ROMEOx47/IrisFlowerClassification

from flask import Flask, render_template, request, jsonify import cv2 import numpy as np import pytesseract

app = Flask(name)

Set the path to the Tesseract OCR executable

pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'

@app.route('/') def index(): return render_template('index.html')

@app.route('/recognize', methods=['POST']) def recognize(): # Get the uploaded image file image = request.files['image']

# Check if an image file was uploaded
if image:
    try:
        # Read the uploaded image
        img = cv2.imdecode(np.frombuffer(image.read(), np.uint8), cv2.IMREAD_COLOR)

        # Convert to grayscale
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        # Apply Gaussian blur to the image
        blur = cv2.GaussianBlur(gray, (5, 5), 0)

        # Perform edge detection
        edges = cv2.Canny(blur, 50, 150)

        # Find contours
        contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        # Sort contours by area (Assuming the license plate will be one of the largest contours)
        contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]

        # Iterate over the contours to find the license plate
        for contour in contours:
            rect = cv2.minAreaRect(contour)
            box = cv2.boxPoints(rect)
            box = np.int0(box)
            crop_img = img[min(box[:, 1]):max(box[:, 1]), min(box[:, 0]):max(box[:, 0])]
            gray_crop = cv2.cvtColor(crop_img, cv2.COLOR_BGR2GRAY)

            # Use pytesseract to extract text from the image
            text = pytesseract.image_to_string(gray_crop)
            if text:
                return jsonify({"result": text})

        # If no license plate is found
        return jsonify({"result": "No license plate found in the image."})

    except Exception as e:
        return jsonify({"error": str(e)})

else:
    return jsonify({"error": "No image file uploaded."})

if name == 'main': app.run()