Merge pull request #6 from asr-aditya/patch-3

Create train_unet_multilabel-segmentation.md

docs/medicalai/examples/train_unet_multilabel-segmentation.md

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+# Example: Image Segmentation (Camvid Dataset)
+The dataset to perform imgage segmentation can be downloaded from [here](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)
+
+### Import libraries
+```python
+import os
+import numpy as np
+import medicalai as ai
+import tensorflow as tf
+```
+### Define the hyperparameters
+Specify the dataset folder which further contains test & train folders each with n class object folders
+```python
+datasetFolderPath = "../data/camvid/"
+```
+Specify the dimensions of image to be fed to network
+```python
+(IMG_HEIGHT,IMG_WIDTH) = (256,256)
+```
+Specify the name of the model to be trained on
+```python
+EXPT_NAME = '1'
+AI_NAME = 'unet' 
+MODEL_SAVE_NAME = '../model/'+AI_NAME+'/Medical_RSNA_'+str(IMG_HEIGHT)+'x'+str(IMG_WIDTH)+'_'+AI_NAME+'_EXPT_'+str(EXPT_NAME)
+```
+Specify remaining hyperparamters
+```
+batch_size = 32
+epochs = 10
+learning_rate = 0.0001
+
+```
+### Define the augmentation for the generator
+(The following augmentation is for image only)
+```python
+augment = ai.AUGMENTATION(rescale= 1./255)
+```
+Load your data from folder using datasetGenFromFolder if your data is in folder structured form. Make sure ```flag_multi_class``` is set to ```True```
+```python
+dsHandler = ai.segmentaionGenerator(folder=datasetFolderPath,targetDim=(IMG_HEIGHT,IMG_WIDTH), 
+                                        augmentation=augment, class_mode=None,
+                                        batch_size = batch_size,
+                                        image_folder_name = "image", mask_folder_name = "masks",
+                                        flag_multi_class=True)
+```
+```python
+trainGen = dsHandler.load_train_generator()
+testGen = dsHandler.load_test_generator()
+```
+### Train model
+Now our image generator is ready to be trained on our model. But first we need to define a tensorflow callback for the model
+```python
+model_checkpoint = tf.keras.callbacks.ModelCheckpoint(
+                                        MODEL_SAVE_NAME+'best.h5', 
+                                        verbose=0,
+                                        mode='auto', 
+                                        save_freq=5,
+                                        save_best_only=True,
+                                        )
+callbacks = [model_checkpoint]
+```
+```python
+TRAIN_STEPS = int(np.ceil(dsHandler.imageGen.generator.n/dsHandler.batch_size))
+trainer = ai.TRAIN_ENGINE()
+trainer.train_and_save_segmentation(AI_NAME=AI_NAME,
+                                    MODEL_SAVE_NAME = MODEL_SAVE_NAME, 
+                                    trainSet=trainGen,inputSize = (IMG_HEIGHT,IMG_WIDTH,3),
+                                    TRAIN_STEPS=TRAIN_STEPS,
+                                    BATCH_SIZE= BATCH_SIZE, EPOCHS= EPOCHS, 
+                                    LEARNING_RATE= LEARNING_RATE, SAVE_BEST_MODEL = SAVE_BEST_MODEL, 
+                                    callbacks = callbacks,
+                                    showModel = False)
+
+```
+Use the above model to predict segmentation masks
+```python
+infEngine = ai.INFERENCE_ENGINE(MODEL_SAVE_NAME)
+predsG = infEngine.predict_segmentation(testGen)
+```
+Save the predicted segmentation masks in a folder
+```python
+infEngine.saveResult(save_path='results',npyfile=predsG,num_class=32,flag_multi_class=True)
+```