<reponame>vjchong888/aml-
############################################################################################
#
# The MIT License (MIT)
# 
# <NAME> Acute Myeloid/Lymphoblastic Leukemia AI Research Project
# Copyright (C) 2018 <NAME> (<EMAIL>)
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# 
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# 
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# Title:         Caffe Acute Lymphoblastic Leukemia CNN Info
# Description:   Used to view info Caffe Acute Lymphoblastic Leukemia CNN
# Configuration: Required/Confs.json
# Last Modified: 2019-03-10
#
############################################################################################

import os, sys, cv2
sys.path.append('/home/upsquared/caffe/python')
import caffe

import numpy as np

from Classes.Helpers import Helpers

class allCNN():

    def __init__(self):

        """
        Sets up all default requirements and placeholders 
        needed for the Caffe Acute Lymphoblastic Leukemia CNN.
        """
        
        self.Helpers = Helpers()
        self.confs = self.Helpers.loadConfs()
        self.logFile = self.Helpers.setLogFile(self.confs["Settings"]["Logs"]["allCNN"])
        
        self.Helpers.logMessage(self.logFile, "allCNN", "Status", "Init complete")

    def loadCaffeNet(self):

        """
        Loads the Caffe network using prototxt layer definition.
        """
        
        self.net = caffe.Net(self.confs["Settings"]["Classifier"]["Caffe"]["layerFile"], caffe.TEST)
        
        print("")
        self.Helpers.logMessage(self.logFile, "allCNN", "Status", "Caffe net initialized")

    def printDetails(self):

        """
        Prints and logs input, blob and parameter info.
        """

        # Prints the Net Inputs
        self.Helpers.logMessage(self.logFile, "allCNN", "Net Inputs", str(self.net.inputs))
        
        # Prints the Net Blobs
        self.Helpers.logMessage(self.logFile, "allCNN", "Net Blobs", str(self.net.blobs))
        
        # Prints the Net Blob shapes
        self.Helpers.logMessage(self.logFile, "allCNN", "Net Blob shapes", str([(k, v.data.shape) for k, v in self.net.blobs.items()]))
        
        # Prints the Net Params
        self.Helpers.logMessage(self.logFile, "allCNN", "Net Params", str(self.net.params))
        
        # Prints the Net Params shapes
        self.Helpers.logMessage(self.logFile, "allCNN", "Net Params", str([(k, v[0].data.shape, v[1].data.shape) for k, v in self.net.params.items()]))
        
        print("")

    def writeOutputImages(self, image):

        """
        Writes the output images for each neuron in the first convolution layer.
        """

        # Transposes the input (50,50,3) -> (3,50,50)
        inp = np.transpose(cv2.imread(image))

        # Reshape the data blob
        self.net.blobs['data'].reshape(1, *inp.shape)
        self.net.blobs['data'].data[...] = inp
        
        # Passes the input data through the network to compute the output
        self.net.forward()

        # Loops through each neuron in the first convolution layer and saves the images in that neuron
        for i in range(30):
            cv2.imwrite(self.confs["Settings"]["Classifier"]["Data"]["dir"] + self.confs["Settings"]["Classifier"]["Info"]["outDir"] + 'conv1/out_' + str(i) + '.jpg', 
                        255 * self.net.blobs['conv1'].data[0,i])

        # Loops through each neuron in the second convolution layer and saves the images in that neuron
        for i in range(30):
            cv2.imwrite(self.confs["Settings"]["Classifier"]["Data"]["dir"] + self.confs["Settings"]["Classifier"]["Info"]["outDir"] + 'conv2/out_' + str(i) + '.jpg', 
                        255 * self.net.blobs['conv2'].data[0,i])

        self.Helpers.logMessage(self.logFile, 
                                "allCNN", 
                                "Output Images", 
                                "Output images written to " + self.confs["Settings"]["Classifier"]["Data"]["dir"] + self.confs["Settings"]["Classifier"]["Info"]["outDir"])

    def saveCaffeNet(self):

        """
        Saves our Caffe network.
        """

        self.net.save(self.confs["Settings"]["Classifier"]["Model"]["file"])
        
        self.Helpers.logMessage(self.logFile, 
                                "allCNN", 
                                "Status", 
                                "Caffe net saved")

allCNN = allCNN()

def main(argv):

    if(len(argv) < 1):

        """
        Incorrect arguments size.
        """

        allCNN.Helpers.logMessage(allCNN.logFile, 
                                  "allCNN", 
                                  "Arguments", 
                                  "Please provide NetworkInfo or Outputs argument")

    elif argv[0] == "NetworkInfo":

        """
        Provides information about our Caffe network.
        """

        allCNN.loadCaffeNet()
        allCNN.printDetails()

    elif argv[0] == "Outputs":

        """
        Plots the outputs of each neuron as images.
        """

        allCNN.loadCaffeNet()
        allCNN.writeOutputImages(allCNN.confs["Settings"]["Classifier"]["Data"]["dir"] + allCNN.confs["Settings"]["Classifier"]["Data"]["dirTest"] + allCNN.confs["Settings"]["Classifier"]["Info"]["testImage"])

    elif argv[0] == "Save":

        """
        Saves our Caffe network.
        """

        allCNN.loadCaffeNet()
        allCNN.saveCaffeNet()

if __name__ == "__main__":
	main(sys.argv[1:])
############################################################################################
#
# The MIT License (MIT)
# 
# <NAME> Acute Myeloid/Lymphoblastic Leukemia AI Research Project
# Copyright (C) 2018 <NAME> (<EMAIL>)
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# 
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# 
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# Title:         Caffe Acute Lymphoblastic Leukemia CNN Info
# Description:   Used to view info Caffe Acute Lymphoblastic Leukemia CNN
# Configuration: Required/Confs.json
# Last Modified: 2019-03-10
#
############################################################################################

import os, sys, cv2
sys.path.append('/home/upsquared/caffe/python')
import caffe

import numpy as np

from Classes.Helpers import Helpers

class allCNN():

    def __init__(self):

        """
        Sets up all default requirements and placeholders 
        needed for the Caffe Acute Lymphoblastic Leukemia CNN.
        """
        
        self.Helpers = Helpers()
        self.confs = self.Helpers.loadConfs()
        self.logFile = self.Helpers.setLogFile(self.confs["Settings"]["Logs"]["allCNN"])
        
        self.Helpers.logMessage(self.logFile, "allCNN", "Status", "Init complete")

    def loadCaffeNet(self):

        """
        Loads the Caffe network using prototxt layer definition.
        """
        
        self.net = caffe.Net(self.confs["Settings"]["Classifier"]["Caffe"]["layerFile"], caffe.TEST)
        
        print("")
        self.Helpers.logMessage(self.logFile, "allCNN", "Status", "Caffe net initialized")

    def printDetails(self):

        """
        Prints and logs input, blob and parameter info.
        """

        # Prints the Net Inputs
        self.Helpers.logMessage(self.logFile, "allCNN", "Net Inputs", str(self.net.inputs))
        
        # Prints the Net Blobs
        self.Helpers.logMessage(self.logFile, "allCNN", "Net Blobs", str(self.net.blobs))
        
        # Prints the Net Blob shapes
        self.Helpers.logMessage(self.logFile, "allCNN", "Net Blob shapes", str([(k, v.data.shape) for k, v in self.net.blobs.items()]))
        
        # Prints the Net Params
        self.Helpers.logMessage(self.logFile, "allCNN", "Net Params", str(self.net.params))
        
        # Prints the Net Params shapes
        self.Helpers.logMessage(self.logFile, "allCNN", "Net Params", str([(k, v[0].data.shape, v[1].data.shape) for k, v in self.net.params.items()]))
        
        print("")

    def writeOutputImages(self, image):

        """
        Writes the output images for each neuron in the first convolution layer.
        """

        # Transposes the input (50,50,3) -> (3,50,50)
        inp = np.transpose(cv2.imread(image))

        # Reshape the data blob
        self.net.blobs['data'].reshape(1, *inp.shape)
        self.net.blobs['data'].data[...] = inp
        
        # Passes the input data through the network to compute the output
        self.net.forward()

        # Loops through each neuron in the first convolution layer and saves the images in that neuron
        for i in range(30):
            cv2.imwrite(self.confs["Settings"]["Classifier"]["Data"]["dir"] + self.confs["Settings"]["Classifier"]["Info"]["outDir"] + 'conv1/out_' + str(i) + '.jpg', 
                        255 * self.net.blobs['conv1'].data[0,i])

        # Loops through each neuron in the second convolution layer and saves the images in that neuron
        for i in range(30):
            cv2.imwrite(self.confs["Settings"]["Classifier"]["Data"]["dir"] + self.confs["Settings"]["Classifier"]["Info"]["outDir"] + 'conv2/out_' + str(i) + '.jpg', 
                        255 * self.net.blobs['conv2'].data[0,i])

        self.Helpers.logMessage(self.logFile, 
                                "allCNN", 
                                "Output Images", 
                                "Output images written to " + self.confs["Settings"]["Classifier"]["Data"]["dir"] + self.confs["Settings"]["Classifier"]["Info"]["outDir"])

    def saveCaffeNet(self):

        """
        Saves our Caffe network.
        """

        self.net.save(self.confs["Settings"]["Classifier"]["Model"]["file"])
        
        self.Helpers.logMessage(self.logFile, 
                                "allCNN", 
                                "Status", 
                                "Caffe net saved")

allCNN = allCNN()

def main(argv):

    if(len(argv) < 1):

        """
        Incorrect arguments size.
        """

        allCNN.Helpers.logMessage(allCNN.logFile, 
                                  "allCNN", 
                                  "Arguments", 
                                  "Please provide NetworkInfo or Outputs argument")

    elif argv[0] == "NetworkInfo":

        """
        Provides information about our Caffe network.
        """

        allCNN.loadCaffeNet()
        allCNN.printDetails()

    elif argv[0] == "Outputs":

        """
        Plots the outputs of each neuron as images.
        """

        allCNN.loadCaffeNet()
        allCNN.writeOutputImages(allCNN.confs["Settings"]["Classifier"]["Data"]["dir"] + allCNN.confs["Settings"]["Classifier"]["Data"]["dirTest"] + allCNN.confs["Settings"]["Classifier"]["Info"]["testImage"])

    elif argv[0] == "Save":

        """
        Saves our Caffe network.
        """

        allCNN.loadCaffeNet()
        allCNN.saveCaffeNet()

if __name__ == "__main__":
	main(sys.argv[1:])
############################################################################################ # # The MIT License (MIT) # # <NAME> Acute Myeloid/Lymphoblastic Leukemia AI Research Project # Copyright (C) 2018 <NAME> (<EMAIL>) # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # # Title:         Caffe Acute Lymphoblastic Leukemia CNN Info # Description:   Used to view info Caffe Acute Lymphoblastic Leukemia CNN # Configuration: Required/Confs.json # Last Modified: 2019-03-10 # ############################################################################################ Sets up all default requirements and placeholders          needed for the Caffe Acute Lymphoblastic Leukemia CNN. Loads the Caffe network using prototxt layer definition. Prints and logs input, blob and parameter info. # Prints the Net Inputs # Prints the Net Blobs # Prints the Net Blob shapes # Prints the Net Params # Prints the Net Params shapes Writes the output images for each neuron in the first convolution layer. # Transposes the input (50,50,3) -> (3,50,50) # Reshape the data blob # Passes the input data through the network to compute the output # Loops through each neuron in the first convolution layer and saves the images in that neuron # Loops through each neuron in the second convolution layer and saves the images in that neuron Saves our Caffe network. Incorrect arguments size. Provides information about our Caffe network. Plots the outputs of each neuron as images. Saves our Caffe network.