'''
Copyright 2025 Vignesh(VK)Kotteeswaran <iamvk888@gmail.com>
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
'''


import numpy as np
from openvino.runtime import Core
import math
import cv2
from utils import CTCLabelDecode







class OCR():

    def __init__(self,model_path):

        ie = Core()

        print('\n',model_path)

        model = ie.read_model(model=model_path)
        self.compiled_model = ie.compile_model(model=model, device_name="CPU")

        self.input_layer = self.compiled_model.input(0)
        self.output_layer = self.compiled_model.output(0)
        self.decoder=CTCLabelDecode('dict.txt',True)
        self.show_frame=None
        self.image_shape=None
        self.dynamic_width=False


       


    def img_decode(self,img):

        img = np.frombuffer(img, dtype='uint8')
        img=cv2.imdecode(img, 1)
        #print(img.shape)
        
        return img
    
    def preprocess_img(self,img):

        grayscale_image = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

        # Create an empty array of shape (height, width, 3) for the stacked image
        stacked_image = np.zeros((grayscale_image.shape[0], grayscale_image.shape[1], 3), dtype=np.uint8)

        # Assign the grayscale image to each channel of the stacked image
        stacked_image[:, :, 0] = grayscale_image
        stacked_image[:, :, 1] = grayscale_image
        stacked_image[:, :, 2] = grayscale_image
    

        return self.resize_norm_img(stacked_image)

    def resize_norm_img(self,img,
                    padding=True,
                    interpolation=cv2.INTER_LINEAR):


        self.image_shape=[3,48,int(img.shape[1]*2)]


        imgC,imgH,imgW=self.image_shape

        
        # todo: change to 0 and modified image shape
        max_wh_ratio = imgW * 1.0 / imgH
        h, w = img.shape[0], img.shape[1]
        ratio = w * 1.0 / h
        max_wh_ratio = min(max(max_wh_ratio, ratio), max_wh_ratio)
        imgW = int(imgH * max_wh_ratio)

        if math.ceil(imgH * ratio) > imgW:
            resized_w = imgW
        else:
            resized_w = int(math.ceil(imgH * ratio))
        resized_image = cv2.resize(img, (resized_w, imgH))

 
        self.show_frame=resized_image
        resized_image = resized_image.astype('float32')
        
        

        if self.image_shape[0] == 1:
            resized_image = resized_image / 255
            
            resized_image = resized_image[np.newaxis, :]
        else:
            
            resized_image = resized_image.transpose((2, 0, 1)) / 255

        
        
        resized_image -= 0.5
        resized_image /= 0.5
        padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
        padding_im[:, :, 0:resized_w] = resized_image

        return padding_im

    def predict(self,src):

        imgs=[]
        show_frames=[]

        for item in src:

            if hasattr(item,'shape'):

                imgs.append(np.expand_dims(self.preprocess_img(item),axis=0))

            elif isinstance(item,str):
                
                with open(item, 'rb') as f:
                    content=f.read()
                imgs.append(np.expand_dims(self.preprocess_img(self.img_decode(content)),axis=0))

            else:
                return "Error: Invalid Input"
            
            show_frames.append(self.show_frame)
            

        blob=np.concatenate(imgs,axis=0).astype(np.float32)
        
        outputs = self.compiled_model([blob])[self.output_layer]

        
        texts=[]

        for output in outputs:

            output=np.expand_dims(output,axis=0)

            curr_text=self.decoder(output)[0][0]


            texts.append(curr_text)

        
   
        return texts[0]