for table in tables:
worksheet = workbook.add_worksheet()
table_entries = table.get_table_entries()
table_roi = image[table.y:table.y + table.h, table.x:table.x + table.w]
table_roi = cv.resize(table_roi, (table.w * mult, table.h * mult))
cv.imwrite(out + table_name, table_roi)
num_img = 0
for i in range(len(table_entries)):
row = table_entries[i]
for j in range(len(row)):
entry = row[j]
entry_roi = table_roi[entry[1] * mult:(entry[1] + entry[3]) * mult,
entry[0] * mult:(entry[0] + entry[2]) * mult]
fname = out + "table/cell" + str(num_img) + ".jpg"
cv.imwrite(fname, entry_roi)
fname = utils.run_textcleaner(fname, num_img)
text = utils.run_tesseract(fname, num_img, psm, oem)
num_img += 1
worksheet.write(i, j, text)
workbook.close()
def getData_1(original_image):
"""GENERIC FUNCTION TO GET TABULAR AND NON TABULAR DATA"""
###############################################
# CONVERT COLORSPACE TO NEGATIVE
###############################################
original_image = cv.resize(
original_image, (830, 1170), interpolation=cv.INTER_AREA
) # might have a lot of overhead depending on img size
gray_image = cv.cvtColor(original_image, cv.COLOR_BGR2GRAY)
###############################################
# APPLY ADAPTIVE THRESHOLD AND NEGATIVE
###############################################
threshold = cv.adaptiveThreshold(gray_image, 255,
cv.ADAPTIVE_THRESH_GAUSSIAN_C,
cv.THRESH_BINARY, 11, 2)
threshold = cv.bitwise_not(threshold)
# debug.showImage(threshold, "thresh", 80)#DEBUG
###############################################
# EXTRACT TABLE LINES
###############################################
horizontal, vertical = utils.extractTableLines(threshold, 150, 30)
###############################################
# CREATE LINE MASK AND FIND EXTERNAL CONTOURS
###############################################
line_mask = horizontal + vertical
# debug.showImage(line_mask, "mask", 80)#DEBUG
table_ctrs, _ = cv.findContours(line_mask, cv.RETR_EXTERNAL,
cv.CHAIN_APPROX_SIMPLE) # table outlines
table_ctrs = utils.removeFlatContours(table_ctrs)
# ###############################################
# EXTRACT TABLES AND INDIVIDUAL CELLS AND OCR
# ###############################################
data = {}
table_num = 1
table_ctrs = utils.sortContours(
table_ctrs,
cv.boundingRect(
line_mask)[2]) # sort contours left-to-right, top-to-bottom
# debug.showContours(table_ctrs)#DEBUG
# for each table outline contour, get cell contours then perform OCR
for table_ctr in table_ctrs:
x, y, w, h = cv.boundingRect(table_ctr)
table_bbox = gray_image[y - 1:y + h + 1, x - 1:x + w + 1]
cell_ctrs = utils.getCellContours(table_bbox, w)
# debug.showContours(cell_ctrs)#DEBUG
key = "table {}".format(table_num)
data[key] = {}
visited_rows = []
row = 0
for cell_ctr in cell_ctrs:
x, y, w, h = cv.boundingRect(cell_ctr)
cell_bbox = table_bbox[y:y + h, x:x + w]
# detect headers for specific table style
if cv.mean(cell_bbox)[0] < 155:
_, cell_bbox = cv.threshold(cell_bbox, 200, 255,
cv.THRESH_BINARY_INV)
# logic to differentiate different rows and cells
if y not in visited_rows:
visited_rows.append(y)
row += 1
col = 1
data[key]["row " + str(row)] = []
else:
col += 1
# signifiy if OCR returned empty string
v = utils.run_tesseract(cell_bbox, 6, 3)
if v == "":
v = "NULL"
data[key]["row " + str(row)].append(("col " + str(col), v))
table_num += 1
# #############################################
# GET NON TABLE DATA
# #############################################
data["non-tabular data"] = utils.getNonTabularData(gray_image, table_ctrs)
# #############################################
# FORMAT JSON
# #############################################
json_data = json.dumps(data, indent=3, ensure_ascii=False)
print(json_data)