voronoi_otsu		274-object_segmentation_using_voronoi_otsu
275-object_segmentation_and_analysis_using_voronoi_otsu_labeling		275-object_segmentation_and_analysis_using_voronoi_otsu_labeling
276 - Grain segmentation using less than 10 lines of code in python		276 - Grain segmentation using less than 10 lines of code in python
277-3D_segmentation_using_voronoi-otsu		277-3D_segmentation_using_voronoi-otsu
278 - IHC color separation followed by nuclei segmentation using python		278 - IHC color separation followed by nuclei segmentation using python
285-Object detection using Mask R CNN with XML annotated data		285-Object detection using Mask R CNN with XML annotated data
286-Object detection using mask RCNN - end to end		286-Object detection using mask RCNN - end to end
290-Deep Learning based edge detection using HED		290-Deep Learning based edge detection using HED
291-Object segmentation using Deep Learning based edge detection (HED)		291-Object segmentation using Deep Learning based edge detection (HED)
299-Evaluating sklearn model using KFold cross validation in python		299-Evaluating sklearn model using KFold cross validation in python
300-Picking the best model and corresponding hyperparameters using Gridsearch		300-Picking the best model and corresponding hyperparameters using Gridsearch
301-Evaluating keras model using KFold cross validation		301-Evaluating keras model using KFold cross validation
302-Tuning deep learning hyperparameters		302-Tuning deep learning hyperparameters
303 - Reinhard color transformation		303 - Reinhard color transformation
304 - Augmentation of histology images		304 - Augmentation of histology images
306 - Content based image retrieval via feature extraction		306 - Content based image retrieval via feature extraction
307 - Segment your images in python without training		307 - Segment your images in python without training
310-Understanding sub-word tokenization used for NLP		310-Understanding sub-word tokenization used for NLP
323-Train a chatbot on your own documents		323-Train a chatbot on your own documents
324-Chat-based data analysis-pandasAI		324-Chat-based data analysis-pandasAI
332 - All about image annotations		332 - All about image annotations
335 - Converting COCO JSON annotations to labeled mask images		335 - Converting COCO JSON annotations to labeled mask images
336-Nuclei-Instance-Detectron2.0_YOLOv8_code		336-Nuclei-Instance-Detectron2.0_YOLOv8_code
337 - Whole Slide Image segmentation for nuclei using Detectron2 and YOLOv8		337 - Whole Slide Image segmentation for nuclei using Detectron2 and YOLOv8
AMT01_extracting_information_from_outlook_emails		AMT01_extracting_information_from_outlook_emails
AMT_02_extract_gmails_from_a_user		AMT_02_extract_gmails_from_a_user
Prompt Engineering for Marketers - Tips Tricks 55		Prompt Engineering for Marketers - Tips Tricks 55
Tips_Tricks_45_white-balance_using_python		Tips_Tricks_45_white-balance_using_python
images		images
other_files		other_files
tips_tricks_36_pyscript_python_in_the_browser		tips_tricks_36_pyscript_python_in_the_browser
tips_tricks_46-Feature engineering vs feature learning		tips_tricks_46-Feature engineering vs feature learning
tips_tricks_52_Hidden Gems - defaultdict		tips_tricks_52_Hidden Gems - defaultdict
tips_tricks_53_Extracting a Targeted Subset from a COCO JSON		tips_tricks_53_Extracting a Targeted Subset from a COCO JSON
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

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