Testing111.py

import cv2
import numpy as np
import pytesseract
#import argparse
#import os
#import urllib
from PIL import Image
#from google.cloud import vision
#from google.cloud.vision import types


img= cv2.imread('frame12.jpg')
#cv2.namedWindow('Original Image',cv2.WINDOW_NORMAL)
#cv2.imshow('Original Image',img)

img_gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
#cv2.namedWindow('Gray Converted Image',cv2.WINDOW_NORMAL)
#cv2.imshow('Gray converted Image',img_gray)

noise_removal=cv2.bilateralFilter(img_gray,9,75,75)
#cv2.namedWindow('Noise Removed image',cv2.WINDOW_NORMAL)
#cv2.imshow('Noise removed image',noise_removal)

equal_histogram = cv2.equalizeHist(noise_removal)
#cv2.namedWindow('After histogram ',cv2.WINDOW_NORMAL)
#cv2.imshow('After histogram',equal_histogram)

kernel= cv2.getStructuringElement(cv2.MORPH_RECT,(5,5))
morph_image=cv2.morphologyEx(equal_histogram,cv2.MORPH_OPEN,kernel,iterations=15)
#cv2.namedWindow("Morphological opening", cv2.WINDOW_NORMAL)
#cv2.imshow("Morphological opening",morph_image)

sub_morph_image=cv2.subtract(equal_histogram,morph_image)
#cv2.namedWindow("Subtraction image",cv2.WINDOW_NORMAL)
#cv2.imshow("Subtract image",sub_morph_image)

ret,thresh_image=cv2.threshold(sub_morph_image,0,255,cv2.THRESH_OTSU)
#cv2.namedWindow("Image after thresholding",cv2.WINDOW_NORMAL)
#cv2.imshow("Image after thresholding",thresh_image)

canny_image= cv2.Canny(thresh_image,250,255)
#cv2.namedWindow("Image after applying canny",cv2.WINDOW_NORMAL)
canny_image= cv2.convertScaleAbs(canny_image)

kernel=np.ones((3,3),np.uint8)
dilated_image=cv2.dilate(canny_image,kernel,iterations=1)
#cv2.namedWindow("Dilation",cv2.WINDOW_NORMAL)
#cv2.imshow("Dilation",dilated_image)

new,contours,hierarchy =cv2.findContours(dilated_image,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours=sorted(contours,key =cv2.contourArea, reverse = True)[:10]
screenCnt = None
for c in contours:
    peri = cv2.arcLength(c,True)
    approx =cv2.approxPolyDP(c,0.06*peri,True)
    if len(approx)==4:
        screenCnt = approx
        break
final=cv2.drawContours(img,[screenCnt],-1,(0,255,0),3)
#cv2.namedWindow("Image with selected contour",cv2.WINDOW_NORMAL)
#cv2.imshow("Image with selected contour",final)

mask=np.zeros(img_gray.shape,np.uint8)
new_image=cv2.drawContours(mask,[screenCnt],0,255,-1)
new_image=cv2.bitwise_and(img,img,mask=mask)
#cv2.namedWindow("Final Image,",cv2.WINDOW_NORMAL)
#cv2.imshow("Final_image",new_image)

y,cr,cb = cv2.split(cv2.cvtColor(new_image,cv2.COLOR_RGB2YCrCb))
y=cv2.equalizeHist(y)
final_image=cv2.cvtColor(cv2.merge([y,cr,cb]),cv2.COLOR_YCrCb2RGB)
cv2.namedWindow("Enhanced Number Plate",cv2.WINDOW_NORMAL)
cv2.imshow("Enhanced Number plate",final_image)
cv2.imwrite('final.png',final_image)

"""

client=vision.ImageAnnotatorClient()

with io.open(final_image,'rb') as image_file:
    content=image_file.read()
image=types.Image(content=content)
response =client.text_detection(image=image)
texts=response.text_annotations
print('Texts:')

for text in texts:
    print('\n"{}"'.format(text.description))

    vetices =(['({},{})'.format(vertex.x, vertex.y)
               for vertex in text.bounding_poly.vertices])
    


"""
"""ap=argparse.ArgumentParser()
ap.add_argument('-i',"--image",required=True, help="path to input image to be OCR'd")
ap.add_argument("-p","--preprocess", type=str,default="thresh",
                help ="type of preprocessing to be done")
agrs =vars(ap.parse_args())


image=final_image
gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
if args["preprocess"]=="thresh":
    gray = cv2.threshold(gray,0,255,cv2.THRESH_BINARY |cv2.THRESH_OTSU)[1]
elif args["preprocess"]=="blur":
    gray=cv2.medianBlur(gray,3)
filename="{}.png".format(os.getpid())
cv2.imwrite(file.txt,gray)
    
"""
cv2.waitKey()