import numpy as np import cv2 import digits import os import video from common import mosaic def main(): cap = video.create_capture() classifier_fn = 'digits_svm.dat' if not os.path.exists(classifier_fn): print '"%s" not found, run digits.py first' % classifier_fn return model = digits.SVM() model.load('digits_svm.dat') SZ = 20 while True: ret, frame = cap.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) bin = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 31, 10) bin = cv2.medianBlur(bin, 3) contours, _ = cv2.findContours( bin.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) boxes = [] for cnt in contours: x, y, w, h = cv2.boundingRect(cnt) if h < 16 or h > 60 or 1.2*h < w: continue cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0)) sub = bin[y:,x:][:h,:w] #sub = ~cv2.equalizeHist(sub) #_, sub_bin = cv2.threshold(sub, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU) s = float(h)/SZ m = cv2.moments(sub) m00 = m['m00'] if m00/255 < 0.1*w*h or m00/255 > 0.9*w*h: continue #frame[y:,x:][:h,:w] = sub[...,np.newaxis] c1 = np.float32([m['m10'], m['m01']]) / 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 sub1 = cv2.warpAffine(sub, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR) sub1 = digits.deskew(sub1) sample = np.float32(sub1).reshape(1,SZ*SZ) / 255.0 digit = model.predict(sample)[0] cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1) boxes.append(sub1) if len(boxes) > 0: cv2.imshow('box', mosaic(10, boxes)) cv2.imshow('frame', frame) cv2.imshow('bin', bin) if cv2.waitKey(1) == 27: break if __name__ == '__main__': main()