step3.py

import cv2
import numpy as np
import time
from model import NeuralNetwork

class Write():

    net = NeuralNetwork()
    load_from_disk = True
    if load_from_disk:
        penval = np.load('penval.npy')

    cap = cv2.VideoCapture(0)
    cap.set(3,1280)
    cap.set(4,720)

    kernel = np.ones((5,5),np.uint8)

    # Initializing the canvas on which we will draw upon
    canvas = None

    # set the window to autosize so we can view this full screen.
    cv2.namedWindow('image', cv2.WINDOW_NORMAL)

    # Initilize x1,y1 points
    x1,y1=0,0

    # Threshold for noise
    noiseth = 800

    while(1):
        ret, frame = cap.read()
        frame = cv2.flip( frame, 1 )
    
        # Initilize the canvas as a black image of same size as the frame.
        if canvas is None:
            #canvas = np.zeros_like(frame)
            canvas = np.zeros_like(frame)
            canvas[100:500,100:500] = 0

        if ret:
            # Convert BGR to HSV
            hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    
        # If you're reading from memory then load the upper and lower ranges from there
        if load_from_disk:
            lower_range = penval[0]
            upper_range = penval[1]
            
        # Otherwise define your own custom values for upper and lower range.
        else:             
            lower_range  = np.array([26,80,147])
            upper_range = np.array([81,255,255])
    
        mask = cv2.inRange(hsv, lower_range, upper_range)
    
        # Perform morphological operations to get rid of the noise
        mask = cv2.erode(mask,kernel,iterations = 1)
        mask = cv2.dilate(mask,kernel,iterations = 2)
    
        # Find Contours
        _, contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
        # Make sure there is a contour present and also its size is bigger than the noise threshold.
        if contours and cv2.contourArea(max(contours, key = cv2.contourArea)) > noiseth:
                
            c = max(contours, key = cv2.contourArea)    
            x2,y2,w,h = cv2.boundingRect(c)
        
            # If there were no previous points then save the detected x2,y2 coordinates as x1,y1. 
            # This is true when we writing for the first time or when writing again when the pen had disapeared from view.
            if x1 == 0 and y1 == 0:
                x1,y1= x2,y2
            
            else:
                # Draw the line on the canvas
                canvas = cv2.line(canvas, (x1,y1),(x2,y2), 255, 15)
        
            # After the line is drawn the new points become the previous points.
            x1,y1= x2,y2

        else:
            # If there were no contours detected then make x1,y1 = 0
            x1,y1 =0,0
    
        # Merge the canvas and the frame.
        frame = cv2.add(frame,canvas)
    
        # Optionally stack both frames and show it.
        stacked = np.hstack((canvas,frame))
        cv2.imshow('Track',cv2.resize(stacked,None,fx=0.6,fy=0.6))

        k = cv2.waitKey(1) & 0xFF
        if k == 27:
            break
        if k == ord('p'):
            image = canvas[100:500,100:500]
            gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
            result = net.predict(gray)
            print("PREDICTION : ",result)
        elif k==ord('s'):
            print(frame.shape)
        
        # When c is pressed clear the canvas
        if k == ord('c'):
            canvas = None

    cv2.destroyAllWindows()
    cap.release()