from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import os import cv2 import imageio import numpy as np import cv2 as cv import scipy import xmltodict from pycocotools import mask as mask_util # ref: https://www.kaggle.com/stainsby/fast-tested-rle-and-input-routines def rle_encode(mask): pixels = mask.T.flatten() # We need to allow for cases where there is a '1' at either end of the sequence. # We do this by padding with a zero at each end when needed. use_padding = False if pixels[0] or pixels[-1]: use_padding = True pixel_padded = np.zeros([len(pixels) + 2], dtype=pixels.dtype) pixel_padded[1:-1] = pixels pixels = pixel_padded rle = np.where(pixels[1:] != pixels[:-1])[0] + 2 if use_padding: rle = rle - 1 rle[1::2] = rle[1::2] - rle[:-1:2] return rle def rle_to_string(runs): return ' '.join(str(x) for x in runs) # This is copied from https://www.kaggle.com/paulorzp/run-length-encode-and-decode. # Thanks to Paulo Pinto. def rle_decode(rle_str, mask_shape, mask_dtype): s = rle_str.split() starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])] starts -= 1 ends = starts + lengths mask = np.zeros(np.prod(mask_shape), dtype=mask_dtype) for lo, hi in zip(starts, ends): mask[lo:hi] = 1 return mask.reshape(mask_shape[::-1]).T def encode_mask_to_poly(mask, mask_id, image_id): if len(mask.shape) == 3: mask = cv.cvtColor(mask, cv.COLOR_BGR2GRAY) kernel = np.ones((2, 2), np.uint8) mask = cv.dilate(mask, kernel, iterations=1) _, C, h = cv.findContours(mask, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE) seg = [[float(x) for x in contour.flatten()] for contour in C] seg = [cont for cont in seg if len(cont) > 4] # filter all polygons that are boxes rle = mask_util.frPyObjects(seg, mask.shape[0], mask.shape[1]) return { 'area': float(sum(mask_util.area(rle))), 'bbox': list(mask_util.toBbox(rle)[0]), 'category_id': 1, 'id': mask_id, 'image_id': image_id, 'iscrowd': 0, 'segmentation': seg } def encode_mask_to_rle(mask, mask_id, image_id): seg = mask_util.encode(np.asarray(mask, order='F')) return encode_rle(seg, mask_id, image_id) def encode_rle(rle, mask_id, image_id): rle['counts'] = rle['counts'].decode('utf-8') return { 'image_id': image_id, 'segmentation': rle, 'category_id': 1, 'id': mask_id, 'area': int(mask_util.area(rle)), 'bbox': list(mask_util.toBbox(rle)), 'iscrowd': 0 } def regions_to_rle(regions, shape): R = [r.flatten() for r in regions] rle = mask_util.frPyObjects(R, shape[0], shape[1]) return rle def parse_xml_annotations(file_path): with open(file_path) as f: xml = f.read() ann = xmltodict.parse(xml) regions = [] if isinstance(ann['Annotations']['Annotation'], list): print('Found Multiple regions') for a in ann['Annotations']['Annotation']: if 'Regions' in a and 'Region' in a['Regions']: for region in a['Regions']['Region']: vertices = [] for v in region['Vertices']['Vertex']: vertices.append([float(v['@X']), float(v['@Y'])]) regions.append(np.asarray(vertices)) else: for region in ann['Annotations']['Annotation']['Regions']['Region']: vertices = [] for v in region['Vertices']['Vertex']: vertices.append([float(v['@X']), float(v['@Y'])]) regions.append(np.asarray(vertices)) return regions def filter_contours(contours, H): C = [] i = 0 while i != -1: j = H[i][2] while j != -1: C.append(contours[j]) j = H[j][0] i = H[i][0] kernel = np.ones((3, 3), np.uint8) def dedupe_contours(rles, dataset): M = mask_util.decode(rles) all_mask = M[:, :, 0].copy() all_mask[:] = False areas = np.sum(M, (0, 1)) sort_idx = areas.argsort() areas = areas[sort_idx] M = M[:, :, sort_idx] res = [] im_size = M.shape[0] * M.shape[1] for idx in range(M.shape[-1]): if areas[idx] < 30 or areas[idx] > im_size * 0.5: continue m = M[:, :, idx] intersection = m & all_mask area_inter = intersection.sum() if area_inter > 30: continue else: mask = m & ~all_mask total_area = mask.sum() if total_area < 30: continue if dataset not in ['2009_ISBI_2DNuclei', 'cluster_nuclei']: m = cv.dilate(m, kernel, iterations=1) all_mask = m | all_mask res.append(m) if not res: return None M2 = np.stack(res).transpose((1, 2, 0)) if dataset == '2009_ISBI_2DNuclei': M2 = scipy.ndimage.zoom(M2, (0.4, 0.4, 1), order=1) rles = mask_util.encode(np.asarray(M2, dtype=np.uint8, order='F')) return rles def parse_segments_from_outlines(outline_path, dataset): if dataset == 'BBBC006': import imread masks = imread.imread(outline_path) rles = [] for idx in range(1, masks.max() + 1): rles.append(mask_util.encode(np.asarray(masks == idx, dtype=np.uint8, order='F'))) return rles if dataset == 'BBBC020': out_dir, prefix = outline_path.rsplit('/', 1) files = os.listdir(out_dir) masks = [] for f_name in files: if prefix in f_name: m = imageio.imread(os.path.join(out_dir, f_name)) m[m > 0] = 1 m = scipy.ndimage.zoom(m, (0.4, 0.4), order=1) masks.append(m) rles = [] for m in masks: rles.append(mask_util.encode(np.asarray(m, dtype=np.uint8, order='F'))) return rles if dataset == '2009_ISBI_2DNuclei': import imread outlines = imread.imread(outline_path) else: outlines = imageio.imread(outline_path) if dataset == 'cluster_nuclei' or dataset == '2009_ISBI_2DNuclei': outlines[outlines != [255, 0, 0]] = 0 imgray = cv2.cvtColor(outlines, cv2.COLOR_RGB2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) elif dataset == 'BBBC007': thresh = np.asarray(outlines, np.uint8) elif dataset == 'BBBC018': thresh = outlines thresh[0, :] = 1 thresh[:, 0] = 1 thresh[:, -1] = 1 thresh[-1, :] = 1 im, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE) seg = [[float(x) for x in c.flatten()] for c in contours] seg = [cont for cont in seg if len(cont) > 4] # filter all polygons that are boxes if not seg: return [] rles = mask_util.frPyObjects(seg, outlines.shape[0], outlines.shape[1]) rles = dedupe_contours(rles, dataset) return rles