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110 lines
3.0 KiB
Python
Executable File
110 lines
3.0 KiB
Python
Executable File
#!/usr/bin/env python
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'''
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Digit recognition from video.
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Run digits.py before, to train and save the SVM.
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Usage:
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digits_video.py [{camera_id|video_file}]
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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# built-in modules
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import os
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import sys
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# local modules
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import video
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from common import mosaic
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from digits import *
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def main():
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try:
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src = sys.argv[1]
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except:
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src = 0
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cap = video.create_capture(src, fallback='synth:bg={}:noise=0.05'.format(cv.samples.findFile('sudoku.png')))
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classifier_fn = 'digits_svm.dat'
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if not os.path.exists(classifier_fn):
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print('"%s" not found, run digits.py first' % classifier_fn)
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return
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model = cv.ml.SVM_load(classifier_fn)
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while True:
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_ret, frame = cap.read()
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gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
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bin = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY_INV, 31, 10)
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bin = cv.medianBlur(bin, 3)
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_, contours, heirs = cv.findContours( bin.copy(), cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE)
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try:
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heirs = heirs[0]
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except:
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heirs = []
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for cnt, heir in zip(contours, heirs):
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_, _, _, outer_i = heir
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if outer_i >= 0:
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continue
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x, y, w, h = cv.boundingRect(cnt)
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if not (16 <= h <= 64 and w <= 1.2*h):
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continue
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pad = max(h-w, 0)
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x, w = x - (pad // 2), w + pad
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cv.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0))
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bin_roi = bin[y:,x:][:h,:w]
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m = bin_roi != 0
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if not 0.1 < m.mean() < 0.4:
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continue
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'''
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gray_roi = gray[y:,x:][:h,:w]
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v_in, v_out = gray_roi[m], gray_roi[~m]
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if v_out.std() > 10.0:
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continue
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s = "%f, %f" % (abs(v_in.mean() - v_out.mean()), v_out.std())
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cv.putText(frame, s, (x, y), cv.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
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'''
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s = 1.5*float(h)/SZ
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m = cv.moments(bin_roi)
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c1 = np.float32([m['m10'], m['m01']]) / m['m00']
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c0 = np.float32([SZ/2, SZ/2])
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t = c1 - s*c0
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A = np.zeros((2, 3), np.float32)
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A[:,:2] = np.eye(2)*s
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A[:,2] = t
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bin_norm = cv.warpAffine(bin_roi, A, (SZ, SZ), flags=cv.WARP_INVERSE_MAP | cv.INTER_LINEAR)
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bin_norm = deskew(bin_norm)
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if x+w+SZ < frame.shape[1] and y+SZ < frame.shape[0]:
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frame[y:,x+w:][:SZ, :SZ] = bin_norm[...,np.newaxis]
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sample = preprocess_hog([bin_norm])
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digit = model.predict(sample)[1].ravel()
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cv.putText(frame, '%d'%digit, (x, y), cv.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
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cv.imshow('frame', frame)
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cv.imshow('bin', bin)
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ch = cv.waitKey(1)
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if ch == 27:
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break
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print('Done')
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if __name__ == '__main__':
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print(__doc__)
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main()
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cv.destroyAllWindows()
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