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digits_video.py prints warning if trained classifier (should be created by digits.py) not found

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Alexander Mordvintsev 2012-06-27 08:29:22 +00:00
parent 3804ca3e20
commit b987154ebc
1 changed files with 66 additions and 55 deletions
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121
samples/python2/digits_video.py
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@ -1,63 +1,74 @@
import numpy as np import numpy as np
import cv2 import cv2
#import video
import digits import digits
import os
import video
from common import mosaic from common import mosaic
#cap = video.create_capture()
cap = cv2.VideoCapture(0)
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 < 20 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 = 1.1*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)*2
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: def main():
cv2.imshow('box', mosaic(10, boxes)) cap = video.create_capture()
cv2.imshow('frame', frame) classifier_fn = 'digits_svm.dat'
cv2.imshow('bin', bin) if not os.path.exists(classifier_fn):
if cv2.waitKey(1) == 27: print '"%s" not found, run digits.py first' % classifier_fn
break 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 < 20 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 = 1.1*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)*2
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()