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Open Source Computer Vision Library
https://opencv.org/
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69 lines
2.2 KiB
69 lines
2.2 KiB
import numpy as np |
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import cv2 |
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import digits |
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import os |
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import video |
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from common import mosaic |
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def main(): |
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cap = video.create_capture() |
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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 = digits.SVM() |
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model.load('digits_svm.dat') |
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SZ = 20 |
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while True: |
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ret, frame = cap.read() |
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) |
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bin = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 31, 10) |
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bin = cv2.medianBlur(bin, 3) |
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contours, _ = cv2.findContours( bin.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) |
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for cnt in contours: |
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x, y, w, h = cv2.boundingRect(cnt) |
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if h < 16 or h > 60 or 1.2*h < w: |
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continue |
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cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0)) |
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sub = bin[y:,x:][:h,:w] |
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#sub = ~cv2.equalizeHist(sub) |
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#_, sub_bin = cv2.threshold(sub, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU) |
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s = float(h)/SZ |
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m = cv2.moments(sub) |
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m00 = m['m00'] |
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if m00/255 < 0.1*w*h or m00/255 > 0.9*w*h: |
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continue |
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c1 = np.float32([m['m10'], m['m01']]) / 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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sub1 = cv2.warpAffine(sub, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR) |
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sub1 = digits.deskew(sub1) |
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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] = sub1[...,np.newaxis] |
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sample = np.float32(sub1).reshape(1,SZ*SZ) / 255.0 |
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digit = model.predict(sample)[0] |
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cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1) |
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cv2.imshow('frame', frame) |
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cv2.imshow('bin', bin) |
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if cv2.waitKey(1) == 27: |
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break |
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if __name__ == '__main__': |
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main()
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