From 7419b66313f5e0302b012259f43ee045500c6088 Mon Sep 17 00:00:00 2001 From: Vlad Khramtsov Date: Tue, 21 Jul 2020 17:36:55 +0300 Subject: [PATCH 01/13] 12601: add main files for integration testing --- clearcut_detection_backend/test/Dockerfile | 21 ++ clearcut_detection_backend/test/__init__.py | 0 clearcut_detection_backend/test/download.py | 288 +++++++++++++++++ clearcut_detection_backend/test/predict.py | 306 ++++++++++++++++++ clearcut_detection_backend/test/preprocess.py | 117 +++++++ .../test/requirements.txt | 88 +++++ clearcut_detection_backend/test/run-docker.sh | 1 + clearcut_detection_backend/test/settings.py | 10 + .../test/test_config.ini | 5 + .../test/test_data_prepare.py | 28 ++ clearcut_detection_backend/test/test_model.py | 124 +++++++ clearcut_detection_backend/test/utils.py | 23 ++ 12 files changed, 1011 insertions(+) create mode 100644 clearcut_detection_backend/test/Dockerfile create mode 100644 clearcut_detection_backend/test/__init__.py create mode 100644 clearcut_detection_backend/test/download.py create mode 100644 clearcut_detection_backend/test/predict.py create mode 100644 clearcut_detection_backend/test/preprocess.py create mode 100644 clearcut_detection_backend/test/requirements.txt create mode 100644 clearcut_detection_backend/test/run-docker.sh create mode 100644 clearcut_detection_backend/test/settings.py create mode 100644 clearcut_detection_backend/test/test_config.ini create mode 100644 clearcut_detection_backend/test/test_data_prepare.py create mode 100644 clearcut_detection_backend/test/test_model.py create mode 100644 clearcut_detection_backend/test/utils.py diff --git a/clearcut_detection_backend/test/Dockerfile b/clearcut_detection_backend/test/Dockerfile new file mode 100644 index 0000000..232a357 --- /dev/null +++ b/clearcut_detection_backend/test/Dockerfile @@ -0,0 +1,21 @@ +FROM python:3.6 + +RUN mkdir /test +WORKDIR /test + +COPY requirements.txt /test +RUN pip install -r requirements.txt + +RUN apt-get update -y && apt-get install -y \ + software-properties-common + +RUN add-apt-repository -r ppa:ubuntugis/ppa && apt-get update +RUN apt-get update +RUN apt-get install gdal-bin -y +RUN apt-get install libgdal-dev -y +RUN export CPLUS_INCLUDE_PATH=/usr/include/gdal +RUN export C_INCLUDE_PATH=/usr/include/gdal +RUN pip install GDAL==$(gdal-config --version | awk -F'[.]' '{print $1"."$2}') + + +ADD . /test \ No newline at end of file diff --git a/clearcut_detection_backend/test/__init__.py b/clearcut_detection_backend/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/clearcut_detection_backend/test/download.py b/clearcut_detection_backend/test/download.py new file mode 100644 index 0000000..b138c60 --- /dev/null +++ b/clearcut_detection_backend/test/download.py @@ -0,0 +1,288 @@ +import os +import re +import datetime +import logging +from enum import Enum + +from concurrent.futures import ThreadPoolExecutor, as_completed +from google.cloud import storage +from google.cloud.exceptions import NotFound +from xml.dom import minidom +from xml.etree.ElementTree import ParseError + +from utils import area_tile_set_test, bands_to_download, date_current_test, date_previous_test +import settings + +logger = logging.getLogger('sentinel') + + +class TillNameError(Exception): + def __init__(self, till_name): + self.message = f'{till_name} is not valid till_name' + Exception.__init__(self, self.message) + + def __str__(self): + return self.message + + +class Bands(Enum): + TCI = 'TCI' + B04 = 'B04' + B08 = 'B08' + B8A = 'B8A' + B11 = 'B11' + B12 = 'B12' + + +class SentinelDownload: + + def __init__(self): + settings.DOWNLOADED_IMAGES_DIR.mkdir(parents=True, exist_ok=True) + os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = './key.json' + self.sequential_dates_count = settings.MAXIMUM_DATES_STORE_FOR_TILE + self.area_tile_set = area_tile_set_test + self.bands_to_download = bands_to_download + self.base_uri = 'gs://gcp-public-data-sentinel-2' # TODO to settings + self.bucket_name = 'gcp-public-data-sentinel-2' # TODO to settings + self.prefix = 'L2/tiles' + self.tiles_and_uris_dict = {tile_name: self.get_tile_uri(tile_name) for tile_name in self.area_tile_set} + self.storage_client = storage.Client() + self.storage_bucket = self.get_storage_bucket() + + logger.info(f'area tile set for download: {self.area_tile_set}') + logger.info(f'bands to download:{self.bands_to_download}') + print(bands_to_download) + + def get_storage_bucket(self): + try: + return self.storage_client.get_bucket(self.bucket_name) + except NotFound: + logger.error('Error\n\n', exc_info=True) + + def process_download(self): + """ + Requests metadata file to define if update is needed for tile + Launches multithread download + """ + tiles_to_update = self.request_google_cloud_storage_for_latest_acquisition(self.tiles_and_uris_dict) + print(tiles_to_update) + self.launch_download_pool(tiles_to_update) + + @staticmethod + def get_tile_uri(tile_name): + """ + Reads config and extract name of the tiles which are needed for application + Converts Tile Name to part of the URI in format [UTM_ZONE]/[LATITUDE_BAND]/[GRID_SQUARE] + Creates URI for full tile path + :tile_name: str + :return: str: tile_uri + """ + try: + match = re.search(r'\b\d+', tile_name) + utm_zone = int(match[0]) if match else None + if not utm_zone or 1 > utm_zone or utm_zone > 60: + raise TillNameError(tile_name) + string = tile_name.replace(match[0], '') + + match = re.search(r'\b[C-Z]', string) + latitude_band = match[0] if match else None + if not latitude_band: + raise TillNameError(tile_name) + string = string.replace(match[0], '', 1) + + match = re.search(r'\b[A-Z][A-Z]\b', string) + grid_square = match[0] if match else None + if not grid_square: + logger.info(string) + raise TillNameError(tile_name) + return f'{utm_zone}/{latitude_band}/{grid_square}' + except TillNameError: + logger.error('Error\n\n', exc_info=True) + + def launch_download_pool(self, tiles_to_update): + """ + For each tile starts own thread processing + :param tiles_to_update: + :return: + """ + with ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor: + future_list = [] + for tile_name, tile_path in tiles_to_update.items(): + future = executor.submit(self.download_images_from_tiles, tile_name, tile_path) + future_list.append(future) + + for future in as_completed(future_list): + if not future.result(): + exit(1) + else: + logger.info(f'images for {future.result()[0]} were downloaded') + + def download_images_from_tiles(self, tile_name, tile_path): + """ + Iterates over folders to fetch tile images folder + Downloads band specified in config + :param tile_name: + :param tile_path: + :return: + """ + tile_prefix = f'{tile_path}/IMG_DATA/R20m/' + blobs = self.storage_bucket.list_blobs(prefix=tile_prefix) + + for blob in blobs: + band, download_needed = self.file_need_to_be_downloaded(blob.name) + if download_needed: + filename = settings.DOWNLOADED_IMAGES_DIR / f'{tile_name}_{band}.jp2' + self.download_file_from_storage(blob, filename) + + tile_prefix = f'{tile_path}/IMG_DATA/R10m/' + blobs = self.storage_bucket.list_blobs(prefix=tile_prefix) + + for blob in blobs: + band, download_needed = self.file_need_to_be_downloaded(blob.name) + if download_needed: + filename = settings.DOWNLOADED_IMAGES_DIR / f'{tile_name}_{band}.jp2' + self.download_file_from_storage(blob, filename) + + + tile_prefix = f'{tile_path}/QI_DATA/' + blobs = self.storage_bucket.list_blobs(prefix=tile_prefix) + + endswith = 'MSK_CLDPRB_20m.jp2' + for blob in blobs: + if blob.name.endswith(endswith): + filename = settings.DOWNLOADED_IMAGES_DIR / f"{tile_name}_{blob.name.split('/')[-1]}" + self.download_file_from_storage(blob, filename) + + return tile_name, tile_path + + def file_need_to_be_downloaded(self, name): + """ + Checks if blob is eligible for download through formats specified in config + :param name: + :return: + """ + for band in self.bands_to_download: + if name.endswith(f'_{band}_10m.jp2') or name.endswith(f'_{band}_20m.jp2'): + return band, True + return None, False + + def request_google_cloud_storage_for_latest_acquisition(self, tiles_path): + """ + Iterates over tile sets and picks latest tile dataset. + Defines if tile is needed to be updated. + :param tiles_path: + :return: + """ + tiles_to_be_downloaded = {} + metadata_file = 'MTD_TL.xml' + + for tile_name, tile_path in tiles_path.items(): + logger.info(f'TILE NAME: {tile_name}') + delimiter = '/' + tile_uri = f'L2/tiles/{tile_path}/' + + blobs = self.storage_client.list_blobs(self.storage_bucket, prefix=tile_uri, delimiter=delimiter) + blobs._next_page() + + prefixes = list(blobs.prefixes) + if not prefixes: + raise NotFound(f'no such tile_uri: {tile_uri}') + prefixes.sort(key=self.get_folders_date, reverse=True) + granule_id_list = [prefix for prefix in prefixes if date_current_test in prefix or date_previous_test in prefix] + + granule_num = 0 + for granule_id in granule_id_list: + if granule_num < self.sequential_dates_count: + prefix = f'{granule_id}GRANULE/' + blobs = self.storage_client.list_blobs(self.storage_bucket, prefix=prefix, delimiter=delimiter) + blobs._next_page() + + nested_granule_id_list = list(blobs.prefixes) + if not nested_granule_id_list: + raise NotFound(f'no such prefix: {prefix}') + + nested_granule_id = nested_granule_id_list[0] + updated_tile_uri = nested_granule_id[:-1] if nested_granule_id.endswith('/') else nested_granule_id + + filename = settings.DOWNLOADED_IMAGES_DIR / f'{tile_name}_{metadata_file}' + print(granule_num, filename) + blob = self.storage_bucket.get_blob(f'{updated_tile_uri}/{metadata_file}') + if not blob: + raise NotFound(f'not found {updated_tile_uri}/{metadata_file}') + + update_needed = self.define_if_tile_update_needed(blob, f'{tile_name}_{granule_num}', filename) + if update_needed: + logger.info(f'Tile {tile_name}_{granule_num} will be downloaded from {updated_tile_uri}') + tiles_to_be_downloaded[f'{tile_name}_{granule_num}'] = f'{updated_tile_uri}' + os.remove(filename) + granule_num += 1 + else: + break + print(tiles_to_be_downloaded) + return tiles_to_be_downloaded + + def define_if_tile_update_needed(self, blob, tile_name, filename) -> bool: + """ + Checks hash of the metadata file for latest image and information from DB + Downloads metadata file if hash is not equal + Checks cloud coverage value from metadata file if it lower then one from settings - allows to download images + :param blob: + :param tile_name: + :param filename: + :return: + """ + filename = str(filename) + self.download_file_from_storage(blob, filename) + nodata_pixel_value = self.define_xml_node_value(filename, 'NODATA_PIXEL_PERCENTAGE') + # print('====== NO DATA PIXEL VALUE ======') + # print(nodata_pixel_value) + if nodata_pixel_value >= settings.MAXIMUM_EMPTY_PIXEL_PERCENTAGE: + return False + cloud_coverage_value = self.define_xml_node_value(filename, 'CLOUDY_PIXEL_PERCENTAGE') + # print('====== CLOUD COVERAGE VALUE ======') + # print(cloud_coverage_value) + if cloud_coverage_value <= settings.MAXIMUM_CLOUD_PERCENTAGE_ALLOWED: + return True + else: + return False + + def get_folders_date(self, path): + match = re.search(r'_\d{8}T\d{6}', path) + if match: + return match[0] + else: + raise ValueError(f'No date in {self.bucket_name} for {path}') + + @staticmethod + def download_file_from_storage(blob, filename): + """ + Downloads blob to local storage + :param blob: + :param filename: + :return: + """ + with open(filename, 'wb') as new_file: + blob.download_to_file(new_file) + + @staticmethod + def define_xml_node_value(xml_file_name, node): + """ + Parsing XML file for passed node name + :param xml_file_name: + :param node: + :return: + """ + xml_dom = minidom.parse(xml_file_name) + try: + xml_node = xml_dom.getElementsByTagName(node) + xml_node_value = xml_node[0].firstChild.data + return float(xml_node_value) + except FileNotFoundError(f'No such file: {xml_file_name}'): + logger.error('Error\n\n', exc_info=True) + except ParseError(f'no such node ({node}) in the {xml_file_name}'): + logger.error('Error\n\n', exc_info=True) + return None + + +sentinel_downloader = SentinelDownload() +sentinel_downloader.process_download() diff --git a/clearcut_detection_backend/test/predict.py b/clearcut_detection_backend/test/predict.py new file mode 100644 index 0000000..9acf78d --- /dev/null +++ b/clearcut_detection_backend/test/predict.py @@ -0,0 +1,306 @@ +import os +import re +import cv2 +import torch +import logging +import imageio +import rasterio +import argparse +import geopandas +import numpy as np +import segmentation_models_pytorch as smp + +from catalyst.dl.utils import UtilsFactory +from geopandas import GeoSeries +from scipy import spatial +from shapely.geometry import Polygon +from shapely.ops import unary_union +from torchvision import transforms +from torch import nn +from tqdm import tqdm +from rasterio.windows import Window +from rasterio.plot import reshape_as_image +from rasterio import features +from skimage.transform import match_histograms + +from test_data_prepare import get_gt_polygons +from utils import path_exists_or_create + +CLOUDS_PROBABILITY_THRESHOLD = 15 +NEAREST_POLYGONS_NUMBER = 10 +DATES_FOR_TILE = 2 + +import warnings +warnings.filterwarnings('ignore') + +logging.basicConfig(format='%(asctime)s %(message)s') + +def load_model(network, model_weights_path, channels, neighbours): + device = 'gpu' if torch.cuda.is_available() else 'cpu' + model = get_model(network) + model.encoder.conv1 = nn.Conv2d( + count_channels(channels)*neighbours, 64, kernel_size=(7, 7), + stride=(2, 2), padding=(3, 3), bias=False + ) + model, device = UtilsFactory.prepare_model(model) + model.load_state_dict(torch.load(model_weights_path, map_location=torch.device(device))) + return model, device + + +def predict(image_tensor, model, channels, neighbours, size, device): + image_shape = 1, count_channels(channels)*neighbours, size, size + prediction, _ = model.predict(image_tensor.view(image_shape).to(device, dtype=torch.float)) + result = prediction.view(size, size).detach().cpu().numpy() + return result + + +def diff(img1, img2): + img2 = match_histograms(img2, img1, multichannel=True) + difference = ( (img1 - img2) / (img1 + img2) ) + difference = (difference + 1) * 127 + return np.concatenate((difference.astype(np.uint8), img1.astype(np.uint8), img2.astype(np.uint8)), axis=-1) + + +def mask_postprocess(mask): + kernel = np.ones((3, 3), np.uint8) + erosion = cv2.erode(mask, kernel, iterations = 1) + kernel = np.ones((5, 5), np.uint8) + closing = cv2.morphologyEx(erosion, cv2.MORPH_CLOSE, kernel) + return closing + + +def predict_raster(img_path, channels, network, model_weights_path, input_size=56, neighbours=3): + tile = os.path.basename(img_path) + tiff_files = [os.path.join(img_path, f'{tile}_{i}', f'{tile}_{i}_merged.tiff') for i in range(DATES_FOR_TILE)] + model, device = load_model(network, model_weights_path, channels, neighbours) + + with rasterio.open(tiff_files[0]) as source_current, \ + rasterio.open(tiff_files[1]) as source_previous: + + meta = source_current.meta + meta['count'] = 1 + clearcut_mask = np.zeros((source_current.height, source_current.width)) + image = np.zeros((source_current.height, source_current.width)) + mask = np.ones((source_current.height, source_current.width)) + + gt_polygons_filename = get_gt_polygons() + gt_polygons = geopandas.read_file(gt_polygons_filename) + gt_polygons = gt_polygons.to_crs(source_current.crs) + mask = features.rasterize(shapes=gt_polygons['geometry'], + out_shape=(source_current.height, source_current.width), + transform=source_current.transform, + default_value=1) + + for i in tqdm(range(source_current.width // input_size)): + for j in range(source_current.height // input_size): + bottom_row = j * input_size + upper_row = (j + 1) * input_size + left_column = i * input_size + right_column = (i + 1) * input_size + + if mask[left_column:right_column, bottom_row:upper_row].sum() > 0: + corners=[ + source_current.xy(bottom_row, left_column), + source_current.xy(bottom_row, right_column), + source_current.xy(upper_row, right_column), + source_current.xy(upper_row, left_column), + source_current.xy(bottom_row, left_column) + ] + + window = Window(bottom_row, left_column, input_size, input_size) + image_current = reshape_as_image(source_current.read(window=window)) + image_previous = reshape_as_image(source_previous.read(window=window)) + + difference_image = diff(image_current, image_previous) + image_tensor = transforms.ToTensor()(difference_image.astype(np.uint8)).to(device, dtype=torch.float) + + predicted = predict(image_tensor, model, channels, neighbours, input_size, device) + predicted = mask_postprocess(predicted) + clearcut_mask[left_column:right_column, bottom_row:upper_row] += predicted + + meta['dtype'] = 'float32' + return clearcut_mask.astype(np.float32), meta + + +def get_model(name, classification_head=True, model_weights_path=None): + if name == 'unet_ch': + aux_params = dict( + pooling='max', # one of 'avg', 'max' + dropout=0.1, # dropout ratio, default is None + activation='sigmoid', # activation function, default is None + classes=1, # define number of output labels + ) + return smp.Unet('resnet18', aux_params=aux_params, + encoder_weights=None, encoder_depth=2, + decoder_channels=(256, 128)) + else: + raise ValueError("Unknown network") + + +def count_channels(channels): + count = 0 + for ch in channels: + if ch == 'rgb': + count += 3 + elif ch in ['ndvi', 'ndmi', 'b8', 'b8a', 'b11', 'b12']: + count += 1 + else: + raise Exception('{} channel is unknown!'.format(ch)) + + return count + + +def filter_by_channels(image_tensor, channels, neighbours=3): + # order: ['TCI','B08','B8A','B11','B12', 'NDVI', 'NDMI'] + result = [] + for i in range(neighbours): + for ch in channels: + if ch == 'rgb': + result.append(image_tensor[:, :, (0+i*9):(3+i*9)]) + elif ch == 'b8': + result.append(image_tensor[:, :, (3+i*9):(4+i*9)]) + elif ch == 'b8a': + result.append(image_tensor[:, :, (4+i*9):(5+i*9)]) + elif ch == 'b11': + result.append(image_tensor[:, :, (5+i*9):(6+i*9)]) + elif ch == 'b12': + result.append(image_tensor[:, :, (6+i*9):(7+i*9)]) + elif ch == 'ndvi': + result.append(image_tensor[:, :, (7+i*9):(8+i*9)]) + elif ch == 'ndmi': + result.append(image_tensor[:, :, (8+i*9):(9+i*9)]) + else: + raise Exception(f'{ch} channel is unknown!') + return np.concatenate(result, axis=2) + + +def scale(tensor, max_value): + max_ = tensor.max() + if max_ > 0: + return tensor / max_ * max_value + return tensor + + +def save_raster(raster_array, meta, save_path, filename): + if not os.path.exists(save_path): + os.makedirs(save_path, exist_ok=True) + logging.info("Data directory created.") + + save_path = os.path.join(save_path, f'predicted_{filename}') + + cv2.imwrite(f'{save_path}.png', raster_array) + + with rasterio.open(f'{save_path}.tif', 'w', **meta) as dst: + for i in range(1, meta['count'] + 1): + dst.write(raster_array, i) + + +def polygonize(raster_array, meta, transform=True, mode=cv2.RETR_TREE): + raster_array = (raster_array * 255).astype(np.uint8) + + contours, _ = cv2.findContours(raster_array, mode, cv2.CHAIN_APPROX_SIMPLE) + + polygons = [] + for i in tqdm(range(len(contours))): + c = contours[i] + n_s = (c.shape[0], c.shape[2]) + if n_s[0] > 2: + if transform: + polys = [tuple(i) * meta['transform'] for i in c.reshape(n_s)] + else: + polys = [tuple(i) for i in c.reshape(n_s)] + polygons.append(Polygon(polys)) + + return polygons + + +def save_polygons(polygons, save_path, filename): + if len(polygons) == 0: + logging.info('no_polygons detected') + return + + if not os.path.exists(save_path): + os.makedirs(save_path, exist_ok=True) + logging.info("Data directory created.") + + logging.info(f'{filename}.geojson saved.') + polygons.to_file(os.path.join(save_path, f'{filename}.geojson'), driver='GeoJSON') + + +def intersection_poly(test_poly, mask_poly): + intersecion_score = False + if test_poly.is_valid and mask_poly.is_valid: + intersection_result = test_poly.intersection(mask_poly) + if not intersection_result.is_valid: + intersection_result = intersection_result.buffer(0) + if not intersection_result.is_empty: + intersecion_score = True + return intersecion_score + + +def polygon_to_geodataframe(polygons, src_crs): + polygons = {'geometry': polygons} + return geopandas.GeoDataFrame(polygons, crs=src_crs) + + +def parse_args(): + parser = argparse.ArgumentParser(description='Script for predicting masks.') + parser.add_argument( + '--image_path', '-ip', dest='image_path', + type=str, required=True, help='Path to source image' + ) + parser.add_argument( + '--model_weights_path', '-mwp', dest='model_weights_path', + default='data/unet_v4.pth', help='Path to directory where pieces will be stored' + ) + parser.add_argument( + '--network', '-net', dest='network', default='unet_ch', + help='Model architecture' + ) + parser.add_argument( + '--save_path', '-sp', dest='save_path', default='data/predicted', + help='Path to directory where results will be stored' + ) + parser.add_argument( + '--channels', '-ch', dest='channels', + default=['rgb', 'b8', 'b8a', 'b11', 'b12', 'ndvi', 'ndmi'], + help='Channel list', nargs='+' + ) + parser.add_argument( + '--threshold', '-t', dest='threshold', + default=0.4, help='Threshold to get binary values in mask', type=float + ) + parser.add_argument( + '--polygonize_only', '-po', dest='polygonize_only', + default=False, help='Flag to skip prediction', type=bool + ) + + return parser.parse_args() + + +def main(): + args = parse_args() + path_exists_or_create(args.save_path) + filename = re.split(r'[./]', args.image_path)[-1] + predicted_filename = f'predicted_{filename}' + + if not args.polygonize_only: + raster_array, meta = predict_raster( + args.image_path, + args.channels, args.network, args.model_weights_path + ) + save_raster(raster_array, meta, args.save_path, filename) + else: + with rasterio.open(os.path.join(args.save_path, f'{predicted_filename}.tif')) as src: + raster_array = src.read() + raster_array = np.moveaxis(raster_array, 0, -1) + meta = src.meta + src.close() + + clearcuts = polygonize(raster_array > args.threshold, meta) + clearcuts = polygon_to_geodataframe(clearcuts, meta['crs']) + save_polygons(clearcuts, args.save_path, predicted_filename) + + +if __name__ == '__main__': + main() diff --git a/clearcut_detection_backend/test/preprocess.py b/clearcut_detection_backend/test/preprocess.py new file mode 100644 index 0000000..8a5e623 --- /dev/null +++ b/clearcut_detection_backend/test/preprocess.py @@ -0,0 +1,117 @@ +import os +from os.path import join + +import imageio +import rasterio +import numpy as np + +from configparser import ConfigParser +from tqdm import tqdm + +from utils import path_exists_or_create, bands_to_download +from settings import DOWNLOADED_IMAGES_DIR, MODEL_TIFFS_DIR + +ROOT = '.SAFE' + +def get_ndvi(b4_file, b8_file, ndvi_file): + os.system( + f'gdal_calc.py -A {b4_file} -B {b8_file} \ + --outfile={ndvi_file} \ + --calc="(B-A)/(A+B+0.001)" --type=Float32 --quiet' + ) + +def to_tiff(input_jp2_file, output_tiff_file, output_type='Float32'): + os.system( + f'gdal_translate -ot {output_type} \ + {input_jp2_file} {output_tiff_file}' + ) + +def scale_img(img_file, output_file=None, min_value=0, max_value=255, output_type='Byte'): + with rasterio.open(img_file) as src: + img = src.read(1) + img = np.nan_to_num(img) + mean_ = img.mean() + std_ = img.std() + min_ = max(img.min(), mean_ - 2 * std_) + max_ = min(img.max(), mean_ + 2 * std_) + + output_file = os.path.splitext(img_file)[0] if output_file is None else output_file + + os.system( + f'gdal_translate -ot {output_type} \ + -scale {min_} {max_} {min_value} {max_value} \ + {img_file} {output_file}' + ) + +def prepare_tiff(filename): + save_path = path_exists_or_create(join(MODEL_TIFFS_DIR, filename.split('_')[0], f"{filename}")) + output_tiffs = {} + bands_to_convert = [band for band in bands_to_download] + + if 'TCI' in bands_to_download: + output_tiffs['tiff_rgb_name'] = join(save_path, f'{filename}_TCI.tif') + to_tiff(join(DOWNLOADED_IMAGES_DIR, f'{filename}_TCI.jp2'), + join(save_path, f'{filename}_TCI.tif'), 'Byte') + bands_to_convert.remove('TCI') + + for band in bands_to_convert: + output_tiffs[f'tiff_{band}_name'] = join(save_path, f'{filename}_{band}.tif') + to_tiff(join(DOWNLOADED_IMAGES_DIR, f'{filename}_{band}.jp2'), + output_tiffs[f'tiff_{band}_name']) + + + if 'B04' in bands_to_download and 'B08' in bands_to_download: + output_tiffs['tiff_ndvi_name'] = join(save_path, f'{filename}_ndvi.tif') + print('\nndvi band is processing...') + get_ndvi(output_tiffs.get('tiff_B04_name'), + output_tiffs.get('tiff_B08_name'), + output_tiffs.get('tiff_ndvi_name')) + bands_to_convert.append('ndvi') + + if 'B11' in bands_to_download and 'B8A' in bands_to_download: + output_tiffs['tiff_ndmi_name'] = join(save_path, f'{filename}_ndmi.tif') + print('\nndmi band is processing...') + get_ndvi(output_tiffs.get('tiff_B11_name'), + output_tiffs.get('tiff_B8A_name'), + output_tiffs.get('tiff_ndmi_name')) + bands_to_convert.append('ndmi') + + for band in bands_to_convert: + output_tiffs[f'scaled_{band}_name'] = f"{output_tiffs[f'tiff_{band}_name']}_scaled.tif" + scale_img(output_tiffs[f'tiff_{band}_name'], output_tiffs[f'scaled_{band}_name']) + + tiff_output_name = join(save_path, f'{filename}_merged.tiff') + + if 'B04' in bands_to_download: + bands_to_convert.remove('B04') + # if 'TCI' in bands_to_download: + # bands_to_convert = [output_tiffs['tiff_rgb_name']] + bands_to_convert + + files_to_merge = [output_tiffs.get(f'scaled_{band}_name') for band in bands_to_convert] + files_to_merge = [output_tiffs['tiff_rgb_name']] + files_to_merge + merged_files = " ".join(files_to_merge) + + print(merged_files) + os.system( + f"gdal_merge.py -separate -o {tiff_output_name} {merged_files}" + ) + + to_tiff(join(DOWNLOADED_IMAGES_DIR, f'{filename}_MSK_CLDPRB_20m.jp2'), + f'{join(save_path, "clouds.tiff")}') + + for item in os.listdir(save_path): + if item.endswith('.tif'): + os.remove(join(save_path, item)) + + + +def preprocess(): + filenames = ["_".join(file.split('_')[:2]) for file in os.listdir(DOWNLOADED_IMAGES_DIR)] + filenames = set(filenames) + for filename in tqdm(filenames): + print(filename) + prepare_tiff(filename) + print('==============') + + +preprocess() \ No newline at end of file diff --git a/clearcut_detection_backend/test/requirements.txt b/clearcut_detection_backend/test/requirements.txt new file mode 100644 index 0000000..8e4f2d2 --- /dev/null +++ b/clearcut_detection_backend/test/requirements.txt @@ -0,0 +1,88 @@ +affine==2.3.0 +argcomplete==1.11.1 +attrs==19.3.0 +boto==2.49.0 +boto3==1.14.11 +botocore==1.17.11 +cachetools==3.1.1 +certifi==2020.6.20 +cffi==1.14.0 +chardet==3.0.4 +click==7.1.2 +click-plugins==1.1.1 +cligj==0.5.0 +crcmod==1.7 +cryptography==2.9.2 +cycler==0.10.0 +Cython==0.29.20 +decorator==4.4.2 +docutils==0.15.2 +fasteners==0.15 +Fiona==1.8.13.post1 +gcs-oauth2-boto-plugin==2.6 +geopandas==0.5.1 +google-api-core==1.14.3 +google-api-python-client==1.7.4 +google-apitools==0.5.31 +google-auth==1.7.1 +google-auth-httplib2==0.0.3 +google-cloud-core==1.0.3 +google-cloud-storage==1.23.0 +google-reauth==0.1.0 +google-resumable-media==0.5.0 +googleapis-common-protos==1.6.0 +gsutil==4.47 +httplib2==0.18.1 +idna==2.9 +imageio==2.5.0 +importlib-metadata==1.6.1 +jmespath==0.10.0 +kiwisolver==1.2.0 +matplotlib==3.2.2 +mercantile==1.1.5 +mock==2.0.0 +monotonic==1.5 +munch==2.5.0 +networkx==2.4 +numpy==1.16.4 +oauth2client==4.1.3 +opencv-python==4.2.0.34 +pandas==0.24.2 +pbr==5.4.5 +Pillow==6.1.0 +protobuf==3.12.2 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +pycparser==2.20 +pyOpenSSL==19.1.0 +pyparsing==2.4.7 +pyproj==2.2.1 +python-dateutil==2.8.1 +pytz==2020.1 +pyu2f==0.1.4 +PyWavelets==1.1.1 +PyYAML==5.1.1 +rasterio==1.1.2 +requests==2.24.0 +retry-decorator==1.1.1 +rio-mbtiles==1.4.2 +rsa==4.0 +s3transfer==0.3.3 +scikit-image==0.17.2 +scipy==1.5.0 +sentinelhub==2.5.3 +Shapely==1.6.4.post2 +six==1.12.0 +snuggs==1.4.7 +SocksiPy-branch==1.1 +tifffile==2020.6.3 +tqdm==4.19.9 +uritemplate==3.0.1 +urllib3==1.25.9 +utm==0.5.0 +zipp==3.1.0 + +catalyst==19.5 +segmentation-models-pytorch==0.1.0 +torch==1.4.0 +torchvision==0.5.0 diff --git a/clearcut_detection_backend/test/run-docker.sh b/clearcut_detection_backend/test/run-docker.sh new file mode 100644 index 0000000..71a03ae --- /dev/null +++ b/clearcut_detection_backend/test/run-docker.sh @@ -0,0 +1 @@ +sudo docker run -v /home/quantum/DS/clearcut/service/testing/clearcut_detection/clearcut_detection_backend/test/:/test/ -ti testing /bin/bash \ No newline at end of file diff --git a/clearcut_detection_backend/test/settings.py b/clearcut_detection_backend/test/settings.py new file mode 100644 index 0000000..26ef822 --- /dev/null +++ b/clearcut_detection_backend/test/settings.py @@ -0,0 +1,10 @@ +from pathlib import Path + +MAXIMUM_CLOUD_PERCENTAGE_ALLOWED = 20.0 +MAXIMUM_EMPTY_PIXEL_PERCENTAGE = 5.0 +MAXIMUM_DATES_STORE_FOR_TILE = 2 +MAX_WORKERS = 4 + +DATA_DIR = Path('./data') +DOWNLOADED_IMAGES_DIR = Path('./data/source_images/') +MODEL_TIFFS_DIR = Path('./data/model_tiffs') \ No newline at end of file diff --git a/clearcut_detection_backend/test/test_config.ini b/clearcut_detection_backend/test/test_config.ini new file mode 100644 index 0000000..042c6a3 --- /dev/null +++ b/clearcut_detection_backend/test/test_config.ini @@ -0,0 +1,5 @@ +[config] +AREA_TILE_SET = 36UYA +BANDS_TO_DOWNLOAD = TCI B04 B08 B8A B11 B12 +DATE_CURRENT = 20190427 +DATE_PREVIOUS = 20190402 diff --git a/clearcut_detection_backend/test/test_data_prepare.py b/clearcut_detection_backend/test/test_data_prepare.py new file mode 100644 index 0000000..04c2311 --- /dev/null +++ b/clearcut_detection_backend/test/test_data_prepare.py @@ -0,0 +1,28 @@ +import os +import pandas as pd +import geopandas + +from datetime import datetime +from shapely.ops import unary_union +from utils import DATE_CURRENT, DATE_PREVIOUS, TEST_POLYGONS, TEST_PATH + + +def save_polygons(polygons, crs, save_path, filename): + if len(polygons) == 0: + return + polygons = geopandas.GeoDataFrame({'geometry': polygons}, crs = crs) + polygons.to_file(os.path.join(save_path, f'{filename}.geojson'), driver='GeoJSON') + return os.path.join(save_path, f'{filename}.geojson') + + +def prepare_testfile(): + test = geopandas.read_file(TEST_POLYGONS) + test['img_date'] = pd.to_datetime(test['img_date'], format='%Y-%m-%d') + test = test[(test['img_date'] > DATE_PREVIOUS) & (test['img_date'] <= DATE_CURRENT)] + return test + + +def get_gt_polygons(): + test = prepare_testfile() + clearcuts = unary_union(test['geometry'].buffer(1e-5)) + return save_polygons(clearcuts, test.crs, TEST_PATH, 'test_clearcuts') diff --git a/clearcut_detection_backend/test/test_model.py b/clearcut_detection_backend/test/test_model.py new file mode 100644 index 0000000..6103316 --- /dev/null +++ b/clearcut_detection_backend/test/test_model.py @@ -0,0 +1,124 @@ +import json +import os +import logging +from concurrent.futures import as_completed, ThreadPoolExecutor +import requests +import yaml +from pathlib import Path +from django.conf import settings +from clearcuts.geojson_save import save +from clearcuts.models import TileInformation, RunUpdateTask, Tile +from test.prepare_tif import prepare_tiff +from services.configuration import area_tile_set_test + +model_call_config = './model_call_config.yml' +logger = logging.getLogger('model_call') + + +class ModelCaller: + """ + Class for asynchronous calling of model's API + """ + def __init__(self): + self.data_dir = settings.DATA_DIR + self.tile_index_distinct = set(area_tile_set_test) + logger.info(f'tile_index_distinct: {self.tile_index_distinct}') + + def start(self): + if settings.PATH_TYPE == 'fs': + path_type = 0 + else: + logger.error(f'Unsupported file path in settings.PATH_TYPE: {settings.PATH_TYPE}') + raise ValueError + + + with ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor: + future_list = [] + for tile_index in self.tile_index_distinct: + tiles = [f'{tile_index}_0', f'{tile_index}_1'] + for tile_name in tiles: + logger.info(f'ThreadPoolExecutor submit {tile_index}, {tile_name}') + future = executor.submit(prepare_tiff, tile_name) + future_list.append(future) + + results = {} + for future in as_completed(future_list): + if future.result()[0]: + self.remove_temp_files(future.result()[0], future.result()[1]) + if future.result()[2]: + tile_index = future.result()[2] + if tile_index not in results: + results[tile_index] = 1 + else: + logger.info(f'start model_predict for {tile_index}') + self.model_predict(self.query.filter(tile_index__exact=tile_index)) + del results[tile_index] + + tile_list = self.query.filter(tile_index__exact=tile_index).order_by('tile_name') + path_img_0 = tile_list[0].model_tiff_location + path_img_1 = tile_list[1].model_tiff_location + image_date_0 = tile_list[0].tile_date + image_date_1 = tile_list[1].tile_date + + tile = Tile.objects.get(tile_index=tile_index) + task = RunUpdateTask(tile_index=tile, + path_type=path_type, + path_img_0=path_img_0, + path_img_1=path_img_1, + image_date_0=image_date_0, + image_date_1=image_date_1, + ) + task.save() + + + + + if len(results) > 0: + logger.error(f'results after model_predict not empty.\n\ + results: {results}') + + @staticmethod + def remove_temp_files(path, tile_name): + logger.info(f'Try remove temp files for {tile_name}') + temp_files = Path(path).glob(f'{tile_name}*.tif') + try: + for file in temp_files: + file.unlink() + logger.info(f'temp files for {tile_name} were removed') + except OSError: + logger.error('Error\n\n', exc_info=True) + + def model_predict(self, tile): + """ + Sending unique tile_index to model and saving results to db + """ + src_tile = tile.first() + tif_path = src_tile.model_tiff_location.split('/')[:-2] + + tif_path = os.path.join(*tif_path) + logger.info(f'raster_prediction {tif_path}') + results = raster_prediction(tif_path) + logger.info(f'results:\n{results}') + results_path = os.path.join(self.data_dir, results[0].get('polygons')) + if os.path.exists(results_path): + save(tile, results_path) + +# TODO: add docstring +def raster_prediction(tif_path): + with open(model_call_config, 'r') as config: + cfg = yaml.load(config, Loader=yaml.SafeLoader) + model_api_cfg = cfg["model-api"] + api_endpoint = "http://{host}:{port}/{endpoint}".format( + host=model_api_cfg["host"], + port=model_api_cfg["port"], + endpoint=model_api_cfg["endpoint"] + ) + data = {"image_path": tif_path} + logger.info(f'sending request to model API for\n{tif_path}') + try: + response = requests.post(url=api_endpoint, json=data) + result = response.text + datastore = json.loads(result) + return datastore + except (ValueError, Exception): + logger.error('Error\n\n', exc_info=True) diff --git a/clearcut_detection_backend/test/utils.py b/clearcut_detection_backend/test/utils.py new file mode 100644 index 0000000..8c1f169 --- /dev/null +++ b/clearcut_detection_backend/test/utils.py @@ -0,0 +1,23 @@ +import os + +from configparser import ConfigParser +from datetime import datetime + +def path_exists_or_create(path): + if not os.path.exists(path): + os.makedirs(path) + return path + +config_test = ConfigParser(allow_no_value=True) +config_test.read('test_config.ini') + +area_tile_set_test = set(config_test.get('config', 'AREA_TILE_SET').split()) +bands_to_download = config_test.get('config', 'BANDS_TO_DOWNLOAD').split() +date_current_test = config_test.get('config', 'DATE_CURRENT') +date_previous_test = config_test.get('config', 'DATE_PREVIOUS') + +TEST_PATH = 'data/test_data' +TEST_POLYGONS = f'{TEST_PATH}/36UYA_test_data.geojson' +TEST_TILES = f'{TEST_PATH}/tiles_to_download.txt' +DATE_CURRENT = datetime.strptime(date_current_test, '%Y%m%d') +DATE_PREVIOUS = datetime.strptime(date_previous_test, '%Y%m%d') From ca941e2a71eeb9592cbe70c417d3b3af689e6a3a Mon Sep 17 00:00:00 2001 From: Vlad Khramtsov Date: Thu, 23 Jul 2020 12:56:48 +0300 Subject: [PATCH 02/13] 12601: implement integration testing pipeline --- .gitignore | 6 +- .../data/test_data/36UYA_test_data.geojson | 285 ++++++++++++++++++ .../test/data/test_data/gold_standard.txt | 10 + .../data/test_data/test_clearcuts.geojson | 54 ++++ .../test/data/test_data/tiles_to_download.txt | 2 + clearcut_detection_backend/test/download.py | 5 +- clearcut_detection_backend/test/evaluation.py | 143 +++++++++ clearcut_detection_backend/test/polyeval.py | 138 +++++++++ clearcut_detection_backend/test/predict.py | 41 +-- clearcut_detection_backend/test/settings.py | 4 +- clearcut_detection_backend/test/test.sh | 6 + clearcut_detection_backend/test/utils.py | 6 + 12 files changed, 676 insertions(+), 24 deletions(-) create mode 100644 clearcut_detection_backend/test/data/test_data/36UYA_test_data.geojson create mode 100644 clearcut_detection_backend/test/data/test_data/gold_standard.txt create mode 100644 clearcut_detection_backend/test/data/test_data/test_clearcuts.geojson create mode 100644 clearcut_detection_backend/test/data/test_data/tiles_to_download.txt create mode 100644 clearcut_detection_backend/test/evaluation.py create mode 100644 clearcut_detection_backend/test/polyeval.py create mode 100644 clearcut_detection_backend/test/test.sh diff --git a/.gitignore b/.gitignore index 5a9235d..d39508c 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,11 @@ +test_status.json + \.idea/ \.vscode clearcut_detection_backend/clearcuts/migrations/ clearcut_detection_backend/model/predicted/ logs.txt -data/ +clearcut_detection_backend/data/ logs/ venv/ _install/ @@ -80,4 +82,4 @@ credentials.json # External files landcover.zip docker-compose-django-debug.yml -dyman_settings.py \ No newline at end of file +dyman_settings.py diff --git a/clearcut_detection_backend/test/data/test_data/36UYA_test_data.geojson b/clearcut_detection_backend/test/data/test_data/36UYA_test_data.geojson new file mode 100644 index 0000000..21c0ffb --- /dev/null +++ b/clearcut_detection_backend/test/data/test_data/36UYA_test_data.geojson @@ -0,0 +1,285 @@ +{ +"type": "FeatureCollection", +"name": "36UYA_Spring_2019imgd", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "tileID": "20190313T085351_36UYA_0", "img_date": "2019\/03\/13" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 36.548569275199696, 49.544220676183897 ], [ 36.548486126720199, 49.544114512842299 ], [ 36.548518313228399, 49.544050118571803 ], [ 36.5483332408064, 49.5439578779825 ], [ 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b/clearcut_detection_backend/test/data/test_data/tiles_to_download.txt @@ -0,0 +1,2 @@ +L1C_T36UYA_A010815_20190402T083714 +L1C_T36UYA_A020081_20190427T083603 \ No newline at end of file diff --git a/clearcut_detection_backend/test/download.py b/clearcut_detection_backend/test/download.py index b138c60..25bc581 100644 --- a/clearcut_detection_backend/test/download.py +++ b/clearcut_detection_backend/test/download.py @@ -42,8 +42,8 @@ def __init__(self): self.sequential_dates_count = settings.MAXIMUM_DATES_STORE_FOR_TILE self.area_tile_set = area_tile_set_test self.bands_to_download = bands_to_download - self.base_uri = 'gs://gcp-public-data-sentinel-2' # TODO to settings - self.bucket_name = 'gcp-public-data-sentinel-2' # TODO to settings + self.base_uri = 'gs://gcp-public-data-sentinel-2' + self.bucket_name = 'gcp-public-data-sentinel-2' self.prefix = 'L2/tiles' self.tiles_and_uris_dict = {tile_name: self.get_tile_uri(tile_name) for tile_name in self.area_tile_set} self.storage_client = storage.Client() @@ -65,7 +65,6 @@ def process_download(self): Launches multithread download """ tiles_to_update = self.request_google_cloud_storage_for_latest_acquisition(self.tiles_and_uris_dict) - print(tiles_to_update) self.launch_download_pool(tiles_to_update) @staticmethod diff --git a/clearcut_detection_backend/test/evaluation.py b/clearcut_detection_backend/test/evaluation.py new file mode 100644 index 0000000..3174699 --- /dev/null +++ b/clearcut_detection_backend/test/evaluation.py @@ -0,0 +1,143 @@ +import os +import json +import argparse + +import imageio +import cv2 as cv +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +from PIL import Image +from tqdm import tqdm +from settings import DATA_DIR +from sklearn.metrics import f1_score, precision_score, recall_score, auc, precision_recall_curve + +from polyeval import * +from utils import GOLD_STANDARD_F1SCORES, GOLD_DICE, GOLD_IOU, SUCCESS_THRESHOLD + +def parse_args(): + parser = argparse.ArgumentParser( + description='Script for evaluating performance of the model.') + parser.add_argument( + '--datasets_path', '-dp', dest='datasets_path', + default=f'{DATA_DIR}/predicted/masks', + help='Path to the directory all the data') + parser.add_argument( + '--predictions_path', '-pp', dest='predictions_path', + default=f'{DATA_DIR}/predicted/preds', + help='Path to the directory with predictions') + parser.add_argument( + '--output_name', '-on', dest='output_name', + default='inference', help='Name for output file') + parser.add_argument( + '--masks_folder', '-mf', dest='masks_folder', + default='masks', + help='Name of folder where masks are storing' + ) + parser.add_argument( + '--mask_type', '-mt', dest='mask_type', + default='png', + help='Type of mask file' + ) + + return parser.parse_args() + +def dice_coef(y_true, y_pred, eps=1e-7): + y_true_f = y_true.flatten() + y_pred_f = y_pred.flatten() + intersection = np.sum(y_true_f * y_pred_f) + return (2. * intersection + eps) / (np.sum(y_true_f) + np.sum(y_pred_f)+eps) + + +def iou(y_true, y_pred, smooth=1.0): + y_true_f = y_true.flatten() + y_pred_f = y_pred.flatten() + intersection = np.sum(y_true_f * y_pred_f) + return (1. * intersection + smooth) / (np.sum(y_true_f) + np.sum(y_pred_f) - intersection + smooth) + +def confusion_matrix(y_true, y_pred): + mm, mn, nm, nn = 0, 0, 0, 0 + M, N = 0, 0 + for i in range(len(y_true)): + if(y_true.iloc[i] == y_pred.iloc[i]): + if(y_true.iloc[i] == 1): + M += 1 + mm += 1 + else: + N += 1 + nn += 1 + else: + if(y_true.iloc[i] == 1): + M += 1 + mn += 1 + else: + N += 1 + nm += 1 + return mm, mn, nm, nn, M, N + +def evaluate(datasets_path, predictions_path, output_name, masks_folder, mask_type): + threshold = 0.3 + res_cols = ['name', 'dice_score', 'iou_score', 'pixel_amount'] + test_df_results = pd.DataFrame(columns=res_cols) + dices, ious = [], [] + filenames = os.listdir(datasets_path) + + test_polys, truth_polys = [], [] + for instance_name in tqdm(filenames): + prediction = cv.imread(os.path.join(predictions_path, instance_name)) + mask = cv.imread(os.path.join(datasets_path, instance_name)) + + test_polys.append(polygonize(prediction[:,:,0].astype(np.uint8))) + truth_polys.append(polygonize(mask[:,:,0].astype(np.uint8))) + + dice_score = dice_coef(mask / 255, (prediction / 255) > threshold) + iou_score = iou(mask / 255, (prediction / 255) > threshold, smooth=1.0) + + dices.append(dice_score) + ious.append(iou_score) + + pixel_amount = mask.sum() / 255 + + test_df_results = test_df_results.append({'name': instance_name, + 'dice_score': dice_score, 'iou_score': iou_score, 'pixel_amount': pixel_amount}, ignore_index=True) + + print("Average dice score - {0}".format(round(np.average(dices), 4))) + print("Average iou score - {0}".format(round(np.average(ious), 4))) + + log_save = os.path.join(predictions_path, f'{output_name}_f1score.csv') + print(log_save) + log = pd.DataFrame(columns=['f1_score', 'threshold', 'TP', 'FP', 'FN']) + for threshold in np.arange(0.1, 1, 0.1): + F1score, true_pos_count, false_pos_count, false_neg_count, total_count = evalfunction(test_polys, truth_polys, threshold=threshold) + log = log.append({'f1_score': round(F1score,4), + 'threshold': round(threshold,2), + 'TP':int(true_pos_count), + 'FP':int(false_pos_count), + 'FN':int(false_neg_count)}, ignore_index=True) + + print(log) + log.to_csv(log_save, index=False) + + test_df_results_path = os.path.join(predictions_path, f'{output_name}_dice.csv') + test_df_results.to_csv(test_df_results_path, index=False) + return log, np.average(dices), np.average(ious) + + + +if __name__ == "__main__": + args = parse_args() + f1_score_test, dice, iou = evaluate(args.datasets_path, args.predictions_path, args.output_name, args.masks_folder, args.mask_type) + f1_score_standard = pd.read_csv(GOLD_STANDARD_F1SCORES) + + result = {} + result['f1_score'] = np.mean(f1_score_standard['f1_score'].to_numpy()[0:3] - f1_score_test['f1_score'].to_numpy()[0:3]) + result['dice_score'] = GOLD_DICE - dice + result['iou_score'] = GOLD_IOU - iou + result['status'] = (result['f1_score'] < SUCCESS_THRESHOLD) \ + & (result['dice_score'] < SUCCESS_THRESHOLD) \ + & (result['iou_score'] < SUCCESS_THRESHOLD) + print(result) + result['status'] = str(result['status']) + with open('test_status.json', 'w') as outfile: + json.dump(result, outfile) \ No newline at end of file diff --git a/clearcut_detection_backend/test/polyeval.py b/clearcut_detection_backend/test/polyeval.py new file mode 100644 index 0000000..efedbf7 --- /dev/null +++ b/clearcut_detection_backend/test/polyeval.py @@ -0,0 +1,138 @@ +import os +import cv2 +import numpy as np + +from scipy import ndimage as ndi +from shapely.geometry import Polygon +from skimage.segmentation import watershed +from skimage.feature import peak_local_max + +import matplotlib.pyplot as plt + +def watershed_segmentation(image): + distance = ndi.distance_transform_edt(image) + local_maxi = peak_local_max(distance, indices=False, footprint=np.ones((3, 3)), + labels=image) + markers = ndi.label(local_maxi)[0] + labels = watershed(-distance, markers, mask=image) + return labels, distance + +def polygonize(raster_array, meta=None, transform=False): + contours, hierarchy = cv2.findContours(raster_array.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + polygons = [] + for i in range(len(contours)): + c = contours[i] + n_s = (c.shape[0], c.shape[2]) + if n_s[0] > 2: + if transform: + polys = [tuple(i) * meta['transform'] for i in c.reshape(n_s)] + else: + polys = [tuple(i) for i in c.reshape(n_s)] + polygons.append(Polygon(polys)) + return polygons + +def iou_poly(test_poly, truth_poly): + iou_score = 0 + intersection_result = test_poly.intersection(truth_poly) + if not intersection_result.is_valid: + intersection_result = intersection_result.buffer(0) + if not intersection_result.is_empty: + intersection_area = intersection_result.area + union_area = test_poly.union(truth_poly).area + iou_score = intersection_area / union_area + else: + iou_score = 0 + return iou_score + + +def score(test_polys, truth_polys, threshold=0.5): + true_pos_count = 0 + true_neg_count = 0 + false_pos_count = 0 + false_neg_count = 0 + total_count = 0 + for test_poly, truth_poly in zip(test_polys, truth_polys): + if len(test_poly)==0 and len(truth_poly)==0: + true_neg_count += 1 + total_count+=1 + elif len(test_poly)==0 and len(truth_poly)>0: + false_pos_count += 1 + total_count+=1 + elif len(test_poly)>0 and len(truth_poly)==0: + false_neg_count += 1 + total_count+=1 + else: + intersected=[] + + for test_p in test_poly: + for truth_p in truth_poly: + if not test_p.is_valid: + test_p = test_p.buffer(0) + if not truth_p.is_valid: + truth_p = truth_p.buffer(0) + if test_p.intersection(truth_p).is_valid: + if not test_p.intersection(truth_p).is_empty: + intersected.append([test_p, truth_p]) + + if len(intersected) < len(test_poly): + false_neg_count += (len(test_poly) - len(intersected)) + total_count+=(len(test_poly) - len(intersected)) + if len(intersected) < len(truth_poly): + false_pos_count += (len(truth_poly) - len(intersected)) + total_count+=(len(truth_poly) - len(intersected)) + for inter in intersected: + iou_score = iou_poly(inter[0], inter[1]) + + ''' + xs, ys = inter[0].exterior.xy + plt.fill(xs, ys, alpha=0.5, fc='r', label='Test') + xs, ys = inter[1].exterior.xy + plt.fill(xs, ys, alpha=0.5, fc='b', label='Truth') + plt.legend() + plt.title(iou_list) + plt.show() + ''' + + if iou_score >= threshold: + true_pos_count += 1 + total_count+=1 + else: + false_pos_count += 1 + total_count+=1 + ''' + for geom in test_poly: + xs, ys = geom.exterior.xy + plt.fill(xs, ys, alpha=0.5, fc='r', label='Test') + + for geom in truth_poly: + xs, ys = geom.exterior.xy + plt.fill(xs, ys, alpha=0.5, fc='b', label='Truth') + plt.legend() + plt.show() + ''' + return true_pos_count, false_pos_count, false_neg_count, total_count + + +def evalfunction(test_polys, truth_polys, threshold = 0.5): + if len(truth_polys)==0 and len(test_polys)!=0: + true_pos_count = 0 + false_pos_count = len(test_polys) + false_neg_count = 0 + total_count = len(test_polys) + elif len(truth_polys)==0 and len(test_polys)==0: + true_pos_count = len(test_polys) + false_pos_count = 0 + false_neg_count = 0 + total_count = len(test_polys) + else: + true_pos_count, false_pos_count, false_neg_count, total_count = score(test_polys, truth_polys, + threshold=threshold + ) + + if (true_pos_count > 0): + precision = float(true_pos_count) / (float(true_pos_count) + float(false_pos_count)) + recall = float(true_pos_count) / (float(true_pos_count) + float(false_neg_count)) + F1score = 2.0 * precision * recall / (precision + recall) + else: + F1score = 0 + return F1score, true_pos_count, false_pos_count, false_neg_count, total_count diff --git a/clearcut_detection_backend/test/predict.py b/clearcut_detection_backend/test/predict.py index 9acf78d..105fccd 100644 --- a/clearcut_detection_backend/test/predict.py +++ b/clearcut_detection_backend/test/predict.py @@ -22,9 +22,11 @@ from rasterio.plot import reshape_as_image from rasterio import features from skimage.transform import match_histograms +from scipy.ndimage import gaussian_filter from test_data_prepare import get_gt_polygons -from utils import path_exists_or_create +from settings import MODEL_TIFFS_DIR, DATA_DIR +from utils import path_exists_or_create, area_tile_set_test CLOUDS_PROBABILITY_THRESHOLD = 15 NEAREST_POLYGONS_NUMBER = 10 @@ -69,7 +71,7 @@ def mask_postprocess(mask): return closing -def predict_raster(img_path, channels, network, model_weights_path, input_size=56, neighbours=3): +def predict_raster(img_path, channels, network, model_weights_path, save_path, input_size=56, neighbours=3): tile = os.path.basename(img_path) tiff_files = [os.path.join(img_path, f'{tile}_{i}', f'{tile}_{i}_merged.tiff') for i in range(DATES_FOR_TILE)] model, device = load_model(network, model_weights_path, channels, neighbours) @@ -118,6 +120,15 @@ def predict_raster(img_path, channels, network, model_weights_path, input_size=5 predicted = mask_postprocess(predicted) clearcut_mask[left_column:right_column, bottom_row:upper_row] += predicted + mask_piece = mask[left_column:right_column, bottom_row:upper_row] + # mask_piece = (gaussian_filter(mask_piece, 0.5) > 0) + + cv2.imwrite(f"{save_path}/preds/{i}_{j}.png", predicted * 255) + cv2.imwrite(f"{save_path}/masks/{i}_{j}.png", mask_piece * 255) + + + + meta['dtype'] = 'float32' return clearcut_mask.astype(np.float32), meta @@ -247,18 +258,18 @@ def parse_args(): parser = argparse.ArgumentParser(description='Script for predicting masks.') parser.add_argument( '--image_path', '-ip', dest='image_path', - type=str, required=True, help='Path to source image' + default=f'{MODEL_TIFFS_DIR}/{area_tile_set_test.pop()}', help='Path to source image' ) parser.add_argument( '--model_weights_path', '-mwp', dest='model_weights_path', - default='data/unet_v4.pth', help='Path to directory where pieces will be stored' + default=f'{DATA_DIR}/unet_v4.pth', help='Path to directory where pieces will be stored' ) parser.add_argument( '--network', '-net', dest='network', default='unet_ch', help='Model architecture' ) parser.add_argument( - '--save_path', '-sp', dest='save_path', default='data/predicted', + '--save_path', '-sp', dest='save_path', default=f'{DATA_DIR}/predicted', help='Path to directory where results will be stored' ) parser.add_argument( @@ -281,21 +292,17 @@ def parse_args(): def main(): args = parse_args() path_exists_or_create(args.save_path) + path_exists_or_create(f"{args.save_path}/preds") + path_exists_or_create(f"{args.save_path}/masks") filename = re.split(r'[./]', args.image_path)[-1] predicted_filename = f'predicted_{filename}' - if not args.polygonize_only: - raster_array, meta = predict_raster( - args.image_path, - args.channels, args.network, args.model_weights_path - ) - save_raster(raster_array, meta, args.save_path, filename) - else: - with rasterio.open(os.path.join(args.save_path, f'{predicted_filename}.tif')) as src: - raster_array = src.read() - raster_array = np.moveaxis(raster_array, 0, -1) - meta = src.meta - src.close() + raster_array, meta = predict_raster( + args.image_path, + args.channels, args.network, args.model_weights_path, + args.save_path + ) + # save_raster(raster_array, meta, args.save_path, filename) clearcuts = polygonize(raster_array > args.threshold, meta) clearcuts = polygon_to_geodataframe(clearcuts, meta['crs']) diff --git a/clearcut_detection_backend/test/settings.py b/clearcut_detection_backend/test/settings.py index 26ef822..1207c68 100644 --- a/clearcut_detection_backend/test/settings.py +++ b/clearcut_detection_backend/test/settings.py @@ -3,8 +3,8 @@ MAXIMUM_CLOUD_PERCENTAGE_ALLOWED = 20.0 MAXIMUM_EMPTY_PIXEL_PERCENTAGE = 5.0 MAXIMUM_DATES_STORE_FOR_TILE = 2 -MAX_WORKERS = 4 +MAX_WORKERS = 6 DATA_DIR = Path('./data') DOWNLOADED_IMAGES_DIR = Path('./data/source_images/') -MODEL_TIFFS_DIR = Path('./data/model_tiffs') \ No newline at end of file +MODEL_TIFFS_DIR = Path('./data/model_tiffs') diff --git a/clearcut_detection_backend/test/test.sh b/clearcut_detection_backend/test/test.sh new file mode 100644 index 0000000..605a20d --- /dev/null +++ b/clearcut_detection_backend/test/test.sh @@ -0,0 +1,6 @@ +# !bin/bash/ + +python download.py +python preprocess.py +python predict.py +python evaluate.py diff --git a/clearcut_detection_backend/test/utils.py b/clearcut_detection_backend/test/utils.py index 8c1f169..2c8b258 100644 --- a/clearcut_detection_backend/test/utils.py +++ b/clearcut_detection_backend/test/utils.py @@ -21,3 +21,9 @@ def path_exists_or_create(path): TEST_TILES = f'{TEST_PATH}/tiles_to_download.txt' DATE_CURRENT = datetime.strptime(date_current_test, '%Y%m%d') DATE_PREVIOUS = datetime.strptime(date_previous_test, '%Y%m%d') + +# Target metrics values +GOLD_STANDARD_F1SCORES = f'{TEST_PATH}/gold_standard.txt' +GOLD_DICE = 0.2811 +GOLD_IOU = 0.2545 +SUCCESS_THRESHOLD = 0.05 From 8e24365aef2db1d3d04cbd9102fa4a85c1948e2c Mon Sep 17 00:00:00 2001 From: Vlad Khramtsov Date: Thu, 23 Jul 2020 13:02:05 +0300 Subject: [PATCH 03/13] 12601: add README --- clearcut_detection_backend/test/README.md | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 clearcut_detection_backend/test/README.md diff --git a/clearcut_detection_backend/test/README.md b/clearcut_detection_backend/test/README.md new file mode 100644 index 0000000..d48d85f --- /dev/null +++ b/clearcut_detection_backend/test/README.md @@ -0,0 +1,7 @@ +# Clearcut_detection testing + +To test the model, run `./test.sh` script. +Before running, be sure that + +1) `unet_v4.pth` is located in the `test/data/` directory, and +2) `key.json` in the `test/` directory. From 3ca643ee7e523bff71fc3bb544016295300071f2 Mon Sep 17 00:00:00 2001 From: Vlad Khramtsov Date: Thu, 23 Jul 2020 13:08:24 +0300 Subject: [PATCH 04/13] 12601: remove run-docker.sh script --- clearcut_detection_backend/test/run-docker.sh | 1 - 1 file changed, 1 deletion(-) delete mode 100644 clearcut_detection_backend/test/run-docker.sh diff --git a/clearcut_detection_backend/test/run-docker.sh b/clearcut_detection_backend/test/run-docker.sh deleted file mode 100644 index 71a03ae..0000000 --- a/clearcut_detection_backend/test/run-docker.sh +++ /dev/null @@ -1 +0,0 @@ -sudo docker run -v /home/quantum/DS/clearcut/service/testing/clearcut_detection/clearcut_detection_backend/test/:/test/ -ti testing /bin/bash \ No newline at end of file From f4d1e9df7c0b1a171cb97af2f3cf189b92bc226a Mon Sep 17 00:00:00 2001 From: Vlad Khramtsov Date: Thu, 23 Jul 2020 13:18:08 +0300 Subject: [PATCH 05/13] 12601: change status field in --- clearcut_detection_backend/test/evaluation.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/clearcut_detection_backend/test/evaluation.py b/clearcut_detection_backend/test/evaluation.py index 3174699..80dcf3c 100644 --- a/clearcut_detection_backend/test/evaluation.py +++ b/clearcut_detection_backend/test/evaluation.py @@ -139,5 +139,10 @@ def evaluate(datasets_path, predictions_path, output_name, masks_folder, mask_ty & (result['iou_score'] < SUCCESS_THRESHOLD) print(result) result['status'] = str(result['status']) + if result['status']: + result['status'] = result['status'].replace('True', 'success') + else: + result['status'] = result['status'].replace('False', 'failed') + with open('test_status.json', 'w') as outfile: json.dump(result, outfile) \ No newline at end of file From d04c5102b36bb2474028e24e0299a97624feeb6e Mon Sep 17 00:00:00 2001 From: Vlad Khramtsov Date: Thu, 23 Jul 2020 15:31:28 +0300 Subject: [PATCH 06/13] 12601: add IOU_THRESHOLD for F1-score --- clearcut_detection_backend/test/evaluation.py | 17 ++++++++++------- clearcut_detection_backend/test/utils.py | 1 + 2 files changed, 11 insertions(+), 7 deletions(-) diff --git a/clearcut_detection_backend/test/evaluation.py b/clearcut_detection_backend/test/evaluation.py index 80dcf3c..9cc239e 100644 --- a/clearcut_detection_backend/test/evaluation.py +++ b/clearcut_detection_backend/test/evaluation.py @@ -14,7 +14,7 @@ from sklearn.metrics import f1_score, precision_score, recall_score, auc, precision_recall_curve from polyeval import * -from utils import GOLD_STANDARD_F1SCORES, GOLD_DICE, GOLD_IOU, SUCCESS_THRESHOLD +from utils import GOLD_STANDARD_F1SCORES, GOLD_DICE, GOLD_IOU, SUCCESS_THRESHOLD, IOU_THRESHOLD def parse_args(): parser = argparse.ArgumentParser( @@ -130,19 +130,22 @@ def evaluate(datasets_path, predictions_path, output_name, masks_folder, mask_ty f1_score_test, dice, iou = evaluate(args.datasets_path, args.predictions_path, args.output_name, args.masks_folder, args.mask_type) f1_score_standard = pd.read_csv(GOLD_STANDARD_F1SCORES) + f1_score_test = f1_score_test[f1_score_test['threshold'] <= IOU_THRESHOLD] + f1_score_standard = f1_score_standard[f1_score_standard['threshold'] <= IOU_THRESHOLD] + result = {} - result['f1_score'] = np.mean(f1_score_standard['f1_score'].to_numpy()[0:3] - f1_score_test['f1_score'].to_numpy()[0:3]) + result['f1_score'] = np.mean(f1_score_standard['f1_score'].to_numpy() - f1_score_test['f1_score'].to_numpy()) result['dice_score'] = GOLD_DICE - dice result['iou_score'] = GOLD_IOU - iou result['status'] = (result['f1_score'] < SUCCESS_THRESHOLD) \ & (result['dice_score'] < SUCCESS_THRESHOLD) \ & (result['iou_score'] < SUCCESS_THRESHOLD) - print(result) - result['status'] = str(result['status']) + if result['status']: - result['status'] = result['status'].replace('True', 'success') + result['status'] = str(result['status']).replace('True', 'success') else: - result['status'] = result['status'].replace('False', 'failed') - + result['status'] = str(result['status']).replace('False', 'failed') + + print(result) with open('test_status.json', 'w') as outfile: json.dump(result, outfile) \ No newline at end of file diff --git a/clearcut_detection_backend/test/utils.py b/clearcut_detection_backend/test/utils.py index 2c8b258..c4b945c 100644 --- a/clearcut_detection_backend/test/utils.py +++ b/clearcut_detection_backend/test/utils.py @@ -26,4 +26,5 @@ def path_exists_or_create(path): GOLD_STANDARD_F1SCORES = f'{TEST_PATH}/gold_standard.txt' GOLD_DICE = 0.2811 GOLD_IOU = 0.2545 +IOU_THRESHOLD = 0.3 SUCCESS_THRESHOLD = 0.05 From 0d44232cf0c3f4c78c54e83547d72d7e00fa7d1e Mon Sep 17 00:00:00 2001 From: Vlad Khramtsov Date: Fri, 24 Jul 2020 18:16:54 +0300 Subject: [PATCH 07/13] 12601: testing code refactoring --- .../docker-compose-test.yml | 29 ++ clearcut_detection_backend/test.Dockerfile | 20 + clearcut_detection_backend/test.py | 5 + clearcut_detection_backend/test/README.md | 7 - .../data/test_data/36UYA_test_data.geojson | 285 ------------- .../test/data/test_data/gold_standard.txt | 10 - .../data/test_data/test_clearcuts.geojson | 54 --- .../test/data/test_data/tiles_to_download.txt | 2 - clearcut_detection_backend/test/evaluation.py | 140 +++---- .../test/{polyeval.py => polygon_metrics.py} | 23 +- clearcut_detection_backend/test/predict.py | 373 +++--------------- .../test/requirements.txt | 8 +- .../test/{ => scripts}/Dockerfile | 2 +- .../test/{ => scripts}/download.py | 0 .../test/{ => scripts}/preprocess.py | 0 clearcut_detection_backend/test/settings.py | 6 +- clearcut_detection_backend/test/test.sh | 6 - .../test/test_config.ini | 5 + .../test/test_data_prepare.py | 7 +- clearcut_detection_backend/test/test_model.py | 124 ------ clearcut_detection_backend/test/utils.py | 59 ++- 21 files changed, 252 insertions(+), 913 deletions(-) create mode 100644 clearcut_detection_backend/docker-compose-test.yml create mode 100644 clearcut_detection_backend/test.Dockerfile create mode 100644 clearcut_detection_backend/test.py delete mode 100644 clearcut_detection_backend/test/README.md delete mode 100644 clearcut_detection_backend/test/data/test_data/36UYA_test_data.geojson delete mode 100644 clearcut_detection_backend/test/data/test_data/gold_standard.txt delete mode 100644 clearcut_detection_backend/test/data/test_data/test_clearcuts.geojson delete mode 100644 clearcut_detection_backend/test/data/test_data/tiles_to_download.txt rename clearcut_detection_backend/test/{polyeval.py => polygon_metrics.py} (84%) rename clearcut_detection_backend/test/{ => scripts}/Dockerfile (97%) rename clearcut_detection_backend/test/{ => scripts}/download.py (100%) rename clearcut_detection_backend/test/{ => scripts}/preprocess.py (100%) delete mode 100644 clearcut_detection_backend/test/test.sh delete mode 100644 clearcut_detection_backend/test/test_model.py diff --git a/clearcut_detection_backend/docker-compose-test.yml b/clearcut_detection_backend/docker-compose-test.yml new file mode 100644 index 0000000..ddde612 --- /dev/null +++ b/clearcut_detection_backend/docker-compose-test.yml @@ -0,0 +1,29 @@ +version: '3' +services: + model: + build: + context: ./model + dockerfile: model.Dockerfile + image: clearcut_detection/model + env_file: + - ./model/model.env + volumes: + - ./model/:/model + - ./data/:/model/data + working_dir: /model + ports: + - '5000:5000' + command: /bin/bash -c "python app.py" + + test: + build: + context: ./ + dockerfile: test.Dockerfile + image: clearcut_detection/test + volumes: + - ./:/code + working_dir: /code + command: /bin/bash -c "pip install -r ./test/requirements.txt && python test.py" + +volumes: + data: diff --git a/clearcut_detection_backend/test.Dockerfile b/clearcut_detection_backend/test.Dockerfile new file mode 100644 index 0000000..da5c155 --- /dev/null +++ b/clearcut_detection_backend/test.Dockerfile @@ -0,0 +1,20 @@ +FROM python:3.6 + +RUN mkdir /test +WORKDIR /test + +COPY ./test/requirements.txt /test +RUN pip install -r requirements.txt + +RUN apt-get update -y && apt-get install -y \ + software-properties-common + +RUN add-apt-repository -r ppa:ubuntugis/ppa && apt-get update +RUN apt-get update +RUN apt-get install gdal-bin -y +RUN apt-get install libgdal-dev -y +RUN export CPLUS_INCLUDE_PATH=/usr/include/gdal +RUN export C_INCLUDE_PATH=/usr/include/gdal +RUN pip install GDAL==$(gdal-config --version | awk -F'[.]' '{print $1"."$2}') + +ADD . /test/ \ No newline at end of file diff --git a/clearcut_detection_backend/test.py b/clearcut_detection_backend/test.py new file mode 100644 index 0000000..3ea5116 --- /dev/null +++ b/clearcut_detection_backend/test.py @@ -0,0 +1,5 @@ +from test.predict import model_predict +from test.evaluation import model_evaluate + +results, test_tile_path = model_predict() +model_evaluate(results, test_tile_path) diff --git a/clearcut_detection_backend/test/README.md b/clearcut_detection_backend/test/README.md deleted file mode 100644 index d48d85f..0000000 --- a/clearcut_detection_backend/test/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Clearcut_detection testing - -To test the model, run `./test.sh` script. -Before running, be sure that - -1) `unet_v4.pth` is located in the `test/data/` directory, and -2) `key.json` in the `test/` directory. diff --git a/clearcut_detection_backend/test/data/test_data/36UYA_test_data.geojson b/clearcut_detection_backend/test/data/test_data/36UYA_test_data.geojson deleted file mode 100644 index 21c0ffb..0000000 --- a/clearcut_detection_backend/test/data/test_data/36UYA_test_data.geojson +++ /dev/null @@ -1,285 +0,0 @@ -{ -"type": "FeatureCollection", -"name": "36UYA_Spring_2019imgd", -"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, -"features": [ -{ "type": "Feature", "properties": { "tileID": "20190313T085351_36UYA_0", "img_date": "2019\/03\/13" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 36.548569275199696, 49.544220676183897 ], [ 36.548486126720199, 49.544114512842299 ], [ 36.548518313228399, 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/dev/null @@ -1,2 +0,0 @@ -L1C_T36UYA_A010815_20190402T083714 -L1C_T36UYA_A020081_20190427T083603 \ No newline at end of file diff --git a/clearcut_detection_backend/test/evaluation.py b/clearcut_detection_backend/test/evaluation.py index 9cc239e..acc8ea5 100644 --- a/clearcut_detection_backend/test/evaluation.py +++ b/clearcut_detection_backend/test/evaluation.py @@ -1,53 +1,24 @@ import os import json -import argparse +import yaml -import imageio -import cv2 as cv import numpy as np import pandas as pd -import matplotlib.pyplot as plt +import rasterio +import geopandas -from PIL import Image from tqdm import tqdm -from settings import DATA_DIR -from sklearn.metrics import f1_score, precision_score, recall_score, auc, precision_recall_curve - -from polyeval import * -from utils import GOLD_STANDARD_F1SCORES, GOLD_DICE, GOLD_IOU, SUCCESS_THRESHOLD, IOU_THRESHOLD - -def parse_args(): - parser = argparse.ArgumentParser( - description='Script for evaluating performance of the model.') - parser.add_argument( - '--datasets_path', '-dp', dest='datasets_path', - default=f'{DATA_DIR}/predicted/masks', - help='Path to the directory all the data') - parser.add_argument( - '--predictions_path', '-pp', dest='predictions_path', - default=f'{DATA_DIR}/predicted/preds', - help='Path to the directory with predictions') - parser.add_argument( - '--output_name', '-on', dest='output_name', - default='inference', help='Name for output file') - parser.add_argument( - '--masks_folder', '-mf', dest='masks_folder', - default='masks', - help='Name of folder where masks are storing' - ) - parser.add_argument( - '--mask_type', '-mt', dest='mask_type', - default='png', - help='Type of mask file' - ) - - return parser.parse_args() +from rasterio import features + +from test.polygon_metrics import f1_score_evaluation, polygonize +from test.utils import GOLD_DICE, GOLD_F1SCORE, GOLD_IOU, SUCCESS_THRESHOLD, IOU_THRESHOLD +from test.test_data_prepare import get_gt_polygons def dice_coef(y_true, y_pred, eps=1e-7): y_true_f = y_true.flatten() y_pred_f = y_pred.flatten() intersection = np.sum(y_true_f * y_pred_f) - return (2. * intersection + eps) / (np.sum(y_true_f) + np.sum(y_pred_f)+eps) + return (2. * intersection + eps) / (np.sum(y_true_f) + np.sum(y_pred_f) + eps) def iou(y_true, y_pred, smooth=1.0): @@ -56,6 +27,7 @@ def iou(y_true, y_pred, smooth=1.0): intersection = np.sum(y_true_f * y_pred_f) return (1. * intersection + smooth) / (np.sum(y_true_f) + np.sum(y_pred_f) - intersection + smooth) + def confusion_matrix(y_true, y_pred): mm, mn, nm, nn = 0, 0, 0, 0 M, N = 0, 0 @@ -76,65 +48,84 @@ def confusion_matrix(y_true, y_pred): nm += 1 return mm, mn, nm, nn, M, N -def evaluate(datasets_path, predictions_path, output_name, masks_folder, mask_type): - threshold = 0.3 + +def get_raster_masks(reference_tif, model_result): + raster = {} + with rasterio.open(reference_tif) as src: + filenames = {} + filenames['mask'] = get_gt_polygons() + filenames['predicted'] = os.path.join('data', model_result[0].get('polygons')) + for name in filenames: + gt_polygons = geopandas.read_file(filenames[name]) + gt_polygons = gt_polygons.to_crs(src.crs) + raster[name] = features.rasterize(shapes=gt_polygons['geometry'], + out_shape=(src.height, src.width), + transform=src.transform, + default_value=1) + + return raster + +def load_config(): + with open('./model/predict_config.yml', 'r') as config: + cfg = yaml.load(config, Loader=yaml.SafeLoader) + + models = cfg['models'] + save_path = cfg['prediction']['save_path'] + threshold = cfg['prediction']['threshold'] + input_size = cfg['prediction']['input_size'] + + return models, save_path, threshold, input_size + + +def evaluate(model_result, test_tile_path): + raster = get_raster_masks(test_tile_path['current'], model_result) + _, _, _, size = load_config() + res_cols = ['name', 'dice_score', 'iou_score', 'pixel_amount'] test_df_results = pd.DataFrame(columns=res_cols) dices, ious = [], [] - filenames = os.listdir(datasets_path) - test_polys, truth_polys = [], [] - for instance_name in tqdm(filenames): - prediction = cv.imread(os.path.join(predictions_path, instance_name)) - mask = cv.imread(os.path.join(datasets_path, instance_name)) - - test_polys.append(polygonize(prediction[:,:,0].astype(np.uint8))) - truth_polys.append(polygonize(mask[:,:,0].astype(np.uint8))) + for i in tqdm(range(raster['mask'].shape[0] // size)): + for j in range(raster['mask'].shape[1] // size): + instance_name = f'{i}_{j}' + mask = raster['mask'][i*size : (i+1)*size, j*size : (j+1)*size] + if mask.sum() > 0: + prediction = raster['predicted'][i*size : (i+1)*size, j*size : (j+1)*size] + test_polys.append(polygonize(prediction.astype(np.uint8))) + truth_polys.append(polygonize(mask.astype(np.uint8))) - dice_score = dice_coef(mask / 255, (prediction / 255) > threshold) - iou_score = iou(mask / 255, (prediction / 255) > threshold, smooth=1.0) + dice_score = dice_coef(mask, prediction) + iou_score = iou(mask, prediction, smooth=1.0) - dices.append(dice_score) - ious.append(iou_score) + dices.append(dice_score) + ious.append(iou_score) - pixel_amount = mask.sum() / 255 + pixel_amount = mask.sum() - test_df_results = test_df_results.append({'name': instance_name, - 'dice_score': dice_score, 'iou_score': iou_score, 'pixel_amount': pixel_amount}, ignore_index=True) + test_df_results = test_df_results.append({'name': instance_name, + 'dice_score': dice_score, 'iou_score': iou_score, 'pixel_amount': pixel_amount}, ignore_index=True) - print("Average dice score - {0}".format(round(np.average(dices), 4))) - print("Average iou score - {0}".format(round(np.average(ious), 4))) - - log_save = os.path.join(predictions_path, f'{output_name}_f1score.csv') - print(log_save) log = pd.DataFrame(columns=['f1_score', 'threshold', 'TP', 'FP', 'FN']) for threshold in np.arange(0.1, 1, 0.1): - F1score, true_pos_count, false_pos_count, false_neg_count, total_count = evalfunction(test_polys, truth_polys, threshold=threshold) + F1score, true_pos_count, false_pos_count, false_neg_count, total_count = f1_score_evaluation(test_polys, truth_polys, threshold=threshold) log = log.append({'f1_score': round(F1score,4), 'threshold': round(threshold,2), 'TP':int(true_pos_count), 'FP':int(false_pos_count), 'FN':int(false_neg_count)}, ignore_index=True) - print(log) - log.to_csv(log_save, index=False) - - test_df_results_path = os.path.join(predictions_path, f'{output_name}_dice.csv') - test_df_results.to_csv(test_df_results_path, index=False) return log, np.average(dices), np.average(ious) -if __name__ == "__main__": - args = parse_args() - f1_score_test, dice, iou = evaluate(args.datasets_path, args.predictions_path, args.output_name, args.masks_folder, args.mask_type) - f1_score_standard = pd.read_csv(GOLD_STANDARD_F1SCORES) +def model_evaluate(model_result, test_tile_path): + f1_score_test, dice, iou = evaluate(model_result, test_tile_path) - f1_score_test = f1_score_test[f1_score_test['threshold'] <= IOU_THRESHOLD] - f1_score_standard = f1_score_standard[f1_score_standard['threshold'] <= IOU_THRESHOLD] + f1_score_test = f1_score_test[f1_score_test['threshold'] == IOU_THRESHOLD]['f1_score'].to_numpy() + f1_score_standard = GOLD_F1SCORE result = {} - result['f1_score'] = np.mean(f1_score_standard['f1_score'].to_numpy() - f1_score_test['f1_score'].to_numpy()) + result['f1_score'] = float(f1_score_standard - f1_score_test[0]) result['dice_score'] = GOLD_DICE - dice result['iou_score'] = GOLD_IOU - iou result['status'] = (result['f1_score'] < SUCCESS_THRESHOLD) \ @@ -146,6 +137,5 @@ def evaluate(datasets_path, predictions_path, output_name, masks_folder, mask_ty else: result['status'] = str(result['status']).replace('False', 'failed') - print(result) with open('test_status.json', 'w') as outfile: - json.dump(result, outfile) \ No newline at end of file + json.dump(result, outfile) diff --git a/clearcut_detection_backend/test/polyeval.py b/clearcut_detection_backend/test/polygon_metrics.py similarity index 84% rename from clearcut_detection_backend/test/polyeval.py rename to clearcut_detection_backend/test/polygon_metrics.py index efedbf7..8ad76bf 100644 --- a/clearcut_detection_backend/test/polyeval.py +++ b/clearcut_detection_backend/test/polygon_metrics.py @@ -83,37 +83,16 @@ def score(test_polys, truth_polys, threshold=0.5): for inter in intersected: iou_score = iou_poly(inter[0], inter[1]) - ''' - xs, ys = inter[0].exterior.xy - plt.fill(xs, ys, alpha=0.5, fc='r', label='Test') - xs, ys = inter[1].exterior.xy - plt.fill(xs, ys, alpha=0.5, fc='b', label='Truth') - plt.legend() - plt.title(iou_list) - plt.show() - ''' - if iou_score >= threshold: true_pos_count += 1 total_count+=1 else: false_pos_count += 1 total_count+=1 - ''' - for geom in test_poly: - xs, ys = geom.exterior.xy - plt.fill(xs, ys, alpha=0.5, fc='r', label='Test') - - for geom in truth_poly: - xs, ys = geom.exterior.xy - plt.fill(xs, ys, alpha=0.5, fc='b', label='Truth') - plt.legend() - plt.show() - ''' return true_pos_count, false_pos_count, false_neg_count, total_count -def evalfunction(test_polys, truth_polys, threshold = 0.5): +def f1_score_evaluation(test_polys, truth_polys, threshold = 0.5): if len(truth_polys)==0 and len(test_polys)!=0: true_pos_count = 0 false_pos_count = len(test_polys) diff --git a/clearcut_detection_backend/test/predict.py b/clearcut_detection_backend/test/predict.py index 105fccd..e81f5c3 100644 --- a/clearcut_detection_backend/test/predict.py +++ b/clearcut_detection_backend/test/predict.py @@ -1,313 +1,66 @@ +import json import os -import re -import cv2 -import torch import logging -import imageio -import rasterio -import argparse -import geopandas -import numpy as np -import segmentation_models_pytorch as smp - -from catalyst.dl.utils import UtilsFactory -from geopandas import GeoSeries -from scipy import spatial -from shapely.geometry import Polygon -from shapely.ops import unary_union -from torchvision import transforms -from torch import nn -from tqdm import tqdm -from rasterio.windows import Window -from rasterio.plot import reshape_as_image -from rasterio import features -from skimage.transform import match_histograms -from scipy.ndimage import gaussian_filter - -from test_data_prepare import get_gt_polygons -from settings import MODEL_TIFFS_DIR, DATA_DIR -from utils import path_exists_or_create, area_tile_set_test - -CLOUDS_PROBABILITY_THRESHOLD = 15 -NEAREST_POLYGONS_NUMBER = 10 -DATES_FOR_TILE = 2 - -import warnings -warnings.filterwarnings('ignore') - -logging.basicConfig(format='%(asctime)s %(message)s') - -def load_model(network, model_weights_path, channels, neighbours): - device = 'gpu' if torch.cuda.is_available() else 'cpu' - model = get_model(network) - model.encoder.conv1 = nn.Conv2d( - count_channels(channels)*neighbours, 64, kernel_size=(7, 7), - stride=(2, 2), padding=(3, 3), bias=False - ) - model, device = UtilsFactory.prepare_model(model) - model.load_state_dict(torch.load(model_weights_path, map_location=torch.device(device))) - return model, device - - -def predict(image_tensor, model, channels, neighbours, size, device): - image_shape = 1, count_channels(channels)*neighbours, size, size - prediction, _ = model.predict(image_tensor.view(image_shape).to(device, dtype=torch.float)) - result = prediction.view(size, size).detach().cpu().numpy() - return result - - -def diff(img1, img2): - img2 = match_histograms(img2, img1, multichannel=True) - difference = ( (img1 - img2) / (img1 + img2) ) - difference = (difference + 1) * 127 - return np.concatenate((difference.astype(np.uint8), img1.astype(np.uint8), img2.astype(np.uint8)), axis=-1) - - -def mask_postprocess(mask): - kernel = np.ones((3, 3), np.uint8) - erosion = cv2.erode(mask, kernel, iterations = 1) - kernel = np.ones((5, 5), np.uint8) - closing = cv2.morphologyEx(erosion, cv2.MORPH_CLOSE, kernel) - return closing - - -def predict_raster(img_path, channels, network, model_weights_path, save_path, input_size=56, neighbours=3): - tile = os.path.basename(img_path) - tiff_files = [os.path.join(img_path, f'{tile}_{i}', f'{tile}_{i}_merged.tiff') for i in range(DATES_FOR_TILE)] - model, device = load_model(network, model_weights_path, channels, neighbours) - - with rasterio.open(tiff_files[0]) as source_current, \ - rasterio.open(tiff_files[1]) as source_previous: - - meta = source_current.meta - meta['count'] = 1 - clearcut_mask = np.zeros((source_current.height, source_current.width)) - image = np.zeros((source_current.height, source_current.width)) - mask = np.ones((source_current.height, source_current.width)) - - gt_polygons_filename = get_gt_polygons() - gt_polygons = geopandas.read_file(gt_polygons_filename) - gt_polygons = gt_polygons.to_crs(source_current.crs) - mask = features.rasterize(shapes=gt_polygons['geometry'], - out_shape=(source_current.height, source_current.width), - transform=source_current.transform, - default_value=1) - - for i in tqdm(range(source_current.width // input_size)): - for j in range(source_current.height // input_size): - bottom_row = j * input_size - upper_row = (j + 1) * input_size - left_column = i * input_size - right_column = (i + 1) * input_size - - if mask[left_column:right_column, bottom_row:upper_row].sum() > 0: - corners=[ - source_current.xy(bottom_row, left_column), - source_current.xy(bottom_row, right_column), - source_current.xy(upper_row, right_column), - source_current.xy(upper_row, left_column), - source_current.xy(bottom_row, left_column) - ] - - window = Window(bottom_row, left_column, input_size, input_size) - image_current = reshape_as_image(source_current.read(window=window)) - image_previous = reshape_as_image(source_previous.read(window=window)) - - difference_image = diff(image_current, image_previous) - image_tensor = transforms.ToTensor()(difference_image.astype(np.uint8)).to(device, dtype=torch.float) - - predicted = predict(image_tensor, model, channels, neighbours, input_size, device) - predicted = mask_postprocess(predicted) - clearcut_mask[left_column:right_column, bottom_row:upper_row] += predicted - - mask_piece = mask[left_column:right_column, bottom_row:upper_row] - # mask_piece = (gaussian_filter(mask_piece, 0.5) > 0) - - cv2.imwrite(f"{save_path}/preds/{i}_{j}.png", predicted * 255) - cv2.imwrite(f"{save_path}/masks/{i}_{j}.png", mask_piece * 255) - - - - - meta['dtype'] = 'float32' - return clearcut_mask.astype(np.float32), meta - - -def get_model(name, classification_head=True, model_weights_path=None): - if name == 'unet_ch': - aux_params = dict( - pooling='max', # one of 'avg', 'max' - dropout=0.1, # dropout ratio, default is None - activation='sigmoid', # activation function, default is None - classes=1, # define number of output labels - ) - return smp.Unet('resnet18', aux_params=aux_params, - encoder_weights=None, encoder_depth=2, - decoder_channels=(256, 128)) - else: - raise ValueError("Unknown network") - - -def count_channels(channels): - count = 0 - for ch in channels: - if ch == 'rgb': - count += 3 - elif ch in ['ndvi', 'ndmi', 'b8', 'b8a', 'b11', 'b12']: - count += 1 - else: - raise Exception('{} channel is unknown!'.format(ch)) - - return count - - -def filter_by_channels(image_tensor, channels, neighbours=3): - # order: ['TCI','B08','B8A','B11','B12', 'NDVI', 'NDMI'] - result = [] - for i in range(neighbours): - for ch in channels: - if ch == 'rgb': - result.append(image_tensor[:, :, (0+i*9):(3+i*9)]) - elif ch == 'b8': - result.append(image_tensor[:, :, (3+i*9):(4+i*9)]) - elif ch == 'b8a': - result.append(image_tensor[:, :, (4+i*9):(5+i*9)]) - elif ch == 'b11': - result.append(image_tensor[:, :, (5+i*9):(6+i*9)]) - elif ch == 'b12': - result.append(image_tensor[:, :, (6+i*9):(7+i*9)]) - elif ch == 'ndvi': - result.append(image_tensor[:, :, (7+i*9):(8+i*9)]) - elif ch == 'ndmi': - result.append(image_tensor[:, :, (8+i*9):(9+i*9)]) - else: - raise Exception(f'{ch} channel is unknown!') - return np.concatenate(result, axis=2) - - -def scale(tensor, max_value): - max_ = tensor.max() - if max_ > 0: - return tensor / max_ * max_value - return tensor - - -def save_raster(raster_array, meta, save_path, filename): - if not os.path.exists(save_path): - os.makedirs(save_path, exist_ok=True) - logging.info("Data directory created.") - - save_path = os.path.join(save_path, f'predicted_{filename}') - - cv2.imwrite(f'{save_path}.png', raster_array) - - with rasterio.open(f'{save_path}.tif', 'w', **meta) as dst: - for i in range(1, meta['count'] + 1): - dst.write(raster_array, i) - - -def polygonize(raster_array, meta, transform=True, mode=cv2.RETR_TREE): - raster_array = (raster_array * 255).astype(np.uint8) - - contours, _ = cv2.findContours(raster_array, mode, cv2.CHAIN_APPROX_SIMPLE) - - polygons = [] - for i in tqdm(range(len(contours))): - c = contours[i] - n_s = (c.shape[0], c.shape[2]) - if n_s[0] > 2: - if transform: - polys = [tuple(i) * meta['transform'] for i in c.reshape(n_s)] - else: - polys = [tuple(i) for i in c.reshape(n_s)] - polygons.append(Polygon(polys)) - - return polygons - - -def save_polygons(polygons, save_path, filename): - if len(polygons) == 0: - logging.info('no_polygons detected') - return - - if not os.path.exists(save_path): - os.makedirs(save_path, exist_ok=True) - logging.info("Data directory created.") - - logging.info(f'{filename}.geojson saved.') - polygons.to_file(os.path.join(save_path, f'{filename}.geojson'), driver='GeoJSON') - - -def intersection_poly(test_poly, mask_poly): - intersecion_score = False - if test_poly.is_valid and mask_poly.is_valid: - intersection_result = test_poly.intersection(mask_poly) - if not intersection_result.is_valid: - intersection_result = intersection_result.buffer(0) - if not intersection_result.is_empty: - intersecion_score = True - return intersecion_score - - -def polygon_to_geodataframe(polygons, src_crs): - polygons = {'geometry': polygons} - return geopandas.GeoDataFrame(polygons, crs=src_crs) - - -def parse_args(): - parser = argparse.ArgumentParser(description='Script for predicting masks.') - parser.add_argument( - '--image_path', '-ip', dest='image_path', - default=f'{MODEL_TIFFS_DIR}/{area_tile_set_test.pop()}', help='Path to source image' - ) - parser.add_argument( - '--model_weights_path', '-mwp', dest='model_weights_path', - default=f'{DATA_DIR}/unet_v4.pth', help='Path to directory where pieces will be stored' - ) - parser.add_argument( - '--network', '-net', dest='network', default='unet_ch', - help='Model architecture' - ) - parser.add_argument( - '--save_path', '-sp', dest='save_path', default=f'{DATA_DIR}/predicted', - help='Path to directory where results will be stored' - ) - parser.add_argument( - '--channels', '-ch', dest='channels', - default=['rgb', 'b8', 'b8a', 'b11', 'b12', 'ndvi', 'ndmi'], - help='Channel list', nargs='+' - ) - parser.add_argument( - '--threshold', '-t', dest='threshold', - default=0.4, help='Threshold to get binary values in mask', type=float - ) - parser.add_argument( - '--polygonize_only', '-po', dest='polygonize_only', - default=False, help='Flag to skip prediction', type=bool +import requests +import yaml +from concurrent.futures import ThreadPoolExecutor + +from test.settings import MODEL_TIFFS_DIR, MAX_WORKERS, DATA_DIR +from test.utils import area_tile_set_test, path_exists_or_create +from test.utils import download_file_from_google_drive +from test.utils import gdrive_ids + +model_call_config = 'model_call_config.yml' +logger = logging.getLogger('model_call') + +def file_download(): + tile = area_tile_set_test.pop() + + test_tile_path = path_exists_or_create(f'{MODEL_TIFFS_DIR}/{tile}') + test_tile_path = {} + + test_tile_path['current'] = path_exists_or_create(f'{MODEL_TIFFS_DIR}/{tile}/{tile}_0/') + f'{tile}_0.tif' + test_tile_path['previous'] = path_exists_or_create(f'{MODEL_TIFFS_DIR}/{tile}/{tile}_1/') + f'{tile}_1.tif' + + test_tile_path['cloud_current'] = path_exists_or_create(f'{MODEL_TIFFS_DIR}/{tile}/{tile}_0/') + 'clouds.tiff' + test_tile_path['cloud_previous'] = path_exists_or_create(f'{MODEL_TIFFS_DIR}/{tile}/{tile}_1/') + 'clouds.tiff' + + with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor: + for order in test_tile_path.keys(): + if not os.path.exists(test_tile_path[order]): + executor.submit(download_file_from_google_drive, gdrive_ids[order], test_tile_path[order]) + + print(test_tile_path) + return test_tile_path + + +def model_predict(): + test_tile_path = file_download() + tif_path = "/".join(test_tile_path['current'].split('/')[:4]) + logger.info(f'raster_prediction {tif_path}') + print(f'raster_prediction {tif_path}') + results = raster_prediction(tif_path) + logger.info(f'results:\n{results}') + print(f'results:\n{results}') + results_path = os.path.join(DATA_DIR, results[0].get('polygons')) + return results, test_tile_path + +# TODO: add docstring +def raster_prediction(tif_path): + with open(model_call_config, 'r') as config: + cfg = yaml.load(config, Loader=yaml.SafeLoader) + model_api_cfg = cfg["model-api"] + api_endpoint = "http://{host}:{port}/{endpoint}".format( + host=model_api_cfg["host"], + port=model_api_cfg["port"], + endpoint=model_api_cfg["endpoint"] ) - - return parser.parse_args() - - -def main(): - args = parse_args() - path_exists_or_create(args.save_path) - path_exists_or_create(f"{args.save_path}/preds") - path_exists_or_create(f"{args.save_path}/masks") - filename = re.split(r'[./]', args.image_path)[-1] - predicted_filename = f'predicted_{filename}' - - raster_array, meta = predict_raster( - args.image_path, - args.channels, args.network, args.model_weights_path, - args.save_path - ) - # save_raster(raster_array, meta, args.save_path, filename) - - clearcuts = polygonize(raster_array > args.threshold, meta) - clearcuts = polygon_to_geodataframe(clearcuts, meta['crs']) - save_polygons(clearcuts, args.save_path, predicted_filename) - - -if __name__ == '__main__': - main() + data = {"image_path": tif_path} + logger.info(f'sending request to model API for\n{tif_path}') + try: + response = requests.post(url=api_endpoint, json=data) + result = response.text + datastore = json.loads(result) + return datastore + except (ValueError, Exception): + logger.error('Error\n\n', exc_info=True) diff --git a/clearcut_detection_backend/test/requirements.txt b/clearcut_detection_backend/test/requirements.txt index 8e4f2d2..c626c53 100644 --- a/clearcut_detection_backend/test/requirements.txt +++ b/clearcut_detection_backend/test/requirements.txt @@ -82,7 +82,7 @@ urllib3==1.25.9 utm==0.5.0 zipp==3.1.0 -catalyst==19.5 -segmentation-models-pytorch==0.1.0 -torch==1.4.0 -torchvision==0.5.0 +# catalyst==19.5 +# segmentation-models-pytorch==0.1.0 +# torch==1.4.0 +# torchvision==0.5.0 diff --git a/clearcut_detection_backend/test/Dockerfile b/clearcut_detection_backend/test/scripts/Dockerfile similarity index 97% rename from clearcut_detection_backend/test/Dockerfile rename to clearcut_detection_backend/test/scripts/Dockerfile index 232a357..39b3193 100644 --- a/clearcut_detection_backend/test/Dockerfile +++ b/clearcut_detection_backend/test/scripts/Dockerfile @@ -18,4 +18,4 @@ RUN export C_INCLUDE_PATH=/usr/include/gdal RUN pip install GDAL==$(gdal-config --version | awk -F'[.]' '{print $1"."$2}') -ADD . /test \ No newline at end of file +ADD . /test diff --git a/clearcut_detection_backend/test/download.py b/clearcut_detection_backend/test/scripts/download.py similarity index 100% rename from clearcut_detection_backend/test/download.py rename to clearcut_detection_backend/test/scripts/download.py diff --git a/clearcut_detection_backend/test/preprocess.py b/clearcut_detection_backend/test/scripts/preprocess.py similarity index 100% rename from clearcut_detection_backend/test/preprocess.py rename to clearcut_detection_backend/test/scripts/preprocess.py diff --git a/clearcut_detection_backend/test/settings.py b/clearcut_detection_backend/test/settings.py index 1207c68..32d533e 100644 --- a/clearcut_detection_backend/test/settings.py +++ b/clearcut_detection_backend/test/settings.py @@ -5,6 +5,6 @@ MAXIMUM_DATES_STORE_FOR_TILE = 2 MAX_WORKERS = 6 -DATA_DIR = Path('./data') -DOWNLOADED_IMAGES_DIR = Path('./data/source_images/') -MODEL_TIFFS_DIR = Path('./data/model_tiffs') +DATA_DIR = Path('./data/test') +DOWNLOADED_IMAGES_DIR = Path(f'{DATA_DIR}/source_images/') +MODEL_TIFFS_DIR = Path(f'{DATA_DIR}/model_tiffs') diff --git a/clearcut_detection_backend/test/test.sh b/clearcut_detection_backend/test/test.sh deleted file mode 100644 index 605a20d..0000000 --- a/clearcut_detection_backend/test/test.sh +++ /dev/null @@ -1,6 +0,0 @@ -# !bin/bash/ - -python download.py -python preprocess.py -python predict.py -python evaluate.py diff --git a/clearcut_detection_backend/test/test_config.ini b/clearcut_detection_backend/test/test_config.ini index 042c6a3..ad34c97 100644 --- a/clearcut_detection_backend/test/test_config.ini +++ b/clearcut_detection_backend/test/test_config.ini @@ -3,3 +3,8 @@ AREA_TILE_SET = 36UYA BANDS_TO_DOWNLOAD = TCI B04 B08 B8A B11 B12 DATE_CURRENT = 20190427 DATE_PREVIOUS = 20190402 +TEST_POLYGONS_URL = https://raw.githubusercontent.com/vldkhramtsov/SentinelClearcutDetection/master/data/time-dependent/36UYA_Spring_time-dependent.geojson +TEST_TILE_CURRENT_GDRIVE_ID = 1uGVCkVnA5Zdp7IlHM2S6STjHbEyf3L3C +TEST_TILE_PREVIOUS_GDRIVE_ID = 1ITe552pKCkN8sqv-qOCd04pJrGymcomi +TEST_CLOUDS_CURRENT_GDRIVE_ID = 1mfg9KpcUoh6Q2-hOJXIXuL4i2HHcGcrf +TEST_CLOUDS_PREVIOUS_GDRIVE_ID = 1yBxZGyzZDjRKXCz6dqK95TnSysGxBxf0 diff --git a/clearcut_detection_backend/test/test_data_prepare.py b/clearcut_detection_backend/test/test_data_prepare.py index 04c2311..e99fab5 100644 --- a/clearcut_detection_backend/test/test_data_prepare.py +++ b/clearcut_detection_backend/test/test_data_prepare.py @@ -4,7 +4,8 @@ from datetime import datetime from shapely.ops import unary_union -from utils import DATE_CURRENT, DATE_PREVIOUS, TEST_POLYGONS, TEST_PATH +from test.utils import DATE_CURRENT, DATE_PREVIOUS, TEST_POLYGONS +from test.settings import DATA_DIR def save_polygons(polygons, crs, save_path, filename): @@ -24,5 +25,5 @@ def prepare_testfile(): def get_gt_polygons(): test = prepare_testfile() - clearcuts = unary_union(test['geometry'].buffer(1e-5)) - return save_polygons(clearcuts, test.crs, TEST_PATH, 'test_clearcuts') + clearcuts = unary_union(test['geometry'].buffer(1e-5)).buffer(-1e-5) + return save_polygons(clearcuts, test.crs, DATA_DIR, 'test_clearcuts') diff --git a/clearcut_detection_backend/test/test_model.py b/clearcut_detection_backend/test/test_model.py deleted file mode 100644 index 6103316..0000000 --- a/clearcut_detection_backend/test/test_model.py +++ /dev/null @@ -1,124 +0,0 @@ -import json -import os -import logging -from concurrent.futures import as_completed, ThreadPoolExecutor -import requests -import yaml -from pathlib import Path -from django.conf import settings -from clearcuts.geojson_save import save -from clearcuts.models import TileInformation, RunUpdateTask, Tile -from test.prepare_tif import prepare_tiff -from services.configuration import area_tile_set_test - -model_call_config = './model_call_config.yml' -logger = logging.getLogger('model_call') - - -class ModelCaller: - """ - Class for asynchronous calling of model's API - """ - def __init__(self): - self.data_dir = settings.DATA_DIR - self.tile_index_distinct = set(area_tile_set_test) - logger.info(f'tile_index_distinct: {self.tile_index_distinct}') - - def start(self): - if settings.PATH_TYPE == 'fs': - path_type = 0 - else: - logger.error(f'Unsupported file path in settings.PATH_TYPE: {settings.PATH_TYPE}') - raise ValueError - - - with ThreadPoolExecutor(max_workers=settings.MAX_WORKERS) as executor: - future_list = [] - for tile_index in self.tile_index_distinct: - tiles = [f'{tile_index}_0', f'{tile_index}_1'] - for tile_name in tiles: - logger.info(f'ThreadPoolExecutor submit {tile_index}, {tile_name}') - future = executor.submit(prepare_tiff, tile_name) - future_list.append(future) - - results = {} - for future in as_completed(future_list): - if future.result()[0]: - self.remove_temp_files(future.result()[0], future.result()[1]) - if future.result()[2]: - tile_index = future.result()[2] - if tile_index not in results: - results[tile_index] = 1 - else: - logger.info(f'start model_predict for {tile_index}') - self.model_predict(self.query.filter(tile_index__exact=tile_index)) - del results[tile_index] - - tile_list = self.query.filter(tile_index__exact=tile_index).order_by('tile_name') - path_img_0 = tile_list[0].model_tiff_location - path_img_1 = tile_list[1].model_tiff_location - image_date_0 = tile_list[0].tile_date - image_date_1 = tile_list[1].tile_date - - tile = Tile.objects.get(tile_index=tile_index) - task = RunUpdateTask(tile_index=tile, - path_type=path_type, - path_img_0=path_img_0, - path_img_1=path_img_1, - image_date_0=image_date_0, - image_date_1=image_date_1, - ) - task.save() - - - - - if len(results) > 0: - logger.error(f'results after model_predict not empty.\n\ - results: {results}') - - @staticmethod - def remove_temp_files(path, tile_name): - logger.info(f'Try remove temp files for {tile_name}') - temp_files = Path(path).glob(f'{tile_name}*.tif') - try: - for file in temp_files: - file.unlink() - logger.info(f'temp files for {tile_name} were removed') - except OSError: - logger.error('Error\n\n', exc_info=True) - - def model_predict(self, tile): - """ - Sending unique tile_index to model and saving results to db - """ - src_tile = tile.first() - tif_path = src_tile.model_tiff_location.split('/')[:-2] - - tif_path = os.path.join(*tif_path) - logger.info(f'raster_prediction {tif_path}') - results = raster_prediction(tif_path) - logger.info(f'results:\n{results}') - results_path = os.path.join(self.data_dir, results[0].get('polygons')) - if os.path.exists(results_path): - save(tile, results_path) - -# TODO: add docstring -def raster_prediction(tif_path): - with open(model_call_config, 'r') as config: - cfg = yaml.load(config, Loader=yaml.SafeLoader) - model_api_cfg = cfg["model-api"] - api_endpoint = "http://{host}:{port}/{endpoint}".format( - host=model_api_cfg["host"], - port=model_api_cfg["port"], - endpoint=model_api_cfg["endpoint"] - ) - data = {"image_path": tif_path} - logger.info(f'sending request to model API for\n{tif_path}') - try: - response = requests.post(url=api_endpoint, json=data) - result = response.text - datastore = json.loads(result) - return datastore - except (ValueError, Exception): - logger.error('Error\n\n', exc_info=True) diff --git a/clearcut_detection_backend/test/utils.py b/clearcut_detection_backend/test/utils.py index c4b945c..6315a10 100644 --- a/clearcut_detection_backend/test/utils.py +++ b/clearcut_detection_backend/test/utils.py @@ -1,30 +1,75 @@ import os +import requests +import urllib.request from configparser import ConfigParser from datetime import datetime +from test.settings import DATA_DIR + +def download_file_from_google_drive(id, destination): + # https://stackoverflow.com/questions/38511444/python-download-files-from-google-drive-using-url + URL = "https://docs.google.com/uc?export=download" + + session = requests.Session() + + response = session.get(URL, params = { 'id' : id }, stream = True) + token = get_confirm_token(response) + + if token: + params = { 'id' : id, 'confirm' : token } + response = session.get(URL, params = params, stream = True) + + save_response_content(response, destination) + +def get_confirm_token(response): + for key, value in response.cookies.items(): + if key.startswith('download_warning'): + return value + + return None + +def save_response_content(response, destination): + CHUNK_SIZE = 32768 + + with open(destination, "wb") as f: + for chunk in response.iter_content(CHUNK_SIZE): + if chunk: # filter out keep-alive new chunks + f.write(chunk) + + def path_exists_or_create(path): if not os.path.exists(path): os.makedirs(path) return path +def download_polygons(polygons_url, polygons_save_path): + urllib.request.urlretrieve(polygons_url, polygons_save_path) + return polygons_save_path + config_test = ConfigParser(allow_no_value=True) -config_test.read('test_config.ini') +config_test.read('./test/test_config.ini') area_tile_set_test = set(config_test.get('config', 'AREA_TILE_SET').split()) bands_to_download = config_test.get('config', 'BANDS_TO_DOWNLOAD').split() date_current_test = config_test.get('config', 'DATE_CURRENT') date_previous_test = config_test.get('config', 'DATE_PREVIOUS') +test_polys_url = config_test.get('config', 'TEST_POLYGONS_URL') + +gdrive_ids = {} +gdrive_ids['current'] = config_test.get('config', 'TEST_TILE_CURRENT_GDRIVE_ID') +gdrive_ids['previous'] = config_test.get('config', 'TEST_TILE_PREVIOUS_GDRIVE_ID') +gdrive_ids['cloud_current'] = config_test.get('config', 'TEST_CLOUDS_CURRENT_GDRIVE_ID') +gdrive_ids['cloud_previous'] = config_test.get('config', 'TEST_CLOUDS_PREVIOUS_GDRIVE_ID') + -TEST_PATH = 'data/test_data' -TEST_POLYGONS = f'{TEST_PATH}/36UYA_test_data.geojson' -TEST_TILES = f'{TEST_PATH}/tiles_to_download.txt' +TEST_POLYGONS = download_polygons(test_polys_url, f'{DATA_DIR}/test_clearcuts.geojson') DATE_CURRENT = datetime.strptime(date_current_test, '%Y%m%d') DATE_PREVIOUS = datetime.strptime(date_previous_test, '%Y%m%d') # Target metrics values -GOLD_STANDARD_F1SCORES = f'{TEST_PATH}/gold_standard.txt' GOLD_DICE = 0.2811 GOLD_IOU = 0.2545 -IOU_THRESHOLD = 0.3 -SUCCESS_THRESHOLD = 0.05 +IOU_THRESHOLD = 0.1 +GOLD_F1SCORE = 0.5283 +SUCCESS_THRESHOLD = 0.06 From 372048846c8273a57aaa92ca66cd508714e55181 Mon Sep 17 00:00:00 2001 From: Vlad Khramtsov Date: Mon, 27 Jul 2020 11:01:49 +0300 Subject: [PATCH 08/13] 12601: move metrics to the remote config file; remove prints --- clearcut_detection_backend/test/predict.py | 3 -- clearcut_detection_backend/test/settings.py | 7 +++-- .../test/test_config.ini | 3 +- clearcut_detection_backend/test/utils.py | 28 +++++++++++-------- 4 files changed, 24 insertions(+), 17 deletions(-) diff --git a/clearcut_detection_backend/test/predict.py b/clearcut_detection_backend/test/predict.py index e81f5c3..c7367cf 100644 --- a/clearcut_detection_backend/test/predict.py +++ b/clearcut_detection_backend/test/predict.py @@ -30,7 +30,6 @@ def file_download(): if not os.path.exists(test_tile_path[order]): executor.submit(download_file_from_google_drive, gdrive_ids[order], test_tile_path[order]) - print(test_tile_path) return test_tile_path @@ -38,10 +37,8 @@ def model_predict(): test_tile_path = file_download() tif_path = "/".join(test_tile_path['current'].split('/')[:4]) logger.info(f'raster_prediction {tif_path}') - print(f'raster_prediction {tif_path}') results = raster_prediction(tif_path) logger.info(f'results:\n{results}') - print(f'results:\n{results}') results_path = os.path.join(DATA_DIR, results[0].get('polygons')) return results, test_tile_path diff --git a/clearcut_detection_backend/test/settings.py b/clearcut_detection_backend/test/settings.py index 32d533e..6b646c4 100644 --- a/clearcut_detection_backend/test/settings.py +++ b/clearcut_detection_backend/test/settings.py @@ -6,5 +6,8 @@ MAX_WORKERS = 6 DATA_DIR = Path('./data/test') -DOWNLOADED_IMAGES_DIR = Path(f'{DATA_DIR}/source_images/') -MODEL_TIFFS_DIR = Path(f'{DATA_DIR}/model_tiffs') +DOWNLOADED_IMAGES_DIR = f'{DATA_DIR}/source_images/' +MODEL_TIFFS_DIR = f'{DATA_DIR}/model_tiffs' + +METRIC_CONFIG = './test/metrics.ini' +TEST_CONFIG = './test/test_config.ini' diff --git a/clearcut_detection_backend/test/test_config.ini b/clearcut_detection_backend/test/test_config.ini index ad34c97..9068d01 100644 --- a/clearcut_detection_backend/test/test_config.ini +++ b/clearcut_detection_backend/test/test_config.ini @@ -3,7 +3,8 @@ AREA_TILE_SET = 36UYA BANDS_TO_DOWNLOAD = TCI B04 B08 B8A B11 B12 DATE_CURRENT = 20190427 DATE_PREVIOUS = 20190402 -TEST_POLYGONS_URL = https://raw.githubusercontent.com/vldkhramtsov/SentinelClearcutDetection/master/data/time-dependent/36UYA_Spring_time-dependent.geojson +TEST_POLYGONS_URL = https://raw.githubusercontent.com/vldkhramtsov/ClearcutServiceDatasets/master/36UYA_Spring_time-dependent.geojson +METRICS_URL = https://raw.githubusercontent.com/vldkhramtsov/ClearcutServiceDatasets/master/metrics.ini TEST_TILE_CURRENT_GDRIVE_ID = 1uGVCkVnA5Zdp7IlHM2S6STjHbEyf3L3C TEST_TILE_PREVIOUS_GDRIVE_ID = 1ITe552pKCkN8sqv-qOCd04pJrGymcomi TEST_CLOUDS_CURRENT_GDRIVE_ID = 1mfg9KpcUoh6Q2-hOJXIXuL4i2HHcGcrf diff --git a/clearcut_detection_backend/test/utils.py b/clearcut_detection_backend/test/utils.py index 6315a10..8c04496 100644 --- a/clearcut_detection_backend/test/utils.py +++ b/clearcut_detection_backend/test/utils.py @@ -5,7 +5,7 @@ from configparser import ConfigParser from datetime import datetime -from test.settings import DATA_DIR +from test.settings import DATA_DIR, METRIC_CONFIG, TEST_CONFIG def download_file_from_google_drive(id, destination): # https://stackoverflow.com/questions/38511444/python-download-files-from-google-drive-using-url @@ -43,18 +43,19 @@ def path_exists_or_create(path): os.makedirs(path) return path -def download_polygons(polygons_url, polygons_save_path): - urllib.request.urlretrieve(polygons_url, polygons_save_path) - return polygons_save_path +def download_dataset(file_url, save_path): + urllib.request.urlretrieve(file_url, save_path) + return save_path config_test = ConfigParser(allow_no_value=True) -config_test.read('./test/test_config.ini') +config_test.read(TEST_CONFIG) area_tile_set_test = set(config_test.get('config', 'AREA_TILE_SET').split()) bands_to_download = config_test.get('config', 'BANDS_TO_DOWNLOAD').split() date_current_test = config_test.get('config', 'DATE_CURRENT') date_previous_test = config_test.get('config', 'DATE_PREVIOUS') test_polys_url = config_test.get('config', 'TEST_POLYGONS_URL') +metrics_url = config_test.get('config', 'METRICS_URL') gdrive_ids = {} gdrive_ids['current'] = config_test.get('config', 'TEST_TILE_CURRENT_GDRIVE_ID') @@ -63,13 +64,18 @@ def download_polygons(polygons_url, polygons_save_path): gdrive_ids['cloud_previous'] = config_test.get('config', 'TEST_CLOUDS_PREVIOUS_GDRIVE_ID') -TEST_POLYGONS = download_polygons(test_polys_url, f'{DATA_DIR}/test_clearcuts.geojson') +TEST_POLYGONS = download_dataset(test_polys_url, f'{DATA_DIR}/test_clearcuts.geojson') DATE_CURRENT = datetime.strptime(date_current_test, '%Y%m%d') DATE_PREVIOUS = datetime.strptime(date_previous_test, '%Y%m%d') # Target metrics values -GOLD_DICE = 0.2811 -GOLD_IOU = 0.2545 -IOU_THRESHOLD = 0.1 -GOLD_F1SCORE = 0.5283 -SUCCESS_THRESHOLD = 0.06 +download_dataset(metrics_url, METRIC_CONFIG) + +config_metrics = ConfigParser(allow_no_value=True) +config_metrics.read(METRIC_CONFIG) + +GOLD_DICE = config_metrics.get('metric', 'GOLD_DICE') +GOLD_IOU = config_metrics.get('metric', 'GOLD_IOU') +IOU_THRESHOLD = config_metrics.get('metric', 'IOU_THRESHOLD') +GOLD_F1SCORE = config_metrics.get('metric', 'GOLD_F1SCORE') +SUCCESS_THRESHOLD = config_metrics.get('metric', 'SUCCESS_THRESHOLD') From 8e62d27a6d9d3743cf42f8d561cab47c87c961e1 Mon Sep 17 00:00:00 2001 From: Vlad Khramtsov Date: Wed, 29 Jul 2020 18:03:18 +0300 Subject: [PATCH 09/13] 12601: add automatic creating DATA_DIR --- clearcut_detection_backend/test/utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/clearcut_detection_backend/test/utils.py b/clearcut_detection_backend/test/utils.py index 8c04496..38ea2a3 100644 --- a/clearcut_detection_backend/test/utils.py +++ b/clearcut_detection_backend/test/utils.py @@ -47,6 +47,8 @@ def download_dataset(file_url, save_path): urllib.request.urlretrieve(file_url, save_path) return save_path +path_exists_or_create(DATA_DIR) + config_test = ConfigParser(allow_no_value=True) config_test.read(TEST_CONFIG) From f4e50a4368d2fa392c89bd02c423c07161460e53 Mon Sep 17 00:00:00 2001 From: Vlad Khramtsov Date: Wed, 29 Jul 2020 19:16:04 +0300 Subject: [PATCH 10/13] 12601: change str values of metrics into float --- clearcut_detection_backend/test/utils.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/clearcut_detection_backend/test/utils.py b/clearcut_detection_backend/test/utils.py index 38ea2a3..0aeb961 100644 --- a/clearcut_detection_backend/test/utils.py +++ b/clearcut_detection_backend/test/utils.py @@ -76,8 +76,8 @@ def download_dataset(file_url, save_path): config_metrics = ConfigParser(allow_no_value=True) config_metrics.read(METRIC_CONFIG) -GOLD_DICE = config_metrics.get('metric', 'GOLD_DICE') -GOLD_IOU = config_metrics.get('metric', 'GOLD_IOU') -IOU_THRESHOLD = config_metrics.get('metric', 'IOU_THRESHOLD') -GOLD_F1SCORE = config_metrics.get('metric', 'GOLD_F1SCORE') -SUCCESS_THRESHOLD = config_metrics.get('metric', 'SUCCESS_THRESHOLD') +GOLD_DICE = float(config_metrics.get('metric', 'GOLD_DICE')) +GOLD_IOU = float(config_metrics.get('metric', 'GOLD_IOU')) +IOU_THRESHOLD = float(float(config_metrics.get('metric', 'IOU_THRESHOLD'))) +GOLD_F1SCORE = float(config_metrics.get('metric', 'GOLD_F1SCORE')) +SUCCESS_THRESHOLD = float(config_metrics.get('metric', 'SUCCESS_THRESHOLD')) From c59c8252f89c5b7bced413276f77d702deb673cb Mon Sep 17 00:00:00 2001 From: Vlad Khramtsov Date: Wed, 5 Aug 2020 16:19:24 +0300 Subject: [PATCH 11/13] 12601: test model to GPU --- clearcut_detection_backend/docker-compose-test.yml | 2 ++ clearcut_detection_backend/model/model.Dockerfile | 10 ++++++---- clearcut_detection_backend/model/predict_raster.py | 2 ++ 3 files changed, 10 insertions(+), 4 deletions(-) diff --git a/clearcut_detection_backend/docker-compose-test.yml b/clearcut_detection_backend/docker-compose-test.yml index ddde612..ebc27ea 100644 --- a/clearcut_detection_backend/docker-compose-test.yml +++ b/clearcut_detection_backend/docker-compose-test.yml @@ -11,6 +11,8 @@ services: - ./model/:/model - ./data/:/model/data working_dir: /model + environment: + - CUDA_VISIBLE_DEVICES=0 ports: - '5000:5000' command: /bin/bash -c "python app.py" diff --git a/clearcut_detection_backend/model/model.Dockerfile b/clearcut_detection_backend/model/model.Dockerfile index 3e6aeb3..f1f7f96 100644 --- a/clearcut_detection_backend/model/model.Dockerfile +++ b/clearcut_detection_backend/model/model.Dockerfile @@ -1,11 +1,13 @@ -FROM python:3.6 +FROM nvidia/cuda:10.0-cudnn7-runtime-ubuntu18.04 + +RUN apt-get update && apt-get install -y python3-pip RUN mkdir /model WORKDIR /model -ADD requirements.txt /model +COPY requirements.txt /model -RUN pip install -r requirements.txt +RUN pip3 install -r requirements.txt -ADD . /model/ +COPY . /model/ diff --git a/clearcut_detection_backend/model/predict_raster.py b/clearcut_detection_backend/model/predict_raster.py index d5c162b..bc7a8e8 100644 --- a/clearcut_detection_backend/model/predict_raster.py +++ b/clearcut_detection_backend/model/predict_raster.py @@ -31,6 +31,8 @@ import warnings warnings.filterwarnings('ignore') +os.environ.get('CUDA_VISIBLE_DEVICES', '0') + logging.basicConfig(format='%(asctime)s %(message)s') def load_model(network, model_weights_path, channels, neighbours): From 5d409c51545df12d6a7f8513e0adbfab7eaf182f Mon Sep 17 00:00:00 2001 From: Vlad Khramtsov Date: Wed, 5 Aug 2020 16:21:55 +0300 Subject: [PATCH 12/13] 12601: fix model/requirements.txt --- clearcut_detection_backend/model/requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/clearcut_detection_backend/model/requirements.txt b/clearcut_detection_backend/model/requirements.txt index 5e736fd..a17305a 100644 --- a/clearcut_detection_backend/model/requirements.txt +++ b/clearcut_detection_backend/model/requirements.txt @@ -1,6 +1,6 @@ catalyst==19.5 Flask==1.1.1 -geopandas==0.5.1 +geopandas==0.8.1 google-api-python-client==1.8.0 google-auth-httplib2==0.0.3 google-auth-oauthlib==0.4.1 @@ -14,3 +14,4 @@ tqdm==4.19.9 oauth2client==4.1.3 Pillow==6.2.2 PyYAML==5.1.1 +opencv-python-headless==4.1.0.25 From b6044d05107563e2634f57ecf260b98a439538f6 Mon Sep 17 00:00:00 2001 From: Vlad Khramtsov Date: Fri, 7 Aug 2020 13:06:58 +0300 Subject: [PATCH 13/13] 12601: change to python3 in docker-compose-test.yml --- clearcut_detection_backend/docker-compose-test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/clearcut_detection_backend/docker-compose-test.yml b/clearcut_detection_backend/docker-compose-test.yml index ebc27ea..3b8a73e 100644 --- a/clearcut_detection_backend/docker-compose-test.yml +++ b/clearcut_detection_backend/docker-compose-test.yml @@ -15,7 +15,7 @@ services: - CUDA_VISIBLE_DEVICES=0 ports: - '5000:5000' - command: /bin/bash -c "python app.py" + command: /bin/bash -c "python3 app.py" test: build: