generateClassifier.py
#!/usr/bin/python
# Import the modules
from sklearn.externals import joblib from sklearn import datasets
from skimage.feature import hog from sklearn.svm import LinearSVC from sklearn import preprocessing import numpy as np
from collections import Counter
# Load the dataset
dataset = datasets.fetch_mldata("MNIST Original")
# Extract the features and labels features = np.array(dataset.data, 'int16') labels = np.array(dataset.target, 'int')
# Extract the hog features list_hog_fd = []
for feature in features:
fd = hog(feature.reshape((28, 28)), orientations=9, pixels_per_cell=(14, 14), cells_per_block=(1, 1), visualise=False) list_hog_fd.append(fd)
hog_features = np.array(list_hog_fd, 'float64')
# Normalize the features
pp = preprocessing.StandardScaler().fit(hog_features) hog_features = pp.transform(hog_features)
print ("Count of digits in dataset", Counter(labels)) # Create an linear SVM object
clf = LinearSVC()
# Perform the training clf.fit(hog_features, labels)
# Save the classifier
joblib.dump((clf, pp), "digits_cls.pkl", compress=3)
performClassifier.py
#!/usr/bin/python
# Import the modules import cv2
from sklearn.externals import joblib from skimage.feature import hog import numpy as np
import argparse as ap
# Get the path of the training set parser = ap.ArgumentParser()
parser.add_argument("-c", "--classiferPath", help="Path to Classifier File", required="True") parser.add_argument("-i", "--image", help="Path to Image", required="True")
args = vars(parser.parse_args())
# Load the classifier
clf, pp = joblib.load(args["classiferPath"])
# Read the input image
im = cv2.imread(args["image"])
# Convert to grayscale and apply Gaussian filtering im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) im_gray = cv2.GaussianBlur(im_gray, (5, 5), 0)
# Threshold the image
ret, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV)
# Find contours in the image
ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Get rectangles contains each contour
rects = [cv2.boundingRect(ctr) for ctr in ctrs]
# For each rectangular region, calculate HOG features and predict # the digit using classifier.
for rect in rects:
# Draw the rectangles
cv2.rectangle(im, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 3) # Make the rectangular region around the digit
leng = int(rect[3] * 1.6)
pt1 = int(rect[1] + rect[3] // 2 - leng // 2) pt2 = int(rect[0] + rect[2] // 2 - leng // 2) roi = im_th[pt1:pt1+leng, pt2:pt2+leng] # Resize the image
roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)
roi = cv2.dilate(roi, (3, 3))
# Calculate the HOG features
roi_hog_fd = hog(roi, orientations=9, pixels_per_cell=(14, 14), cells_per_block=(1, 1), visualise=False) roi_hog_fd = pp.transform(np.array([roi_hog_fd], 'float64'))
nbr = clf.predict(roi_hog_fd)
cv2.putText(im, str(int(nbr[0])), (rect[0], rect[1]),cv2.FONT_HERSHEY_DUPLEX, 2, (0, 255, 255), 3)
cv2.namedWindow("Resulting Image with Rectangular ROIs", cv2.WINDOW_NORMAL) cv2.imshow("Resulting Image with Rectangular ROIs", im)
cv2.waitKey()
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