n=0
m=0
lastCVTime = 0
timeForCV = 0
for filename in files:
is_found = False
if filename.find('/') != -1:
myDir,filename = os.path.split(filename)
myDir += '/'
lastCVTime = time.time()
cv_orig,cv_smoo,cv_final = findcode.findAndOrient(myDir,filename,do_display, verbose)
timeForCV += (time.time() - lastCVTime)
for img,name in [#[cv_smoo,"smooth"],
[cv_final,"clipped"],
[cv_orig,"original"]]:
if is_found:
break
dmtx_im = Image.fromstring("L", cv.GetSize(img), img.tostring())
if name != "original":
padding = 0.1
else:
def parseImages(self, files = None):
if files != None:
self.files = files
n=0
m=0
failNum=0
lastCVTime = 0
timeForCV = 0
print "\nFiles to decode: ",len(self.files)
stop = False
# import pdb;pdb.set_trace()
for filename in self.files:
is_found = False
# if filename.find('/') != -1:
# self.myDir,filename = path.split(filename)
# self.myDir += '/'
lastCVTime = time()
cv_orig,cv_smoo,cv_final = findcode.findAndOrient(self.myDir,
filename,
self.do_display,
self.verbose)
timeForCV += (time() - lastCVTime)
cv.SaveImage(self.myDir+filename.replace('tif','jpg'),cv_final)
test = cv.Avg(cv_final)
if stop == True:
pdb.set_trace()
if test[0] < 130 and test[0] > 40: # hard threshold works for avision
for img,name in [#[cv_smoo,"smooth"], #seems to introduce many errors
[cv_final,"clipped"],
[cv_orig,"original"]]:
if is_found:
break
dmtx_im = Image.fromstring("L", cv.GetSize(img), img.tostring())
if name != "original":
padding = 0.1
else:
padding = 0
ncols,nrows = dmtx_im.size
padw = (ncols)*padding
padh = (nrows)*padding
isize = (int(round(ncols+2*padw)),int(round(nrows+2*padh)))
# Create a new color image onto which we can paste findcode output
dmtx_image = Image.new('RGB',isize,0)
bbox = (round(padw),round(padh),ncols+round(padw),nrows+round(padh))
dmtx_image.paste(dmtx_im,map(int,bbox))
(width, height) = dmtx_image.size
# Send to datamatrix library
if findcode.low_res:
dm_read = DataMatrix(max_count = 1, timeout = 300, min_edge = 20, max_edge = 32, threshold = 5, deviation = 10)
else:
dm_read = DataMatrix(max_count = 1, timeout = 300, min_edge = 20, max_edge = 32, threshold = 5, deviation = 10, shrink = 2)
#dm_read = DataMatrix(max_count = 1, timeout = 300, shape = DataMatrix.DmtxSymbol12x12, min_edge = 20, max_edge = 32, threshold = 5, deviation = 10)
dmtx_code = dm_read.decode (width, height, buffer(dmtx_image.tostring()))
if dmtx_code is not None and len(dmtx_code) == EXPECTED_LEN:
how = "Quick Search: "+str(name)
is_found = True
out = dmtx_code
if not is_found:
self.failed.append(filename)
else:
failNum+=1
if is_found:
n+=1
self.output[filename] = out
#print filename, out, test[0]
else:
#print filename, None, test[0]
pass
#self.failed.append(filename)
m+=1
print failNum, "failed to produce images worth decoding"
print n,"of the",m-failNum,"remaining were successfully decoded."
self.status += "\nFound %d of %d in "%(n,m)
self.status += str(time()-self.totalTime)+" seconds.\n"
self.status += "(OpenCV: "+str(timeForCV)+" sec)\n"
if not(len(self.failed) == 0):# and verbose:
dirr = ' '+self.myDir
self.status+= "\nmissing: "+str(self.failed)+'\n'
return self.output,self.failed,self.status