def StructureExtraction(self, image, sc=10):
SCALE = sc
# Isolate horizontal and vertical lines using morphological operations
horizontal = image.copy()
vertical = image.copy()
horizontal_size = int(horizontal.shape[1] / SCALE)
horizontal_structure = cv.getStructuringElement(
cv.MORPH_RECT, (horizontal_size, 1))
utils.isolate_lines(horizontal, horizontal_structure)
vertical_size = int(vertical.shape[0] / SCALE)
vertical_structure = cv.getStructuringElement(cv.MORPH_RECT,
(1, vertical_size))
utils.isolate_lines(vertical, vertical_structure)
# Create an image mask with just the horizontal
# and vertical lines in the image. Then find
# all contours in the mask.
mask = horizontal + vertical
(contours, _) = cv.findContours(mask, cv.RETR_EXTERNAL,
cv.CHAIN_APPROX_SIMPLE)
# Find intersections between the lines
# to determine if the intersections are table joints.
intersections = cv.bitwise_and(horizontal, vertical)
return contours, intersections
def get_cells(self,
ori_img,
table_coords,
debug=False) -> List[np.ndarray]:
# raise RuntimeError("此模块调试未完成！请选择其他获取表格框的方法！")
cells = []
for coord in table_coords: # for each boarded table
table_cell = []
xmin, ymin, xmax, ymax = [int(k) for k in coord
] # used for cropping & shifting
table_img = ori_img[ymin:ymax, xmin:xmax]
with utils.Timer("Traditional Cell Detection"):
grayscale = cv.cvtColor(table_img, cv.COLOR_BGR2GRAY)
filtered = cv.adaptiveThreshold(~grayscale,
self.MAX_THRESHOLD_VALUE,
cv.ADAPTIVE_THRESH_MEAN_C,
cv.THRESH_BINARY,
self.BLOCK_SIZE,
self.THRESHOLD_CONSTANT)
if debug:
cv.namedWindow('filtered', 0)
cv.resizeWindow('filtered', 900, 700)
cv.imshow('filtered', filtered)
cv.waitKey(0)
horizontal = filtered.copy()
vertical = filtered.copy()
horizontal_size = int(horizontal.shape[1] / self.SCALE)
horizontal_structure = cv.getStructuringElement(
cv.MORPH_RECT, (horizontal_size, 1))
utils.isolate_lines(horizontal, horizontal_structure)
vertical_size = int(vertical.shape[0] / self.SCALE)
vertical_structure = cv.getStructuringElement(
cv.MORPH_RECT, (1, vertical_size))
utils.isolate_lines(vertical, vertical_structure)
mask = horizontal + vertical
if debug:
cv.namedWindow('mask', 0)
cv.resizeWindow('mask', 900, 700)
cv.imshow('mask', mask)
cv.waitKey(0)
(contours, _) = cv.findContours(mask, cv.RETR_EXTERNAL,
cv.CHAIN_APPROX_SIMPLE)
intersections = cv.bitwise_and(horizontal, vertical)
if debug:
cv.namedWindow('intersections', 0)
cv.resizeWindow('intersections', 900, 700)
cv.imshow('intersections', intersections)
cv.waitKey(0)
for i in range(len(contours)):
# Verify that region of interest is a table
(rect, table_joints) = utils.verify_table(
contours[i], intersections)
if rect == None or table_joints is None:
continue
# Create a new instance of a table
table = TraditionalTable(rect[0], rect[1], rect[2],
rect[3])
# Get an n-dimensional array of the coordinates of the table joints
joint_coords = []
for j in range(len(table_joints)):
joint_coords.append(table_joints[j][0][0])
joint_coords = np.asarray(joint_coords)
# Returns indices of coordinates in sorted order
# Sorts based on parameters (aka keys) starting from the last parameter, then second-to-last, etc
# joint_coords:
# [[913 179], [695 179], [548 179], [285 179], [182 179],
# [ 72 179], [913 119], [695 119], [548 119], [285 119],
# [182 119], [ 72 119], [913 31], [695 31], [548 31],
# [457 31], [376 31], [285 31], [182 31], [ 72 31],
# [913 0], [695 0], [548 0], ... ]
sorted_indices = np.lexsort(
(joint_coords[:, 0], joint_coords[:, 1]))
joint_coords = joint_coords[sorted_indices]
# Store joint coordinates in the table instance
table.set_joints(joint_coords)
table_entries = table.get_table_entries()
for k in range(len(table_entries)):
row = table_entries[k]
for j in range(len(row)):
entry = row[j] # xyxy
table_cell.append([
entry[0], entry[1], entry[2], entry[1],
entry[2], entry[3], entry[0], entry[3]
]) # xyxyxyxy
cells.append(np.array(table_cell))
return cells
To isolate the vertical and horizontal lines,
1. Set a scale.
2. Create a structuring element.
3. Isolate the lines by eroding and then dilating the image.
"""
SCALE = 15
# Isolate horizontal and vertical lines using morphological operations
horizontal = filtered.copy()
vertical = filtered.copy()
horizontal_size = int(horizontal.shape[1] / SCALE)
horizontal_structure = cv.getStructuringElement(cv.MORPH_RECT,
(horizontal_size, 1))
utils.isolate_lines(horizontal, horizontal_structure)
vertical_size = int(vertical.shape[0] / SCALE)
vertical_structure = cv.getStructuringElement(cv.MORPH_RECT,
(1, vertical_size))
utils.isolate_lines(vertical, vertical_structure)
# =====================================================
# TABLE EXTRACTION
# =====================================================
# Create an image mask with just the horizontal
# and vertical lines in the image. Then find
# all contours in the mask.
mask = horizontal + vertical
(_, contours, _) = cv.findContours(mask, cv.RETR_EXTERNAL,
cv.CHAIN_APPROX_SIMPLE)
def conversion_algorithm(path):
# scanning the image, applying perspective warping
# and adaptive thresholding
filtered, warped = scan_img(path)
# line isolation
SCALE = 23
# isolate horizontal and vertical lines using morphological operations
horizontal = filtered.copy()
vertical = filtered.copy()
horizontal_size = int(horizontal.shape[1] / SCALE)
horizontal_structure = cv.getStructuringElement(cv.MORPH_RECT,
(horizontal_size, 1))
utils.isolate_lines(horizontal, horizontal_structure)
vertical_size = int(vertical.shape[0] / SCALE)
vertical_structure = cv.getStructuringElement(cv.MORPH_RECT,
(1, vertical_size))
utils.isolate_lines(vertical, vertical_structure)
# TABLE EXTRACTION
# create an image mask with just the horizontal
# and vertical lines in the image. Then find
# all contours in the mask.
mask = horizontal + vertical
cv.imwrite("processing_data/detected_lines.jpg", mask)
(contours, _) = cv.findContours(mask, cv.RETR_EXTERNAL,
cv.CHAIN_APPROX_SIMPLE)
# find intersections between the lines
# to determine if the intersections are table joints.
intersections = cv.bitwise_and(horizontal, vertical)
# get tables from the images
tables = [] # list of tables
for i in range(len(contours)):
# verify that region of interest is a table
(rect, table_joints) = utils.verify_table(contours[i], intersections)
if rect == None or table_joints == None:
continue
# create a new instance of a table
table = Table(rect[0], rect[1], rect[2], rect[3])
# get an n-dimensional array of the coordinates of the table joints
joint_coords = []
for i in range(len(table_joints)):
joint_coords.append(table_joints[i][0][0])
joint_coords = np.asarray(joint_coords)
# returns indices of coordinates in sorted order
# sorts based on parameters (aka keys) starting from the last parameter, then second-to-last, etc
sorted_indices = np.lexsort((joint_coords[:, 0], joint_coords[:, 1]))
joint_coords = joint_coords[sorted_indices]
# store joint coordinates in the table instance
table.set_joints(joint_coords)
tables.append(table)
cv.rectangle(warped, (table.x, table.y),
(table.x + table.w, table.y + table.h), (0, 255, 0), 1, 8,
0)
cv.imwrite("processing_data/table_boundaries.jpg", warped)
def find_table(image): # Convert resized RGB image to grayscale NUM_CHANNELS = 3 if len(image.shape) == NUM_CHANNELS: grayscale = cv.cvtColor(image, cv.COLOR_BGR2GRAY) # ===================================================== # IMAGE FILTERING (using adaptive thresholding) # ===================================================== MAX_THRESHOLD_VALUE = 255 BLOCK_SIZE = 15 THRESHOLD_CONSTANT = 0 # Filter image filtered = cv.adaptiveThreshold(~grayscale, MAX_THRESHOLD_VALUE, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, BLOCK_SIZE, THRESHOLD_CONSTANT) # ===================================================== # LINE ISOLATION # ===================================================== SCALE = 15 # Isolate horizontal and vertical lines using morphological operations horizontal = filtered.copy() vertical = filtered.copy() horizontal_size = int(horizontal.shape[1] / SCALE) horizontal_structure = cv.getStructuringElement(cv.MORPH_RECT, (horizontal_size, 1)) utils.isolate_lines(horizontal, horizontal_structure) vertical_size = int(vertical.shape[0] / SCALE) vertical_structure = cv.getStructuringElement(cv.MORPH_RECT, (1, vertical_size)) utils.isolate_lines(vertical, vertical_structure) # ===================================================== # TABLE EXTRACTION # ===================================================== # Create an image mask with just the horizontal # and vertical lines in the image. Then find # all contours in the mask. mask = horizontal + vertical (contours, _) = cv.findContours(mask, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) # Find intersections between the lines # to determine if the intersections are table joints. intersections = cv.bitwise_and(horizontal, vertical) # Get tables from the images tables = [] # list of tables for i in range(len(contours)): # Verify that region of interest is a table (rect, table_joints) = utils.verify_table(contours[i], intersections) if rect == None or table_joints == None: continue # Create a new instance of a table table = Table(rect[0], rect[1], rect[2], rect[3]) # Get an n-dimensional array of the coordinates of the table joints joint_coords = [] for i in range(len(table_joints)): joint_coords.append(table_joints[i][0][0]) joint_coords = np.asarray(joint_coords) # Returns indices of coordinates in sorted order # Sorts based on parameters (aka keys) starting from the last parameter, then second-to-last, etc sorted_indices = np.lexsort((joint_coords[:, 0], joint_coords[:, 1])) joint_coords = joint_coords[sorted_indices] # Store joint coordinates in the table instance table.set_joints(joint_coords) tables.append(table) if len(tables)!=0: return tables else: return []
