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import cv2
import pytesseract
import numpy as np
import pandas as pd
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
def load_local_image(path: str) -> np.ndarray:
img = Image.open(path).convert("RGB")
return cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
def sort_contours(cnts, method="top-to-bottom"):
reverse = False
i = 1 if method == "top-to-bottom" or method == "bottom-to-top" else 0
if method == "right-to-left" or method == "bottom-to-top":
reverse = True
boundingBoxes = [cv2.boundingRect(c) for c in cnts]
(cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
key=lambda b: b[1][i], reverse=reverse))
return cnts, boundingBoxes
from collections import Counter
def extract_cells_from_grid(table_img: np.ndarray) -> pd.DataFrame:
gray = cv2.cvtColor(table_img, cv2.COLOR_BGR2GRAY)
_, binary = cv2.threshold(~gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# Detect horizontal lines
horizontal = binary.copy()
cols = horizontal.shape[1]
horizontal_size = cols // 15
horizontal_structure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontal_size, 1))
horizontal = cv2.erode(horizontal, horizontal_structure)
horizontal = cv2.dilate(horizontal, horizontal_structure)
# Detect vertical lines
vertical = binary.copy()
rows = vertical.shape[0]
vertical_size = rows // 15
vertical_structure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, vertical_size))
vertical = cv2.erode(vertical, vertical_structure)
vertical = cv2.dilate(vertical, vertical_structure)
# Combine mask
mask = cv2.add(horizontal, vertical)
contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cells = []
# Inside loop over contours
for c in contours:
x, y, w, h = cv2.boundingRect(c)
# NEW: filter out garbage boxes (lines, dash artifacts, etc.)
if w < 20 or h < 20:
continue # noise
# Heuristic: skip cell if mostly empty image (white)
roi = table_img[y:y+h, x:x+w]
white_ratio = cv2.countNonZero(cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)) / (w * h + 1e-5)
if white_ratio < 0.05: # >95% empty
continue
cells.append((x, y, w, h))
if not cells:
return pd.DataFrame()
# Sort top-to-bottom
cells = sorted(cells, key=lambda b: (b[1], b[0]))
# Group by row using y-coordinate proximity
row_tolerance = 15
rows = []
current_row = []
last_y = None
for cell in cells:
x, y, w, h = cell
if last_y is None or abs(y - last_y) <= row_tolerance:
current_row.append(cell)
else:
rows.append(sorted(current_row, key=lambda b: b[0]))
current_row = [cell]
last_y = y
if current_row:
rows.append(sorted(current_row, key=lambda b: b[0]))
# Determine most common number of columns (mode)
col_counts = [len(r) for r in rows]
if not col_counts:
return pd.DataFrame()
most_common_cols = Counter(col_counts).most_common(1)[0][0]
# Extract text
table_data = []
for row in rows:
sorted_row = sorted(row, key=lambda b: b[0])
row_data = []
for x, y, w, h in sorted_row:
cell_img = table_img[y:y+h, x:x+w]
cell_text = pytesseract.image_to_string(cell_img, config="--psm 7").strip()
row_data.append(cell_text)
# Adjust row length to match majority column count
if len(row_data) < most_common_cols:
row_data += [""] * (most_common_cols - len(row_data))
elif len(row_data) > most_common_cols:
row_data = row_data[:most_common_cols]
table_data.append(row_data)
return pd.DataFrame(table_data)
def detect_table_boxes(image: np.ndarray) -> list[tuple[int, int, int, int]]:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, binary = cv2.threshold(~gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
horizontal = binary.copy()
cols = horizontal.shape[1]
horizontalsize = cols // 15
horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontalsize, 1))
horizontal = cv2.erode(horizontal, horizontalStructure)
horizontal = cv2.dilate(horizontal, horizontalStructure)
vertical = binary.copy()
rows = vertical.shape[0]
verticalsize = rows // 15
verticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, verticalsize))
vertical = cv2.erode(vertical, verticalStructure)
vertical = cv2.dilate(vertical, verticalStructure)
mask = horizontal + vertical
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
boxes = []
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
if w > 100 and h > 50:
boxes.append((x, y, w, h))
return boxes
def extract_non_table_text(image: np.ndarray, table_boxes: list[tuple]) -> str:
mask = np.zeros(image.shape[:2], dtype=np.uint8)
for x, y, w, h in table_boxes:
cv2.rectangle(mask, (x, y), (x + w, y + h), 255, -1)
inverse_mask = cv2.bitwise_not(mask)
non_table_img = cv2.bitwise_and(image, image, mask=inverse_mask)
gray = cv2.cvtColor(non_table_img, cv2.COLOR_BGR2GRAY)
custom_config = r'--oem 3 --psm 6'
return pytesseract.image_to_string(gray, config=custom_config)
def dataframe_to_markdown(df: pd.DataFrame) -> str:
return df.to_markdown(index=False)
def extract_image(filepath: str) -> str:
image = load_local_image(filepath)
table_boxes = detect_table_boxes(image)
tables = []
for i, (x, y, w, h) in enumerate(table_boxes):
cropped = image[y:y+h, x:x+w]
try:
df = extract_cells_from_grid(cropped)
tables.append((df, (x, y, w, h)))
except Exception as e:
print(f"[Warning] Skipping table {i} due to error: {e}")
non_table_text = extract_non_table_text(image, table_boxes)
output = ""
if non_table_text.strip():
output += f"### Non-Table Text:\n{non_table_text.strip()}\n\n"
for i, (df, _) in enumerate(tables):
output += f"### Table {i+1} (Markdown):\n{dataframe_to_markdown(df)}\n\n"
return output.strip()
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