app.py

from flask import Flask,render_template,Response, request
from main import text_to_speech
import cv2
from tensorflow.keras.models import model_from_json
from tensorflow.keras.preprocessing import image
import numpy as np
import imageio


app=Flask(__name__)
camera=cv2.VideoCapture(0)


d = {0: ' ', 1: 'A', 2: 'B', 3: 'C',
    4: 'D', 5: 'E', 6: 'F', 7: 'G',
    8: 'H', 9: 'I', 10: 'J', 11: 'K',
    12: 'L', 13: 'M', 14: 'N', 15: 'O',
    16: 'P', 17: 'Q', 18: 'R', 19: 'S',
    20: 'T', 21: 'U', 22: 'V', 23: 'W',
    24: 'X', 25: 'Y',26: 'Z'}

upper_left = (335, 3)
bottom_right = (635, 303)

json_file = open('model-bw.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model-bw.h5")

def function(img):
  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
  blur = cv2.GaussianBlur(gray,(5,5),1)
  th3 = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,11,2)
  ret, res = cv2.threshold(th3, 70, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
  return res

def generate_frames():
    while True:
        ## read the camera frame
        success,frame=camera.read()
        
        if not success:
            break
        else:
            frame= cv2.flip(frame, 1)
            r = cv2.rectangle(frame, upper_left, bottom_right, (0, 0, 0), 5)
            rect_img = frame[upper_left[1] : bottom_right[1], upper_left[0] : bottom_right[0]]
            sketcher_rect = rect_img
            sketcher_rect = function(sketcher_rect)
            sketcher_rect_rgb = cv2.cvtColor(sketcher_rect, cv2.COLOR_GRAY2RGB)
            frame[upper_left[1] : bottom_right[1], upper_left[0] : bottom_right[0]] = sketcher_rect_rgb
            
            ret,buffer=cv2.imencode('.jpg',frame)
            frame=buffer.tobytes()

        yield(b'--frame\r\n'
                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')

l=[]
str1=""
@app.route('/predict',methods=['POST','GET'])
def predictions():
    
    while True:
        success,frame=camera.read()
        frame= cv2.flip(frame, 1)
        r = cv2.rectangle(frame, upper_left, bottom_right, (0, 0, 0), 5)
        rect_img = frame[upper_left[1] : bottom_right[1], upper_left[0] : bottom_right[0]]
        sketcher_rect = rect_img
        sketcher_rect = function(sketcher_rect)
        sketcher_rect_rgb = cv2.cvtColor(sketcher_rect, cv2.COLOR_GRAY2RGB)
        frame[upper_left[1] : bottom_right[1], upper_left[0] : bottom_right[0]] = sketcher_rect_rgb
        
        sketcher_rect = cv2.resize(sketcher_rect,(128, 128))
        x = image.img_to_array(sketcher_rect)
        x = np.expand_dims(x, axis=0)
        x = x/255.0
        pre = loaded_model.predict(x)
        p_test=np.argmax(pre)
        a = d[p_test]
        l.append(a)
        str1=""
        print(l)
        
        for ele in l:
            str1 += ele  
        return render_template("index.html",pred=str1)

@app.route('/stop',methods=['POST','GET'])
def stopping():
    while True:
        ## read the camera frame
        success,frame=camera.read()
        
        if not success:
            break
        else:
            frame= cv2.flip(frame, 1)
            r = cv2.rectangle(frame, upper_left, bottom_right, (0, 0, 0), 5)
            rect_img = frame[upper_left[1] : bottom_right[1], upper_left[0] : bottom_right[0]]
            sketcher_rect = rect_img
            sketcher_rect = function(sketcher_rect)
            sketcher_rect_rgb = cv2.cvtColor(sketcher_rect, cv2.COLOR_GRAY2RGB)
            frame[upper_left[1] : bottom_right[1], upper_left[0] : bottom_right[0]] = sketcher_rect_rgb
            str1=""
            for ele in l:
                str1 += ele 
            text_to_speech(str1, 'Female')
            l.clear()
            return render_template("index.html",pred=str1)

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/video')
def video():
    return Response(generate_frames(),mimetype='multipart/x-mixed-replace; boundary=frame')


if __name__=="__main__":
    app.run(debug=True)