mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
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84 lines
2.7 KiB
84 lines
2.7 KiB
import numpy as np |
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import cv2 |
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import os |
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import sys |
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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: src = sys.argv[1] |
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except: src = 0 |
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cap = video.create_capture(src) |
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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 = SVM() |
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model.load(classifier_fn) |
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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, heirs = cv2.findContours( bin.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE) |
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try: heirs = heirs[0] |
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except: 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 = cv2.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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cv2.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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gray_roi = gray[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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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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cv2.putText(frame, s, (x, y), cv2.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 = cv2.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 = cv2.warpAffine(bin_roi, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.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)[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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ch = cv2.waitKey(1) |
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if ch == 27: |
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break |
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if __name__ == '__main__': |
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main()
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