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from utils import *
from flask import Blueprint,render_template,request,jsonify
import tensorflow as tf
import base64
import io
from PIL import Image
views = Blueprint("views",__name__)
ocr_model = None
ocr_sub_model = None
MODEL_PATH = 'models/ocr_big_1.h5'
SUB_MODEL_PATH= 'models/ocr_new_1.h5'
@views.route('/',methods=['GET','POST'])
def index():
global ocr_model
global ocr_sub_model
if request.method == 'POST':
data = request.json['image']
head, data = data.split(',', 1)
image_data = base64.b64decode(data)
image = Image.open(io.BytesIO(image_data)).convert('RGB')
processed_image = np.array(image)
if not ocr_model:
ocr_model = tf.keras.models.load_model(MODEL_PATH)
if not ocr_sub_model:
ocr_sub_model = tf.keras.models.load_model(SUB_MODEL_PATH)
detected_contours,output_image, coords = get_results(processed_image)
output_string = ""
for i in range(len(detected_contours)):
predicted_char = predict(ocr_model, ocr_sub_model,detected_contours[i],mappings)
if predicted_char == '0':
output_string += 'O'
cv2.putText(output_image, 'O', (coords[i][0]+5,coords[i][1]-5), font, font_scale, color, thickness)
else:
cv2.putText(output_image, predicted_char, (coords[i][0]+5,coords[i][1]-5), font, font_scale, color, thickness)
output_string += predicted_char
output_image = Image.fromarray(output_image, 'RGB')
data = io.BytesIO()
output_image.save(data, "JPEG")
output_image = base64.b64encode(data.getvalue()).decode('utf-8')
return jsonify({'output_image': output_image, 'output_string': output_string})
return render_template('index.html')
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