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#!/usr/bin/env python
'''
Digit recognition from video.
Run digits.py before, to train and save the SVM.
Usage:
digits_video.py [{camera_id|video_file}]
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
# built-in modules
import os
import sys
# local modules
import video
from common import mosaic
from digits import *
def main():
try:
src = sys.argv[1]
except:
src = 0
cap = video.create_capture(src, fallback='synth:bg={}:noise=0.05'.format(cv.samples.findFile('sudoku.png')))
classifier_fn = 'digits_svm.dat'
if not os.path.exists(classifier_fn):
print('"%s" not found, run digits.py first' % classifier_fn)
return
model = cv.ml.SVM_load(classifier_fn)
while True:
_ret, frame = cap.read()
gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
bin = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY_INV, 31, 10)
bin = cv.medianBlur(bin, 3)
contours, heirs = cv.findContours( bin.copy(), cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE)
try:
heirs = heirs[0]
except:
heirs = []
for cnt, heir in zip(contours, heirs):
_, _, _, outer_i = heir
if outer_i >= 0:
continue
x, y, w, h = cv.boundingRect(cnt)
if not (16 <= h <= 64 and w <= 1.2*h):
continue
pad = max(h-w, 0)
x, w = x - (pad // 2), w + pad
cv.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0))
bin_roi = bin[y:,x:][:h,:w]
m = bin_roi != 0
if not 0.1 < m.mean() < 0.4:
continue
'''
gray_roi = gray[y:,x:][:h,:w]
v_in, v_out = gray_roi[m], gray_roi[~m]
if v_out.std() > 10.0:
continue
s = "%f, %f" % (abs(v_in.mean() - v_out.mean()), v_out.std())
cv.putText(frame, s, (x, y), cv.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
'''
s = 1.5*float(h)/SZ
m = cv.moments(bin_roi)
c1 = np.float32([m['m10'], m['m01']]) / m['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
bin_norm = cv.warpAffine(bin_roi, A, (SZ, SZ), flags=cv.WARP_INVERSE_MAP | cv.INTER_LINEAR)
bin_norm = deskew(bin_norm)
if x+w+SZ < frame.shape[1] and y+SZ < frame.shape[0]:
frame[y:,x+w:][:SZ, :SZ] = bin_norm[...,np.newaxis]
sample = preprocess_hog([bin_norm])
digit = model.predict(sample)[1].ravel()
cv.putText(frame, '%d'%digit, (x, y), cv.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
cv.imshow('frame', frame)
cv.imshow('bin', bin)
ch = cv.waitKey(1)
if ch == 27:
break
print('Done')
if __name__ == '__main__':
print(__doc__)
main()
cv.destroyAllWindows()
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