70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
import numpy as np
|
|
import cv2
|
|
import digits
|
|
import os
|
|
import video
|
|
from common import mosaic
|
|
|
|
|
|
|
|
def main():
|
|
cap = video.create_capture()
|
|
|
|
classifier_fn = 'digits_svm.dat'
|
|
if not os.path.exists(classifier_fn):
|
|
print '"%s" not found, run digits.py first' % classifier_fn
|
|
return
|
|
|
|
model = digits.SVM()
|
|
model.load('digits_svm.dat')
|
|
|
|
SZ = 20
|
|
|
|
while True:
|
|
ret, frame = cap.read()
|
|
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
|
|
|
bin = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 31, 10)
|
|
bin = cv2.medianBlur(bin, 3)
|
|
contours, _ = cv2.findContours( bin.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
for cnt in contours:
|
|
x, y, w, h = cv2.boundingRect(cnt)
|
|
if h < 16 or h > 60 or 1.2*h < w:
|
|
continue
|
|
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0))
|
|
sub = bin[y:,x:][:h,:w]
|
|
#sub = ~cv2.equalizeHist(sub)
|
|
#_, sub_bin = cv2.threshold(sub, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
|
|
|
|
s = float(h)/SZ
|
|
m = cv2.moments(sub)
|
|
m00 = m['m00']
|
|
if m00/255 < 0.1*w*h or m00/255 > 0.9*w*h:
|
|
continue
|
|
|
|
c1 = np.float32([m['m10'], m['m01']]) / m00
|
|
c0 = np.float32([SZ/2, SZ/2])
|
|
t = c1 - s*c0
|
|
A = np.zeros((2, 3), np.float32)
|
|
A[:,:2] = np.eye(2)*s
|
|
A[:,2] = t
|
|
sub1 = cv2.warpAffine(sub, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
|
|
sub1 = digits.deskew(sub1)
|
|
if x+w+SZ < frame.shape[1] and y+SZ < frame.shape[0]:
|
|
frame[y:,x+w:][:SZ, :SZ] = sub1[...,np.newaxis]
|
|
|
|
sample = np.float32(sub1).reshape(1,SZ*SZ) / 255.0
|
|
digit = model.predict(sample)[0]
|
|
|
|
cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
|
|
|
|
|
|
cv2.imshow('frame', frame)
|
|
cv2.imshow('bin', bin)
|
|
if cv2.waitKey(1) == 27:
|
|
break
|
|
|
|
if __name__ == '__main__':
|
|
main()
|