Add application file

app.py

@@ -0,0 +1,61 @@
+import cv2
+import imghdr
+import pytesseract
+
+def extract_number_plate(image_path):
+    # Load the image
+    image = cv2.imread(image_path)
+    
+    # Check if the image is valid
+    if image is None:
+        print("Invalid image file!")
+        return
+    
+    # Convert the image to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    
+    # Apply Gaussian blur to reduce noise
+    blurred = cv2.GaussianBlur(gray, (7, 7), 0)
+    
+    # Perform edge detection using Canny algorithm
+    edges = cv2.Canny(blurred, 30, 150)
+    
+    # Find contours in the edge-detected image
+    contours, _ = cv2.findContours(edges.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    
+    # Filter contours based on area to select potential number plates
+    number_plate_contours = []
+    for contour in contours:
+        x, y, w, h = cv2.boundingRect(contour)
+        area = cv2.contourArea(contour)
+        if area > 1000 and w > h:
+            number_plate_contours.append(contour)
+    
+    # Draw bounding rectangles around the number plates
+    for contour in number_plate_contours:
+        x, y, w, h = cv2.boundingRect(contour)
+        cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
+        
+        # Extract the region of interest (number plate)
+        plate = gray[y:y+h, x:x+w]
+        
+        # Apply OCR to the number plate region
+        plate_text = pytesseract.image_to_string(plate, config='--psm 7')
+        
+        # Print the extracted text
+        print("Number Plate Text:", plate_text)
+    
+    # Display the image with bounding rectangles
+    cv2.imshow("Number Plates", image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+# Path to the input image
+image_path = "cars/car2.jpg"
+
+# Check if the file is an image
+if imghdr.what(image_path) is not None:
+    # Extract the number plates and print the text
+    extract_number_plate(image_path)
+else:
+    print("Invalid image file format!")