def get_index_from_region(coorRegion, imageRegion, coorCCRegion, suffix):
bdResult = {}
index1 = []
index2 = []
bdResult = bd_rec(imageRegion, client, imgForm=suffix, api='accurate')
if "words_result" not in bdResult:
print(bdResult)
print("BaiDu recognized error, exit...")
exit(0)
if len(bdResult["words_result"]) == 0:
return index1, index2
words = []
for j, w in enumerate(bdResult["words_result"]):
words.append((j, dic2length(w["location"])))
graphs = []
for j, c in enumerate(coorCCRegion):
if c[2] > heightOfLine * 3 or c[3] > heightOfLine * 1.5:
# if c[2] > heightOfLine*0.5 or c[3] > heightOfLine*0.5:
graphs.append(c)
for w in words:
flag = False
for g in graphs:
g = expand_box(g)
if is_within(w[1], g):
index2.append(bdResult["words_result"][w[0]]["words"])
flag = True
break
if not flag:
index1.append(bdResult["words_result"][w[0]]["words"])
return index1, index2
def get_index_from_region(coorRegion, imageRegion, coorCCRegion, suffix):
"""Gets index from region.
Args:
coorRegion: Coordinate of region.
imageRegion: The image data of region.
coorCCRegion: The coordinate of connected components in region.
suffix: Image name's suffix.
Returns:
Index out graph and Index in graph.
"""
bdResult = {}
index1 = []
index2 = []
bdResult = bd_rec(imageRegion, client, imgForm=suffix, api='accurate')
if "words_result" not in bdResult:
print(bdResult)
print("BaiDu recognized error, exit...")
exit(0)
if len(bdResult["words_result"]) == 0:
return index1, index2
words = []
for j, w in enumerate(bdResult["words_result"]):
words.append((j, dic2length(w["location"])))
graphs = []
for j, c in enumerate(coorCCRegion):
if c[2] > heightOfLine * 3 or c[3] > heightOfLine * 1.5:
# if c[2] > heightOfLine*0.5 or c[3] > heightOfLine*0.5:
graphs.append(c)
for w in words:
flag = False
for g in graphs:
g = expand_box(g)
if is_within(w[1], g):
index2.append(bdResult["words_result"][w[0]]["words"])
flag = True
break
if not flag:
index1.append(bdResult["words_result"][w[0]]["words"])
return index1, index2
def deal_one_page(srcPath,
desPath='',
stanPath='',
saveImage=True,
charsOnly=False,
rectification=False):
"""
"""
global heightOfLine
coor5 = []
mistakes = 0
bdResult = {}
name = os.path.basename(srcPath)
if not os.path.exists(srcPath):
print("Image path not exists!")
return None
try:
imgBgr = cv2.imread(srcPath)
if rectification:
imgBgr = rectify(imgBgr.copy())
imgData = cv2.cvtColor(imgBgr, cv2.COLOR_BGR2GRAY)
# imgData = cv2.imread(srcPath, cv2.IMREAD_GRAYSCALE)
# imgBgr = cv2.cvtColor(imgData, cv2.COLOR_GRAY2BGR)
except Exception as imageError:
print(imageError, 'Could not read the image file, skip...')
# cv2.namedWindow("imgData", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("imgData", imgData)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# exit(0)
imgEliVer = eli_ver(imgData.copy()) #
# cv2.namedWindow("imgEliVer", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("imgEliVer", imgEliVer)
coor1 = divide_ver(imgEliVer) #
# print('coor1: {}'.format(coor1))
if heightOfLine == 0: #
coor2 = divide_hor(imgEliVer, coor1)
heightOfLines = [c[3] - c[1] for c in coor2]
heightOfLine = median(heightOfLines)
imgInv = imu.preprocess_bw_inv(imgData.copy())
cv2.imwrite('imgInv.jpg', imgInv)
coorCC = get_no_intersect_boxes('imgInv.jpg')
imgEliCC = eli_large_cc(coorCC, imgData.copy(), heightOfLine)
os.remove('imgInv.jpg')
# cv2.namedWindow("imgEliCC", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("imgEliCC", imgEliCC)
imgEli = eli_ver(imgEliCC) #1
# cv2.namedWindow("imgEli", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("imgEli", imgEli)
coor2 = divide_hor(imgEli, coor1) #2
# print('coor2: {}'.format(coor2))
# img2 = mark_box(imgBgr, coor2, color=(0,255,0))
# cv2.namedWindow("img2", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("img2", img2)
coor3 = find_char(imgEli, coor2) #3
# print(np.array(coor3))
# for l in coor3:
# img3 = mark_box(imgBgr, l, color=(0,255,0))
# cv2.namedWindow("img3", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("img3", img3)
if charsOnly:
return coor3
# print('SVM classifying...')
coor4 = svm_classify(imgEli, coor3) #4
# print(*coor4, sep='\n')
imgBw = imu.preprocess_bw(imgEli, boxSize=(4, 4), morph=False) #re
coor3Bw = find_char(imgBw, coor2)
coor4Bw = svm_classify(imgBw, coor3Bw)
coor5 = update_result(coor4, coor4Bw)
try:
# imgBd = preprocess_bw(imgEliCC)
# print('BaiDu recognizing...')
bdResult = bd_rec(imgEliCC,
client,
imgForm=srcPath[-4:],
api='general')
# except ConnectionResetError as e:
# print('Connection reset by peer, repeat BaiDu recognizion...')
# bdResult = bd_rec(imgEliCC, client, api='accurate')
except Exception as e:
print(
'Baidu recognition error, check your internet connection. exit...')
exit(0)
# print(bdResult)
addResult = additional_questions(bdResult)
# print(addResult)
if addResult:
coor5.append(([addResult], 2))
# print(*coor4, sep='\n')
if stanPath:
stanResult = output_stan(coor5, os.path.basename(srcPath))
mistakes = verification(stanResult, stanPath)
# print(mistakes)
# if saveImage:
# for l in coor5: #5
# # print(l)
# color = (255,255,0)
# if l[1] != 0:
# color = (255,0,0) if l[1] == 2 else (0,255,0)
# img5 = mark_box(imgBgr, l[0], color)
# cv2.imwrite(desPath, img5) #6
# show_image(img5)
# exit(0)
# cv2.namedWindow("img5", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("img5", img5)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# imgEliVer2 = detect_vertical_line2(imgData)
# coor2 = divide_hor(imgEliVer2, coor1)
sa = detect_self_assessment(bdResult)
if sa:
# imgEliSa = imgData.copy()
imgData[sa:, coor1[1][0]:coor1[1][1] + 10] = 255
# coor22 = divide_hor(imgEliSa, coor1)
coor22 = divide_hor(imgData, coor1)
region = integrate_lines(coor5, coor22, imgData.shape[1], imgData.shape[0])
# print('region: {}'.format(region))
# imgEliVerBgr = cv2.cvtColor(imgEliVer2, cv2.COLOR_GRAY2BGR)
# imgRegion = mark_box(imgBgr, region, color=(0,255,0))
# cv2.imwrite(desPath, imgRegion)
# cv2.imwrite('imgInv.jpg', get_dilation(imgData.copy()))
# coorCCDil = get_no_intersect_boxes('imgInv.jpg')
# os.remove('imgInv.jpg')
'''
CCAndType = []
wordBoxes = [w["location"] for w in bdResult["words_result"]]
idxWords = index.Index()
for j,b in enumerate(wordBoxes):
idxWords.insert(j, (b["left"], b["top"], b["left"]+b["width"], b["top"]+b["height"]))
idxRegion = index.Index()
for j,r in enumerate(region):
idxRegion.insert(j, r)
idxCC = index.Index()
for j,c in enumerate(coorCC):
idxCC.insert(j, (c[0], c[1], c[0]+c[2], c[1]+c[3]))
boxOfSpecialWord = get_match_location(bdResult, keyWordsConfig)
hitsOfCC = []
for j,s in enumerate(boxOfSpecialWord):
b = (s[0], s[1], s[0]+s[2], s[1]+s[3])
hitsOfRegion = list(idxRegion.intersection(b, objects=True))
items = [item for item in hitsOfRegion if item.bbox[1] < s[1]+s[3]/2 < item.bbox[3]]
for item in items:
boxOfGraph = (item.bbox[0], b[3]+10, item.bbox[2]-item.bbox[0], item.bbox[3]-b[3]-20)
boxOfGraph1 = (item.bbox[0], b[3]+10, item.bbox[2], item.bbox[3]-10)
coorCC.append(boxOfGraph)
hitsOfCC += list(idxCC.intersection(boxOfGraph1, objects=False))
# print(hitsOfCC)
coorCCCopy = coorCC.copy()
coorCC = [coorCCCopy[i] for i in range(len(coorCCCopy)) if i not in hitsOfCC]
boxRest = []
for i,c in enumerate(coorCC.copy()):
r = (c[0], c[1], c[0]+c[2], c[1]+c[3])
hitsOfRegion = list(idxRegion.intersection(r))
if len(hitsOfRegion) == 0:
continue
if c[2] > heightOfLine*3 or c[3] > heightOfLine*1.5:
CCAndType.append((c, 1))
# del(coorCC[i])
continue
elif c[2] < heightOfLine*0.5 and c[3] < heightOfLine*0.5:
CCAndType.append((c, 0))
continue
hits = list(idxWords.intersection(r, objects=True))
if hits:
flag = False
for item in hits:
wordBox = (wordBoxes[item.id]["left"], wordBoxes[item.id]["top"], wordBoxes[item.id]["width"], wordBoxes[item.id]["height"])
if is_within(c, wordBox):
CCAndType.append((c, 0))
flag = True
# del(coorCC[i])
continue
if flag:
continue
boxRest.append(c)
typesOfCC = tell_me_text_or_graph(imgBgr.copy(), imgData.copy(), boxRest)
CCAndType += zip(boxRest, typesOfCC)
resultFinal = get_items(region, imgData.copy(), bdResult, CCAndType, name)
'''
imgData1 = detect_vertical_line3(imgData.copy())
resultFinal = get_items_v02(region, imgData1.copy(), coorCC, name)
# coorRegion = []
# coorGraph = []
# coorText = []
# resultFinalA = absolute_coor(resultFinal)
# for x in resultFinalA["questions"]:
# coorRegion.append(dic2coor(x["location"]))
# coorGraph.extend([dic2coor(i["location"]) for i in x["content"] if i["type"]=="graph"])
# coorText.extend([dic2coor(i["location"]) for i in x["content"] if i["type"]=="text"])
# imgRegion = mark_box(imgBgr, coorRegion, color=(0,255,0))
# imgGraph = mark_box(imgRegion, coorGraph, color=(0,0,255))
# imgText = mark_box(imgGraph, coorText, color=(255,255,0))
if desPath:
with open(desPath[:-4] + '.json', 'w') as f:
json.dump(resultFinal, f)
# if saveImage:
# cv2.imwrite(desPath, imgText)
# cv2.namedWindow("imgData", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("imgData", imgData)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
#return coor5, mistakes
return resultFinal
def get_items_from_region(coorRegion, imageRegion, coorCCRegion, itemDir,
suffix, idGraph):
"""
Args:
coorRegion: The coordinate of region.
imageRegion: The image of region.
coorCCRegion: The coordinate of CC in region.
suffix: Image form suffix.
idGraph: The id of graph in paper.
Returns:
A dic of question result.
"""
xiaoti = {}
bdResult = {}
bdResult = bd_rec(imageRegion, client, imgForm=suffix, api='accurate')
title = ''
if "words_result" in bdResult and len(
bdResult["words_result"]) > 0: #结果返回错误处理
title = bdResult["words_result"][0]["words"]
# i = 0
# if "words_result" in bdResult:
# while i < len(bdResult["words_result"]) and \
# not title.endswith(('。', '？', ')')):
# title += bdResult["words_result"][i]["words"]
# i += 1
xiaoti["title"] = title
xiaoti["location"] = coor2dic(coorRegion)
xiaoti["content"] = []
if is_special(title):
locationOfTitle = bdResult["words_result"][0]["location"]
boxOfGraph = (10, dic2coor(locationOfTitle)[3] + 10,
coorRegion[2] - coorRegion[0] - 10,
coorRegion[3] - coorRegion[1] - 10)
itemText = {}
itemText["id"] = 0
itemText["location"] = locationOfTitle
itemText["type"] = "text"
itemText["value"] = title
xiaoti["content"].append(itemText)
itemGraph = {}
itemPath = os.path.join(itemDir, str(idGraph) + '.png')
cv2.imwrite(
itemPath, imageRegion[boxOfGraph[1]:boxOfGraph[3],
boxOfGraph[0]:boxOfGraph[2]])
itemGraph["id"] = 1
itemGraph["location"] = coor2dic(boxOfGraph)
itemGraph["type"] = "graph"
itemGraph["value"] = itemPath
xiaoti["content"].append(itemGraph)
return xiaoti
wordBoxes = []
if "words_result" in bdResult:
for j, w in enumerate(bdResult["words_result"]):
wordBoxes.append((j, dic2length(w["location"])))
graphBoxes = []
for j, b in wordBoxes:
# print(j,b)
b = expand_box(b)
coorCCRegion = [c for c in coorCCRegion if not is_within(c, b)]
n = 0
for j, c in enumerate(coorCCRegion):
# if c[2] > heightOfLine*3 or c[3] > heightOfLine*1.5:
if c[2] > heightOfLine * 0.5 or c[3] > heightOfLine * 0.5:
graphBoxes.append((n, c))
n += 1
c = expand_box(c)
wordBoxes = [it for it in wordBoxes if not is_within(it[1], c)]
mixBoxes = []
for j, w in enumerate(wordBoxes.copy()):
if w[1][2] > len(
bdResult["words_result"][w[0]]["words"]) * heightOfLine * 1.5:
del (wordBoxes[j - len(mixBoxes)])
mixBoxes.append((n, w[1]))
n += 1
# Fuses image
graphBoxes += mixBoxes
idxWords = index.Index()
for j, b in enumerate(wordBoxes):
if b[1][2] < heightOfLine * 5:
idxWords.insert(b[0], length2coor(b[1]))
wordBoxes = [b for b in wordBoxes if b[1][2] >= heightOfLine * 5]
count = 0
idDeleted = []
for j, b in graphBoxes.copy():
bEx = expand_box(b)
items = list(idxWords.intersection(length2coor(bEx), objects=True))
if len(items) > 0:
boxes = [num2int(i.bbox) for i in items] + [length2coor(b)]
box = fuse_box(boxes)
for i, it in enumerate(items):
# del(wordBoxes[it.id-i-len(idDeleted)])
idxWords.delete(it.id, it.bbox)
idDeleted.append(it.id)
del (graphBoxes[j - count])
count += 1
graphBoxes.append((n, box))
n += 1
if len(idxWords.leaves()[0][1]) > 0:
wordItems = list(
idxWords.intersection(idxWords.get_bounds(), objects=True))
wordBoxes += [(i.id, coor2length(num2int(i.bbox))) for i in wordItems]
idxGraphs = index.Index()
for j, b in enumerate(graphBoxes):
idxGraphs.insert(j, length2coor(b[1]))
count = 0
for j, b in enumerate(graphBoxes.copy()):
bEx = expand_box(b[1])
items = list(idxGraphs.intersection(length2coor(bEx), objects=True))
if len(items) > 0:
boxes = [num2int(i.bbox) for i in items] + [length2coor(b[1])]
box = fuse_box(boxes)
for i, it in enumerate(items):
# if it.id-i not in range(0, len(graphBoxes)):
# print(it.id-i)
# print(graphBoxes)
# continue
idxGraphs.delete(it.id, it.bbox)
idxGraphs.insert(len(graphBoxes) + count, length2coor(box))
count += 1
# graphBoxes.append((n, box))
# n += 1
# print(idxGraphs.get_bounds())
# print(idxGraphs.leaves())
# exit(0)
if len(graphBoxes) > 0:
graphItems = list(
idxGraphs.intersection(idxGraphs.get_bounds(), objects=True))
graphBoxes = [(i.id, coor2length(num2int(i.bbox))) for i in graphItems]
for j, b in graphBoxes:
bEx = expand_box(b)
wordBoxes = [
it for it in wordBoxes
if not is_within(it[1], bEx) and it[1][3] < it[1][2] * 1.5
]
items = [(it, 0) for it in wordBoxes] + [(it, 1) for it in graphBoxes]
idxItems = index.Index()
for i, it in enumerate(items):
idxItems.insert(it[0][0], length2coor(it[0][1]), obj=it[1])
count = 0
# flag = False
while True:
tempItems = []
for it in items:
itemInter = list(
idxItems.intersection(length2coor(it[0][1]), objects=True))
if len(itemInter) > 1:
boxes = [num2int(i.bbox) for i in itemInter]
box = fuse_box(boxes)
for it1 in itemInter:
idxItems.delete(it1.id, it1.bbox)
idxItems.insert(len(items) + count, length2coor(box), obj=1)
count += 1
itemExs = list(
idxItems.intersection(idxItems.get_bounds(), objects=True))
tempItems = [((i.id, coor2length(num2int(i.bbox))), i.object)
for i in itemExs]
break
if len(tempItems) > 0:
items = tempItems.copy()
else:
itemExs = list(
idxItems.intersection(idxItems.get_bounds(), objects=True))
items = [((i.id, coor2length(num2int(i.bbox))), i.object)
for i in itemExs]
break
# Sort
items.sort(key=take_key)
g = 0
for j, it in enumerate(items):
c = length2coor(it[0][1])
item = {}
item["id"] = j
item["location"] = coor2dic(c)
if it[1] == 0:
item["type"] = "text"
words = bdResult["words_result"][it[0][0]]["words"]
item["value"] = words
elif it[1] == 1:
item["type"] = "graph"
itemPath = os.path.join(itemDir, str(idGraph + g) + '.png')
cv2.imwrite(itemPath, imageRegion[c[1]:c[3], c[0]:c[2]])
item["value"] = itemPath
g += 1
xiaoti["content"].append(item)
return xiaoti
def deal_one_page(srcPath, desPath='', charsOnly=False, rectification=False):
"""Process a paper image.
Args:
srcPath: File path of image.
desPath: Destination path.
charsOnly: Whether only return chars coordinate result.
rectification: Whether rectify image.
Returns:
Final result.
"""
global heightOfLine
coor5 = []
mistakes = 0
bdResult = {}
ratio = 0
name = os.path.basename(srcPath)
if not os.path.exists(srcPath):
print("Image path not exists!")
return None
try:
imgBgr = cv2.imread(srcPath)
if rectification:
imgBgr = rectify(imgBgr.copy())
imgData = cv2.cvtColor(imgBgr, cv2.COLOR_BGR2GRAY)
except Exception as imageError:
print(imageError, 'Could not read the image file, skip...')
# 根据图像长宽判断是否为单栏
ori_size = imgData.shape[0:2]
single_column = True if ori_size[0] > ori_size[1] else False
# 缩放到固定大小(双栏宽度为4000pixels, 单栏为2000pixels)
if single_column and ori_size[1] != 2000:
ratio = 2000 / ori_size[1]
elif not single_column and ori_size[1] != 4000:
ratio = 4000 / ori_size[1]
if ratio:
imgData = resize(imgData, x=ratio)
new_size = imgData.shape[0:2]
#原始图像去除竖线（长度大于100pixels，可能去除部分题目信息）
imgEliVer = eli_ver(imgData.copy()) #
# cv2.namedWindow("imgEliVer", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("imgEliVer", imgEliVer)
#纵向划分试题区域
coor1 = divide_ver(imgEliVer)
# print('coor1: {}'.format(coor1))
#计算行高（所有行高的中位数）
if heightOfLine == 0: #
coor2 = divide_hor(imgEliVer, coor1)
heightOfLines = [c[3] - c[1] for c in coor2]
heightOfLine = median(heightOfLines)
#获取原始图像联通区域以及消去较大的联通区域
imgInv = imu.preprocess_bw_inv(imgData.copy())
cv2.imwrite('imgInv.jpg', imgInv)
coorCC = get_no_intersect_boxes('imgInv.jpg')
imgEliCC = eli_large_cc(coorCC, imgData.copy(), heightOfLine)
os.remove('imgInv.jpg')
# cv2.namedWindow("imgEliCC", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("imgEliCC", imgEliCC)
#去除较大联通区域的图像上去除竖线
imgEli = eli_ver(imgEliCC) #1
# cv2.namedWindow("imgEli", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("imgEli", imgEli)
#行划分
coor2 = divide_hor(imgEli, coor1) #2
# print('coor2: {}'.format(coor2))
# img2 = mark_box(imgBgr, coor2, color=(0,255,0))
# cv2.namedWindow("img2", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("img2", img2)
#获取每行前三个字符
coor3 = find_char(imgEli, coor2) #3
# print(np.array(coor3))
# for l in coor3:
# l = [length2coor(list(map(lambda x: x / ratio, coor2length(c)))) for c in l]
# img3 = mark_box(imgBgr, l, color=(0,255,0))
# cv2.namedWindow("img3", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("img3", img3)
# cv2.imwrite("img3.jpg", img3)
if charsOnly:
coor_chars = []
#坐标换算
for l in coor3:
coor_chars.append([
length2coor(list(map(lambda x: int(x / ratio),
coor2length(c)))) for c in l
])
return coor_chars
#SVM分类字符图像
# print('SVM classifying...')
coor4 = svm_classify(imgEli, coor3) #4
# print(*coor4, sep='\n')
#在二值图上重新获取字符以及SVM分类（补全部分漏检）
imgBw = imu.preprocess_bw(imgEli, boxSize=(4, 4), morph=False) #re
coor3Bw = find_char(imgBw, coor2)
coor4Bw = svm_classify(imgBw, coor3Bw)
coor5 = update_result(coor4, coor4Bw)
#百度识别
try:
# print('BaiDu recognizing...')
bdResult = bd_rec(imgEliCC,
client,
imgForm=srcPath[-4:],
api='general')
except Exception as e:
print(
'Baidu recognition error, check your internet connection. exit...')
exit(0)
# print(bdResult)
#附加题处理
addResult = additional_questions(bdResult)
# print(addResult)
if addResult:
coor5.append(([addResult], 2))
# print(*coor4, sep='\n')
#自我评测处理
sa = detect_self_assessment(bdResult)
if sa:
imgData[sa:, coor1[1][0]:coor1[1][1] + 10] = 255
coor22 = divide_hor(imgData, coor1)
# print(coor22)
#生成题目区域
region = integrate_lines(coor5, coor22, new_size)
# print('region: {}'.format(region))
#去除装订线及中间分割线（不会去除题目信息）
imgData1 = detect_vertical_line3(imgData.copy())
#导出最后结果
resultFinal = get_items_v02(region, imgData1.copy(), coorCC, name)
if desPath:
with open(desPath[:-4] + '.json', 'w') as f:
json.dump(resultFinal, f)
# cv2.namedWindow("imgData", cv2.WINDOW_AUTOSIZE)
# cv2.imshow("imgData", imgData)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
return resultFinal
def testpaper(file_path, save_path):
"""Process a paper image.
Args:
file_path: File path of image.
save_path: Destination path.
Returns:
Final result.
"""
name = os.path.basename(file_path)
image = cv2.imread(file_path)
if image is None:
print('ImageError: Could not read the image file, exit...')
return None
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 根据图像长宽判断是否为单栏
ori_size = image_gray.shape[0:2]
single_column = True if ori_size[0] > ori_size[1] else False
# 缩放到固定大小(双栏宽度为4000pixels, 单栏为2000pixels)
if single_column and ori_size[1] != 2000:
ratio = 2000 / ori_size[1]
elif not single_column and ori_size[1] != 4000:
ratio = 4000 / ori_size[1]
else:
ratio = 0
if ratio:
image_gray_scale = resize(image_gray, x=ratio)
new_size = image_gray_scale.shape[0:2]
img_bw_inv = imu.preprocess_bw_inv(image_gray_scale)
img_bw = imu.preprocess_bw(image_gray_scale)
img_bw2 = imu.preprocess_bw(image_gray_scale, boxSize=(5, 5))
#原始图像去除竖线（长度大于100pixels，可能去除部分题目信息）
imgEliVer = eli_ver(img_bw2)
#纵向划分试题区域
coor1 = divide_ver(imgEliVer)
if not coor1:
print('coor1: {}'.format(coor1))
print("Can't divide vertically, skip...")
return None
#计算行高（所有行高的中位数）
coor2 = divide_hor(imgEliVer, coor1)
if coor2:
heightOfLines = [c[3] - c[1] for c in coor2]
heightOfLine = median(heightOfLines)
else:
print("Can't divide by line, skip...")
return None
#获取原始图像联通区域以及消去较大的联通区域
imgInv = imu.preprocess_bw_inv(img_bw2.copy())
cv2.imwrite('imgInv.jpg', imgInv)
coorCC = get_no_intersect_boxes('imgInv.jpg')
imgEliCC = eli_large_cc(coorCC, img_bw2.copy(), heightOfLine)
os.remove('imgInv.jpg')
#去除较大联通区域的图像上去除竖线
imgEli = eli_ver(imgEliCC)
#行划分
coor2 = divide_hor(imgEli, coor1)
#获取每行前三个字符
coor3 = find_char(img_bw, coor2)
#SVM分类字符图像
coor4 = svm_classify(img_bw, coor3)
#在二值图上重新获取字符以及SVM分类（补全部分漏检）
imgBw = imu.preprocess_bw(imgEli, boxSize=(4, 4), morph=False)
coor3Bw = find_char(imgBw, coor2)
coor4Bw = svm_classify(imgBw, coor3Bw)
coor5 = update_result(coor4, coor4Bw)
# 百度文字识别API客户端
client = bd_access()
try:
bdResult = bd_rec(imgEliCC, client, imgForm=name[-4:], api='general')
except Exception as e:
print(e, 'Baidu recognition error, exit...', sep='\n')
return None
# 附加题处理
addResult = additional_questions(bdResult)
if addResult:
coor5.append(([addResult], 2))
#自我评测处理
sa = detect_self_assessment(bdResult)
if sa:
image_gray_scale[sa:, coor1[1][0]:coor1[1][1] + 10] = 255
coor22 = divide_hor(image_gray_scale, coor1)
# 生成题目区域
region = integrate_lines(coor5, coor22, new_size)
region = [
length2coor(list(map(lambda x: int(x / ratio), coor2length(r))))
for r in region
]
#背景标记
region_close = []
for r in region:
if r[2] <= r[0] or r[3] <= r[1]: continue
border = imu.strip_white_boder2(image_gray[r[1]:r[3], r[0]:r[2]])
r = num2int([
border[0] + r[0], border[1] + r[1], border[2] + r[0],
border[3] + r[1]
])
region_close.append(r)
background = np.zeros((r[3] - r[1], r[2] - r[0], 3), np.uint8)
background[:] = (200, 170, 150)
image[r[1]:r[3],
r[0]:r[2]] = cv2.addWeighted(image[r[1]:r[3], r[0]:r[2]], 0.7,
background, 0.3, 0)
# image_process = np.concatenate([image_gray_scale, img_bw,
# imgEliVer, imgEliCC, imgEli], axis=0)
# cv2.imwrite("image_process.jpg", image_process)
dic_result = {"positions": region_close}
print(f'region: {region_close}')
print('output to directory {}'.format(save_path))
with open(save_path + '/' + name.replace('.jpg', '.json'), 'w') as f:
json.dump(dic_result, f)
cv2.imwrite(os.path.join(save_path, name), image)
return region
#去除装订线及中间分割线（不会去除题目信息）
imgData1 = detect_vertical_line3(imgData.copy())
#导出最后结果
resultFinal = get_items_v02(region, imgData1.copy(), coorCC, name)
if desPath:
with open(desPath[:-4] + '.json', 'w') as f:
json.dump(resultFinal, f)
return resultFinal