utils_rssrai.py

import csv
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor
from itertools import islice
import logging
import json
from pathlib import Path
import pickle
from typing import Dict, Tuple
import sys

import cv2
import numpy as np
from shapely.geometry import MultiPolygon, Polygon
import shapely.wkt
import shapely.affinity
import shapely.geometry
from libtiff import TIFF


csv.field_size_limit(sys.maxsize)


_x_max_y_min = None
_wkt_data = None

COLOR_DICT = {0: (0, 0, 0),
              1: (0, 200, 0),
              2: (150, 250, 0),
              3: (150, 200, 150),
              4: (200, 0, 200),
              5: (150, 0, 250),
              6: (150, 150, 250),
              7: (250, 200, 0),
              8: (200, 200, 0),
              9: (200, 0, 0),
              10: (250, 0, 150),
              11: (200, 150, 150),
              12: (250, 150, 150),
              13: (0, 0, 200),
              14: (0, 150, 200),
              15: (0, 200, 250)}

def get_x_max_y_min(im_id: str) -> Tuple[float, float]:
    global _x_max_y_min
    if _x_max_y_min is None:
        with open('./grid_sizes.csv') as f:
            _x_max_y_min = {im_id: (float(x), float(y))
                          for im_id, x, y in islice(csv.reader(f), 1, None)}
    return _x_max_y_min[im_id]


def get_wkt_data() -> Dict[str, Dict[int, str]]:
    global _wkt_data
    if _wkt_data is None:
        _wkt_data = {}
        with open('./train_wkt_v4.csv') as f:
            for im_id, poly_type, poly in islice(csv.reader(f), 1, None):
                _wkt_data.setdefault(im_id, {})[int(poly_type)] = poly
    return _wkt_data


def load_image(im_id: str, rgb_only=False, align=True) -> np.ndarray:
    image_path = '/data/data_gWkkHSkq/kaggle-dstl/rssrai_dataset/train/{}.tif'.format(im_id)
    tif = TIFF.open(image_path, mode="r")
    im = tif.read_image()

    return im

def rgb_to_onehot(rgb_arr, color_dict):
    num_classes = len(color_dict)
    shape = rgb_arr.shape[:2]+(num_classes,)
    #print(shape)
    arr = np.zeros( shape, dtype=np.int8 )
    for i, cls in enumerate(color_dict):
        arr[:,:,i] = np.all(rgb_arr.reshape( (-1,3) ) == color_dict[i], axis=1).reshape(shape[:2])
    return arr

def onehot_to_rgb(onehot, color_dict):
    single_layer = np.argmax(onehot, axis=-1)
    output = np.zeros( onehot.shape[:2]+(3,) )
    for k in color_dict.keys():
        output[single_layer==k] = color_dict[k]
    return np.uint8(output)


def load_mask(im_id:str):
    image_path = '/data/data_gWkkHSkq/kaggle-dstl/rssrai_dataset/train/{}_label.tif'.format(im_id)
    tif = TIFF.open(image_path, mode="r")
    im = tif.read_image()

    onehot_mask = rgb_to_onehot(im,COLOR_DICT)

    return onehot_mask.transpose([2,0,1])


def _preprocess_for_alignment(im):
    im = np.squeeze(im)
    if len(im.shape) == 2:
        im = scale_percentile(np.expand_dims(im, 2))
    else:
        assert im.shape[2] == 3, im.shape
        im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    return im.astype(np.float32)


def _aligned(im_ref, im, im_to_align=None, key=None):
    w, h = im.shape[:2]
    im_ref = cv2.resize(im_ref, (h, w), interpolation=cv2.INTER_CUBIC)
    im_ref = _preprocess_for_alignment(im_ref)
    if im_to_align is None:
        im_to_align = im
    im_to_align = _preprocess_for_alignment(im_to_align)
    assert im_ref.shape[:2] == im_to_align.shape[:2]
    try:
        cc, warp_matrix = _get_alignment(im_ref, im_to_align, key)
    except cv2.error as e:
        logger.info('Error getting alignment: {}'.format(e))
        return im, False
    else:
        im = cv2.warpAffine(im, warp_matrix, (h, w),
                            flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP)
        im[im == 0] = np.mean(im)
        return im, True


def _get_alignment(im_ref, im_to_align, key):
    cached_path = Path('align_cache').joinpath('{}.alignment'.format(key))
    if key is not None:
        #cached_path = Path('align_cache').joinpath('{}.alignment'.format(key))
        if cached_path.exists():
            with cached_path.open('rb') as f:
                return pickle.load(f)
    logger.info('Getting alignment for {}'.format(key))
    warp_mode = cv2.MOTION_TRANSLATION
    warp_matrix = np.eye(2, 3, dtype=np.float32)
    criteria = (
        cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 5000,  1e-8)
    cc, warp_matrix = cv2.findTransformECC(
        im_ref, im_to_align, warp_matrix, warp_mode, criteria)
    if key is not None:
        align_path = Path('align_cache')
        align_path.mkdir(exist_ok=True)
        with cached_path.open('wb') as f:
            pickle.dump((cc, warp_matrix), f)
    logger.info('Got alignment for {} with cc {:.3f}: {}'
                .format(key, cc, str(warp_matrix).replace('\n', '')))
    return cc, warp_matrix


def load_polygons(im_id: str, im_size: Tuple[int, int])\
        -> Dict[int, MultiPolygon]:
    return {
        int(poly_type): scale_to_mask(im_id, im_size, shapely.wkt.loads(poly))
        for poly_type, poly in get_wkt_data()[im_id].items()}


def scale_to_mask(im_id: str, im_size: Tuple[int, int], poly: MultiPolygon)\
        -> MultiPolygon:
    x_scaler, y_scaler = get_scalers(im_id, im_size)
    return shapely.affinity.scale(
        poly, xfact=x_scaler, yfact=y_scaler, origin=(0, 0, 0))


def dump_polygons(im_id: str, im_size: Tuple[int, int], polygons: MultiPolygon)\
        -> str:
    """ Save polygons for submission.
    """
    x_scaler, y_scaler = get_scalers(im_id, im_size)
    x_scaler = 1 / x_scaler
    y_scaler = 1 / y_scaler
    polygons = shapely.affinity.scale(
        polygons, xfact=x_scaler, yfact=y_scaler, origin=(0, 0, 0))
    return shapely.wkt.dumps(polygons)


def get_scalers(im_id: str, im_size: Tuple[int, int]) -> Tuple[float, float]:
    h, w = im_size  # they are flipped so that mask_for_polygons works correctly
    w_ = w * (w / (w + 1))
    h_ = h * (h / (h + 1))
    x_max, y_min = get_x_max_y_min(im_id)
    x_scaler = w_ / x_max
    y_scaler = h_ / y_min
    return x_scaler, y_scaler


def mask_for_polygons(
        im_size: Tuple[int, int], polygons: MultiPolygon) -> np.ndarray:
    """ Return numpy mask for given polygons.
    polygons should already be converted to image coordinates.
    """
    img_mask = np.zeros(im_size, np.uint8)
    if not polygons:
        return img_mask
    int_coords = lambda x: np.array(x).round().astype(np.int32)
    exteriors = [int_coords(poly.exterior.coords) for poly in polygons]
    interiors = [int_coords(pi.coords) for poly in polygons
                 for pi in poly.interiors]
    cv2.fillPoly(img_mask, exteriors, 1)
    cv2.fillPoly(img_mask, interiors, 0)
    return img_mask


def rotated(patch: np.ndarray, angle: float) -> np.ndarray:
    patch = patch.transpose([1, 2, 0]).astype(np.float32)
    size = patch.shape[:2]
    center = tuple(np.array(size) / 2)
    rot_mat = cv2.getRotationMatrix2D(center, angle, 1.0)
    patch = cv2.warpAffine(patch, rot_mat, size, flags=cv2.INTER_LINEAR)
    if len(patch.shape) == 2:
        return np.expand_dims(patch, 0)
    else:
        return patch.transpose([2, 0, 1])


def scale_percentile(matrix: np.ndarray) -> np.ndarray:
    """ Fixes the pixel value range to 2%-98% original distribution of values.
    """
    w, h, d = matrix.shape
    matrix = matrix.reshape([w * h, d])
    # Get 2nd and 98th percentile
    mins = np.percentile(matrix, 1, axis=0)
    maxs = np.percentile(matrix, 99, axis=0) - mins
    matrix = (matrix - mins[None, :]) / maxs[None, :]
    return matrix.reshape([w, h, d]).clip(0, 1)


def chunks(lst, n):
    for i in range(0, len(lst), n):
        yield lst[i: i + n]


def mask_to_polygons(mask: np.ndarray, epsilon=5., min_area=10.,
                     fix=False) -> MultiPolygon:
    if fix:
        epsilon *= 4
    image, contours, hierarchy = cv2.findContours(
        ((mask == 1) * 255).astype(np.uint8),
        cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS)
    approx_contours = [cv2.approxPolyDP(cnt, epsilon, True) for cnt in contours]
    if not contours:
        return MultiPolygon()

    cnt_children = defaultdict(list)
    child_contours = set()
    assert hierarchy.shape[0] == 1
    # http://docs.opencv.org/3.1.0/d9/d8b/tutorial_py_contours_hierarchy.html
    for idx, (_, _, _, parent_idx) in enumerate(hierarchy[0]):
        if parent_idx != -1:
            child_contours.add(idx)
            cnt_children[parent_idx].append(approx_contours[idx])

    all_polygons = []
    for idx, cnt in enumerate(approx_contours):
        if idx not in child_contours and cv2.contourArea(cnt) >= min_area:
            assert cnt.shape[1] == 1
            poly = Polygon(
                shell=cnt[:, 0, :],
                holes=[c[:, 0, :] for c in cnt_children.get(idx, [])
                       if cv2.contourArea(c) >= min_area])
            all_polygons.append(poly)

    all_polygons = to_multipolygon(MultiPolygon(all_polygons).buffer(0))
    # return all_polygons - this was used to generate the final merges
    if fix:
        all_polygons = all_polygons.buffer(-1e-7)
        all_polygons = all_polygons.buffer(-1e-7)
    # FIXME - a great idea, but should be done after conversion to final coordinates
    all_polygons = shapely.wkt.loads(
        shapely.wkt.dumps(all_polygons, rounding_precision=8))
    while not all_polygons.is_valid:
        all_polygons = to_multipolygon(all_polygons.buffer(0))
        all_polygons = shapely.wkt.loads(
            shapely.wkt.dumps(all_polygons, rounding_precision=8))
    return all_polygons


def to_multipolygon(poly):
    # Sometimes buffer() converts a simple Multipolygon to just a Polygon,
    # need to keep it a Multi throughout
    return MultiPolygon([poly]) if poly.type == 'Polygon' else poly


def mask_tp_fp_fn_tn(pred_mask: np.ndarray, true_mask: np.ndarray,
                  threshold: float) -> Tuple[int, int, int,int]:
    pred_mask = pred_mask >= threshold
    true_mask = true_mask == 1
    return (( pred_mask &  true_mask).sum(),
            ( pred_mask & ~true_mask).sum(),
            (~pred_mask &  true_mask).sum(),
            (~pred_mask & ~true_mask).sum())


def get_logger(name):
    logger = logging.getLogger(name)
    logger.setLevel(logging.INFO)
    ch = logging.StreamHandler()
    ch.setFormatter(logging.Formatter(
        '%(asctime)s [%(levelname)s] %(module)s: %(message)s'))
    logger.addHandler(ch)
    return logger


logger = get_logger(__name__)


def imap_fixed_output_buffer(fn, it, threads: int):
    with ThreadPoolExecutor(max_workers=threads) as executor:
        futures = []
        max_futures = threads + 1
        for x in it:
            while len(futures) >= max_futures:
                future, futures = futures[0], futures[1:]
                yield future.result()
            futures.append(executor.submit(fn, x))
        for future in futures:
            yield future.result()


def dist_mask(mask, max_dist=10):
    mask = mask.astype(np.uint8)

    def get_dist(m):
        d = cv2.distanceTransform(m, cv2.DIST_L2, maskSize=3)
        d[d > max_dist] = max_dist
        return d / max_dist

    dist = get_dist(mask) - get_dist(1 - mask)
    # TODO - check in the notebook
    # TODO - what is the proper power?
   #pow = 0.5
   #dist[dist > 0] = dist[dist > 0] ** pow
   #dist[dist < 0] = -((-dist[dist < 0]) ** pow)
    return (1 + dist) / 2  # from 0 to 1