Update app.py

app.py

@@ -1,80 +1,97 @@
 import numpy as np
 import cv2
-import imutils
+import  imutils
 import pytesseract
 import pandas as pd
 import time
 import os.path
-from PIL import Image
 
-import io
+from PIL import Image
 import streamlit as st
-bytes_data = None
+bytes_data=None
 
-img_file_buffer = st.camera_input("Take a picture")
+img_file_buffer=st.camera_input("Take a picture")
 if img_file_buffer is not None:
-    test_image = Image.open(img_file_buffer)
-    image = imutils.resize(np.array(test_image), width=500)
-    
-    cv2.imshow("Original Image", image)
-    
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    # cv2.imshow("1 - Grayed image", gray)
-    
-    gray = cv2.bilateralFilter(gray, 11, 17, 17)
-    # cv2.imshow("2 - Smoothened image", gray)
-    
-    edged = cv2.Canny(gray, 170, 200)
+     
+     test_image = Image.open(img_file_buffer)
+     st.image(test_image, use_column_width=True)
+     image = np.asarray(test_image)
+
+     image = imutils.resize(image, width=500)
+
+     cv2.imshow("Original Image", image)
+
+     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    #cv2.imshow("1 - Grayed image", gray)
+
+     gray = cv2.bilateralFilter(gray, 11, 17, 17)
+    #cv2.imshow("2 - Smoothened image", gray)
+
+     edged = cv2.Canny(gray, 170, 200)
     # cv2.imshow("3 - Edged image", edged)
-    
-    cnts, new = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
-    image1 = image.copy()
-    cv2.drawContours(image1, cnts, -1, (0, 255, 0), 3)
-    # cv2.imshow("countours", image1)
-    
-    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:30]
-    screenCnt = None
-    image2 = image.copy()
-    cv2.drawContours(image2, cnts, -1, (0, 0, 255), 3)
-    # cv2.imshow("Top 30 contours", image2)
-    
-    i = 1
-    for c in cnts:
-        perimeter = cv2.arcLength(c, True)
-        approx = cv2.approxPolyDP(c, 0.018 * perimeter, True)
-        if len(approx) == 4:
-            screenCnt = approx
-            x, y, w, h = cv2.boundingRect(c)
-            new_img = image[y:y+h, x:x+w]
-            cv2.imwrite('./'+str(i)+'.png', new_img)
-            i += 1
-            break
-    
-    cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 3)
-    # cv2.imshow("image with detected license plate", image)
-    
-    Cropped_loc = './1.png'
-    cv2.imshow("cropped", cv2.imread(Cropped_loc))
-    
-    # Configuration for tesseract
-    pytesseract.pytesseract.tesseract_cmd = r"D:\number plate\tessract\tesseract.exe"
-    
-    # Run tesseract OCR on image
-    text = pytesseract.image_to_string(Cropped_loc, lang="eng")
-    
-    # Data is stored in CSV file
-    raw_data = {'date': [time.asctime(time.localtime(time.time()))], 'v_number': [text]}
-    
-    df = pd.DataFrame(raw_data, columns=['date', 'v_number'])
-    
-    file_path = 'data.csv'
-    header = not os.path.exists(file_path)  # Check if the file exists and set header flag accordingly
-    
-    df.to_csv('data.csv', mode='a', header=header, index=False)
-    
-    # Print recognized text
-    print(text)
-    cv2.waitKey(0)
-
-if bytes_data is None:
-     st.stop()
+
+
+
+     cnts,new=cv2.findContours(edged.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
+     image1=image.copy()
+     cv2.drawContours(image1,cnts,-1,(0,255,0),3)
+    # cv2.imshow("countours",image1)
+
+     cnts=sorted(cnts,key=cv2.contourArea,reverse=True)[:30]
+     screenCnt=None
+     image2=image.copy()
+     cv2.drawContours(image2,cnts,-1,(0,0,255),3)
+     # cv2.imshow("Top 30 contours",image2)
+
+     i=1
+     for c in cnts:
+             
+             perimeter = cv2.arcLength(c, True)
+             approx = cv2.approxPolyDP(c, 0.018 * perimeter, True)
+             if len(approx) == 4: 
+                     
+                     screenCnt = approx
+ 
+                     x,y,w,h = cv2.boundingRect(c) 
+                     new_img=image[y:y+h,x:x+w]
+                     cv2.imwrite('./'+str(i)+'.png',new_img)
+                     i+=1
+                     break
+ 
+     cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 3)
+     # cv2.imshow("image with detected license plate", image)
+ 
+     Cropped_loc = './1.png'
+     cv2.imshow("cropped", cv2.imread(Cropped_loc))
+ 
+
+
+     # Configuration for tesseract
+     pytesseract.pytesseract.tesseract_cmd=r"D:\number plate\tessract\tesseract.exe"
+
+     # Run tesseract OCR on image
+     text = pytesseract.image_to_string(Cropped_loc,lang="eng")
+ 
+     #Data is stored in CSV file
+     raw_data = {'date': [time.asctime( time.localtime(time.time()) )],'v_number': [text] }
+
+     df = pd.DataFrame(raw_data, columns = ['date', 'v_number'])
+
+     file_path = 'data.csv'
+     header = not os.path.exists(file_path)  # Check if the file exists and set header flag accordingly
+
+
+     df.to_csv('data.csv',mode='a',header=header,index=False)
+     """the mode='a' parameter ensures that each new entry is appended to the existing CSV file instead of replacing it. 
+
+     The header=False parameter prevents the column headers from being written repeatedly when appending entries. 
+
+     The index=False parameter excludes the index column from being written to the CSV file.
+     """
+
+
+
+     # Print recognized text
+     st.write(text)
+# if bytes_data is None:
+#        st.stop()