test_on_image.py

'''
Author:Kaustubh Devkar
(kdevkar1998@gmail.com)
Code is updated for running on google colab
'''
#importing required modules
import numpy as np,cv2,imutils
from sklearn.externals import joblib
from google.colab.patches import cv2_imshow

#reading image
img = cv2.imread('Handwritten-Digit-Recognition-Using-Deep-Learning/sample_image2.jpg')
#resizing image
img = imutils.resize(img,width=300)
#showing original image
cv2_imshow(img)
#converting image to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#showing grayscale image
cv2_imshow(gray)

#creating a kernel
kernel = np.ones((40,40),np.uint8)

#applying blackhat thresholding
blackhat = cv2.morphologyEx(gray,cv2.MORPH_BLACKHAT,kernel)


#applying OTSU's thresholding
ret,thresh = cv2.threshold(blackhat,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

#performing erosion and dilation
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)

#finding countours in image
cnts,hie = cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

#loading our ANN model
model = joblib.load('model.pkl')
for c in cnts:
    try:
        #creating a mask
        mask = np.zeros(gray.shape,dtype="uint8")
        
    
        (x,y,w,h) = cv2.boundingRect(c)
        
        hull = cv2.convexHull(c)
        cv2.drawContours(mask,[hull],-1,255,-1)    
        mask = cv2.bitwise_and(thresh,thresh,mask=mask)

        
        #Getting Region of interest
        roi = mask[y-7:y+h+7,x-7:x+w+7]       
        roi = cv2.resize(roi,(28,28))
        roi = np.array(roi)
        #reshaping roi to feed image to our model
        roi = roi.reshape(1,784)

        #predicting
        prediction = model.predict(roi)
    
        cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),1)
        cv2.putText(img,str(int(prediction)),(x,y),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,0,0),1)
        
    except Exception as e:
        print(e)
        
img = imutils.resize(img,width=500)

#showing the output
cv2_imshow(img)
cv2.imwrite('result2.jpg',img)