Hand Written Recognition with Random Forest

This notebook can be downloaded here.

This notebook demonstrate how to use random forest to do hand written recognition.

Instruction

Make the following three folders. The notebook should be in the same path as these three folders

• train images: Containing few subfolders. Each subfolder contains images of a class. In this example, data has two subfolders: 1 (containing number 1 images) and 2 (containing number 2 images). If your categories are letters a, b and c, you need to name the folders a (containg letter a images), b (containg letter a images), and c (containg letter a images) and so on.

• test images: Containing only images for testing. There is NO subfolder within test images

• The notebook should be in the same folder as these two above folders.

Import some packages

import numpy as np
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import matplotlib
 
# import the necessary packages
from sklearn.model_selection import train_test_split
import os
import tkinter
from os import listdir
from PIL import Image

# Set image resolution
dim = 100

# Set the forest
number_of_trees = 1000

train_folder = 'train images'
test_folder = 'test images'
lab = os.listdir(train_folder)

Xtrain = np.empty((0,3*dim*dim), float)
ytrain = np.empty((0,), int)

for i in range(0, len(lab)): 
    path = train_folder + '/' + lab[i]
    names = [f for f in os.listdir(path)]
    for n in names[0:]:
        img = Image.open(path +'/'+ n)
        image = img.convert('RGB')
        image = image.resize((dim, dim))
        image = np.array(image)
        image = image.ravel().reshape(1,-1)
        Xtrain = np.append(Xtrain, image,0)
        ytrain = np.append(ytrain, i)  

Training a Random Forest

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=number_of_trees)
model.fit(Xtrain, ytrain)

RandomForestClassifier(n_estimators=1000)

Accuracy on Training Data

ypred=model.predict(Xtrain)
from sklearn.metrics import confusion_matrix
mat = confusion_matrix(ypred, ytrain)
mat

array([[21,  0,  0],
       [ 0, 29,  0],
       [ 0,  0, 23]], dtype=int64)

Predicting New Images

In the next codes, we will use our fitted model to classify new images of dogs and cats. The model will label as Dog or Cat. These labeled images is then save in the predict folder.

onlyfiles = [f for f in listdir(test_folder)]

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 10))

for i, images in enumerate(onlyfiles):
    
    ax = plt.subplot(int(1+len(onlyfiles)/3), 3, i + 1)
    img = Image.open(test_folder+'/'+images)
    image = img.convert('RGB')
    image = image.resize((dim, dim))
    image_print = image.resize((dim, dim))
    image = np.array(image)
    image = image.ravel().reshape(1,-1)

    predictions = model.predict(image)
    
    confidence = (100*model.predict_proba(image).max()).round(1)
    
    plt.imshow(image_print)
    plt.title(str(confidence)+'% '+lab[predictions[0]])
    plt.axis("off")