自动登录脚本之图像识别
仅做个保留吧，等完成之后一起写。import Image,ImageFont,ImageDrawimport os,sysfrom math import atan2,piimport pickleDURATION = 1000DIAMETER = 20COLORDIFF = 10TEXTCOLOR = (128,128,128)BACKGROUND = (255,255,255)MODE = 'sample'samples = Nonedef purifyIM(image): frame = image.load() (w,h)=image.size for i in range(h): for j in range(w): if frame < TEXTCOLOR: image.putpixel( (j,i), TEXTCOLOR ) else: image.putpixel( (j,i), BACKGROUND ) return imagedef purify(region): frame = region.getdata() (w,h)=region.size for i in range(h): for j in range(w): if frame != BACKGROUND and frame != (0,0,0): region.putpixel( (j,i), TEXTCOLOR ) else: region.putpixel( (j,i), BACKGROUND ) return regiondef printregion(region): frame = region.getdata() (w,h)=region.size f = file('testCode2.txt','w') for i in range(h): for j in range(w): if frame != BACKGROUND: f.write('*') else: f.write(' ') f.write('\n') f.close()def getImage(fname): im = Image.open(fname) return imdef normalize(im): regions = imdiv(im) if len(regions)!=4: regoins = imdiv2(im) for k in range(len(regions)): regions = dorotate(regions) regions = purify( docrop(regions) ) return regionsdef dorotate(region): deg = 0 maxdens = 0 for i in range(-30,31): dens = density( docrop( region.rotate(i) ) ) if dens > maxdens: deg = i maxdens = dens return region.rotate(deg)def density(region): frame = region.getdata() (w,h) = region.size area_all = w*h area = 0 for i in range(h): for j in range(w): if frame != BACKGROUND and frame != (0,0,0): area += 1 return 1.0*area/area_alldef docrop(region): croppos = getcrop(region) newregion = region.crop(croppos) return newregiondef getcrop(region): frame = region.getdata() (w,h)=region.size pts = [] ptsi = [] for i in range(h): for j in range(w): if frame != BACKGROUND and frame != (0,0,0): pts.append((i,j)) ptsi.append((j,i)) if pts == []: return pp1 = min(pts) pp2 = max(pts) pp3 = min(ptsi) pp4 = max(ptsi) return ,pp1,pp4+1,pp2+1]def crackcode(im): global samples if not samples: samples = loadsamples() regions = normalize(im) s = [] ans = [] for r in regions: s.append(match(r,samples).upper()) messup = ['TFY7','FE','38','72YT','CQGR6','G6C','XK','HK','89B','YV','VY'] for i in range(len(s)): for mess in messup: if s == mess: s = mess if len(s) != 4: return ['failed'] else: for s1 in s: for s2 in s: for s3 in s: for s4 in s: t = s1+s2+s3+s4 ans.append(t) return ansdef match(region,samples): if samples == {}: return None dists = [] for (k,v) in samples.items(): dists.append( (distance(region,k),v) ) dists.sort() if MODE == 'sample': return dists else: i = 0 while dists in ['H','I']: i += 1 return distsdef distance(r1,r2): den1 = density(r1) den2 = density(r2) if 1.0*den1/den2>1: (den1,den2) = (den2,den1) r1 = r1.resize(r2.size) d1 = r1.getdata() d2 = r2.getdata() same = total = for i in xrange(len(d1)): if d1 != BACKGROUND: total += 1 if d1 == d2: same += 1 if d2 != BACKGROUND: total += 1 if d1 == d2: same += 1 return 1 - 1.0*same/total * 1.0*same/total * 1.0*den1/den2def loadsamples(): pks = pickle.load(open('samples.pk','rb')) samples = {} for (pk,v) in pks.items(): im = Image.new('RGB',pk) r = im.crop((0,0,pk,pk)) r.fromstring(pk) samples = v return samplesdef loadttf(): files = [ 'ttf/'+x for x in os.listdir('ttf') ] fonts = [] for f in files: fonts.append( ImageFont.truetype(f,32) ) regions = [] regionsv = [] for font in fonts: im = Image.new( 'RGB', (1000,50), BACKGROUND ) draw = ImageDraw.Draw(im) draw.text((0,0),"B C E F G H J K M P Q R T V W X Y 2 3 4 6 7 8 9"\ ,font=font,fill=TEXTCOLOR ) regions.extend( imdiv(im) ) regionsv.extend( 'B C E F G H J K M P Q R T V W X Y 2 3 4 6 7 8 9'.split(' ') ) for i in xrange(len(regions)): regions = docrop(regions) printregion( regions ) kv = {} for i in range(len(regions)): kv] = regionsv return kvdef imdiv(im): frame = im.load() (w,h) = im.size horis =[] for i in range(w): for j in range(h): if frame != BACKGROUND: horis.append(i) break horis2 = -2,0)] for i in range(1,len(horis)-1): if horis!=horis-1: horis2.append((horis+horis)/2) horis2.append(min(horis[-1]+3,w)) boxes=[] for i in range(len(horis2)-1): boxes.append( ,0,horis2,h]) for k in range(len(boxes)): verts = [] for j in range(h): for i in range(boxes,boxes): if frame != BACKGROUND: verts.append(j) boxes = max(verts-2,0) boxes = min(verts[-1]+3,h) if boxes == []: return None regions = [] for box in boxes: regions.append( im.crop(box) ) return regionsdef imdiv2(im): divs = {} frame = im.load() (w,h) = im.size for i in range(w): for j in range(h): color = frame if color != BACKGROUND: if divs.has_key( color ): divs[ color ].append( (i,j) ) else: divs[ color ] = [ (i,j) ] regions = [] divs = [ (x,sorted(x,cmp=lambda x,y:cmp(x,y))) for x indivs.items() ] divs.sort(cmp=lambda x,y:cmp(x,y)) for (color,pts) in divs: xs = [ x for x in pts ] ys = [ x for x in pts ] box = ( min(xs), min(ys), min(max(xs)+1,w), min(max(ys)+1,h) ) regions.append(im.crop(box)) return regionsdef train(im): print 1 global samples try: samples = pickle.load(open('samples.pk','rb')) except: samples = {} pickle.dump(samples,open('samples.pk','wb')) regions = normalize(im) for region in regions: printregion(region) smps = loadsamples() printframeBy(region) print match(region,smps).upper() print 'Enter to add to library: ' ans = raw_input() if len(ans) == 1: key = (region.size,region.tostring()) samples = ans pickle.dump(samples,open('samples.pk','wb'))def printframeBy(im,code=-1): frame = im.load() (w,h) = im.size for j in xrange(h): for i in xrange(w): if (code == -1 and frame !=BACKGROUND) or (code != -1 and frame==code) : print '*', else: print ' ', printdef identify(fname): image = getImage(fname) image = purifyIM(image) regions = normalize(image) ans = crackcode(image) return ans #printregion(regions)def trainBy(fname): if sys.argv.startswith('train'): trainfiles = os.listdir(sys.argv) trainfiles.sort() for trainfile in trainfiles: trainfile = sys.argv+'/'+trainfile print trainfile im = getImage(trainfile) im = purifyIM(im) train(im)if __name__ == '__main__': if len(sys.argv) == 2: print "Run training" trainBy('genimg3.jpg') else: print "Run indentify" identify('genimg3.jpg')
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