def recogChars(seg_block, mid):
name = ""
confidence = 0.00
refined = refineCrop(seg_block, mid - 1)
for i, one in enumerate(refined):
res_pre = cRP.SimplePredict(one, i)
confidence += res_pre[0]
name += res_pre[1]
return seg_block, name, confidence
def slidingWindowsEval(image):
windows_size = 16
stride = 1
height = image.shape[0]
t0 = time.time()
data_sets = []
for i in range(0, image.shape[1] - windows_size + 1, stride):
data = image[0:height, i:i + windows_size]
data = cv2.resize(data, (23, 23))
# cv2.imshow("image",data)
data = cv2.equalizeHist(data)
data = data.astype(np.float) / 255
data = np.expand_dims(data, 3)
data_sets.append(data)
res = model.predict(np.array(data_sets))
# print "分割",time.time() - t0
pin = res
p = 1 - (res.T)[1]
p = f.gaussian_filter1d(np.array(p, dtype=np.float), 3)
lmin = l.argrelmax(np.array(p), order=3)[0]
interval = []
for i in xrange(len(lmin) - 1):
interval.append(lmin[i + 1] - lmin[i])
if (len(interval) > 3):
mid = get_median(interval)
else:
return []
pin = np.array(pin)
res = searchOptimalCuttingPoint(image, pin, 0, mid, 3)
cutting_pts = res[1]
last = cutting_pts[-1] + mid
if last < image.shape[1]:
cutting_pts.append(last)
else:
cutting_pts.append(image.shape[1] - 1)
name = ""
confidence = 0.00
seg_block = []
for x in xrange(1, len(cutting_pts)):
if x != len(cutting_pts) - 1 and x != 1:
section = image[0:36, cutting_pts[x - 1] - 2:cutting_pts[x] + 2]
elif x == 1:
c_head = cutting_pts[x - 1] - 2
if c_head < 0:
c_head = 0
c_tail = cutting_pts[x] + 2
section = image[0:36, c_head:c_tail]
elif x == len(cutting_pts) - 1:
end = cutting_pts[x]
diff = image.shape[1] - end
c_head = cutting_pts[x - 1]
c_tail = cutting_pts[x]
if diff < 7:
section = image[0:36, c_head - 5:c_tail + 5]
else:
diff -= 1
section = image[0:36, c_head - diff:c_tail + diff]
elif x == 2:
section = image[0:36,
cutting_pts[x - 1] - 3:cutting_pts[x - 1] + mid]
else:
section = image[0:36, cutting_pts[x - 1]:cutting_pts[x]]
seg_block.append(section)
refined = refineCrop(seg_block, mid - 1)
t0 = time.time()
for i, one in enumerate(refined):
res_pre = cRP.SimplePredict(one, i)
# cv2.imshow(str(i),one)
# cv2.waitKey(0)
confidence += res_pre[0]
name += res_pre[1]
# print "字符识别",time.time() - t0
return refined, name, confidence
def slidingWindowsEval(image):
windows_size = 16;
stride = 1
height= image.shape[0]
# print image.shape[1]
p = []
ch_p = []
gain = []
pin=[]
for i in range(0,image.shape[1]-windows_size+1,stride):
data = image[0:height,i:i+windows_size]
data = cv2.resize(data,(23,23))
# cv2.imshow("image",data)
data = cv2.equalizeHist(data)
data = data.astype(np.float)/255
data= np.expand_dims(data,3)
res = model.predict(np.array([data]))
pin.append(res[0])
p.append(res[0][0]+res[0][2])
ch_p.append(res[0][2])
gain.append(res.argmax())
p = np.insert(p,0,0);
p = np.insert(p,len(p),0);
p = f.gaussian_filter1d(np.array(p,dtype=np.float),3)
# print p
sum = image.sum(axis=0)
lmin = l.argrelmax(np.array(p),order = 3)[0]
interval = []
for i in xrange(len(lmin)-1):
interval.append(lmin[i+1]-lmin[i])
if(len(interval)>3):
mid = get_median(interval)
else:
return []
ch_p = np.array(ch_p)
pin = np.array(pin)
res = searchOptimalCuttingPoint(image,pin,0,mid,3)
cutting_pts = res[1]
last = cutting_pts[-1] + mid
if last < image.shape[1]:
cutting_pts.append(last)
else:
cutting_pts.append(image.shape[1]-1)
name = ""
confidence =0.00;
seg_block = []
for x in xrange(1,len(cutting_pts)):
if x != len(cutting_pts)-1 and x!=1:
section = image[0:36,cutting_pts[x-1]-2:cutting_pts[x]+2]
elif x==1:
c_head = cutting_pts[x - 1]- 2
if c_head<0:
c_head=0
c_tail = cutting_pts[x] + 2
section = image[0:36, c_head:c_tail]
elif x==len(cutting_pts)-1:
end = cutting_pts[x]
diff = image.shape[1]-end
c_head = cutting_pts[x - 1]
c_tail = cutting_pts[x]
if diff<7 :
section = image[0:36, c_head-5:c_tail+5]
else:
diff-=1
section = image[0:36, c_head - diff:c_tail + diff]
elif x==2:
section = image[0:36, cutting_pts[x - 1] - 3:cutting_pts[x-1]+ mid]
else:
section = image[0:36,cutting_pts[x-1]:cutting_pts[x]]
seg_block.append(section)
refined = refineCrop(seg_block,mid-1)
for i,one in enumerate(refined):
res_pre = cRP.SimplePredict(one, i )
confidence+=res_pre[0]
name+= res_pre[1]
return seg_block,name,confidence