clean up/sanity check

HDRData.py

@@ -88,11 +88,98 @@ class HDRDatabaseInterface(ABC):
 
     def getMSLabelArray(self, imageId: str) -> np.array:
         '''
+        Returns the Mean Shift Segmentation label array as a numpy array
         '''
         raise NotImplementedError("getMSLabelArray: Method Not Implemented")
 
 
 
+    def saveMask(self, sliceId: str, maskImage: Image.Image, maskName: str="Mask"):
+        '''
+        Saves a mask image associated with a slice
+        '''
+        raise NotImplementedError("saveMask: Method Not Implemented")
+
+
+    def importAndProcessImage(self, imagePath: str):
+        '''
+        Imports an image from a path into the database and preforms all preprocessing
+        '''
+        raise NotImplementedError("importAndProcessImage: Method Not Implemented")
+
+
+    def getModelList(self, imageId, size):
+        '''
+        Should return a list of available models
+        i.e.
+            [
+                { 
+                    "id"  : id,
+                    "name": name
+                }
+            ]
+        '''
+        raise NotImplementedError("getModelList: Method Not Implemented")
+
+
+    def createModel(self, imageId, size, modelName):
+        '''
+        Should create and save a model to the database
+        '''
+        raise NotImplementedError("createModel: Method Not Implemented")
+
+
+    def getMaskList(self, imageId, size):
+        '''
+        Should return a list of available masks for a given slice
+        i.e.
+            [
+                { 
+                    "id"  : id,
+                    "name": name
+                }
+            ]
+        '''
+        raise NotImplementedError("getMaskList: Method Not Implemented")
+
+
+    # NOTE: DOESN'T COPY DUPLICATES
+    def saveSamplesTemp(self, modelId, sliceSize, sampleIds):
+        '''
+        Should save desired samples and corresponding masks to a temporary directory.
+        Return the path to the directory
+        |-- Temp
+        |   |-- 0-sample
+        |   |   |-- input.jpg
+        |   |   |-- mask.jpg
+        '''
+        raise NotImplementedError("saveSamplesTemp: Method Not Implemented")
+
+
+    def saveSlicesForPrediction(self, modelId, sliceSize, sliceIds):
+        '''
+        Should create a temporary directory to save slices to.
+        Return path to temporary dir.
+        |-- Temp
+        |   |-- img-0.jpg
+        |   |-- img-1.jpg
+        '''
+        raise NotImplementedError("saveSlicesForPrediction: Method Not Implemented")
+
+
+    def getModel(self, modelId):
+        '''
+        Should load and return a model from the database
+        '''
+        raise NotImplementedError("getModel: Method Not Implemented")
+
+
+    def updateModel(self, modelId, model):
+        '''
+        Should replace model in the databse with a new model
+        '''
+        raise NotImplementedError("updateModel: Method Not Implemented")
+
 
 
 class HDRFileDB(HDRDatabaseInterface):
@@ -206,7 +293,7 @@ class HDRFileDB(HDRDatabaseInterface):
         return np.load(msLabelsPath)
 
 
-    def saveMask(self, sliceId: str, maskImage: Image.Image, maskName: str="Mask"):
+    def saveMask(self, sliceId: str, maskImage: Image.Image, maskName: str="Mask") -> None:
         maskName = maskName.replace("-", "_")
         baseDir = self._idToBasePath(sliceId)
         # Find slice directory with id
@@ -249,7 +336,7 @@ class HDRFileDB(HDRDatabaseInterface):
     MODEL METHODS
     '''
 
-    def getModelList(self, imageId, size):
+    def getModelList(self, imageId: str, size: int) -> list:
 
         modelList = []
 
@@ -263,7 +350,7 @@ class HDRFileDB(HDRDatabaseInterface):
         return modelList
 
 
-    def createModel(self, imageId, size, modelName):
+    def createModel(self, imageId: str, size: int, modelName: str) -> None:
         baseDir = self._idToBasePath(imageId)
         modelsDir = baseDir.joinpath(self.MODELS_FOLDER)
 
@@ -273,7 +360,7 @@ class HDRFileDB(HDRDatabaseInterface):
         hdrModel.save(saveDir)
 
 
-    def getMaskList(self, imageId, size):
+    def getMaskList(self, imageId: str, size: int) -> list:
 
         maskList = []
 
@@ -298,7 +385,7 @@ class HDRFileDB(HDRDatabaseInterface):
 
 
     # NOTE: DOESN'T COPY DUPLICATES
-    def saveSamplesTemp(self, modelId, sliceSize, sampleIds):
+    def saveSamplesTemp(self, modelId: str, size: int, sampleIds: list) -> Path:
         baseDir = self._idToBasePath(modelId)
         modelDir = baseDir.joinpath(self.MODELS_FOLDER).joinpath(modelId.split("-")[-1])
 
@@ -307,10 +394,10 @@ class HDRFileDB(HDRDatabaseInterface):
         os.mkdir(tempTrainDir)
 
         # Get all mask ids in db
-        maskIds = [mask["id"] for mask in self.getMaskList(baseDir.name, sliceSize)]
+        maskIds = [mask["id"] for mask in self.getMaskList(baseDir.name, str(size))]
 
         idParts = self._idParts(modelId)
-        sliceDir = baseDir.joinpath(self.SLICE_FOLDER).joinpath(sliceSize)
+        sliceDir = baseDir.joinpath(self.SLICE_FOLDER).joinpath(str(size))
 
         sampleIds = json.loads(sampleIds)
 
@@ -332,7 +419,7 @@ class HDRFileDB(HDRDatabaseInterface):
         return tempTrainDir
 
 
-    def saveSlicesForPrediction(self, modelId, sliceSize, sliceIds):
+    def saveSlicesForPrediction(self, modelId: str, size: int, sliceIds: list) -> Path:
         baseDir = self._idToBasePath(modelId)
         modelDir = baseDir.joinpath(self.MODELS_FOLDER).joinpath(modelId.split("-")[-1])
         # Create create new folder to hold slices
@@ -352,13 +439,13 @@ class HDRFileDB(HDRDatabaseInterface):
         return predDir
 
 
-    def getModel(self, modelId):
+    def getModel(self, modelId: str) -> HDRModel:
         baseDir = self._idToBasePath(modelId)
         modelDir = baseDir.joinpath(self.MODELS_FOLDER).joinpath(modelId.split("-")[-1])
         return HDRModel(loadDir=modelDir)
 
 
-    def updateModel(self, modelId, model):
+    def updateModel(self, modelId: str, model: HDRModel) -> None:
         baseDir = self._idToBasePath(modelId)
         modelDir = baseDir.joinpath(self.MODELS_FOLDER).joinpath(modelId.split("-")[-1])
         # Remove old model and save new 
@@ -415,6 +502,6 @@ class HDRFileDB(HDRDatabaseInterface):
     
     def _idToBasePath(self, imageId: str) -> Path:
         return self.imagesPath.joinpath("{}-{}-{}-{}".format(self._idUid(imageId),
-                                                               self._idImageExt(imageId),
-                                                               self._idName(imageId),
-                                                               "OR"))
\ No newline at end of file
+                                                             self._idImageExt(imageId),
+                                                             self._idName(imageId),
+                                                             "OR"))
\ No newline at end of file

app.py

@@ -16,6 +16,7 @@ import base64
 
 from celery import Celery
 
+
 def make_celery(app):
     celery = Celery(
         app.import_name,
@@ -31,6 +32,7 @@ def make_celery(app):
 
     celery.Task = ContextTask
     return celery
+    
 
 '''
 Global Variables
@@ -91,7 +93,6 @@ def getSamples(dataDir: Path) -> [(np.array, np.array)]:
     return (np.array(X_TRAIN), np.array(Y_TRAIN))
 
 
-
 @celery.task(name="app.trainModelTask")
 def trainModelTask(modelId, sliceSize, sampleIds):
     # Copy slices and masks to temp folder
@@ -109,6 +110,7 @@ def trainModelTask(modelId, sliceSize, sampleIds):
 
     return "OK"
 
+
 @celery.task(name="app.predictModelTask")
 def predictModelTask(modelId, sliceSize, sliceIds):
     # Copy slices and masks to temp folder
@@ -126,6 +128,7 @@ def predictModelTask(modelId, sliceSize, sliceIds):
 
     return "OK"
 
+
 '''
 App Routes
 '''
@@ -134,9 +137,11 @@ App Routes
 def index():
     return render_template("index.html")
 
+
 '''
 DATA DISPLAY
 '''
+
 @app.route("/image-select")
 def imageSelect():
     '''
@@ -213,10 +218,8 @@ def createNewModel(imageId, size, modelName):
 
 @app.route("/manage-model/<string:imageId>/<string:modelId>/<int:size>")
 def manageModel(imageId, modelId, size):
-    print("managing model:", modelId)
     availableMasks = imageDB.getMaskList(imageId, str(size))
-    sliceList = imageDB.getSliceList(imageId, str(size))
-    print(availableMasks)
+    sliceList      = imageDB.getSliceList(imageId, str(size))
     return render_template("manageModel.html",
                            imageId=imageId,
                            sliceSize=size,
@@ -227,24 +230,17 @@ def manageModel(imageId, modelId, size):
 
 @app.route("/train-model/<string:modelId>/<string:sliceSize>", methods=["POST"])
 def trainModel(modelId, sliceSize):
-    print(modelId)
     if request.method == "POST":
-        print("=========================")
         maskIds = request.form["maskIds"]
-        print(type(maskIds))
-        result = trainModelTask.delay(modelId, sliceSize, maskIds)
+        trainModelTask.delay(modelId, sliceSize, maskIds)
     return "OK"
 
 
 @app.route("/predict-model/<string:modelId>/<string:sliceSize>", methods=["POST"])
 def predictModel(modelId, sliceSize):
-    print(modelId)
     if request.method == "POST":
-        print("=========================")
         sliceIds = request.form["sliceIds"]
-        print(sliceIds)
         predictModelTask.delay(modelId, sliceSize, sliceIds)
-        #result = trainModelTask.delay(modelId, sliceSize, maskIds)
     return "OK"
 
 '''
@@ -286,14 +282,13 @@ def uploadImage():
 
 @app.route("/save-mask", methods=["POST"])
 def saveMask():
-    imageId   = request.values["imageId"]
-    sliceId   = request.values["sliceId"]
-    maskName  = request.values["maskName"]
+    imageId    = request.values["imageId"]
+    sliceId    = request.values["sliceId"]
+    maskName   = request.values["maskName"]
     maskBase64 = request.values["maskBase64"].split(',')[1]
 
     maskImage = Image.open(io.BytesIO(base64.b64decode(maskBase64)))
     maskImage = maskImage.convert("RGB")
-    print("TTESSSSTTT")
     np.array(maskImage).shape
     imageDB.saveMask(sliceId, maskImage, maskName=maskName)
 

static/css/createModel.css

-
 .main-section-container {
     width: 100%;
     height: 100%;

static/css/imageUpload.css

-
-
 .upload-file-container {
     width: 30%;
     height: 30%;

static/css/manageModel.css

-
-
 .main-section-container{
     width: 100%;
     height: 100%;

static/css/selectModel.css

-
 .main-section-container{
     width: 100%;
     height: 100%;

static/js/createMask.js

-
 /*
  * Handles event onchange for id: #processed-images
  */

static/js/imageSliceContent.js

@@ -120,9 +120,9 @@ function loadMaskCanvas() {
 
 // ==================== CREATEM MODEL BUTTON
 
-function createModel(imageId, sliceSize) {
-    var modelName = prompt("Give Model a Name:", "");
-}
+// /function createModel(imageId, sliceSize) {
+// /    var modelName = prompt("Give Model a Name:", "");
+// /}
 
 window.addEventListener("load", (event) => {
    

static/js/manageModel.js

@@ -16,7 +16,6 @@ function appendToSelect(selectElement, inputId, inputName) {
 }
 
 
-
 function getRadioValAndName(radioNodeList) {
     var val, name;
     // get list of radio buttons with specified name
@@ -33,6 +32,7 @@ function getRadioValAndName(radioNodeList) {
     return [val, name]; // return value of checked radio or undefined if none checked
 }
 
+
 /*
  * Add sample to batch to use for training
  */
@@ -62,7 +62,6 @@ function trainModel(modelId, sliceSize, name) {
 }
 
 
-
 function addSlice(name) {
     var selectedReturn = getRadioValAndName(document.getElementsByName(name));
     id   = selectedReturn[0];

static/js/selectModel.js

-
-
+/*
+ * Send request to backend to create a new model for selected image and size
+ */
 function createNewModel(imageId, size) {
     var modelName = prompt("Enter a model name", "");
-    modelName = modelName.replace(/\s/g, "-")
+    modelName     = modelName.replace(/\s/g, "-") 
     
     if (modelName != null && modelName != "") {
         $.post(`/create-new-model/${imageId}/${size}/${modelName}`, function(data) {