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read_images.py

+# import the necessary packages
+from tensorflow.keras.models import load_model
+from imutils.contours import sort_contours
+import numpy as np
+import imutils
+from cv2 import cv2
+import os
+import csv
+import smtplib
+import mimetypes
+import email
+import email.mime.application
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+from email.mime.base import MIMEBase
+from email import encoders
+import glob
+import db
+import email_template as et
+
+current_path =  os.path.abspath(os.path.join(os.path.dirname(__file__)))
+
+model = "handwriting_recognition.model"
+img_path = current_path+"\\Outputs"
+
+EMAIL_ADDRESS = "eduhelp.finance@gmail.com"
+rec_email = "dinujasm@gmail.com"
+password = "eduhelp@12"
+value_arr = []
+
+# load the handwriting OCR model
+model = load_model(model)
+
+def main():
+	Image_list = os.listdir(img_path)
+	print(Image_list)
+	for j,y in enumerate(Image_list):
+		# load the input image from disk, convert it to grayscale, and blur
+		# it to reduce noise
+		image = cv2.imread(img_path + "\\"+y)
+		gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+		blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+		# perform edge detection, find contours in the edge map, and sort the
+		# resulting contours from left-to-right
+		edged = cv2.Canny(blurred, 30, 150)
+		cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
+			cv2.CHAIN_APPROX_SIMPLE)
+		cnts = imutils.grab_contours(cnts)
+		cnts = sort_contours(cnts, method="left-to-right")[0]
+		# initialize the list of contour bounding boxes and associated
+		#print(cnts)
+		chars = []
+
+		# loop over the contours
+		for c in cnts:
+			# compute the bounding box of the contour
+			(x, y, w, h) = cv2.boundingRect(c)
+
+			# filter out bounding boxes, ensuring they are neither too small
+			if (w >= 10 and w <= 150) and (h >= 20 and h <= 200):
+				# extract the character and threshold it to make the character
+				# appear as *white* (foreground) on a *black* background, then
+				roi = gray[y:y + h, x:x + w]
+				thresh = cv2.threshold(roi, 0, 255,
+					cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
+				(tH, tW) = thresh.shape
+				# if the width is greater than the height, resize along the
+				# width dimension
+				if tW > tH:
+					thresh = imutils.resize(thresh, width=32)
+				# otherwise, resize along the height
+				else:
+					thresh = imutils.resize(thresh, height=32)
+
+        		# re-grab the image dimensions (now that its been resized)
+				# and then determine how much we need to pad the width and
+				# height such that our image will be 32x32
+				(tH, tW) = thresh.shape
+				dX = int(max(0, 32 - tW) / 2.0)
+				dY = int(max(0, 32 - tH) / 2.0)
+				# pad the image and force 32x32 dimensions
+				padded = cv2.copyMakeBorder(thresh, top=dY, bottom=dY,
+					left=dX, right=dX, borderType=cv2.BORDER_CONSTANT,
+					value=(0, 0, 0))
+				padded = cv2.resize(padded, (32, 32))
+				# prepare the padded image for classification via our
+				# handwriting OCR model
+				padded = padded.astype("float32") / 255.0
+				padded = np.expand_dims(padded, axis=-1)
+				# update our list of characters that will be OCR'd
+				chars.append((padded, (x, y, w, h)))
+
+		# extract the bounding box locations and padded characters
+		boxes = [b[1] for b in chars]
+		chars = np.array([c[0] for c in chars], dtype="float32")
+		# OCR the characters using our handwriting recognition model
+		preds = model.predict(chars)
+		# define the list of label names
+		labelNames = "0123456789"
+		labelNames += "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+		labelNames = [l for l in labelNames]
+		value = ''
+		# loop over the predictions and bounding box locations together
+		for (pred, (x, y, w, h)) in zip(preds, boxes):
+			# find the index of the label with the largest corresponding
+			# probability, then extract the probability and label
+			i = np.argmax(pred)
+			prob = pred[i]
+			label = labelNames[i]
+			# draw the prediction on the image
+			#print("[INFO] {} - {:.2f}%".format(label, prob * 100))
+			if(prob * 100 > 50):
+				value += label
+			cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
+			cv2.putText(image, label, (x - 10, y - 10),
+			cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 255, 0), 2)
+		# # show the image
+		# cv2.imshow("Image", image)
+		# cv2.waitKey(0)
+		value_arr.append(value)
+	
+	name = ""
+	val = 0
+	for i in value_arr[1]:
+		val += 1
+		if val < 3:
+			name = name + i + " "
+		else:
+			name = name + i
+
+	temp = et.getTemplate(name,value_arr[2],value_arr[3],value_arr[4])
+	msg = MIMEMultipart('alternative')
+	msg = email.mime.multipart.MIMEMultipart()
+	msg['Subject'] = "Payement Verification"
+	msg['From'] = EMAIL_ADDRESS
+	msg.attach(temp)
+
+	server = smtplib.SMTP('smtp.gmail.com',587)
+	server.starttls()
+	server.login(EMAIL_ADDRESS, password)
+#	msg = "Your bank slip was successfully received by Eduhelp.\n\nName: "+value_arr[1]+"\nRegistration Number:"+value_arr[2]+"\nCourse: "+value_arr[3]+"\nAmount:"+value_arr[4]
+#	message = 'Subject:{} \n\n{}'.format("Payment verification",msg)
+	server.sendmail(EMAIL_ADDRESS, rec_email, msg.as_string())
+	server.quit()
+
+	con = db.connection()
+	cursor = con.cursor()
+	sql = "INSERT INTO payment (slip_name,reg_no,amount,course,varified) VALUES(%s, %s, %s, %s,%s)"
+	val = [name,value_arr[2], value_arr[4],value_arr[3],0]
+	cursor.execute(sql,val)
+	con.commit()
+
+	files = glob.glob(img_path+'/*')
+	for f in files:
+		os.remove(f)
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