def __init__(self,img, bin_threshold, kernel, iterations, areaRange, filename, border=10, show=True, write=True,):

'''

:param img: 输入图像

:param bin_threshold: 二值化的阈值大小

:param kernel: 形态学kernel

:param iterations: 迭代次数

:param areaRange: 面积范围

:param filename:保留json数据的文件名称

:param border: 留边大小

:param show: 是否显示结果图，默认是显示

:param write: 是否把结果写到文件，默认是写入

'''

self.img = img

self.bin_threshold = bin_threshold

self.kernel = kernel

self.iterations = iterations

self.areaRange = areaRange

self.border = border

self.show = show

self.write = write

self.filename = filename

def getRes(self):

# 获取格网线图和格网点图,都是像素级别的

img_erode, joint = detectTable(self.img).run()

cv2.imshow("src", img_erode)

image, contours, hierarchy = cv2.findContours(img_erode, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

area_coord_roi = []

for i in range(len(contours)):

cnt = contours[i]

area = cv2.contourArea(cnt)

if area >self.areaRange[0] and area <self.areaRange[1]:

x, y, w, h = cv2.boundingRect(cnt)

roi = self.img[(y+self.border):((y+h)-self.border), (x+self.border):((x+w)-self.border)]

area_coord_roi.append((area,(x,y,w,h),roi))

#最大面积

max_area = max([info[0] for info in area_coord_roi])

for info in area_coord_roi:

if info[0]==max_area:

max_rect = info[1]

max_info = info

bScript = cutImage.SkipMaxArea(self,max_info,area_coord_roi)

maxW = 0

totalH = 0

#计算拼接的图片大小

for each in area_coord_roi:

x,y,w,h = each[1]

#if 1 == 0: #x>max_rect[0] and y>max_rect[1] and (x+w)<(max_rect[0]+max_rect[2]) and (y+h) <(max_rect[1]+max_rect[3]):

if bScript and each[0] == max_area: #跳过最大面积的表格

pass

else:

if maxW < w:

maxW = w

totalH += h

to_image = Image.new('RGB', (maxW+1, totalH + 1),color='white') # 创建一个新图 size必须是元组

print(maxW+1)

print(totalH + 1)

sumH = 0

area_coord_map = [] #原图中的位置与拼接图的位置映射

for each in area_coord_roi:

#if 1 == 0: #x>max_rect[0] and y>max_rect[1] and (x+w)<(max_rect[0]+max_rect[2]) and (y+h) <(max_rect[1]+max_rect[3]):

if bScript and each[0] == max_area: #跳过最大面积的表格

pass

else:

x, y, w, h = each[1]

dst_dir = 'clip'

if not os.path.exists(dst_dir):

os.mkdir(dst_dir)

name = '%s/%d_%d_%d_%d.png' % (dst_dir, x, y, x + w, y + h)

cv2.imwrite(name, each[2]) #png 0-9 ,[int(cv2.CV_IMWRITE_PNG_COMPRESSION),9]

imgtemp = Image.open(name).convert("RGB") #图片临时保存再来

to_image.paste(imgtemp,(0,sumH)) #((x - 1) * IMAGE_SIZE, (y - 1) * IMAGE_SIZE)

area_coord_map.append(((x,y,w,h),(0,sumH,0+w,sumH+h)))

sumH += h

MeargeImageName = 'clip/final.png'

to_image.save(MeargeImageName) #95最佳 默认75 增采样

imgtempV2 = Image.open(MeargeImageName).convert("RGB") # 图片临时保存再来

W, H = imgtempV2.size

timeTake = time.time()

_, result, angle = model.model(imgtempV2,

detectAngle=DETECTANGLE, ##是否进行文字方向检测

config=dict(MAX_HORIZONTAL_GAP=10, ##字符之间的最大间隔，用于文本行的合并

MIN_V_OVERLAPS=0.7,

MIN_SIZE_SIM=0.7,

TEXT_PROPOSALS_MIN_SCORE=0.1,

TEXT_PROPOSALS_NMS_THRESH=0.3,

TEXT_LINE_NMS_THRESH=0.99, ##文本行之间测iou值

MIN_RATIO=1.0,

LINE_MIN_SCORE=0.2,

TEXT_PROPOSALS_WIDTH=0,

MIN_NUM_PROPOSALS=0,

),

leftAdjust=True, ##对检测的文本行进行向左延伸

rightAdjust=True, ##对检测的文本行进行向右延伸

alph=0.2, ##对检测的文本行进行向右、左延伸的倍数

ifadjustDegree=False ##是否先小角度调整文字倾斜角度

)

print(result)

boxes = []

for line in result:

cx = line['cx']

cy = line['cy']

w = line['w']

h = line['h']

text = line['text']

print(text)

return

def SkipMaxArea(self,maxinfo,area_coord_roi):

mx,my,mw,mh = maxinfo[1]

for info in area_coord_roi:

if info[0]==maxinfo[0]:

pass

else:

x,y,w,h = info[1]

if x>mx and y>my and x+w<mx+mw and y+h<my+mh:

return 1

return 0

def getRes_By_Cell(self):

#获取格网线图和格网点图

img_line, img_joint = detectTable(self.img).run()

cv2.imshow("line", img_line)

cv2.imshow("point", img_joint)

#获取格网图边界 x,y 最大最小值，兼容表格最外边没有边界的情况

contours, hierarchy = cv2.findContours(img_line, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

xmin , xmax, ymin, ymax = img_line.shape[1], 0, img_line.shape[0], 0

for i in range(len(contours)):

cnt = contours[i]

x, y, w, h = cv2.boundingRect(cnt)

xmin = min(xmin, x)

xmax = max(xmax, x + w)

ymin = min(ymin, y)

ymax = max(ymax, y + h)

#通过格网点及格网线图找出每一个单元格并裁切

idx = np.argwhere(img_joint > 1)

idx_unirow = np.unique(idx[:, 0])

idx_unicol = np.unique(idx[0, :]);

def __init__(self, src_img):

self.src_img = src_img

def run(self):

#转换为灰度图

if len(self.src_img.shape) == 2: # 灰度图

gray_img = self.src_img

elif len(self.src_img.shape) ==3:

gray_img = cv2.cvtColor(self.src_img, cv2.COLOR_BGR2GRAY)

#二值化

thresh_img = cv2.adaptiveThreshold(~gray_img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,15,-2)

h_img = thresh_img.copy()

v_img = thresh_img.copy()

scale = 15

h_size = int(h_img.shape[1]/scale)

#提取水平方向格网线（通过腐蚀膨胀）

h_structure = cv2.getStructuringElement(cv2.MORPH_RECT,(h_size,1)) # 形态学因子

h_erode_img = cv2.erode(h_img,h_structure, 1) #腐蚀

#cv2.imshow("1",h_erode_img)

h_dilate_img = cv2.dilate(h_erode_img,h_structure, 1) #膨胀

#cv2.imshow("2",h_dilate_img)

v_size = int(v_img.shape[0] / scale)

# 提取竖直方向格网线（通过腐蚀膨胀）

v_structure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, v_size)) # 形态学因子

v_erode_img = cv2.erode(v_img, v_structure, 1) #腐蚀

#cv2.imshow("3", v_erode_img)

v_dilate_img = cv2.dilate(v_erode_img, v_structure, 1) #膨胀

#cv2.imshow("4", v_dilate_img)

#合并格网线（水平+竖直）

mask_img = h_dilate_img+v_dilate_img

#cv2.imshow("5", mask_img)

#计算格网线交叉点

joints_img = cv2.bitwise_and(h_dilate_img,v_dilate_img)

#cv2.imshow("joints",joints_img)

#cv2.imshow("mask",mask_img)