Reading a Meter using OpenCV - python-3.x

Question

I am trying to read values on my electricity meter LCD display using opencv, From my picture I am able to find meter using HoughCircles method, I am able to find LCD display on meter using contours, the lcd display isn't so clear so again I search for contours to extract digits from display. Now I am unable to read values on the display using tesseract or ssocr, how can i read the values on LCD display. I just started using opencv (Beginner), don't know the right way to go from here and if my approach is correct, would appreciate any help. Below is my code snippet and the meter images links are in comments.
def process_image(path, index):
img = cv2.imread(path)
img = cv2.resize(img,(0,0),fx=2.0,fy=2.0)
height, width, depth = img.shape
print("\n---------------------------------------------\n")
print("In Process Image Path is %s height is %d Width is %d depth is %d" %(path, height, width, depth))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.medianBlur(gray, 15)
circles = cv2.HoughCircles(blur, cv2.HOUGH_GRADIENT,1.2,100)
# ensure at least one circles is found, which is our meter
if circles is not None:
circles = np.uint16(np.around(circles))
print("Meter Found")
for i in circles[0]:
CenterX = i[0]
CenterY = i[1]
Radius = i[2]
circle_img = np.zeros((height, width), np.uint8)
cv2.circle(circle_img, (CenterX, CenterY), Radius, 1, thickness=-1)
masked_data = cv2.bitwise_and(img, img, mask=circle_img)
output = masked_data.copy()
cv2.circle(output, (i[0], i[1]), i[2], (0, 255, 0), 2)
cv2.circle(output, (i[0], i[1]), 2, (0, 0, 255), 3)
cv2.imwrite("output_" + str(index) + ".jpg", output)
break
gray = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray,(5,5),1)
edged = cv2.Canny(blurred, 5,10,200)
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
displayCnt = None
contour_list = []
# loop over the contours
for c in cnts:
# approximate the contour
peri = 0.02 * cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c,peri, True)
# if the contour has four vertices, then we have found
# the meter display
if len(approx) == 4:
contour_list.append(c)
cv2.contourArea(c)
displayCnt = approx
break
warped = four_point_transform(gray, displayCnt.reshape(4, 2))
output = four_point_transform(output, displayCnt.reshape(4, 2))
thresh = cv2.adaptiveThreshold(warped, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY, 31, 2)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
digitCnts = []
# loop over the digit area candidates
for c in cnts:
# compute the bounding box of the contour
(x, y, w, h) = cv2.boundingRect(c)
# if the contour is sufficiently large, it must be a digit
if (w > 5 and w < 100) and (h >= 15 and h <= 150) :
digitCnts.append(c)
# sort the contours from left-to-right, then initialize the
# actual digits themselves
digitCnts = contours.sort_contours(digitCnts,method="left-to-right")[0]
mask = np.zeros(thresh.shape, np.uint8)
cv2.drawContours(mask, digitCnts, -80, (255, 255, 255),-1)
mask = cv2.bitwise_not(mask)
mask = cv2.resize(mask, (0, 0), fx=2.0, fy=2.0)
result = os.popen('/usr/local/bin/ssocr --number-digits=-1 -t 10 Mask.jpg')
output = result.read()
print("Output is " + output)
output = output[2:8]
return str(round(float(output) * 0.1, 1))
else:
print("Circle not Found")
print("\n---------------------------------------------\n")
return None