calcu_model_testing.py

import numpy as np
import cv2
import os
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, Activation, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization
from tensorflow.keras.initializers import glorot_uniform
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam, SGD
from tensorflow.keras.regularizers import l2
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import LearningRateScheduler, ModelCheckpoint
from tensorflow.keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelEncoder
import seaborn as sn
import matplotlib.pyplot as plt
import pandas as pd
import imutils
from imutils.contours import sort_contours

model = tf.keras.models.load_model('maths_symbol_and_digit_recognition.h5')

def test_pipeline(image_path):
    img = cv2.imread(image_path)
    img = cv2.resize(img, (800, 800))
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # blurred = cv2.GaussianBlur(img_gray, (3, 3), 0)
    edged = cv2.Canny(img_gray, 30, 150)
    contours = cv2.findContours(edged.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', 'add', 'div', 'mul', 'sub']

    for c in contours:
        (x, y, w, h) = cv2.boundingRect(c)
        if 20<=w and 30<=h:
            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=32)
            if th > tw:
                thresh = imutils.resize(thresh, height=32)
            (th, tw) = thresh.shape
            dx = int(max(0, 32 - tw)/2.0)
            dy = int(max(0, 32 - 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, (32, 32))
            padded = np.array(padded)
            padded = padded/255.
            padded = np.expand_dims(padded, axis=0)
            padded = np.expand_dims(padded, axis=-1)
            pred = model.predict(padded)
            pred = np.argmax(pred, axis=1)
            label = labels[pred[0]]
            cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
            cv2.putText(img, label, (x-5, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))

    figure = plt.figure(figsize=(10, 10))
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    plt.imshow(img)
    plt.axis('off')
    plt.show()
    

test_pipeline(r'C:\Users\srikr\OneDrive\Desktop\Interview Project\69_image.png')