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performRecognition.py
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# Import the modules
import cv2
from sklearn.externals import joblib
from skimage.feature import hog
import numpy as np
# Load the classifier
clf = joblib.load("digits_cls.pkl")
# Read the input image
im = cv2.imread("black.jpg")
im = cv2.bitwise_not(im)
im = cv2.GaussianBlur(im, (3, 3),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
# im = cv2.GaussianBlur(im, (1, 1),1)
cv2.imwrite("black2.jpg",im)
# cv2.imshow("aa",im)
# cv2.waitKey()
im = cv2.imread("black2.jpg")
# im = im[0:120,50:150]
# Convert to grayscale and apply Gaussian filtering
# im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
# im_gray = cv2.GaussianBlur(im_gray, (0, 0), 1)
cv2.imshow("aa",im)
cv2.waitKey()
im_gray = im
# 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 Linear SVM.
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 )
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]
cv2.imshow("aa",im_th)
cv2.waitKey()
# Resize the image
# print (roi.shape)
# print(roi)
# roi = cv2.resize(roi,(28,28), interpolation=cv2.INTER_AREA)
roi = cv2.dilate(im_th, (3, 3))
# Calculate the HOG features
roi_hog_fd = hog(roi, orientations=9, pixels_per_cell=(14, 14), cells_per_block=(1, 1), visualize=False)
nbr = clf.predict(np.array([roi_hog_fd], 'float64'))
cv2.putText(im, str(int(nbr[0])), (rect[0], rect[1]),cv2.FONT_HERSHEY_DUPLEX, 2, (0, 255, 255), 3)
cv2.imshow("Resulting Image with Rectangular ROIs", im)
cv2.waitKey()
cv2.destroyallwindows()