<gh_stars>0
'''
This class deals with the LandmarkDetection model and the various operations associated with it.
'''
import os
import sys
import time
import logging as log
import numpy as np
import cv2
from openvino.inference_engine import IENetwork, IECore


class LandmarkDetection:
    '''
    Class for the landmark Detection Model.
    '''
    def __init__(self, path, device='CPU', extensions=None):
        '''
        TODO: Set instance variables.
        '''
        # TODO: Initialize attributes
        self.network = None
        self.core = None
        self.input = None
        self.output = None
        self.exec_net = None
        self.device = device
        self.extension = extensions
        self.count = 1

        # TODO: Save path of .bin and .xml files of model
        # landmark_xml = os.path.abspath('../intel/landmarks-regression-retail-0009/FP32/landmarks-regression-retail-0009.xml')
        landmark_xml = os.path.abspath(path)
        landmark_bin = os.path.splitext(landmark_xml)[0]+'.bin'

        # TODO: Initialize IENetwork object
        try:
            self.network = IENetwork(landmark_xml, landmark_bin)
        except Exception as e:
            print('Error occurred, refer `CPC.log` file for details')
            log.error('Landmark Detection IENetwork object could not be initialized/loaded. Check if model files are stored in correct path.', e)

        self.input = next(iter(self.network.inputs))
        self.input_shape = self.network.inputs[self.input].shape
        self.output = next(iter(self.network.outputs))
        self.output_shape = self.network.outputs[self.output].shape


    def load_model(self):

            # TODO: Initialize IECore object and load the network as ExecutableNetwork object
        try:
            self.core = IECore()
            self.exec_net = self.core.load_network(network= self.network, device_name= self.device, num_requests= 1)
            if self.extension is not None:
                self.core.add_extension(extension_path= self.extension, device_name= self.device)
        except Exception as e:
            print('Error occurred, refer `CPC.log` file for details')
            log.error('landmark Detection IECore object could not be initialized/loaded.', e)
        return

    def predict(self, image, pflag):

        # TODO: Predict and dump perf_count if pflag

        try:
            landmark_input = {self.input: image}
            landmark_result = self.exec_net.infer(landmark_input)
            landmark_result = np.squeeze(landmark_result['95'])

            # TODO: Post stats for every 5th frame

            if pflag == 1 and (self.count == 1 or self.count%5) == 0:
                perf_count = self.exec_net.requests[0].get_perf_counts()
                self.get_model_perf(perf_count, self.count)

            self.count += 1

        except Exception as e:
            print('Error occurred, refer `CPC.log` file for details')
            log.error('Landmark Detection inference error: ', e)
        return landmark_result

    # TODO: Method to dump perf_count stats

    def get_model_perf(self, perf_count, count):
        with open('./model_perf/landmark.txt', 'a') as fh:
            fh.write('Frame: '+ str(count) + '\n\n')
        for layer in perf_count:
            # TODO: Check for layers with `EXECUTED` status only

            if perf_count[layer]['status'] == 'EXECUTED':
                # TODO: Extract major parameters

                perf_dict = {'index': perf_count[layer]['execution_index'], 'layer_name': layer,  'exec_time': perf_count[layer]['cpu_time'] }
                with open('./model_perf/landmark.txt', 'a') as fh:
                    fh.write(str(perf_dict))
                    fh.write('\n')


    def check_model(self):
        log.info('landmark Model Input shape: {0}'.format( str(self.input_shape) ))
        log.info('landmark Model Output shape: {0}'.format( str(self.output_shape) ))

        supported_layers = self.core.query_network(network= self.network, device_name= self.device)

        ### TODO: Check for any unsupported layers, and let the user
        ###       know if anything is missing. Exit the program, if so.
        unsupported_layers = [l for l in self.network.layers.keys() if l not in supported_layers]
        if len(unsupported_layers) != 0:
            log.warning("Unsupported layers found: {}".format(unsupported_layers))
            sys.exit("Add necessary extension for given hardware")

    def preprocess_input(self, image):
        '''
        Before feeding the data into the model for inference,
        you might have to preprocess it. This function does that.
        '''
        temp = image.copy()
        temp = cv2.resize(temp, (self.input_shape[3], self.input_shape[2] ) ) # n,c,h,w
        temp = temp.transpose((2, 0, 1))
        temp = temp.reshape(1, *temp.shape)
        return temp

    def preprocess_output(self, frame, outputs):
        '''
        Before feeding the output of this model to the next model,
        you might have to preprocess the output. This function does that.
        '''
        temp_op = frame.copy()
        width = int(frame.shape[1]) #1920
        height = int(frame.shape[0]) #1080
        r_radius = 18
        c_radius = 5

        # TODO: (x,y)*5 coordinates of landmarks
        x_lEye= int(outputs[0] * width)
        y_lEye = int(outputs[1] * height)
        x_rEye = int(outputs[2] * width)
        y_rEye = int(outputs[3] * height)
        x_nose = int(outputs[4] * width)
        y_nose = int(outputs[5] * height)
        x_lLip = int(outputs[6] * width)
        y_lLip = int(outputs[7] * height)
        x_rLip = int(outputs[8] * width)
        y_rLip = int(outputs[9] * height)

        min_x_lEye= x_lEye-r_radius
        min_y_lEye = y_lEye-r_radius
        max_x_lEye= x_lEye+r_radius
        max_y_lEye = y_lEye+r_radius

        min_x_rEye = x_rEye-r_radius
        min_y_rEye = y_rEye-r_radius
        max_x_rEye = x_rEye+r_radius
        max_y_rEye = y_rEye+r_radius

        # TODO: Visualize output if vflag

        lEye = temp_op[y_lEye-r_radius : y_lEye+r_radius , x_lEye-r_radius : x_lEye+r_radius]
        cv2.rectangle(temp_op, (min_x_lEye, min_y_lEye), (max_x_lEye, max_y_lEye), (255, 0, 0), 2)
        # cv2.imshow('lEye', lEye)

        rEye = temp_op[y_rEye-r_radius : y_rEye+r_radius , x_rEye-r_radius : x_rEye+r_radius]
        cv2.rectangle(temp_op, (min_x_rEye, min_y_rEye), (max_x_rEye, max_y_rEye), (0, 255, 0), 2)
        # cv2.imshow('rEye', rEye)


        return temp_op, lEye, rEye

'''
This class deals with the LandmarkDetection model and the various operations associated with it.
'''
import os
import sys
import time
import logging as log
import numpy as np
import cv2
from openvino.inference_engine import IENetwork, IECore


class LandmarkDetection:
    '''
    Class for the landmark Detection Model.
    '''
    def __init__(self, path, device='CPU', extensions=None):
        '''
        TODO: Set instance variables.
        '''
        # TODO: Initialize attributes
        self.network = None
        self.core = None
        self.input = None
        self.output = None
        self.exec_net = None
        self.device = device
        self.extension = extensions
        self.count = 1

        # TODO: Save path of .bin and .xml files of model
        # landmark_xml = os.path.abspath('../intel/landmarks-regression-retail-0009/FP32/landmarks-regression-retail-0009.xml')
        landmark_xml = os.path.abspath(path)
        landmark_bin = os.path.splitext(landmark_xml)[0]+'.bin'

        # TODO: Initialize IENetwork object
        try:
            self.network = IENetwork(landmark_xml, landmark_bin)
        except Exception as e:
            print('Error occurred, refer `CPC.log` file for details')
            log.error('Landmark Detection IENetwork object could not be initialized/loaded. Check if model files are stored in correct path.', e)

        self.input = next(iter(self.network.inputs))
        self.input_shape = self.network.inputs[self.input].shape
        self.output = next(iter(self.network.outputs))
        self.output_shape = self.network.outputs[self.output].shape


    def load_model(self):

            # TODO: Initialize IECore object and load the network as ExecutableNetwork object
        try:
            self.core = IECore()
            self.exec_net = self.core.load_network(network= self.network, device_name= self.device, num_requests= 1)
            if self.extension is not None:
                self.core.add_extension(extension_path= self.extension, device_name= self.device)
        except Exception as e:
            print('Error occurred, refer `CPC.log` file for details')
            log.error('landmark Detection IECore object could not be initialized/loaded.', e)
        return

    def predict(self, image, pflag):

        # TODO: Predict and dump perf_count if pflag

        try:
            landmark_input = {self.input: image}
            landmark_result = self.exec_net.infer(landmark_input)
            landmark_result = np.squeeze(landmark_result['95'])

            # TODO: Post stats for every 5th frame

            if pflag == 1 and (self.count == 1 or self.count%5) == 0:
                perf_count = self.exec_net.requests[0].get_perf_counts()
                self.get_model_perf(perf_count, self.count)

            self.count += 1

        except Exception as e:
            print('Error occurred, refer `CPC.log` file for details')
            log.error('Landmark Detection inference error: ', e)
        return landmark_result

    # TODO: Method to dump perf_count stats

    def get_model_perf(self, perf_count, count):
        with open('./model_perf/landmark.txt', 'a') as fh:
            fh.write('Frame: '+ str(count) + '\n\n')
        for layer in perf_count:
            # TODO: Check for layers with `EXECUTED` status only

            if perf_count[layer]['status'] == 'EXECUTED':
                # TODO: Extract major parameters

                perf_dict = {'index': perf_count[layer]['execution_index'], 'layer_name': layer,  'exec_time': perf_count[layer]['cpu_time'] }
                with open('./model_perf/landmark.txt', 'a') as fh:
                    fh.write(str(perf_dict))
                    fh.write('\n')


    def check_model(self):
        log.info('landmark Model Input shape: {0}'.format( str(self.input_shape) ))
        log.info('landmark Model Output shape: {0}'.format( str(self.output_shape) ))

        supported_layers = self.core.query_network(network= self.network, device_name= self.device)

        ### TODO: Check for any unsupported layers, and let the user
        ###       know if anything is missing. Exit the program, if so.
        unsupported_layers = [l for l in self.network.layers.keys() if l not in supported_layers]
        if len(unsupported_layers) != 0:
            log.warning("Unsupported layers found: {}".format(unsupported_layers))
            sys.exit("Add necessary extension for given hardware")

    def preprocess_input(self, image):
        '''
        Before feeding the data into the model for inference,
        you might have to preprocess it. This function does that.
        '''
        temp = image.copy()
        temp = cv2.resize(temp, (self.input_shape[3], self.input_shape[2] ) ) # n,c,h,w
        temp = temp.transpose((2, 0, 1))
        temp = temp.reshape(1, *temp.shape)
        return temp

    def preprocess_output(self, frame, outputs):
        '''
        Before feeding the output of this model to the next model,
        you might have to preprocess the output. This function does that.
        '''
        temp_op = frame.copy()
        width = int(frame.shape[1]) #1920
        height = int(frame.shape[0]) #1080
        r_radius = 18
        c_radius = 5

        # TODO: (x,y)*5 coordinates of landmarks
        x_lEye= int(outputs[0] * width)
        y_lEye = int(outputs[1] * height)
        x_rEye = int(outputs[2] * width)
        y_rEye = int(outputs[3] * height)
        x_nose = int(outputs[4] * width)
        y_nose = int(outputs[5] * height)
        x_lLip = int(outputs[6] * width)
        y_lLip = int(outputs[7] * height)
        x_rLip = int(outputs[8] * width)
        y_rLip = int(outputs[9] * height)

        min_x_lEye= x_lEye-r_radius
        min_y_lEye = y_lEye-r_radius
        max_x_lEye= x_lEye+r_radius
        max_y_lEye = y_lEye+r_radius

        min_x_rEye = x_rEye-r_radius
        min_y_rEye = y_rEye-r_radius
        max_x_rEye = x_rEye+r_radius
        max_y_rEye = y_rEye+r_radius

        # TODO: Visualize output if vflag

        lEye = temp_op[y_lEye-r_radius : y_lEye+r_radius , x_lEye-r_radius : x_lEye+r_radius]
        cv2.rectangle(temp_op, (min_x_lEye, min_y_lEye), (max_x_lEye, max_y_lEye), (255, 0, 0), 2)
        # cv2.imshow('lEye', lEye)

        rEye = temp_op[y_rEye-r_radius : y_rEye+r_radius , x_rEye-r_radius : x_rEye+r_radius]
        cv2.rectangle(temp_op, (min_x_rEye, min_y_rEye), (max_x_rEye, max_y_rEye), (0, 255, 0), 2)
        # cv2.imshow('rEye', rEye)


        return temp_op, lEye, rEye
This class deals with the LandmarkDetection model and the various operations associated with it. Class for the landmark Detection Model. TODO: Set instance variables. # TODO: Initialize attributes # TODO: Save path of .bin and .xml files of model # landmark_xml = os.path.abspath('../intel/landmarks-regression-retail-0009/FP32/landmarks-regression-retail-0009.xml') # TODO: Initialize IENetwork object # TODO: Initialize IECore object and load the network as ExecutableNetwork object # TODO: Predict and dump perf_count if pflag # TODO: Post stats for every 5th frame # TODO: Method to dump perf_count stats # TODO: Check for layers with `EXECUTED` status only # TODO: Extract major parameters ### TODO: Check for any unsupported layers, and let the user ###       know if anything is missing. Exit the program, if so. Before feeding the data into the model for inference,         you might have to preprocess it. This function does that. # n,c,h,w Before feeding the output of this model to the next model,         you might have to preprocess the output. This function does that. #1920 #1080 # TODO: (x,y)*5 coordinates of landmarks # TODO: Visualize output if vflag # cv2.imshow('lEye', lEye) # cv2.imshow('rEye', rEye)