dpi = 80.0

xpixels, ypixels = data.shape[::-1]

margin = 0.05

figsize = (1 + margin) * ypixels / dpi, (1 + margin) * xpixels / dpi

fig = plt.figure(figsize=figsize, dpi=dpi)

ax = fig.add_axes([margin, margin, 1 - 2 * margin, 1 - 2 * margin])

ax.imshow(data, interpolation='none')

dot = cv2.imread("/dot.7.7.jpg")

dirs = []

dir = []

fo = open("ocr_detection.txt", "a+", encoding='utf-8')

dir_left,dir_right,dirs_left,dirs_right=[],[],[],[]

d.p2=img_gray = cv2.cvtColor(mat, cv2.COLOR_BGR2GRAY)

w, h = img_gray.shape[::-1]

l = get_content_roi(d, img_gray) #去边框

img_struct = img_gray[slice(l[0], l[1]), slice(l[2], l[3])]

loc = find_mid_line(d, img_struct) #切割线,原函数是img2part

half_img =cut_image(d,img_struct,loc) #切分图形, 返回是图形的左右图，在list中

# for half in half_img:

# d.p2=no_circle = find_circles(d,half,dir) #返回的e二值化的图

# dir=dp_split_height2(d,no_circle,dir)

# # d.p2 = no_circle = find_circles(d, half_img[1], dir) # 返回的e二值化的图

# break

d.p2 = no_circle = find_circles(d, half_img[0], dir_left, "left") # 返回的e二值化的图

dir_left = dp_split_height2(d, no_circle, dir_left, "left")

dirs_left = sort_content(dir_left, dirs_left)

d.p2 = no_circle = find_circles(d, half_img[1], dir_right, "right") # 返回的e二值化的图

dir_right = dp_split_height2(d, no_circle, dir_right, "right")

dirs_right = sort_content(dir_right, dirs_right)

dirs_left.extend(dirs_right)

# all_dirs.extend(dirs_left)

read_target(dirs_left)

ocr_content = ocr_test(dirs_left)

copy_mat = mat

d.f = "get_content_roi"

d.p2 = img = cv2.adaptiveThreshold(copy_mat, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 55, 25)

w, h = mat.shape[::-1]

w4, h4 = int(w/4), int(h/4)

d.p2 = top_roi = img[slice(0, h4), slice(w4, 3*w4)]

ret,top_thresh = cv2.threshold(top_roi,0,255,cv2.THRESH_OTSU)

d.p2 = mask = cv2.dilate(top_thresh, tools.box(150,80))

_, contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

_, boundingBoxes = tools.sort_contours(contours)

top = boundingBoxes[0][3]

d.p2 = left_roi = img[slice(h4, 3*h4), slice(0, w4)]

ret, left_thresh = cv2.threshold(left_roi, 0, 255, cv2.THRESH_OTSU)

d.p2 = mask = cv2.dilate(left_thresh, tools.box(100, 10))

_, contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

_, boundingBoxes = tools.sort_contours(contours, method="left-right")

left = boundingBoxes[0][2]

d.p2 = right_roi = img[slice(h4, 3*h4), slice(3*w4, w)]

ret, right_thresh = cv2.threshold(right_roi, 0, 255, cv2.THRESH_OTSU)

d.p2 = mask = cv2.dilate(right_thresh, tools.box(100, 10))

_, contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

_, boundingBoxes = tools.sort_contours(contours, method="left-right")

right = 3*w4+boundingBoxes[-1][0]

d.p2 = bottom_roi = img[slice(3*h4, h), slice(w4, 3*w4)]

ret, botton_thresh = cv2.threshold(bottom_roi, 0, 255, cv2.THRESH_OTSU)

d.p2 = mask = cv2.dilate(botton_thresh, tools.box(2, 100))

_, contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

_, boundingBoxes = tools.sort_contours(contours)

bottom = 3*h4+boundingBoxes[-1][1]

d.p2 = content = copy_mat[slice(top,bottom), slice(left,right)]

# return content

# return [slice(top,bottom), slice(left,right)]

mat=img

w,h =mat.shape[::-1]

d.p2 = mat = mat[:,int(0.4*w):int(0.6*w)]

minLineLength = int(h / 2)

maxLineGap = 15

d.p2 = mat_adaptive = cv2.adaptiveThreshold(mat, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 101, 15)

d.p2 = mask = cv2.dilate(mat_adaptive, tools.box(3, 10))

lines = cv2.HoughLinesP(mask, 1, np.pi / 180, 30, minLineLength, maxLineGap)

for x1, y1, x2, y2 in lines[0]:

print("x1=", x1, "; y1=", y1, "; x2=", x2, "; y2=", y2)

d.p2 = drae_line = cv2.line(mat,(x1,y1),(x2,y2),(255,255,0),10)

loc = int(0.5*( x1 + x2 )+0.4*w)

w, h = img.shape[::-1]

d.p2=img_left = img[:,0:loc]

d.p2=img_right = img[:,loc:w]

img_gray = img

width,hight = img.shape[::-1]

# d.p2=img

d.p2=roi_adaptive = cv2.adaptiveThreshold(img_gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 275, 15)

d.p2=roi_adaptive_weak = cv2.adaptiveThreshold(img_gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 55, 25)

# cv2.GaussianBlur(img_gray,img_gray,tools.box(2,2),2)

circles = cv2.HoughCircles(img_gray, cv2.HOUGH_GRADIENT, 1, 100, param1=100, param2=30, minRadius=130, maxRadius=160)

cx ,cy,cr= [],[],[]

ContentPath1,Height1,PagePath1,ClassPath =[],[],[],[]

k = 1

for circle in circles[0,:]:

# print("i = ", i)

x,y,r = circle[0],circle[1],circle[2]

cx.append(x)

cy.append(y)

cr.append(r)

d.p2 =cir_demo= cv2.circle(img, (x, y),r, (0, 0, 255),5 )

# cv2.circle(img, (x, y), r, (0, 0, 255), 1)

a,b,c,dd = int(y - r+20),int(y),int(x - r / 2),int(x + r / 2)

print (x," ",y," ",r)

d.p2 = roi = roi_adaptive[a:b,c:dd] #一目录的数字

d.p2 = content = roi_adaptive_weak[slice(int(y-r),int(y+r)),slice(int(x+r+40),width)]#一级目录的文字

# ocr_number(roi)

# ocr(content)

content_path="./image/"+d.n+"-content-1-"+str(k)+isleft+".jpg"

ContentPath1.append(content_path)

cv2.imwrite(content_path, content)

Height1.append(y-r)

roi_path="./image/"+d.n+"-class-1-"+str(k)+isleft+".jpg"

PagePath1.append(0)

ClassPath.append(roi_path)

cv2.imwrite(roi_path,roi)

roi_adaptive_weak[slice(int(y - r-30), int(y + r+20)), slice(0, width)]=0

k += 1

dir.extend(list(zip(ContentPath1, Height1, PagePath1, ClassPath)))

# initialize the reverse flag and sort index

reverse = False

i = 0

# handle if we need to sort in reverse

if method == "right-to-left" or method == "bottom-to-top":

reverse = True

# handle if we are sorting against the y-coordinate rather than

# the x-coordinate of the bounding box

if method == "top-to-bottom" or method == "bottom-to-top":

i = 1

# construct the list of bounding boxes and sort them from top to

# bottom

boundingBoxes = [cv2.boundingRect(c) for c in cnts]

(cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),

key=lambda b: b[1][i], reverse=reverse))

# return the list of sorted contours and bounding boxes

(cnts, boundingBoxes) = sort_contours(contours)

lines = list([])

last_y = -999

for contour in boundingBoxes:

x, y, w, h = contour

if abs(y - last_y) > 20:

lines.append([x, y, w, h])

else:

min_x = min(lines[-1][0], x)

max_x = max(lines[-1][0] + lines[-1][2], x + w)

lines[-1][0] = min_x

lines[-1][2] = max_x - min_x

last_y = y

# roi_copy = roi

# roi = cv2.adaptiveThreshold(roi, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 55, 25)

mask = cv2.dilate(roi, tools.box(2, 2))

tf = cv2.imread("./dot.7.7.jpg")

tf2 = cv2.cvtColor(tf, cv2.COLOR_RGB2GRAY)

#template = cv2.adaptiveThreshold(tf2, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 55, 25)

res = cv2.matchTemplate(roi, tf2, cv2.TM_CCORR_NORMED)

threshold = 0.7

loc = np.where(res >= threshold)

regions = np.zeros_like(roi)

# found all match dot image point

for pt in zip(*loc[::-1]):

cv2.rectangle(regions, pt, (pt[0] + 10, pt[1] + 5), (255, 255, 0), 2)

d.p2 = regions

regions = cv2.dilate(regions, tools.box(5, 5))

_, contours, _ = cv2.findContours(regions, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

lines = tools.contact_contours(contours)

regions = np.zeros_like(roi)

i = 0

width, height = tf2.shape[::-1]

for contour in lines:

x, y, w, h = contour

contour[2]=contour[2]+width-10

contour[3] = height+2

for contour in lines:

x, y, w, h = contour

cv2.rectangle(regions, (x, y), (x + w, y + h), (255, 255, 0), 2)

# cv2.putText(regions, "{}".format(i + 1), (x + 20, y + 20), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 0), 2)

i += 1

d.p2 = regions

cv2.imwrite("./dot_match.png",regions)

# ret,roi_for_contents = cv2.threshold(roi_copy,100,255,cv2.THRESH_OTSU)

content = contents(d,roi,lines,dir,isleft)

roi_cpy = roi

# roi = cv2.imread("/home/xxy/project/tsl_project/dir1_right.jpg",0)

width,hight = roi.shape[::-1]

kk=0

content_height = []

ContentPath3,Height3,PagePath3 ,ClassPath= [],[],[],[]

for contour in lines:

x,y,w,h = contour

#cv2.rectangle(img, (x, y), (x + w, y + h), (255, 255, 0), 2)

d.p2=img1=content_target = roi[slice(y,h+y+20),slice(0,x)]

d.p2=img2=page_target = roi[slice(y,h+y+20),slice(x+w-5,width-50)]

content_height.extend([y])

if mean(content_target)<10:

content_target = roi[slice(y-h-30,y+10),slice(60,width)]

elif len(content_height)>1:

height = content_height[kk]-content_height[kk-1]

# print("高的差值为： ",height)

if height>(h+10+30)*1.5 and height<2.2*(h+10+30): #此处的10+30借鉴上面content_target = roi[slice(y-h-30,y+10),slice(60,width)]

img1=roi[slice(y, h + y + 20), slice(0, x)]

img2=roi[slice(y-h-20, y ), slice(0, width)]#拼接在前面的图像

content_target=img_concatenate(img2,img1)

sh3(content_target)

content_target = cv2.erode(content_target, tools.box(2,2))

page_target = cv2.erode(page_target, tools.box(2,2))

# content_target = img_concatenate(img1, img2)

cv2.imwrite("./image/" + d.n + "-content-3-" + str(kk)+ isleft + ".jpg", content_target)

cv2.imwrite("./image/" + d.n + "-page-3-" + str(kk)+ isleft + ".jpg",page_target)

ContentPath3.append("./image/" + d.n + "-content-3-" + str(kk) + isleft+ ".jpg")

PagePath3.append("./image/" + d.n + "-page-3-" + str(kk) +isleft+ ".jpg")

Height3.append(h+y+10)

ClassPath.append(0)

roi[slice(y , y+h+10), slice(0, width)]=0

roi[slice(y - h - 30, y + 10), slice(0, width)] = 0

kk+=1

dir.extend(list(zip(ContentPath3,Height3,PagePath3,ClassPath)))

d.p2 = roi

imgCopy = cv2.erode(img, tools.box(2, 2))

w,h_origin = img.shape[::-1]

img = img[slice(0,h_origin-200),:]

d.p2 = erosion=cv2.erode(img,tools.box(2,10))

d.p2 = dilate = cv2.dilate(erosion,tools.box(200,20))

_, contours, _ = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

ContentPath2,Hight2,PagePath2=[],[],[]

regions = np.zeros_like(dilate)

for i in range(0, len(contours)):

x, y, w, h = cv2.boundingRect(contours[i])

cv2.rectangle(regions, (x, y), (x + w, y + h), (255, 255, 255), 5)

print("x = ", x, " ;y = ", y, " ;w = ", w, " ;h = ", h)

# cv2.imwrite("/image/"+d.n+"_class2_content2_"+str(i+1)+".jpg",imgCopy[slice(y,y+h),slice(x,x+w)])

ContentPath2.append("/image/"+d.n+"_class2_content2_"+str(i+1)+".jpg")

Hight2.append(h+y)

PagePath2.append(0)

d.p2 = regions

cv2.imwrite("_ocr_h.png", mat)

os.system("tesseract" + " " + "_ocr_h.png" + " ocr -l chi_sim --psm 7")

fs = open("ocr.txt", 'r', encoding='utf-8')

string = fs.read()

string = re.sub(r'\n', "", string)

print("ocr: " + string)

cv2.imwrite("_ocr_h.png", mat)

os.system("tesseract" + " " + "_ocr_h.png" + " ocr --oem 0 --psm 7 test_c")

fs = open("ocr.txt", 'r', encoding='utf-8')

string = fs.read()

string = re.sub(r'\D', "", string)

print("ocr: " + string)

k=1

f= open("./image/target.txt","w",encoding='utf-8')

for i in dir:

f.write(str(i))

f.write("\n")

page, class_ocr,l = [], [],[]

for imgpath in dirs:

print ("imagepath : ",imgpath)

img = cv2.imread(imgpath[0],0)

ocr_content = tools.ocr(img)

img_page_num = imgpath[2]

class_path = imgpath[3]

if class_path != 0:

class_img = cv2.imread(class_path,0)

class_name = tools.ocr_number(class_img)

else:

class_name=0

if img_page_num != 0 : #or img_num_path != '0':

page_num = cv2.imread(img_page_num,0)

ocr_num = tools.ocr_number(page_num)

page.append(ocr_num)

else:

ocr_num =''

page.append(ocr_num)

if class_name !=0:

class_name=class_name.replace("\f","")

ocr_content=ocr_content.replace("\f","")

class_ocr.append(class_name+ocr_content)

else:

ocr_content=ocr_content.replace("\f","")

ocr_num=ocr_num.replace("\f","")

# class_ocr.append(ocr_content+ocr_num)

class_ocr.append(ocr_content)

l.extend(list(zip(class_ocr, page)))

"""书的目录树

[

{"text":"Unit 11","start_page":1,"rank":0},

{"text":"Unit 11","start_page":1,"rank":0}

]

"""

page=[]

i = 0

for j in ocr_list:

page.append(list(j))

if page[-1][-1] == '':

page[-1][-1] = list(ocr_list[i+1])[-1]

i += 1

for i,j in page:

print (i,j)

all_dirs.append({"text":i,"start_page":int(j),"rank":0})

# img1=cv2.imread("/home/ccs-pc/project/dir1.jpg",0)

# img2=cv2.imread("/home/ccs-pc/project/dir2.jpg",0)

img = np.concatenate([img1,img2],axis=1)

# image = np.concatenate((gray1, gray2)) # 纵向连接=np.vstack((gray1, gray2))

# 横向连接image = np.concatenate([gray1, gray2], axis=1)

imgfiles = ["Dir.jpg","Dir2.jpg"]

all_dirs = []

for imgfile in imgfiles:

basename = os.path.basename(imgfile)

name, _ = os.path.splitext(basename)

img = cv2.imread(imgfile)

d = tools.DD(name)

dp(d, img, all_dirs)

# break

fo = open("dir.txt", 'w+', encoding='utf-8')

fs = json.dumps(all_dirs,ensure_ascii=False)

fo.write(fs)