{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.9","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# import the necessary packages\nfrom tensorflow.keras.datasets import mnist\nimport numpy as np","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-06-02T07:52:36.698713Z","iopub.execute_input":"2023-06-02T07:52:36.699111Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install imutils","metadata":{"execution":{"iopub.status.busy":"2023-06-02T06:46:28.545122Z","iopub.execute_input":"2023-06-02T06:46:28.545862Z","iopub.status.idle":"2023-06-02T06:46:45.385223Z","shell.execute_reply.started":"2023-06-02T06:46:28.545826Z","shell.execute_reply":"2023-06-02T06:46:45.384045Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Collecting imutils\n  Downloading imutils-0.5.4.tar.gz (17 kB)\n  Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hBuilding wheels for collected packages: imutils\n  Building wheel for imutils (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for imutils: filename=imutils-0.5.4-py3-none-any.whl size=25859 sha256=4c76f4be6f4e7fd3652eed07e7113462c513cbdc80a4a52d79ea6b9fbc6a455f\n  Stored in directory: /root/.cache/pip/wheels/85/cf/3a/e265e975a1e7c7e54eb3692d6aa4e2e7d6a3945d29da46f2d7\nSuccessfully built imutils\nInstalling collected packages: imutils\nSuccessfully installed imutils-0.5.4\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}]},{"cell_type":"code","source":"# set the matplotlib backend so figures can be saved in the background\nimport matplotlib\nmatplotlib.use(\"Agg\")\n# import the necessary package\n#from pyimagesearch.models import ResNet\n#from pyimagesearch.az_dataset import load_mnist_dataset\n#from pyimagesearch.az_dataset import load_az_dataset\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.optimizers import SGD\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\nfrom imutils import build_montages\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport argparse\nimport cv2\nimport csv\nimport tensorflow as tf\nimport keras\nfrom matplotlib.patches import Rectangle\nfrom PIL import Image","metadata":{"execution":{"iopub.status.busy":"2023-06-02T07:23:11.269525Z","iopub.execute_input":"2023-06-02T07:23:11.269885Z","iopub.status.idle":"2023-06-02T07:23:11.277066Z","shell.execute_reply.started":"2023-06-02T07:23:11.269857Z","shell.execute_reply":"2023-06-02T07:23:11.276127Z"},"trusted":true},"execution_count":49,"outputs":[]},{"cell_type":"code","source":"classes = {\n     'a':10,\n     'b':11,\n     'c':12,\n     'd':13,\n     'e':14,\n     'f':15,\n     'g':16,\n     'h':17,\n     'i':18,\n     'j':19,\n     'k':20,\n     'l':21,\n     'm':22,\n     'n':23,\n     'o':24,\n     'p':25,\n     'q':26,\n     'r':27,\n     's':28,\n     't':29,\n     'u':30,\n     'v':31,\n     'w':32,\n     'x':33,\n     'y':34,\n     'z':35,\n     'A':36,\n     'B':37,\n     'C':38,\n     'D':39,\n     'E':40,\n     'F':41,\n     'G':42,\n     'H':43,\n     'I':44,\n     'J':45,\n     'K':46,\n     'L':47,\n     'M':48,\n     'N':49,\n     'O':50,\n     'P':51,\n     'Q':52,\n     'R':53,\n     'S':54,\n     'T':55,\n     'U':56,\n     'V':57,\n     'W':58,\n     'X':59,\n     'Y':60,\n     'Z':61,\n      '1': 1,\n      '2': 2,\n      '3': 3,\n      '4': 4,\n      '5': 5,\n      '6': 6,\n      '7': 7,\n      '8': 8,\n      '9': 9,\n      '0': 0\n}","metadata":{"execution":{"iopub.status.busy":"2023-06-02T06:59:05.92928Z","iopub.execute_input":"2023-06-02T06:59:05.930104Z","iopub.status.idle":"2023-06-02T06:59:05.943871Z","shell.execute_reply.started":"2023-06-02T06:59:05.930044Z","shell.execute_reply":"2023-06-02T06:59:05.942788Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"def load_dataset(datasetPath):\n    # initialize the list of data and labels\n    data = []\n    labels = []\n    # loop over the rows of the A-Z handwritten digit dataset\n    x=open(datasetPath)\n    reader = csv.reader(x)\n    tmp=next(reader)\n    for row in reader:\n        # parse the label and image from the row\n        label = classes[row[1]]\n        img=cv2.imread(row[0])\n        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n        img.resize((28, 28))\n        image = np.array(img,dtype=\"uint8\")    \n        #image = np.array([int(x) for x in row[:-1]], dtype=\"uint8\")\n        # images are represented as single channel (grayscale) images\n        # that are 28x28=784 pixels -- we need to take this flattened\n        # 784-d list of numbers and repshape them into a 28x28 matrix\n        image = image.reshape((28, 28))\n        # update the list of data and labels\n        data.append(image)\n        labels.append(label)\n        # convert the data and labels to NumPy arrays\n    data = np.array(data, dtype=\"float32\")\n    labels = np.array(labels, dtype=\"int\")\n    # return a 2-tuple of the A-Z data and labels\n    return (data, labels)    ","metadata":{"execution":{"iopub.status.busy":"2023-06-02T07:23:39.951469Z","iopub.execute_input":"2023-06-02T07:23:39.951812Z","iopub.status.idle":"2023-06-02T07:23:39.960262Z","shell.execute_reply.started":"2023-06-02T07:23:39.951783Z","shell.execute_reply":"2023-06-02T07:23:39.959126Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"# initialize the number of epochs to train for, initial learning rate,\n# and batch size\nEPOCHS = 35\nINIT_LR = 1e-1\nBS = 128\n# load the A-Z and MNIST datasets, respectively\nprint(\"[INFO] loading datasets...\")\n(Data, Labels) = load_dataset(\"/kaggle/input/english-fontnumber-recognition/train.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-06-02T07:23:43.310952Z","iopub.execute_input":"2023-06-02T07:23:43.312004Z","iopub.status.idle":"2023-06-02T07:32:59.232007Z","shell.execute_reply.started":"2023-06-02T07:23:43.311968Z","shell.execute_reply":"2023-06-02T07:32:59.230928Z"},"trusted":true},"execution_count":55,"outputs":[{"name":"stdout","text":"[INFO] loading datasets...\n","output_type":"stream"}]},{"cell_type":"code","source":"len(Labels)","metadata":{"execution":{"iopub.status.busy":"2023-06-02T07:32:59.271508Z","iopub.execute_input":"2023-06-02T07:32:59.271891Z","iopub.status.idle":"2023-06-02T07:32:59.283751Z","shell.execute_reply.started":"2023-06-02T07:32:59.271857Z","shell.execute_reply":"2023-06-02T07:32:59.282883Z"},"trusted":true},"execution_count":59,"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"62992"},"metadata":{}}]},{"cell_type":"code","source":"for i in range (len(Labels)):\n    print(Labels[i])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# each image in the A-Z and MNIST digts datasets are 28x28 pixels;\n# however, the architecture we're using is designed for 32x32 images,\n# so we need to resize them to 32x32\ndata = [cv2.resize(image, (32, 32)) for image in Data]\ndata = np.array(data, dtype=\"float32\")\n# add a channel dimension to every image in the dataset and scale the\n# pixel intensities of the images from [0, 255] down to [0, 1]\ndata = np.expand_dims(data, axis=-1)\ndata /= 255.0","metadata":{"execution":{"iopub.status.busy":"2023-06-02T07:32:59.999031Z","iopub.execute_input":"2023-06-02T07:32:59.999538Z","iopub.status.idle":"2023-06-02T07:33:00.539929Z","shell.execute_reply.started":"2023-06-02T07:32:59.99949Z","shell.execute_reply":"2023-06-02T07:33:00.538832Z"},"trusted":true},"execution_count":61,"outputs":[]},{"cell_type":"code","source":"# convert the labels from integers to vectors\nle = LabelBinarizer()\nlabels = le.fit_transform(Labels)\ncounts = labels.sum(axis=0)\n# account for skew in the labeled data\nclassTotals = labels.sum(axis=0)\nclassWeight = {}\n# loop over all classes and calculate the class weight\nfor i in range(0, len(classTotals)):\n    classWeight[i] = classTotals.max() / classTotals[i]\n# partition the data into training and testing splits using 80% of\n# the data for training and the remaining 20% for testing\n(trainX, testX, trainY, testY) = train_test_split(data,\n    labels, test_size=0.20, stratify=labels, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2023-06-02T07:33:17.377098Z","iopub.execute_input":"2023-06-02T07:33:17.377484Z","iopub.status.idle":"2023-06-02T07:33:23.721793Z","shell.execute_reply.started":"2023-06-02T07:33:17.377449Z","shell.execute_reply":"2023-06-02T07:33:23.720763Z"},"trusted":true},"execution_count":63,"outputs":[]},{"cell_type":"code","source":"# construct the image generator for data augmentation\naug = ImageDataGenerator(\n    rotation_range=10,\n    zoom_range=0.05,\n    width_shift_range=0.1,\n    height_shift_range=0.1,\n    shear_range=0.15,\n    horizontal_flip=False,\n    fill_mode=\"nearest\")","metadata":{"execution":{"iopub.status.busy":"2023-06-02T07:33:27.421001Z","iopub.execute_input":"2023-06-02T07:33:27.422046Z","iopub.status.idle":"2023-06-02T07:33:27.42741Z","shell.execute_reply.started":"2023-06-02T07:33:27.422009Z","shell.execute_reply":"2023-06-02T07:33:27.426434Z"},"trusted":true},"execution_count":64,"outputs":[]},{"cell_type":"code","source":"from keras.layers import BatchNormalization\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.convolutional import AveragePooling2D\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.layers.convolutional import ZeroPadding2D\nfrom keras.layers.core import Activation\nfrom keras.layers.core import Dense\nfrom keras.layers import Flatten\nfrom keras.layers import Input\nfrom keras.models import Model\nfrom keras.layers import add\nfrom keras.regularizers import l2\nfrom keras import backend as K\n\nclass ResNet:\n    @staticmethod\n    def residual_module(data, K, stride, chanDim, red=False,\n        reg=0.0001, bnEps=2e-5, bnMom=0.9):\n        # the shortcut branch of the ResNet module should be\n        # initialize as the input (identity) data\n        shortcut = data\n\n        # the first block of the ResNet module are the 1x1 CONVs\n        bn1 = BatchNormalization(axis=chanDim, epsilon=bnEps,\n            momentum=bnMom)(data)\n        act1 = Activation(\"relu\")(bn1)\n        conv1 = Conv2D(int(K * 0.25), (1, 1), use_bias=False,\n            kernel_regularizer=l2(reg))(act1)\n\n        # the second block of the ResNet module are the 3x3 CONVs\n        bn2 = BatchNormalization(axis=chanDim, epsilon=bnEps,\n            momentum=bnMom)(conv1)\n        act2 = Activation(\"relu\")(bn2)\n        conv2 = Conv2D(int(K * 0.25), (3, 3), strides=stride,\n            padding=\"same\", use_bias=False,\n            kernel_regularizer=l2(reg))(act2)\n\n        # the third block of the ResNet module is another set of 1x1\n        # CONVs\n        bn3 = BatchNormalization(axis=chanDim, epsilon=bnEps,\n            momentum=bnMom)(conv2)\n        act3 = Activation(\"relu\")(bn3)\n        conv3 = Conv2D(K, (1, 1), use_bias=False,\n            kernel_regularizer=l2(reg))(act3)\n\n        # if we are to reduce the spatial size, apply a CONV layer to\n        # the shortcut\n        if red:\n            shortcut = Conv2D(K, (1, 1), strides=stride,\n                use_bias=False, kernel_regularizer=l2(reg))(act1)\n\n        # add together the shortcut and the final CONV\n        x = add([conv3, shortcut])\n\n        # return the addition as the output of the ResNet module\n        return x\n\n    @staticmethod\n    def build(width, height, depth, classes, stages, filters,\n        reg=0.0001, bnEps=2e-5, bnMom=0.9, dataset=\"cifar\"):\n        # initialize the input shape to be \"channels last\" and the\n        # channels dimension itself\n        inputShape = (height, width, depth)\n        chanDim = -1\n\n        # if we are using \"channels first\", update the input shape\n        # and channels dimension\n        if K.image_data_format() == \"channels_first\":\n            inputShape = (depth, height, width)\n            chanDim = 1\n\n        # set the input and apply BN\n        inputs = Input(shape=inputShape)\n        x = BatchNormalization(axis=chanDim, epsilon=bnEps,\n            momentum=bnMom)(inputs)\n\n        # check if we are utilizing the CIFAR dataset\n        if dataset == \"cifar\":\n            # apply a single CONV layer\n            x = Conv2D(filters[0], (3, 3), use_bias=False,\n                padding=\"same\", kernel_regularizer=l2(reg))(x)\n\n        # check to see if we are using the Tiny ImageNet dataset\n        elif dataset == \"tiny_imagenet\":\n            # apply CONV => BN => ACT => POOL to reduce spatial size\n            x = Conv2D(filters[0], (5, 5), use_bias=False,\n                padding=\"same\", kernel_regularizer=l2(reg))(x)\n            x = BatchNormalization(axis=chanDim, epsilon=bnEps,\n                momentum=bnMom)(x)\n            x = Activation(\"relu\")(x)\n            x = ZeroPadding2D((1, 1))(x)\n            x = MaxPooling2D((3, 3), strides=(2, 2))(x)\n\n        # loop over the number of stages\n        for i in range(0, len(stages)):\n            # initialize the stride, then apply a residual module\n            # used to reduce the spatial size of the input volume\n            stride = (1, 1) if i == 0 else (2, 2)\n            x = ResNet.residual_module(x, filters[i + 1], stride,\n                chanDim, red=True, bnEps=bnEps, bnMom=bnMom)\n\n            # loop over the number of layers in the stage\n            for j in range(0, stages[i] - 1):\n                # apply a ResNet module\n                x = ResNet.residual_module(x, filters[i + 1],\n                    (1, 1), chanDim, bnEps=bnEps, bnMom=bnMom)\n\n        # apply BN => ACT => POOL\n        x = BatchNormalization(axis=chanDim, epsilon=bnEps,\n            momentum=bnMom)(x)\n        x = Activation(\"relu\")(x)\n        x = AveragePooling2D((8, 8))(x)\n\n        # softmax classifier\n        x = Flatten()(x)\n        x = Dense(classes, kernel_regularizer=l2(reg))(x)\n        x = Activation(\"softmax\")(x)\n\n        # create the model\n        model = Model(inputs, x, name=\"resnet\")\n\n        # return the constructed network architecture\n        return model","metadata":{"execution":{"iopub.status.busy":"2023-06-02T07:33:31.363992Z","iopub.execute_input":"2023-06-02T07:33:31.364466Z","iopub.status.idle":"2023-06-02T07:33:31.402556Z","shell.execute_reply.started":"2023-06-02T07:33:31.364431Z","shell.execute_reply":"2023-06-02T07:33:31.400396Z"},"trusted":true},"execution_count":65,"outputs":[]},{"cell_type":"code","source":"# initialize and compile our deep neural network\nprint(\"[INFO] compiling model...\")\nopt = tf.keras.optimizers.experimental.SGD(learning_rate=INIT_LR,weight_decay=INIT_LR / EPOCHS)\nmodel = ResNet.build(32, 32, 1, len(le.classes_), (3, 3, 3),\n    (64, 64, 128, 256), reg=0.0005)\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=opt,\n    metrics=[\"accuracy\"])","metadata":{"execution":{"iopub.status.busy":"2023-06-02T07:33:41.042844Z","iopub.execute_input":"2023-06-02T07:33:41.043246Z","iopub.status.idle":"2023-06-02T07:33:47.68879Z","shell.execute_reply.started":"2023-06-02T07:33:41.043213Z","shell.execute_reply":"2023-06-02T07:33:47.687795Z"},"trusted":true},"execution_count":66,"outputs":[{"name":"stdout","text":"[INFO] compiling model...\n","output_type":"stream"}]},{"cell_type":"code","source":"# train the network\nprint(\"[INFO] training network...\")\nH = model.fit(\n    aug.flow(trainX, trainY, batch_size=BS),\n    validation_data=(testX, testY),\n    steps_per_epoch=len(trainX) // BS,\n    epochs=EPOCHS,\n    class_weight=classWeight,\n    verbose=1)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# define the list of label names\nlabelNames = \"0123456789\"\nlabelNames += \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\"\nlabelNames = [l for l in labelNames]\n# evaluate the network\nprint(\"[INFO] evaluating network...\")\npredictions = model.predict(testX, batch_size=BS)\nprint(classification_report(testY.argmax(axis=1),\n    predictions.argmax(axis=1), target_names=labelNames))","metadata":{"execution":{"iopub.status.busy":"2023-05-31T13:29:20.457018Z","iopub.execute_input":"2023-05-31T13:29:20.457378Z","iopub.status.idle":"2023-05-31T13:29:24.813039Z","shell.execute_reply.started":"2023-05-31T13:29:20.457348Z","shell.execute_reply":"2023-05-31T13:29:24.811971Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"[INFO] evaluating network...\n217/217 [==============================] - 4s 14ms/step\n              precision    recall  f1-score   support\n\n           0       0.99      0.82      0.89       864\n           1       0.99      0.91      0.95       972\n           2       0.82      0.92      0.86       871\n           3       0.93      0.91      0.92       905\n           4       0.99      0.67      0.80       838\n           5       0.93      0.91      0.92       786\n           6       0.97      0.85      0.91       859\n           7       0.92      0.86      0.89       906\n           8       0.84      0.94      0.88       848\n           9       0.95      0.85      0.89       867\n           A       0.80      0.67      0.73       722\n           B       0.93      0.87      0.90       702\n           C       0.91      0.82      0.86       722\n           D       0.85      0.86      0.85       702\n           E       0.72      0.88      0.79       709\n           F       0.86      0.91      0.89       701\n           G       0.92      0.47      0.62       712\n           H       0.95      0.79      0.86       709\n           I       0.71      0.59      0.64       717\n           J       0.95      0.85      0.90       712\n           K       0.85      0.90      0.88       718\n           L       0.68      0.71      0.69       716\n           M       0.89      0.92      0.91       715\n           N       0.95      0.72      0.82       705\n           O       0.86      0.87      0.86       709\n           P       0.96      0.91      0.94       711\n           Q       0.66      0.87      0.75       711\n           R       0.90      0.84      0.87       711\n           S       0.91      0.90      0.91       702\n           T       0.79      0.88      0.84       709\n           U       0.86      0.88      0.87       707\n           V       0.87      0.83      0.85       707\n           W       0.92      0.86      0.89       708\n           X       0.76      0.95      0.84       709\n           Y       0.84      0.87      0.85       713\n           Z       0.91      0.86      0.88       709\n           a       0.32      0.89      0.47        28\n           b       0.13      0.95      0.23        22\n           c       0.28      0.79      0.42        24\n           d       0.23      1.00      0.38        23\n           e       0.63      0.79      0.70        24\n           f       0.47      0.58      0.52        24\n           g       0.31      0.91      0.46        22\n           h       0.13      1.00      0.23        23\n           i       0.90      0.39      0.55        23\n           j       0.90      0.41      0.56        22\n           k       0.10      0.17      0.12        23\n           l       0.20      0.86      0.33        22\n           m       0.44      0.77      0.56        22\n           n       0.10      0.74      0.17        23\n           o       0.29      0.61      0.39        23\n           p       0.38      0.91      0.54        22\n           q       0.61      0.88      0.72        25\n           r       0.27      0.61      0.38        23\n           s       0.19      0.57      0.28        23\n           t       0.14      0.09      0.11        11\n           u       0.44      0.91      0.60        22\n           v       0.47      0.75      0.58        24\n           w       0.25      0.77      0.38        22\n           x       0.40      0.83      0.54        24\n           y       0.57      0.70      0.63        23\n           z       0.30      0.43      0.36        23\n\n    accuracy                           0.84     27774\n   macro avg       0.66      0.78      0.67     27774\nweighted avg       0.87      0.84      0.84     27774\n\n","output_type":"stream"}]},{"cell_type":"code","source":"# save the model to disk\nprint(\"[INFO] serializing network...\")\nmodel.save(\"/kaggle/working/model\", save_format=\"h5\")\n# construct a plot that plots and saves the training history\nN = np.arange(0, EPOCHS)\nplt.style.use(\"ggplot\")\nplt.figure()\nplt.plot(N, H.history[\"loss\"], label=\"train_loss\")\nplt.plot(N, H.history[\"val_loss\"], label=\"val_loss\")\nplt.title(\"Training Loss and Accuracy\")\nplt.xlabel(\"Epoch #\")\nplt.ylabel(\"Loss/Accuracy\")\nplt.legend(loc=\"lower left\")\nplt.savefig(\"/kaggle/working/plots\")","metadata":{"execution":{"iopub.status.busy":"2023-05-31T13:29:47.285856Z","iopub.execute_input":"2023-05-31T13:29:47.286262Z","iopub.status.idle":"2023-05-31T13:29:47.859153Z","shell.execute_reply.started":"2023-05-31T13:29:47.28623Z","shell.execute_reply":"2023-05-31T13:29:47.858068Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"[INFO] serializing network...\n","output_type":"stream"}]},{"cell_type":"code","source":"# initialize our list of output test images\nimages = []\n# randomly select a few testing characters\nfor i in np.random.choice(np.arange(0, len(trainY)), size=(49,)):\n# classify the character\n    probs = model.predict(trainX[np.newaxis, i])\n    prediction = probs.argmax(axis=1)\n    label = labelNames[prediction[0]]\n    # extract the image from the test data and initialize the text\n    # label color as green (correct)\n    image = (trainX[i] * 255).astype(\"uint8\")\n    color = (0, 255, 0)\n    # otherwise, the class label prediction is incorrect\n    if prediction[0] != np.argmax(trainY[i]):\n        color = (0, 0, 255)\n    # merge the channels into one image, resize the image from 32x32\n    # to 96x96 so we can better see it and then draw the predicted\n    # label on the image\n    image = cv2.merge([image] * 3)\n    image = cv2.resize(image, (96, 96), interpolation=cv2.INTER_LINEAR)\n    cv2.putText(image, label, (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75,\n        color, 2)\n    # add the image to our list of output images\n    images.append(image)\n# construct the montage for the images\nmontage = build_montages(images, (96, 96), (7, 7))[0]\n# show the output montage\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimgplot = plt.imshow(montage)\nplt.show()\n#cv2.imshow(montage)\n#cv2.waitKey(0)","metadata":{"execution":{"iopub.status.busy":"2023-05-31T13:29:58.863122Z","iopub.execute_input":"2023-05-31T13:29:58.863528Z","iopub.status.idle":"2023-05-31T13:30:03.250059Z","shell.execute_reply.started":"2023-05-31T13:29:58.863494Z","shell.execute_reply":"2023-05-31T13:30:03.248955Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 0s 447ms/step\n1/1 [==============================] - 0s 22ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 24ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 23ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 52ms/step\n1/1 [==============================] - 0s 36ms/step\n1/1 [==============================] - 0s 23ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 20ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 21ms/step\n1/1 [==============================] - 0s 25ms/step\n1/1 [==============================] - 0s 25ms/step\n1/1 [==============================] - 0s 26ms/step\n1/1 [==============================] - 0s 25ms/step\n1/1 [==============================] - 0s 24ms/step\n1/1 [==============================] - 0s 26ms/step\n1/1 [==============================] - 0s 27ms/step\n1/1 [==============================] - 0s 26ms/step\n1/1 [==============================] - 0s 26ms/step\n1/1 [==============================] - 0s 26ms/step\n1/1 [==============================] - 0s 25ms/step\n1/1 [==============================] - 0s 24ms/step\n1/1 [==============================] - 0s 25ms/step\n1/1 [==============================] - 0s 24ms/step\n1/1 [==============================] - 0s 25ms/step\n1/1 [==============================] - 0s 25ms/step\n1/1 [==============================] - 0s 24ms/step\n1/1 [==============================] - 0s 25ms/step\n1/1 [==============================] - 0s 23ms/step\n1/1 [==============================] - 0s 25ms/step\n1/1 [==============================] - 0s 25ms/step\n1/1 [==============================] - 0s 25ms/step\n1/1 [==============================] - 0s 25ms/step\n1/1 [==============================] - 0s 26ms/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"# import the necessary packages\nfrom tensorflow.keras.models import load_model\nfrom imutils.contours import sort_contours\nimport numpy as np\nimport argparse\nimport imutils\nimport cv2\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Rectangle\nfrom PIL import Image","metadata":{"execution":{"iopub.status.busy":"2023-06-01T07:21:19.131536Z","iopub.execute_input":"2023-06-01T07:21:19.131902Z","iopub.status.idle":"2023-06-01T07:21:19.138201Z","shell.execute_reply.started":"2023-06-01T07:21:19.131872Z","shell.execute_reply":"2023-06-01T07:21:19.136885Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"# load the handwriting OCR model\nprint(\"[INFO] loading handwriting OCR model...\")\nmodel = load_model(\"/kaggle/working/model\")","metadata":{"execution":{"iopub.status.busy":"2023-05-31T13:30:18.414526Z","iopub.execute_input":"2023-05-31T13:30:18.415769Z","iopub.status.idle":"2023-05-31T13:30:19.464393Z","shell.execute_reply.started":"2023-05-31T13:30:18.415722Z","shell.execute_reply":"2023-05-31T13:30:19.463445Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"[INFO] loading handwriting OCR model...\n","output_type":"stream"}]},{"cell_type":"code","source":"# load the input image from disk, convert it to grayscale, and blur\n# it to reduce noise\nimage = cv2.imread(\"../input/english-fontnumber-recognition/Font/Font/Sample060/img060-00761.png\")\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nblurred = cv2.GaussianBlur(gray, (5, 5), 0)\n# perform edge detection, find contours in the edge map, and sort the\n# resulting contours from left-to-right\nedged = cv2.Canny(blurred, 30, 150)\ncnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,\n    cv2.CHAIN_APPROX_SIMPLE)\ncnts = imutils.grab_contours(cnts)\ncnts = sort_contours(cnts, method=\"left-to-right\")[0]\n# initialize the list of contour bounding boxes and associated\n# characters that we'll be OCR'ing\nchars = []","metadata":{"execution":{"iopub.status.busy":"2023-06-01T07:16:41.809743Z","iopub.execute_input":"2023-06-01T07:16:41.810435Z","iopub.status.idle":"2023-06-01T07:16:41.822846Z","shell.execute_reply.started":"2023-06-01T07:16:41.810402Z","shell.execute_reply":"2023-06-01T07:16:41.821897Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"len(cnts)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# loop over the contours\nfor c in cnts:\n    # compute the bounding box of the contour\n    (x, y, w, h) = cv2.boundingRect(c)\n    # filter out bounding boxes, ensuring they are neither too small\n    # nor too large\n    if (w >= 5 and w <= 150) and (h >= 15 and h <= 120):\n        # extract the character and threshold it to make the character\n        # appear as *white* (foreground) on a *black* background, then\n        # grab the width and height of the thresholded image\n        roi = gray[y:y + h, x:x + w]\n        thresh = cv2.threshold(roi, 0, 255,\n            cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]\n        (tH, tW) = thresh.shape\n        # if the width is greater than the height, resize along the\n        # width dimension\n        if tW > tH:\n            thresh = imutils.resize(thresh, width=32)\n        # otherwise, resize along the height\n        else:\n            thresh = imutils.resize(thresh, height=32)\n            # re-grab the image dimensions (now that its been resized)\n        # and then determine how much we need to pad the width and\n        # height such that our image will be 32x32\n        (tH, tW) = thresh.shape\n        dX = int(max(0, 32 - tW) / 2.0)\n        dY = int(max(0, 32 - tH) / 2.0)\n        # pad the image and force 32x32 dimensions\n        padded = cv2.copyMakeBorder(thresh, top=dY, bottom=dY,\n            left=dX, right=dX, borderType=cv2.BORDER_CONSTANT,\n            value=(0, 0, 0))\n        padded = cv2.resize(padded, (32, 32))\n        # prepare the padded image for classification via our\n        # handwriting OCR model\n        padded = padded.astype(\"float32\") / 255.0\n        padded = np.expand_dims(padded, axis=-1)\n        # update our list of characters that will be OCR'd\n        chars.append((padded, (x, y, w, h)))","metadata":{"execution":{"iopub.status.busy":"2023-06-01T07:16:53.217114Z","iopub.execute_input":"2023-06-01T07:16:53.21753Z","iopub.status.idle":"2023-06-01T07:16:53.230795Z","shell.execute_reply.started":"2023-06-01T07:16:53.217499Z","shell.execute_reply":"2023-06-01T07:16:53.229701Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"# extract the bounding box locations and padded characters\nboxes = [b[1] for b in chars]\nchars = np.array([c[0] for c in chars], dtype=\"float32\")\n# OCR the characters using our handwriting recognition model\npreds = model.predict(chars)\n# define the list of label names\nlabelNames = \"0123456789\"\nlabelNames += \"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\"\nlabelNames = [l for l in labelNames]","metadata":{"execution":{"iopub.status.busy":"2023-06-01T07:16:58.338009Z","iopub.execute_input":"2023-06-01T07:16:58.338729Z","iopub.status.idle":"2023-06-01T07:16:58.344853Z","shell.execute_reply.started":"2023-06-01T07:16:58.338698Z","shell.execute_reply":"2023-06-01T07:16:58.343899Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"code","source":"boxes","metadata":{"execution":{"iopub.status.busy":"2023-06-01T07:18:51.59607Z","iopub.execute_input":"2023-06-01T07:18:51.597243Z","iopub.status.idle":"2023-06-01T07:18:51.605733Z","shell.execute_reply.started":"2023-06-01T07:18:51.597188Z","shell.execute_reply":"2023-06-01T07:18:51.604461Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"[(27, 14, 38, 100), (65, 14, 37, 100)]"},"metadata":{}}]},{"cell_type":"code","source":"# loop over the predictions and bounding box locations together\nfor (pred, (x, y, w, h)) in zip(preds, boxes):\n    # find the index of the label with the largest corresponding\n    # probability, then extract the probability and label\n    i = np.argmax(pred)\n    prob = pred[i]\n    label = labelNames[i]\n    # draw the prediction on the image\n    print(\"[INFO] {} - {:.2f}%\".format(label, prob * 100))\n    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n    cv2.putText(image, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 255, 0), 2)\n    # show the image\n    imgplot = plt.imshow(image)\nplt.show()\n    #cv2.imshow(\"Image\", image)\n    #cv2.waitKey(0)","metadata":{"execution":{"iopub.status.busy":"2023-06-01T07:21:50.411943Z","iopub.execute_input":"2023-06-01T07:21:50.4124Z","iopub.status.idle":"2023-06-01T07:21:50.712379Z","shell.execute_reply.started":"2023-06-01T07:21:50.412359Z","shell.execute_reply":"2023-06-01T07:21:50.711341Z"},"trusted":true},"execution_count":41,"outputs":[]}]}