main.py

import os
from flask import Flask, request, redirect, jsonify
import numpy as np
from flask import render_template
from asgiref.wsgi import WsgiToAsgi
import numpy as np
import cv2
from sklearn.preprocessing import LabelEncoder
import imutils
from imutils.contours import sort_contours
from keras.models import load_model
import warnings
from flask_cors import CORS

# Suppress specific TensorFlow and Keras warnings
warnings.filterwarnings("ignore", category=DeprecationWarning, module="tensorflow")
warnings.filterwarnings("ignore", category=DeprecationWarning, module="keras")

# Get the path to the directory containing this script
script_dir = os.path.dirname(os.path.abspath(__file__))

# Load the model using the relative path
model_path = os.path.join(script_dir, "./ocr_perfecto_experiment.h5")
model = load_model(model_path)


def test_pipeline(image_data):

    img = cv2.imdecode(image_data, cv2.IMREAD_COLOR)
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    height, width = img_gray.shape
    half_width = round(width / 2)
    half_height = round(height / 2)
    img_gray = cv2.resize(img_gray, (half_width, half_height))
    img_gray = cv2.GaussianBlur(img_gray, (5, 5), 0)
    edged = cv2.Canny(img_gray, 30, 150)
    dilated = cv2.dilate(edged.copy(), None, iterations=6)
    normalized_image = cv2.normalize(dilated, None, 0, 255, cv2.NORM_MINMAX)

    contours = cv2.findContours(
        normalized_image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
    )
    contours = imutils.grab_contours(contours)
    contours = sort_contours(contours, method="left-to-right")[0]
    labels = [
        "0",
        "1",
        "2",
        "3",
        "4",
        "5",
        "6",
        "7",
        "8",
        "9",
        "_",
        "-",
        "[",
        "]",
        "+",
        "%",
    ]

    real_labels = [
        "0",
        "1",
        "2",
        "3",
        "4",
        "5",
        "6",
        "7",
        "8",
        "9",
        "*",
        "-",
        "(",
        ")",
        "+",
        "/",
    ]

    label_encoder = LabelEncoder()
    label_class = label_encoder.fit_transform(labels)

    results = []

    for c in contours:
        if cv2.contourArea(c) < 1000:
            continue
        (x, y, w, h) = cv2.boundingRect(c)
        if 20 <= w:
            roi = img_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 tw > th:
                thresh = imutils.resize(thresh, width=28)
            if th > tw:
                thresh = imutils.resize(thresh, height=28)
            (th, tw) = thresh.shape
            dx = int(max(0, 28 - tw) / 2.0)
            dy = int(max(0, 28 - th) / 2.0)
            padded = cv2.copyMakeBorder(
                thresh,
                top=dy,
                bottom=dy,
                left=dx,
                right=dx,
                borderType=cv2.BORDER_CONSTANT,
                value=(0, 0, 0),
            )
            padded = cv2.resize(padded, (28, 28))
            padded = np.array(padded)
            padded = padded / 255.0
            padded = np.expand_dims(padded, axis=0)
            padded = np.expand_dims(padded, axis=-1)
            pred = model.predict(padded)
            pred = np.argmax(pred, axis=1)
            results.append(real_labels[np.where(label_class == pred[0])[0][0]])

    return results


ALLOWED_EXTENSIONS = {"png", "jpg", "jpeg"}
app = Flask(__name__, template_folder="./src/templates", static_folder="./src/public")
app.secret_key = "1234"
cors = CORS(app, resources={r"/*": {"origins": "*"}})


def allowed_file(filename):
    return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXTENSIONS


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


@app.route("/api/photo-upload", methods=["POST"])
def upload_file():
    try:
        if "file" not in request.files:
            raise ValueError("File not found in the request.")

        file = request.files["file"]

        if file.filename == "":
            raise ValueError("Empty filename in the request.")

        if file and allowed_file(file.filename):
            image = file.read()
            image_data = np.frombuffer(image, np.uint8)
            results = test_pipeline(image_data)
            return jsonify(results), 200
        else:
            raise ValueError("Invalid file type.")

    except Exception as e:
        return f"Error processing file: {str(e)}", 500


@app.route("/predict", methods=["POST"])
def predict():
    try:
        if "file" not in request.files:
            raise ValueError("File not found in the request.")

        file = request.files["file"]

        if file.filename == "":
            raise ValueError("Empty filename in the request.")

        if file and allowed_file(file.filename):
            image = file.read()
            image_data = np.frombuffer(image, np.uint8)
            results = test_pipeline(image_data)
            return jsonify(results), 200
        else:
            raise ValueError("Invalid file type.")

    except Exception as e:
        return f"Error processing file: {str(e)}", 500


wsgi = WsgiToAsgi(app)


def create_app():
    return app