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| import cv2 | |
| import math | |
| import argparse | |
| from tensorflow.keras.models import load_model | |
| from flask import Flask, request, jsonify | |
| import cv2 | |
| import json | |
| import numpy as np | |
| from tensorflow.keras import backend as K | |
| from get_coordinate import get_object_coordinates | |
| import requests | |
| import gradio as gr | |
| import os | |
| file_urls = [ | |
| 'https://www.dropbox.com/scl/fi/skt4o9a37ccrxvruojk3o/2.png?rlkey=kxppvdnvbs9852rj6ly123xfk&dl=0', | |
| 'https://www.dropbox.com/scl/fi/3opkr5aoca1fq0wrudlcx/3.png?rlkey=wm4vog7yyk5naoqu68vr6v48s&dl=0', | |
| 'https://www.dropbox.com/scl/fi/t74nd09fod52x0gua93ty/1.png?rlkey=er4ktuephlapzyvh5glkym5b4&dl=0'] | |
| def download_file(url, save_name): | |
| url = url | |
| if not os.path.exists(save_name): | |
| file = requests.get(url) | |
| open(save_name, 'wb').write(file.content) | |
| for i, url in enumerate(file_urls): | |
| if 'mp4' in file_urls[i]: | |
| download_file( | |
| file_urls[i], | |
| f"video.mp4" | |
| ) | |
| else: | |
| download_file( | |
| file_urls[i], | |
| f"image_{i}.jpg" | |
| ) | |
| class OCR(): | |
| def __init__(self,path="model-ocr-0.1829.h5",config_path="config.json"): | |
| # Read the config JSON file | |
| with open(config_path, 'r',encoding="utf-8") as file: | |
| self.config_data = json.load(file) | |
| # Get the threshold value | |
| self.threshold = self.config_data['hiragana']['threshold'] | |
| # Get the label dictionary | |
| self.label_dict = self.config_data['hiragana']['label'] | |
| # load the model from local | |
| self.model = load_model(path,custom_objects={"K": K}) | |
| def run(self,image): | |
| # extract the character coordinates using the cv2 contours | |
| coordinate,thresholdedImage = get_object_coordinates(image) | |
| image_batch = np.zeros((1,64,64,1)) | |
| output =[] | |
| for row in range(len(coordinate)): | |
| temp = {} | |
| # crop the image | |
| cropImg = thresholdedImage[coordinate[row][1]:coordinate[row][3],coordinate[row][0]:coordinate[row][2]] | |
| # resize the image | |
| image_batch[0,:,:,0] = cv2.resize(cropImg,(64,64))*255 | |
| # predict the results | |
| predict = self.model.predict(image_batch) | |
| position = np.argmax(predict) | |
| label_name = self.label_dict[str(position)] | |
| temp["text"] = label_name | |
| temp["prob"] = predict[position] | |
| temp["coord"] = coordinate[row] # Xmin,Ymin,Xmax,Ymax | |
| output.append(temp) | |
| return output | |
| def getOCRResults(image_path): | |
| temp0 =[] | |
| for i in range(len(image_path)): | |
| image = cv2.imread(image_path[i]) | |
| results0 = ocrAPP.run(image) | |
| temp0.append(results0) | |
| result_json={} | |
| result_json["result"] = temp0 | |
| response = jsonify(result_json) | |
| response.headers['Content-Type'] = 'application/json; charset=utf-8' | |
| return response | |
| ocrAPP = OCR() | |
| video_path = [['video.mp4']] | |
| path = [['image_0.jpg'], ['image_1.jpg']] | |
| inputs_image = [ | |
| gr.components.Image(type="filepath", label="Input Image"), | |
| ] | |
| outputs = [ | |
| gr.components.JSON(label="Output Json"), | |
| ] | |
| interface_image = gr.Interface( | |
| fn=getOCRResults, | |
| inputs=inputs_image, | |
| outputs=outputs, | |
| title="Hiragana Character Recognition", | |
| examples=path, | |
| cache_examples=False, | |
| ) | |
| gr.TabbedInterface( | |
| [interface_image], | |
| tab_names=['Image inference'] | |
| ).queue().launch() | |