import cv2

def extract_cell_images_from_table(image):
    BLUR_KERNEL_SIZE = (17, 17)
    STD_DEV_X_DIRECTION = 0
    STD_DEV_Y_DIRECTION = 0
    blurred = cv2.GaussianBlur(image, BLUR_KERNEL_SIZE, STD_DEV_X_DIRECTION, STD_DEV_Y_DIRECTION)
    MAX_COLOR_VAL = 255
    BLOCK_SIZE = 15
    SUBTRACT_FROM_MEAN = -2
    
    img_bin = cv2.adaptiveThreshold(
        ~blurred,
        MAX_COLOR_VAL,
        cv2.ADAPTIVE_THRESH_MEAN_C,
        cv2.THRESH_BINARY,
        BLOCK_SIZE,
        SUBTRACT_FROM_MEAN,
    )
    vertical = horizontal = img_bin.copy()
    SCALE = 5
    image_width, image_height = horizontal.shape
    horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (int(image_width / SCALE), 1))
    horizontally_opened = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel)
    vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, int(image_height / SCALE)))
    vertically_opened = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel)
    
    horizontally_dilated = cv2.dilate(horizontally_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (40, 1)))
    vertically_dilated = cv2.dilate(vertically_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (1, 60)))
    
    mask = horizontally_dilated + vertically_dilated
    contours, heirarchy = cv2.findContours(
        mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE,
    )
    
    perimeter_lengths = [cv2.arcLength(c, True) for c in contours]
    epsilons = [0.05 * p for p in perimeter_lengths]
    approx_polys = [cv2.approxPolyDP(c, e, True) for c, e in zip(contours, epsilons)]
    
    # Filter out contours that aren't rectangular. Those that aren't rectangular
    # are probably noise.
    approx_rects = [p for p in approx_polys if len(p) == 4]
    bounding_rects = [cv2.boundingRect(a) for a in approx_polys]
    
    # Filter out rectangles that are too narrow or too short.
    MIN_RECT_WIDTH = 40
    MIN_RECT_HEIGHT = 10
    bounding_rects = [
        r for r in bounding_rects if MIN_RECT_WIDTH < r[2] and MIN_RECT_HEIGHT < r[3]
    ]
    
    # The largest bounding rectangle is assumed to be the entire table.
    # Remove it from the list. We don't want to accidentally try to OCR
    # the entire table.
    largest_rect = max(bounding_rects, key=lambda r: r[2] * r[3])
    bounding_rects = [b for b in bounding_rects if b is not largest_rect]
    
    cells = [c for c in bounding_rects]
    def cell_in_same_row(c1, c2):
        c1_center = c1[1] + c1[3] - c1[3] / 2
        c2_bottom = c2[1] + c2[3]
        c2_top = c2[1]
        return c2_top < c1_center < c2_bottom
    
    orig_cells = [c for c in cells]
    rows = []
    while cells:
        first = cells[0]
        rest = cells[1:]
        cells_in_same_row = sorted(
            [
                c for c in rest
                if cell_in_same_row(c, first)
            ],
            key=lambda c: c[0]
        )
    
        row_cells = sorted([first] + cells_in_same_row, key=lambda c: c[0])
        rows.append(row_cells)
        cells = [
            c for c in rest
            if not cell_in_same_row(c, first)
        ]
    
    # Sort rows by average height of their center.
    def avg_height_of_center(row):
        centers = [y + h - h / 2 for x, y, w, h in row]
        return sum(centers) / len(centers)
    
    rows.sort(key=avg_height_of_center)
    cell_images_rows = []
    for row in rows:
        cell_images_row = []
        for x, y, w, h in row:
            cell_images_row.append(image[y:y+h, x:x+w])
        cell_images_rows.append(cell_images_row)
    return cell_images_rows
