import onnxruntime
import numpy as np
import time

from queue import Queue
import os, sys, cv2

MAX_BATCH_SIZE = 32

class BatchedInferenceProvider :

    
    def __init__(self, context, batch_size = 8, wait = True):
        if not isinstance(context, ONNXContext):
            print('context must be an instance of KerasContext')
            sys.exit(0)
        self.context = context
        self.batch_size = batch_size
        self.wait = wait
       
        self.queue = Queue(MAX_BATCH_SIZE)
    

    def add_to_batch(self, image_data):

        image_data = preprocess(image_data)

        if not self.wait : return True, [(self.context.infer(image_data)[0]).tolist()]

        self.queue.put(image_data)
        if self.queue.qsize() < self.batch_size :
            return (False, self.batch_size - self.queue.qsize())
        
        batch = list()
        print()
        for i in range(self.batch_size):
            image = self.queue.get()
            batch.append(image)
        
        batch = np.array(batch, dtype = 'float32')
        return True,[int(np.argmax(tensor)) for tensor in self.context.infer(batch)]


class ONNXContext :

    def __init__(self, model_file):
        if not os.path.exists(model_file):
            print('Model {} not found'.format(model_file))
            sys.exit(0)
        self.session = onnxruntime.InferenceSession(model_file)
    
    def infer(self, image_data):#inference function
        if not type(image_data) == np.ndarray or image_data.ndim < 3:#to check weather image_data is in numpy array with 3 dimentions
            print('Provide a numpy array  WHC format')
            sys.exit(0)
        if image_data.ndim == 3 :
            image_data = np.expand_dims(image_data, axis = 0)#expand the dimenstions of the array
        
        image_data = [image_data.astype('float32')]
        feed = dict([ (input.name, image_data[i]) for i, input in  enumerate(self.session.get_inputs()) ])
        outputs = [ output.name for output in self.session.get_outputs() ]

        return self.session.run(outputs, feed)#input  

def preprocess(image_data):#function to check the data is in bytes 
    
    if not type(image_data) == bytes :
        print('image_data should be a byte-string')#to check the data is in bytes 
        sys.exit(0)

    image_data = np.fromstring(image_data, dtype = 'uint8')
    image_np = cv2.imdecode(image_data, cv2.IMREAD_COLOR)

    print(image_np.shape)
    
    if image_np.shape[-1] != 3 :#to check the image is coloured or not
        print('Provide an RGB image')
        sys.exit(0)
    
    return ( cv2.resize(image_np, (225, 225)) / 255. )#resize the image

        


5f84cd54ad8dc599dd5cbfbb405051af6164e489
<mask token>


class BatchedInferenceProvider:
    <mask token>
    <mask token>


class ONNXContext:

    def __init__(self, model_file):
        if not os.path.exists(model_file):
            print('Model {} not found'.format(model_file))
            sys.exit(0)
        self.session = onnxruntime.InferenceSession(model_file)

    def infer(self, image_data):
        if not type(image_data) == np.ndarray or image_data.ndim < 3:
            print('Provide a numpy array  WHC format')
            sys.exit(0)
        if image_data.ndim == 3:
            image_data = np.expand_dims(image_data, axis=0)
        image_data = [image_data.astype('float32')]
        feed = dict([(input.name, image_data[i]) for i, input in enumerate(
            self.session.get_inputs())])
        outputs = [output.name for output in self.session.get_outputs()]
        return self.session.run(outputs, feed)


<mask token>

<mask token>


class BatchedInferenceProvider:

    def __init__(self, context, batch_size=8, wait=True):
        if not isinstance(context, ONNXContext):
            print('context must be an instance of KerasContext')
            sys.exit(0)
        self.context = context
        self.batch_size = batch_size
        self.wait = wait
        self.queue = Queue(MAX_BATCH_SIZE)

    def add_to_batch(self, image_data):
        image_data = preprocess(image_data)
        if not self.wait:
            return True, [self.context.infer(image_data)[0].tolist()]
        self.queue.put(image_data)
        if self.queue.qsize() < self.batch_size:
            return False, self.batch_size - self.queue.qsize()
        batch = list()
        print()
        for i in range(self.batch_size):
            image = self.queue.get()
            batch.append(image)
        batch = np.array(batch, dtype='float32')
        return True, [int(np.argmax(tensor)) for tensor in self.context.
            infer(batch)]


class ONNXContext:

    def __init__(self, model_file):
        if not os.path.exists(model_file):
            print('Model {} not found'.format(model_file))
            sys.exit(0)
        self.session = onnxruntime.InferenceSession(model_file)

    def infer(self, image_data):
        if not type(image_data) == np.ndarray or image_data.ndim < 3:
            print('Provide a numpy array  WHC format')
            sys.exit(0)
        if image_data.ndim == 3:
            image_data = np.expand_dims(image_data, axis=0)
        image_data = [image_data.astype('float32')]
        feed = dict([(input.name, image_data[i]) for i, input in enumerate(
            self.session.get_inputs())])
        outputs = [output.name for output in self.session.get_outputs()]
        return self.session.run(outputs, feed)


<mask token>

<mask token>


class BatchedInferenceProvider:

    def __init__(self, context, batch_size=8, wait=True):
        if not isinstance(context, ONNXContext):
            print('context must be an instance of KerasContext')
            sys.exit(0)
        self.context = context
        self.batch_size = batch_size
        self.wait = wait
        self.queue = Queue(MAX_BATCH_SIZE)

    def add_to_batch(self, image_data):
        image_data = preprocess(image_data)
        if not self.wait:
            return True, [self.context.infer(image_data)[0].tolist()]
        self.queue.put(image_data)
        if self.queue.qsize() < self.batch_size:
            return False, self.batch_size - self.queue.qsize()
        batch = list()
        print()
        for i in range(self.batch_size):
            image = self.queue.get()
            batch.append(image)
        batch = np.array(batch, dtype='float32')
        return True, [int(np.argmax(tensor)) for tensor in self.context.
            infer(batch)]


class ONNXContext:

    def __init__(self, model_file):
        if not os.path.exists(model_file):
            print('Model {} not found'.format(model_file))
            sys.exit(0)
        self.session = onnxruntime.InferenceSession(model_file)

    def infer(self, image_data):
        if not type(image_data) == np.ndarray or image_data.ndim < 3:
            print('Provide a numpy array  WHC format')
            sys.exit(0)
        if image_data.ndim == 3:
            image_data = np.expand_dims(image_data, axis=0)
        image_data = [image_data.astype('float32')]
        feed = dict([(input.name, image_data[i]) for i, input in enumerate(
            self.session.get_inputs())])
        outputs = [output.name for output in self.session.get_outputs()]
        return self.session.run(outputs, feed)


def preprocess(image_data):
    if not type(image_data) == bytes:
        print('image_data should be a byte-string')
        sys.exit(0)
    image_data = np.fromstring(image_data, dtype='uint8')
    image_np = cv2.imdecode(image_data, cv2.IMREAD_COLOR)
    print(image_np.shape)
    if image_np.shape[-1] != 3:
        print('Provide an RGB image')
        sys.exit(0)
    return cv2.resize(image_np, (225, 225)) / 255.0

import onnxruntime
import numpy as np
import time
from queue import Queue
import os, sys, cv2
MAX_BATCH_SIZE = 32


class BatchedInferenceProvider:

    def __init__(self, context, batch_size=8, wait=True):
        if not isinstance(context, ONNXContext):
            print('context must be an instance of KerasContext')
            sys.exit(0)
        self.context = context
        self.batch_size = batch_size
        self.wait = wait
        self.queue = Queue(MAX_BATCH_SIZE)

    def add_to_batch(self, image_data):
        image_data = preprocess(image_data)
        if not self.wait:
            return True, [self.context.infer(image_data)[0].tolist()]
        self.queue.put(image_data)
        if self.queue.qsize() < self.batch_size:
            return False, self.batch_size - self.queue.qsize()
        batch = list()
        print()
        for i in range(self.batch_size):
            image = self.queue.get()
            batch.append(image)
        batch = np.array(batch, dtype='float32')
        return True, [int(np.argmax(tensor)) for tensor in self.context.
            infer(batch)]


class ONNXContext:

    def __init__(self, model_file):
        if not os.path.exists(model_file):
            print('Model {} not found'.format(model_file))
            sys.exit(0)
        self.session = onnxruntime.InferenceSession(model_file)

    def infer(self, image_data):
        if not type(image_data) == np.ndarray or image_data.ndim < 3:
            print('Provide a numpy array  WHC format')
            sys.exit(0)
        if image_data.ndim == 3:
            image_data = np.expand_dims(image_data, axis=0)
        image_data = [image_data.astype('float32')]
        feed = dict([(input.name, image_data[i]) for i, input in enumerate(
            self.session.get_inputs())])
        outputs = [output.name for output in self.session.get_outputs()]
        return self.session.run(outputs, feed)


def preprocess(image_data):
    if not type(image_data) == bytes:
        print('image_data should be a byte-string')
        sys.exit(0)
    image_data = np.fromstring(image_data, dtype='uint8')
    image_np = cv2.imdecode(image_data, cv2.IMREAD_COLOR)
    print(image_np.shape)
    if image_np.shape[-1] != 3:
        print('Provide an RGB image')
        sys.exit(0)
    return cv2.resize(image_np, (225, 225)) / 255.0

import onnxruntime
import numpy as np
import time

from queue import Queue
import os, sys, cv2

MAX_BATCH_SIZE = 32

class BatchedInferenceProvider :

    
    def __init__(self, context, batch_size = 8, wait = True):
        if not isinstance(context, ONNXContext):
            print('context must be an instance of KerasContext')
            sys.exit(0)
        self.context = context
        self.batch_size = batch_size
        self.wait = wait
       
        self.queue = Queue(MAX_BATCH_SIZE)
    

    def add_to_batch(self, image_data):

        image_data = preprocess(image_data)

        if not self.wait : return True, [(self.context.infer(image_data)[0]).tolist()]

        self.queue.put(image_data)
        if self.queue.qsize() < self.batch_size :
            return (False, self.batch_size - self.queue.qsize())
        
        batch = list()
        print()
        for i in range(self.batch_size):
            image = self.queue.get()
            batch.append(image)
        
        batch = np.array(batch, dtype = 'float32')
        return True,[int(np.argmax(tensor)) for tensor in self.context.infer(batch)]


class ONNXContext :

    def __init__(self, model_file):
        if not os.path.exists(model_file):
            print('Model {} not found'.format(model_file))
            sys.exit(0)
        self.session = onnxruntime.InferenceSession(model_file)
    
    def infer(self, image_data):#inference function
        if not type(image_data) == np.ndarray or image_data.ndim < 3:#to check weather image_data is in numpy array with 3 dimentions
            print('Provide a numpy array  WHC format')
            sys.exit(0)
        if image_data.ndim == 3 :
            image_data = np.expand_dims(image_data, axis = 0)#expand the dimenstions of the array
        
        image_data = [image_data.astype('float32')]
        feed = dict([ (input.name, image_data[i]) for i, input in  enumerate(self.session.get_inputs()) ])
        outputs = [ output.name for output in self.session.get_outputs() ]

        return self.session.run(outputs, feed)#input  

def preprocess(image_data):#function to check the data is in bytes 
    
    if not type(image_data) == bytes :
        print('image_data should be a byte-string')#to check the data is in bytes 
        sys.exit(0)

    image_data = np.fromstring(image_data, dtype = 'uint8')
    image_np = cv2.imdecode(image_data, cv2.IMREAD_COLOR)

    print(image_np.shape)
    
    if image_np.shape[-1] != 3 :#to check the image is coloured or not
        print('Provide an RGB image')
        sys.exit(0)
    
    return ( cv2.resize(image_np, (225, 225)) / 255. )#resize the image

        

