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img_to_text.py

+#Importing libraries
+from cv2 import cv2
+import numpy as np
+import os
+import matplotlib.pyplot as plt
+from watchdog.observers import Observer
+from watchdog.events import LoggingEventHandler
+import time
+import read_images as ri
+
+# Declaring Paths
+current_path =  os.path.abspath(os.path.join(os.path.dirname(__file__)))
+img_path = current_path+"\\Images"
+test_images_path = current_path+"\\Test_images"
+
+per = 25
+scale = 0.5
+
+roi = [[(285, 230), (2000, 364), 'text', 'Validation'], #(x,y) (width+x,height+y)
+       [(1150, 660), (2400, 780), 'text', 'Name'], 
+       [(2654, 660), (3400, 730), 'text', 'RegNo'], 
+       [(784, 960), (2350, 1040), 'text', 'Course'], 
+       [(2412, 1350), (3250, 1450),  'text', 'Amount']]
+
+imageT = cv2.imread("template.jpg")
+h,w,c  = imageT.shape
+
+orb = cv2.ORB_create(1000)
+
+# unique key points/elements to image,  decsripters are the representation of these key points that will be easier for
+#the computer to understand
+kp1, des1 = orb.detectAndCompute(imageT, None)
+
+Image_list = os.listdir(test_images_path)
+def on_created(event):
+    print(event.src_path)
+    img = cv2.imread(event.src_path)
+    fileName = (event.src_path).split("\\")
+    fileName1 = fileName[len(fileName)-1]
+    fileName = fileName1.split(".")
+    h,w,c  = img.shape
+    kp2, des2 = orb.detectAndCompute(img, None)
+    bf = cv2.BFMatcher(cv2.NORM_HAMMING)
+    matches = bf.match(des2,des1)
+    matches.sort(key= lambda x: x.distance)
+    good = matches[:int(len(matches)*(per/100))]
+    imgMatch = cv2.drawMatches(img,kp2,imageT,kp1,good[:100],None,flags=2)
+
+    srcPoints = np.float32([kp2[m.queryIdx].pt for m in good]).reshape(-1,1,2)
+    dstPoints = np.float32([kp1[m.trainIdx].pt for m in good]).reshape(-1,1,2)
+
+    M, _ = cv2.findHomography(srcPoints,dstPoints,cv2.RANSAC,5.0)
+    imgScan = cv2.warpPerspective(img,M,(w,h))
+
+    imgShow = imgScan.copy()
+    imgMask = np.zeros_like(imgShow)
+
+    Data_list = []
+    i =0
+    for x,r in enumerate(roi):
+        i += 1
+        cv2.rectangle(imgMask, ((r[0][0]), r[0][1]), ((r[1][0]), r[1][1]), (0,255,0), cv2.FILLED)
+        imgShow = cv2.addWeighted(imgShow, 0.99, imgMask, 0.1,0)
+
+        imgCrop = imgScan[r[0][1]:r[1][1], r[0][0]:r[1][0]]
+      #  cv2.imshow(str(x), imgCrop)
+        plt.imsave('Outputs\\'+fileName[0]+str(i)+'.png', imgCrop)
+    ri.main()
+#     img = cv2.resize(img, (w//4,h//4), None, scale, scale)
+#     imgShow = cv2.resize(imgShow, (w//4,h//4), None, scale, scale)
+#     cv2.imshow(y+"2", imgShow)
+
+# cv2.waitKey(0)
+
+
+if __name__ == "__main__":
+    # An agent has been created here to monitor video-data folder. when a new video is uploaded to that folder,
+    # it automatically starts processing that video.
+    event_handler = LoggingEventHandler()
+    event_handler.on_created = on_created
+
+    observer = Observer()
+    observer.schedule(event_handler, img_path, recursive=True)
+    observer.start()
+
+    try:
+        print("Started")
+        while True:
+            time.sleep(1)
+    finally:
+        observer.stop()
+    observer.join()
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