def recogniseOne(svm, img, path0, paths, charIndex):
print("---recogniseOne---")
height, width = img.shape
start = Constants.SPILTLINE_RANGES[charIndex][0]
end = Constants.SPILTLINE_RANGES[charIndex][1]
char1 = []
ret = []
choosedPaths = []
for path in paths:
if path[0][0] >= start and path[0][0] <= end:
#这里要再进行一次搜索的，因为之前的path是不完全的
if path[len(path) - 1][1] == 0:
SpiltUtil.searchRecursionFromTop(img, path, path[0][0],
path[0][1], charIndex)
else:
SpiltUtil.searchRecursionFromBottom(img, path, path[0][0],
path[0][1], charIndex)
path.sort(key=lambda y: y[1])
path1 = Util.fillPath(path, height)
oneChar = ReadImg.getOneChar(img, path0, path1)
print("NonZero in oneChar:" + str(np.count_nonzero(oneChar)))
#如果这个分割出来的字符中黑色像素点太少，那直接不要了，因为肯定是错误的
if np.count_nonzero(oneChar) < 30:
continue
c1 = Util.conventToTrainChar(oneChar)
choosedPaths.append(path1)
#Util.printCV2Image(c1)
for angle in range(Constants.LEFT_ANGLE, Constants.RIGHT_ANGLE):
c2 = Util.rotate(c1, angle)
#Util.printCV2Image(c2)
#cv2.imshow('img' + str(angle), c2)
char1.append(c2)
#最后一个字符，特殊处理
if end == width - 1:
path1 = Util.getRightBoder(width, height)
choosedPaths.append(path1)
c1 = Util.conventToTrainChar(ReadImg.getOneChar(img, path0, path1))
for angle in range(Constants.LEFT_ANGLE, Constants.RIGHT_ANGLE):
c2 = Util.rotate(c1, angle)
#Util.printCV2Image(c2)
#cv2.imshow('img' + str(angle), c2)
char1.append(c2)
'''for p1 in range(start, end):
path1 = Util.fillPath(SpiltUseCV2.spiltOne(img, p1), height)
c1 = Util.conventToTrainChar(ReadImg.getOneChar(img, path0, path1))
for angle in range(Constants.LEFT_ANGLE, Constants.RIGHT_ANGLE):
c2 = Util.rotate(c1, angle)
#Util.printCV2Image(c2)
#cv2.imshow('img' + str(angle), c2)
char1.append(c2)'''
#要训练的是(height, width, 20, 20)这样的四维数组
ret.append(char1)
result = predictInMyWay(svm, ret)
#print("result:" + str(result))
maxValue = getMaxNumChar(result)
bestSplitPath = findBestSplitPath(result, maxValue, choosedPaths)
print("bses Path:" + str(bestSplitPath))
return bestSplitPath, maxValue
def searchAllPossible(img, path0, path1, char1, choosedPaths, charIndex):
height, width = img.shape
#在这里想加上对这个开始的分割点左右5个点分别进行搜索，以尽量获得一个最大概率的
#新加的左右五个点search，先把旋转去掉了
#从上搜索
searchRange = Constants.SEARCH_ALL_POSSIBLE_RANGE
for start in range(max(path1[0][0] - searchRange, 0), path1[0][0]):
newPath = Util.fillPath(
SpiltUtil.spiltOne(img, start, Constants.SEARCH_FROM_TOP,
charIndex), height)
char1.append(
Util.conventToTrainChar(
ReadImg.getOneChar(img, path0, newPath)[0]))
choosedPaths.append(newPath)
#从上搜索
for start in range(path1[0][0] + 1,
min(path1[0][0] + searchRange, height - 1)):
newPath = Util.fillPath(
SpiltUtil.spiltOne(img, start, Constants.SEARCH_FROM_TOP,
charIndex), height)
char1.append(
Util.conventToTrainChar(
ReadImg.getOneChar(img, path0, newPath)[0]))
choosedPaths.append(newPath)
#从下搜索
for start in range(max(path1[height - 1][0] - searchRange, 0),
path1[height - 1][0]):
newPath = Util.fillPath(
SpiltUtil.spiltOne(img, start, Constants.SEARCH_FROM_BOTTOM,
charIndex), height)
char1.append(
Util.conventToTrainChar(
ReadImg.getOneChar(img, path0, newPath)[0]))
choosedPaths.append(newPath)
#从下搜索
for start in range(path1[height - 1][0] + 1,
min(path1[height - 1][0] + searchRange, height - 1)):
newPath = Util.fillPath(
SpiltUtil.spiltOne(img, start, Constants.SEARCH_FROM_BOTTOM,
charIndex), height)
char1.append(
Util.conventToTrainChar(
ReadImg.getOneChar(img, path0, newPath)[0]))
choosedPaths.append(newPath)
def recogniseOne(svm, img, path0, paths, charIndex):
print("---recogniseOne---")
height, width = img.shape
start = Constants.SPILTLINE_RANGES[charIndex][0]
end = Constants.SPILTLINE_RANGES[charIndex][1]
char1 = []
ret = []
choosedPaths = []
for path in paths:
if path[0][0] >= start and path[0][0] <= end:
#这里要再进行一次搜索的，因为之前的path是不完全的
if path[len(path) - 1][1] == 0:
SpiltUtil.searchRecursionFromTop(img, path, path[0][0],
path[0][1], charIndex)
else:
SpiltUtil.searchRecursionFromBottom(img, path, path[0][0],
path[0][1], charIndex)
path.sort(key=lambda y: y[1])
path1 = Util.fillPath(path, height)
oneChar = ReadImg.getOneChar(img, path0, path1)
#因为在getOneChar里加了wrapAffine，导致了getOneChar是返回的一个字符数组
#但我们识别字符时现在还不想作wrapaffine，所以就默认去第一个原始字符了
oneChar = oneChar[0]
print("NonZero in oneChar:" + str(np.count_nonzero(oneChar)))
#如果这个分割出来的字符中黑色像素点太少，那直接不要了，因为肯定是错误的
if np.count_nonzero(oneChar) < 40:
continue
c1 = Util.conventToTrainChar(oneChar)
choosedPaths.append(path1)
char1.append(c1)
searchAllPossible(img, path0, path1, char1, choosedPaths,
charIndex)
#即在上面的paths中并没有找到合适的分割线
if len(char1) == 0:
'''
因为上面并没有合适的分割线，所以这里手动选取两个合适的分割点
1.path0.x + 20
2.path0.x + 2 * Constants.SEARCH_ALL_POSSIBLE_RANGE
'''
startX1 = path0[0][0] + 20
path1 = Util.fillPath(
SpiltUtil.spiltOne(img, startX1, Constants.SEARCH_FROM_TOP,
charIndex), height)
searchAllPossible(img, path0, path1, char1, choosedPaths, charIndex)
startX1 = startX1 + (2 * Constants.SEARCH_ALL_POSSIBLE_RANGE)
path1 = Util.fillPath(
SpiltUtil.spiltOne(img, startX1, Constants.SEARCH_FROM_TOP,
charIndex), height)
searchAllPossible(img, path0, path1, char1, choosedPaths, charIndex)
#最后一个字符，特殊处理
if end == width - 1:
path1 = Util.getRightBoder(width, height)
choosedPaths.append(path1)
#因为在getOneChar里加了wrapAffine，导致了getOneChar是返回的一个字符数组
#但我们识别字符时现在还不想作wrapaffine，所以就默认去第一个原始字符了
c1 = Util.conventToTrainChar(ReadImg.getOneChar(img, path0, path1)[0])
searchAllPossible(img, path0, path1, char1, choosedPaths, charIndex)
#要训练的是(height, width, 20, 20)这样的四维数组
ret.append(char1)
indexs, probs = predictAndCalc(svm, ret)
print("indexs:" + str(indexs))
print("probs:" + str(probs))
maxIndex, spiltIndex = getMaxNumChar_New(indexs, probs)
print("maxIndex:" + str(maxIndex))
print("spiltIndex:" + str(spiltIndex))
bestSplitPath = findBestSplitPath_New(spiltIndex, choosedPaths)
print("best Path:" + str(bestSplitPath))
return bestSplitPath, maxIndex
