| |
|
| | '''
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| | Digit recognition from video.
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| |
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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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| |
|
| |
|
| | 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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| |
|
| |
|
| | import os
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| | import sys
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| |
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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]
|
| | except:
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| | src = 0
|
| | 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'
|
| | if not os.path.exists(classifier_fn):
|
| | print('"%s" not found, run digits.py first' % classifier_fn)
|
| | return
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| |
|
| | model = cv.ml.SVM_load(classifier_fn)
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| |
|
| | while True:
|
| | _ret, frame = cap.read()
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| | gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
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| |
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| |
|
| | 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 = []
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| |
|
| | 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))
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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:
|
| | continue
|
| | '''
|
| | gray_roi = gray[y:,x:][:h,:w]
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| | 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]
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| |
|
| | 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)
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| |
|
| |
|
| | 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()
|
| |
|
