Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
 
 

69 lines
2.2 KiB

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()