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recognize_img.py
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recognize_img.py
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import cv2
from sklearn.externals import joblib
from skimage.feature import hog
import numpy as np
import matplotlib.pyplot as plt
def recognize(image_path):
clf1 = joblib.load("digits_svn.pkl")
clf2 = joblib.load("digits_rf.pkl")
im = cv2.imread(image_path)
im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
im_gray = cv2.GaussianBlur(im_gray, (5, 5), 0)
im_gray = cv2.adaptiveThreshold(im_gray, im_gray.mean(), 1, 1, 11, 2)
ctrs, hier = cv2.findContours(im_gray, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
rects = []
for ctr in ctrs:
if cv2.contourArea(ctr) > 200:
[x, y, w, h] = cv2.boundingRect(ctr)
if min(h, w) * 4 > max(h, w):
rects.append([x, y, w, h])
# rects = [cv2.boundingRect(ctr) for ctr in ctrs]
for rect in rects:
cv2.rectangle(im, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 3)
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_gray[pt1:pt1 + leng, pt2:pt2 + leng]
if roi.size > 0:
roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)
roi_hog_fd = hog(roi, orientations=9, pixels_per_cell=(14, 14), cells_per_block=(1, 1), visualise=False)
nbr1 = clf1.predict(np.array([roi_hog_fd], 'float64'))
nbr2 = clf2.predict(np.array([roi_hog_fd], 'float64'))
answ = "s:" + str(int(nbr1[0])) + ",f:" + str(int(nbr2[0]))
cv2.putText(im, answ, (rect[0], rect[1]), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 255, 255), 1)
cv2.imwrite(image_path,im)