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How to use _parse_annotation method in pandera
Best Python code snippet using pandera_python
imdb.py
Source:imdb.py 
...29 for attr in self.cache_attr:30 if attr in cache:31 setattr(self, attr, cache[attr])32 except:33 self._parse_annotation()34 self._set_bbox_range()35 self._bbox_sanity()36 self._set_minmax()37 self._save_cache()38 self._filter()39 self._order()40 else:41 self.imgpath = np.empty(0)42 self.imgsize = np.empty((0, 2), dtype=np.int32)43 self.bbox_original = np.empty((0, 4), dtype=np.int32)44 self.bbox_min = np.empty((0, 4), dtype=np.int32)45 self.bbox_max = np.empty((0, 4), dtype=np.int32)46 self.minsize = np.empty(0, dtype=np.int32)47 self.maxsize = np.empty(0, dtype=np.int32)48 # For UMDFaces49 self.angle = np.empty((0, 3), dtype=np.float32) # yaw, pitch, roll50 self.points = np.empty((0, 21, 2), dtype=np.int32) # 21 points51 self.lab = np.empty((0, 3), dtype=np.float32) # mean LAB of face region52 # For Photoshop53 self.partname = np.empty(0)54 self.level = np.empty(0, dtype=np.int32)55 self.bbox_mask = np.empty((0, 4), dtype=np.int32)56 def _set(self, kwargs):57 self.ignore_cache = False 58 def _parse_annotation(self):59 raise NotImplementedError()60 def _set_bbox_range(self):61 raise NotImplementedError()62 def _bbox_sanity(self):63 self.bbox_min[:,:2] = np.maximum(0, self.bbox_min[:,:2]) # top, left (minbox)64 self.bbox_max[:,:2] = np.maximum(0, self.bbox_max[:,:2]) # top, left (maxbox)65 if hasattr(self, 'imgsize'):66 self.bbox_min[:,2] = np.minimum(self.imgsize[:,0], self.bbox_min[:,2]) # bottom67 self.bbox_max[:,2] = np.minimum(self.imgsize[:,0], self.bbox_max[:,2]) # bottom68 self.bbox_min[:,3] = np.minimum(self.imgsize[:,1], self.bbox_min[:,3]) # right69 self.bbox_max[:,3] = np.minimum(self.imgsize[:,1], self.bbox_max[:,3]) # right70 def _set_minmax(self):71 min_width = self.bbox_min[:,3] - self.bbox_min[:,1]72 min_height = self.bbox_min[:,2] - self.bbox_min[:,0]73 max_width = self.bbox_max[:,3] - self.bbox_max[:,1]74 max_height = self.bbox_max[:,2] - self.bbox_max[:,0]75 self.minsize = np.maximum(min_width, (min_height / self.cfg.dataset.ar).astype(np.int32))76 self.maxsize = np.minimum(max_width, (max_height / self.cfg.dataset.ar).astype(np.int32))77 def _filter(self):78 reso_th = self.cfg.dataset.resolution_thres_factor * self.cfg.dataset.resolution79 reso_th = max(self.cfg.dataset.min_original_resolution, reso_th)80 mask_w = (self.bbox_max[:,3] - self.bbox_max[:,1]) > reso_th81 mask_h = (self.bbox_max[:,2] - self.bbox_max[:,0]) > reso_th * self.cfg.dataset.ar82 mask_minmax = self.minsize <= self.maxsize83 mask = mask_w & mask_h & mask_minmax 84 if hasattr(self, 'angle'):85 angle_th = self.cfg.dataset.angle_th86 mask_angle = (-angle_th < self.angle) & (self.angle < angle_th)87 mask_angle = mask_angle.all(1)88 mask = mask & mask_angle89 90 self.imgpath = self.imgpath[mask]91 self.bbox_original = self.bbox_original[mask]92 self.bbox_min = self.bbox_min[mask]93 self.bbox_max = self.bbox_max[mask]94 self.minsize = self.minsize[mask]95 self.maxsize = self.maxsize[mask]96 if hasattr(self, 'imgsize'):97 self.imgsize = self.imgsize[mask]98 if hasattr(self, 'angle'):99 self.angle = self.angle[mask]100 if hasattr(self, 'points'):101 self.points = self.points[mask]102 if hasattr(self, 'lab'):103 self.lab = self.lab[mask]104 if hasattr(self, 'partname'):105 self.partname = self.partname[mask]106 if hasattr(self, 'level'):107 self.level = self.level[mask]108 if hasattr(self, 'bbox_mask'):109 self.bbox_mask = self.bbox_mask[mask]110 def _order(self):111 idx = np.argsort(self.imgpath, axis=0)112 self.imgpath = self.imgpath[idx]113 self.bbox_original = self.bbox_original[idx]114 self.bbox_min = self.bbox_min[idx]115 self.bbox_max = self.bbox_max[idx]116 self.minsize = self.minsize[idx]117 self.maxsize = self.maxsize[idx]118 if hasattr(self, 'imgsize'):119 self.imgsize = self.imgsize[idx]120 if hasattr(self, 'angle'):121 self.angle = self.angle[idx]122 if hasattr(self, 'points'):123 self.points = self.points[idx]124 if hasattr(self, 'lab'):125 self.lab = self.lab[idx]126 if hasattr(self, 'partname'):127 self.partname = self.partname[idx]128 if hasattr(self, 'level'):129 self.level = self.level[idx]130 if hasattr(self, 'bbox_mask'):131 self.bbox_mask = self.bbox_mask[idx]132 133 def split(self, num_2):134 imdb_1 = Imdb(self.cfg, self.name, virtual=True, logger=self.logger)135 imdb_2 = Imdb(self.cfg, self.name, virtual=True, logger=self.logger)136 # num_val = self.cfg.dataset.num_val[self.name]137 num_1 = len(self.imgpath) - num_2138 imdb_1.imgpath = self.imgpath[:num_1]139 imdb_1.bbox_original = self.bbox_original[:num_1]140 imdb_1.bbox_min = self.bbox_min[:num_1]141 imdb_1.bbox_max = self.bbox_max[:num_1]142 imdb_1.minsize = self.minsize[:num_1]143 imdb_1.maxsize = self.maxsize[:num_1]144 imdb_2.imgpath = self.imgpath[num_1:]145 imdb_2.bbox_original = self.bbox_original[num_1:]146 imdb_2.bbox_min = self.bbox_min[num_1:]147 imdb_2.bbox_max = self.bbox_max[num_1:]148 imdb_2.minsize = self.minsize[num_1:]149 imdb_2.maxsize = self.maxsize[num_1:]150 if hasattr(self, 'imgsize'):151 imdb_1.imgsize = self.imgsize[:num_1]152 imdb_2.imgsize = self.imgsize[num_1:]153 if hasattr(self, 'angle'):154 imdb_1.angle = self.angle[:num_1]155 imdb_2.angle = self.angle[num_1:]156 if hasattr(self, 'points'):157 imdb_1.points = self.points[:num_1]158 imdb_2.points = self.points[num_1:]159 160 if hasattr(self, 'lab'):161 imdb_1.lab = self.lab[:num_1]162 imdb_2.lab = self.lab[num_1:]163 if hasattr(self, 'partname'):164 imdb_1.partname = self.partname[:num_1]165 imdb_2.partname = self.partname[num_1:]166 if hasattr(self, 'level'):167 imdb_1.level = self.level[:num_1]168 imdb_2.level = self.level[num_1:]169 if hasattr(self, 'bbox_mask'):170 imdb_1.bbox_mask = self.bbox_mask[:num_1]171 imdb_2.bbox_mask = self.bbox_mask[num_1:]172 return imdb_1, imdb_2 173 def _check_cache(self, cfg):174 assert not self.ignore_cache175 with open(self.cache_filename, 'rb') as f:176 cache = pickle.load(f)177 return cache178 def _save_cache(self):179 os.makedirs(self.cfg.cache_path, exist_ok=True)180 with open(self.cache_filename, 'wb') as f:181 cache_data = {}182 for attr in self.cache_attr:183 if hasattr(self, attr):184 cache_data[attr] = getattr(self, attr)185 pickle.dump(cache_data, f)186class Union(Imdb):187 def _set(self, kwargs):188 self.train = kwargs['train']189 self.imdbs = dict()190 self.weights = dict()191 self.total = 0192 def merge(self, imdb, weight=0.0):193 assert self.virtual194 self.imdbs[imdb.name] = imdb195 self.weights[imdb.name] = weight196 def initialize(self):197 if self.train:198 total_candidates = [199 int(len(self.imdbs[imdb_name].imgpath) / self.weights[imdb_name]) \200 for imdb_name in self.imdbs.keys()]201 self.total = np.array(total_candidates).min()202 else:203 self.total = 0204 def reset(self):205 self.imgpath = np.empty(0)206 self.imgsize = np.empty((0, 2), dtype=np.int32)207 self.bbox_original = np.empty((0, 4), dtype=np.int32)208 self.bbox_min = np.empty((0, 4), dtype=np.int32)209 self.bbox_max = np.empty((0, 4), dtype=np.int32)210 self.minsize = np.empty(0, dtype=np.int32)211 self.maxsize = np.empty(0, dtype=np.int32)212 # For UMDFaces213 self.angle = np.empty((0, 3), dtype=np.float32) # yaw, pitch, roll214 self.points = np.empty((0, 21, 2), dtype=np.int32) # 21 points215 self.lab = np.empty((0, 3), dtype=np.float32) # mean LAB of face region216 # For Photoshop217 self.partname = np.empty(0)218 self.level = np.empty(0, dtype=np.int32)219 self.bbox_mask = np.empty((0, 4), dtype=np.int32)220 for imdb_name in self.imdbs.keys():221 imdb = self.imdbs[imdb_name]222 length = len(imdb.imgpath)223 weight = self.weights[imdb_name]224 if self.train:225 num_select = int(self.total * weight)226 select = np.random.choice(length, size=num_select, replace=False)227 else:228 select = np.arange(length)229 num_select = len(select)230 self.imgpath = np.append(self.imgpath, imdb.imgpath[select], axis=0)231 self.bbox_original = np.append(self.bbox_original, imdb.bbox_original[select], axis=0)232 self.bbox_min = np.append(self.bbox_min, imdb.bbox_min[select], axis=0)233 self.bbox_max = np.append(self.bbox_max, imdb.bbox_max[select], axis=0)234 self.minsize = np.append(self.minsize, imdb.minsize[select], axis=0)235 self.maxsize = np.append(self.maxsize, imdb.maxsize[select], axis=0)236 if hasattr(imdb, 'imgsize') and len(imdb.imgsize) == length:237 self.imgsize = np.append(self.imgsize, imdb.imgsize[select], axis=0)238 else:239 self.imgsize = np.append(self.imgsize, np.zeros((num_select, 2)), axis=0)240 if hasattr(imdb, 'angle') and len(imdb.angle) == length:241 self.angle = np.append(self.angle, imdb.angle[select], axis=0)242 else:243 self.angle = np.append(self.angle, np.ones((num_select, 3)) * (-360), axis=0)244 245 if hasattr(imdb, 'points') and len(imdb.points) == length:246 self.points = np.append(self.points, imdb.points[select], axis=0)247 else:248 self.points = np.append(self.points, np.zeros((num_select, 21, 2)), axis=0)249 250 if hasattr(imdb, 'lab') and len(imdb.lab) == length:251 self.lab = np.append(self.lab, imdb.lab[select], axis=0)252 else:253 self.lab = np.append(self.lab, np.zeros((num_select, 3)), axis=0)254class AFLW(Imdb):255 def _set(self, kwargs):256 raise NotImplementedError()257 def _parse_annotation(self):258 root = os.path.join(self.cfg.data_path, self.name)259 import sqlite3 as sq260 aflw_sq_path = os.path.join(root, 'data/aflw.sqlite') 261 aflw_sq = sq.connect(aflw_sq_path)262 aflw_cur = aflw_sq.cursor()263 aflw_cur.execute("SELECT name FROM sqlite_master WHERE type='table';")264 table_name = aflw_cur.fetchall()265 # fetch image_name, face_rect and feature coordinates from db# fetch i 266 faces = aflw_cur.execute("SELECT * FROM Faces")267 face_ids = faces.fetchall()268 face_names = []269 face_rects = []270 face_features = []271 imgpath = list()272 bbox = list()273 for i in range(len(face_ids)): 274 # get face_id and file_id275 face_id = face_ids[i][0]276 file_id_sqlite = "SELECT file_id FROM Faces WHERE face_id ='" + str(face_id) + "'"277 file_id = aflw_cur.execute(file_id_sqlite).fetchall()278 file_id = file_id[0][0] # 'img00035.jpg'279 if len(file_id) < 1:280 continue281 282 # get file_path283 face_name_query = "SELECT filepath FROM FaceImages WHERE file_id = '"+ file_id + "'"284 face_name = aflw_cur.execute(face_name_query).fetchall()285 face_name = face_name[0][0] # '3/image00035.jpg'286 # rect287 feature_rect_query = "SELECT FaceRect.x,FaceRect.y,FaceRect.w,FaceRect.h FROM FaceRect WHERE face_id ='" + str(face_id) + "'"288 feature_rect = aflw_cur.execute(feature_rect_query).fetchall() # [(62, 64, 348, 348)]289 if len(feature_rect) < 1:290 continue291 292 feature_rect = feature_rect[0]293 x = feature_rect[0]294 y = feature_rect[1]295 w = feature_rect[2]296 h = feature_rect[3]297 298 # coor (normalize to 0~1)299 feature_coor_query = "SELECT descr,FeatureCoords.x,FeatureCoords.y FROM FeatureCoords,FeatureCoordTypes WHERE face_id ='" + str(face_id) + "' AND FeatureCoords.feature_id = FeatureCoordTypes.feature_id"300 feature_coor = aflw_cur.execute(feature_coor_query).fetchall() 301 coor_x = [-1 for k in range(5)]302 coor_y = [-1 for k in range(5)]303 for j in range(len(feature_coor)):304 if feature_coor[j][0] == 'LeftEyeCenter':305 coor_x[0] = feature_coor[j][1]306 coor_y[0] = feature_coor[j][2]307 elif feature_coor[j][0] == 'RightEyeCenter':308 coor_x[1] = feature_coor[j][1]309 coor_y[1] = feature_coor[j][2]310 elif feature_coor[j][0] == 'NoseCenter':311 coor_x[2] = feature_coor[j][1]312 coor_y[2] = feature_coor[j][2]313 elif feature_coor[j][0] == 'MouthLeftCorner':314 coor_x[3] = feature_coor[j][1]315 coor_y[3] = feature_coor[j][2]316 elif feature_coor[j][0] == 'MouthRightCorner':317 coor_x[4] = feature_coor[j][1]318 coor_y[4] = feature_coor[j][2]319 320 coor = []321 coor.append(coor_x)322 coor.append(coor_y)323 imgpath.append(os.path.join(self.name, 'aflw/data/flickr', face_name))324 bbox.append([y, x, y + h, x + w])325 # self.coor.append(coor)326 aflw_cur.close()327 aflw_sq.close()328 self.imgpath = np.array(imgpath)329 self.bbox_original = np.array(bbox, dtype=np.int32)330 def _set_bbox_range(self):331 top, left, bottom, right = np.split(self.bbox_original, np.arange(1, 4), axis=1)332 h = bottom - top333 w = right - left334 size_min = np.max((h, w), 0) * 1.2335 size_max = np.max((h, w), 0) * 1.4336 cx = np.mean((left, right), 0)337 cy = np.mean((top, bottom), 0)338 cx_min = cx339 cx_max = cx340 cy_min = cy - 0.15 * size_min341 cy_max = cy - 0.15 * size_max342 self.bbox_min = np.hstack((343 cy_min - 0.5 * size_min,344 cx_min - 0.4 * size_min,345 cy_min + 0.5 * size_min,346 cx_min + 0.4 * size_min)).astype(np.int32)347 self.bbox_max = np.hstack((348 cy_max - 0.5 * size_max,349 cx_max - 0.5 * size_max,350 cy_max + 0.5 * size_max,351 cx_max + 0.5 * size_max)).astype(np.int32)352class CelebA(Imdb):353 354 def _set(self, kwargs):355 raise NotImplementedError()356 def _parse_annotation(self):357 root = os.path.join(self.cfg.data_path, self.name)358 anno_file = open(os.path.join(root, 'Anno/list_bbox_celeba.txt'), 'r')359 lines = anno_file.readlines()360 for line in lines[2:]:361 assert '.jpg' in line362 elem = list(filter(lambda a: a != '', line.strip().split(' ')))363 imgname = elem[0]364 left, top, width, height = tuple(elem[1:])365 left = int(left)366 top = int(top)367 width = int(width)368 height = int(height)369 bbox = [top, left, top + height, left + width]370 relpath = os.path.join(self.name, 'Img/img_celeba')371 self.imgpath.append(os.path.join(relpath, imgname))372 self.bbox_original.append(bbox)373 anno_file.close()374 375 def _set_bbox_range(self):376 for bb_o in self.bbox_original:377 top, left, bottom, right = bb_o378 h = bottom - top379 w = right - left380 center_x = (left + right) / 2381 w_min = 0.8 * w382 top_min = int(top + 0.1 * h)383 left_min = int(center_x - w_min / 2)384 bottom_min = int(bottom - 0.25 * h)385 right_min = int(center_x + w_min / 2)386 center_y = (top_min + bottom_min) / 2387 h_new = 2 * (bottom_min - top_min)388 w_new = 2 * (right_min - left_min)389 top_pad = int(center_y - h_new / 2)390 left_pad = int(center_x - w_new / 2)391 bottom_pad = int(center_y + h_new / 2)392 right_pad = int(center_x + w_new / 2)393 bbox_min = [top_min, left_min, bottom_min, right_min]394 bbox_max = [top_pad, left_pad, bottom_pad, right_pad]395 self.bbox_min.append(bbox_min)396 self.bbox_max.append(bbox_max)397class CelebA_HQ(Imdb):398 def _set(self, kwargs):399 self.num_img = 30 * 1000400 def _parse_annotation(self):401 imgpath = list()402 imgsize = list()403 bbox = list()404 for i in range(self.num_img):405 imgname = '{:06d}.png'.format(i)406 imgpath.append(os.path.join(self.name, imgname))407 imgsize.append([1024, 1024])408 bbox.append([0, 0, 1024, 1024])409 410 self.imgpath = np.array(imgpath)411 self.imgsize = np.array(imgsize, dtype=np.int32)412 self.bbox_original = np.array(bbox, dtype=np.int32)413 414 def _set_bbox_range(self):415 self.bbox_min = np.copy(self.bbox_original)416 self.bbox_max = np.copy(self.bbox_original)417 # offset_x = 212418 # offset_y = 170419 # min_size_x = 600420 # min_size_y = 650421 # top = offset_y422 # bottom = top + min_size_y423 # left = offset_x424 # right = left + min_size_x425 # length = len(self.bbox_original)426 # self.bbox_min = np.array([[top, left, bottom, right]] * length, dtype=np.int32)427 # self.bbox_max = np.array([[0, 0, 1024, 1024]] * length, dtype=np.int32)428class IJB_C(Imdb):429 def _set(self, kwargs):430 raise NotImplementedError()431 def _parse_annotation(self):432 root = os.path.join(self.cfg.data_path, self.name)433 anno_file = open(os.path.join(root, 'protocols/ijbc_face_detection_ground_truth.csv'))434 lines = anno_file.readlines()435 for line in lines[1:]:436 imgpath, left, top, width, height, ignore = tuple(line.strip().split(','))437 if imgpath.split('/')[0] != 'nonfaces':438 left = int(left)439 top = int(top)440 width = int(width)441 height = int(height)442 ignore = int(ignore)443 imgpath = os.path.join(self.name, 'images', imgpath)444 bbox = [top, left, top + height, left + width]445 self.imgpath.append(imgpath)446 self.bbox_original.append(bbox)447 # self.ignore.append(ignore)448 anno_file.close()449 def _set_bbox_range(self):450 for bb_o in self.bbox_original:451 top, left, bottom, right = bb_o452 center_y = (top + bottom) / 2453 center_x = (left + right) / 2454 h = bottom - top455 w = right - left456 # top_min = int(top)457 # left_min = int(left + 0.1 * w)458 # bottom_min = int(bottom)459 # right_min = int(right - 0.1 * w)460 h_new = 2 * h461 w_new = 2 * w462 top_pad = int(center_y - h_new / 2)463 left_pad = int(center_x - w_new / 2)464 bottom_pad = int(center_y + h_new / 2)465 right_pad = int(center_x + w_new / 2)466 # bbox_min = [top_min, left_min, bottom_min, right_min]467 bbox_min = bb_o468 bbox_max = [top_pad, left_pad, bottom_pad, right_pad]469 self.bbox_min.append(bbox_min)470 self.bbox_max.append(bbox_max)471class UMDFaces(Imdb):472 473 def _parse_annotation(self):474 with open(os.path.join(self.cfg.cache_path, 'UMDFaces_img.csv')) as f:475 imgpath = list()476 imgsize = list()477 bbox = list()478 angle = list() # yaw, pitch, roll479 points = list() # 21 points480 lab = list()481 rdr = csv.DictReader(f)482 for row in rdr:483 imgpath.append(os.path.join(self.name, 'umdfaces_batch{}'.format(int(row['fold'])), row['FILE']))484 imgsize.append([int(row['img_h']), int(row['img_w'])])485 bbox.append([486 float(row['FACE_Y']),487 float(row['FACE_X']),488 float(row['FACE_Y']) + float(row['FACE_HEIGHT']),489 float(row['FACE_X']) + float(row['FACE_WIDTH'])])490 angle.append([491 float(row['YAW']),492 float(row['PITCH']),493 float(row['ROLL'])])494 pts = []495 for i in range(21):496 pts.append([497 float(row['P{}X'.format(i + 1)]),498 float(row['P{}Y'.format(i + 1)])499 # int(float(row['VIS{}'.format(i + 1)]))500 ])501 points.append(pts)502 lab.append([503 float(row['meanL']) * 100 / 255,504 float(row['meanA']) - 128,505 float(row['meanB']) - 128])506 self.imgpath = np.array(imgpath)507 self.imgsize = np.array(imgsize, dtype=np.int32)508 self.bbox_original = np.array(bbox, dtype=np.int32)509 self.angle = np.array(angle, dtype=np.float32)510 self.points = np.array(points, dtype=np.int32)511 self.lab = np.array(lab, dtype=np.float32)512 def _set_bbox_range(self):513 top, left, bottom, right = np.split(self.bbox_original, np.arange(1, 4), axis=1)514 h = bottom - top515 w = right - left516 size_min = np.max((h, w), 0) * 1.3517 size_max = np.max((h, w), 0) * 1.7518 cx = np.mean((left, right), 0)519 cy = np.mean((top, bottom), 0)520 th_x, th_y = 0.2, 0.2521 yaw, pitch, roll = np.split(self.angle, 3, axis=1)522 # x523 cx_delta_min = 0.3 * np.sin(np.radians(yaw))524 cx_delta_max = 0.3 * np.sin(np.radians(yaw))525 cx_min = cx - w * np.clip(cx_delta_min, -th_x, th_x)526 cx_max = cx - w * np.clip(cx_delta_max, -th_x, th_x)527 # y528 cy_delta_min = 0.1 * h * np.sin(np.radians(pitch)) + 0.15 * size_min529 cy_delta_max = 0.1 * h * np.sin(np.radians(pitch)) + 0.15 * size_max530 cy_min = cy - np.clip(cy_delta_min, -th_y * size_min, th_y * size_min)531 cy_max = cy - np.clip(cy_delta_max, -th_y * size_max, th_y * size_max)532 533 self.bbox_min = np.hstack((534 cy_min - 0.5 * size_min,535 cx_min - 0.4 * size_min,536 cy_min + 0.5 * size_min,537 cx_min + 0.4 * size_min)).astype(np.int32)538 self.bbox_max = np.hstack((539 cy_max - 0.5 * size_max,540 cx_max - 0.5 * size_max,541 cy_max + 0.5 * size_max,542 cx_max + 0.5 * size_max)).astype(np.int32)543class PGGAN_published_100k(CelebA_HQ):544 def _set(self, kwargs):545 self.num_img = 100 * 1000546class PGGAN_published_nGPUs(Imdb):547 def _parse_annotation(self):548 root = os.path.join(self.cfg.data_path, self.name)549 imgpath = list()550 bbox_original = list()551 imgsize = list()552 for dirpath, dirnames, filenames in os.walk(root):553 if len(filenames) > 0 and len(dirnames) == 0:554 for fn in filenames:555 if 'tick=' in fn:556 p = re.compile('_tick=\d+_')557 tick = int(p.search(fn).group()[6:-1])558 # tick = int(fn.split('_')[-2].split('=')[-1])559 resolution = 2 ** int((tick - 1 + 3000) / 1200)560 bbox = [0, 0, resolution, resolution]561 _dirpath = dirpath[len(self.cfg.data_path) + 1:]562 imgpath.append(os.path.join(_dirpath, fn))563 bbox_original.append(bbox)564 imgsize.append([resolution, resolution])565 self.imgpath = np.array(imgpath)566 self.bbox_original = np.array(bbox_original, dtype=np.int32)567 self.imgsize = np.array(imgsize, dtype=np.int32)568 569 def _set_bbox_range(self):570 self.bbox_min = np.copy(self.bbox_original)571 self.bbox_max = np.copy(self.bbox_original)572 # ox_ratio = 212 / 1024573 # oy_ratio = 170 / 1024574 # minx_ratio = 600 / 1024575 # miny_ratio = 650 / 1024576 # for i in range(len(self.bbox_original)):577 # h = self.bbox_original[i][2] - self.bbox_original[i][0]578 # w = self.bbox_original[i][3] - self.bbox_original[i][1]579 # if h >= 512 and w >= 512:580 # top = int(h * ox_ratio)581 # left = int(w * oy_ratio)582 # height = int(h * minx_ratio)583 # width = int(w * miny_ratio)584 # self.bbox_min.append([top, left, top + height, left + width])585 # self.bbox_max.append(self.bbox_original[i])586 # else:587 # self.bbox_min.append(self.bbox_original[i])588 # self.bbox_max.append(self.bbox_original[i])589 # self.bbox_min = np.array(self.bbox_min, dtype=np.int32)590 # self.bbox_max = np.array(self.bbox_max, dtype=np.int32)591class PGGAN_trained(PGGAN_published_nGPUs):592 pass593class Glow(Imdb):594 def _set(self, kwargs):595 if 'test' in kwargs:596 self.test = kwargs['test']597 else:598 self.test = None599 def _parse_annotation(self):600 if self.test:601 imglist = glob(os.path.join(self.cfg.data_path, self.name, 'test', '*.jpg'))602 else:603 imglist = glob(os.path.join(self.cfg.data_path, self.name, 'train', '*.jpg'))604 imglist = ['/'.join(_.split('/')[-3:]) for _ in imglist]605 self.imgpath = np.array(imglist)606 self.bbox_original = np.array([[0, 0, 256, 256]] * len(imglist), dtype=np.int32)607 self.imgsize = np.array([[256, 256]] * len(imglist), dtype=np.int32)608 609 def _set_bbox_range(self):610 self.bbox_min = np.copy(self.bbox_original)611 self.bbox_max = np.copy(self.bbox_original)612class Glow_CelebA_HQ(Glow):613 pass614class Glow_UMDFaces(Glow):615 pass616class StarGAN_CelebA(Glow):617 pass618class BEGAN(Imdb):619 def _set(self, kwargs):620 raise NotImplementedError()621 def _parse_annotation(self):622 root = os.path.join(self.cfg.data_path, self.name)623 for dirpath, dirnames, filenames in os.walk(root):624 if len(filenames) > 0 and len(dirnames) == 0:625 for fn in filenames:626 p = re.compile('_size=\d+_')627 size = int(p.search(fn).group()[6:-1])628 bbox = [0, 0, size, size]629 _dirpath = dirpath[len(self.cfg.data_path) + 1:]630 self.imgpath.append(os.path.join(_dirpath, fn))631 self.bbox_original.append(bbox)632 633 def _set_bbox_range(self):634 self.bbox_min = np.copy(self.bbox_original)635 self.bbox_max = np.copy(self.bbox_original)636class DCGAN(Imdb):637 def _set(self, kwargs):638 raise NotImplementedError()639 640 def _parse_annotation(self):641 root = os.path.join(self.cfg.data_path, self.name)642 for dirpath, dirnames, filenames in os.walk(root):643 if len(filenames) > 0 and len(dirnames) == 0:644 for fn in filenames:645 bbox = [0, 0, 64, 64]646 _dirpath = dirpath[len(self.cfg.data_path) + 1:]647 self.imgpath.append(os.path.join(_dirpath, fn))648 self.bbox_original.append(bbox)649 650 def _set_bbox_range(self):651 self.bbox_min = np.copy(self.bbox_original)652 self.bbox_max = np.copy(self.bbox_original)653class LSGAN(DCGAN):654 pass655class DRAGAN(DCGAN):656 pass657class WGAN_GP(DCGAN):658 pass659class Photoshop(Imdb):660 def __init__(self, cfg, name=None, virtual=False, logger=None, **kwargs):661 super(Photoshop, self).__init__(cfg, name=name, virtual=virtual, logger=logger, **kwargs)662 weight = 0663 for k, v in self.cfg.dataset.swap_parts.items():664 weight += v665 assert math.isclose(weight, 1.0), 'Sum of swap_parts weight should be 1.0, but {}'.format(weight)666 def _parse_annotation(self):667 with open(os.path.join(self.cfg.data_path, 'Photoshop', 'Photoshop_result.csv')) as f:668 imgpath = list()669 imgsize = list()670 bbox = list()671 partname = list()672 level = list()673 bbox_mask = list()674 rdr = csv.DictReader(f)675 for row in rdr:676 imgpath.append(os.path.join(677 'Photoshop', 678 'Photoshop_' + self.cfg.dataset.photoshop_ext, 679 row['filename'] + '.' + self.cfg.dataset.photoshop_ext)680 )681 imgsize.append([int(row['h']), int(row['w'])])682 bbox.append([683 int(row['face_y']),684 int(row['face_x']),685 int(row['face_y']) + int(row['face_h']),686 int(row['face_x']) + int(row['face_w'])])687 partname.append(row['partname'])688 level.append(int(row['level']))689 bbox_mask.append([690 int(row['mask_y']),691 int(row['mask_x']),692 int(row['mask_y']) + int(row['mask_h']),693 int(row['mask_x']) + int(row['mask_w'])694 ])695 self.imgpath = np.array(imgpath)696 self.imgsize = np.array(imgsize, dtype=np.int32)697 self.bbox_original = np.array(bbox, dtype=np.int32)698 self.partname = np.array(partname)699 self.level = np.array(level, dtype=np.int32)700 self.bbox_mask = np.array(bbox_mask, dtype=np.int32)701 def _filter(self):702 super(Photoshop, self)._filter()703 partname_valid = [key for key, value in self.cfg.dataset.swap_parts.items() if value > 0] 704 mask_part = np.zeros(len(self.imgpath), dtype=np.bool)705 for pv in partname_valid:706 mask_part = mask_part | (self.partname == pv)707 708 mask = mask_part709 self.imgpath = self.imgpath[mask]710 self.imgsize = self.imgsize[mask]711 self.bbox_original = self.bbox_original[mask]712 self.bbox_min = self.bbox_min[mask]713 self.bbox_max = self.bbox_max[mask]714 self.minsize = self.minsize[mask]715 self.maxsize = self.maxsize[mask]716 if hasattr(self, 'angle'):717 self.angle = self.angle[mask]718 if hasattr(self, 'points'):719 self.points = self.points[mask]720 if hasattr(self, 'lab'):721 self.lab = self.lab[mask]722 if hasattr(self, 'partname'):723 self.partname = self.partname[mask]724 if hasattr(self, 'level'):725 self.level = self.level[mask]726 if hasattr(self, 'bbox_mask'):727 self.bbox_mask = self.bbox_mask[mask]728 def _set_bbox_range(self):729 top, left, bottom, right = np.split(self.bbox_original, np.arange(1, 4), axis=1)730 h = bottom - top731 w = right - left732 size_min = np.max((h, w), 0) * 1.1733 size_max = np.max((h, w), 0) * 1.5734 cx = np.mean((left, right), 0)735 cy = np.mean((top, bottom), 0) - 0.1 * h736 self.bbox_min = np.hstack((737 cy - 0.5 * size_min,738 cx - 0.4 * size_min,739 cy + 0.5 * size_min,740 cx + 0.4 * size_min)).astype(np.int32)741 self.bbox_max = np.hstack((742 cy - 0.5 * size_max,743 cx - 0.5 * size_max,744 cy + 0.5 * size_max,745 cx + 0.5 * size_max)).astype(np.int32)746class Sample_1_M1_Real(Imdb):747 ''' 201~400 real images from Sample_1, task 1 '''748 def _parse_annotation(self):749 with open(os.path.join(self.cfg.data_path, 'RnDChallenge', 'Sample_1_M1_imsize.csv'), 'r') as f:750 rdr = csv.DictReader(f)751 imgpath = list()752 bbox_original = list()753 imgsize = list()754 for row in rdr:755 idx = int(re.findall('\d{5}', row['filename'])[0])756 if idx > 200:757 imgpath.append(os.path.join('RnDChallenge', 'Sample_1', row['filename']))758 h = int(row['h'])759 w = int(row['w'])760 bbox_original.append([0, 0, h, w])761 imgsize.append([h, w])762 763 self.imgpath = np.array(imgpath)764 self.bbox_original = np.array(bbox_original, dtype=np.int32)765 self.imgsize = np.array(imgsize, dtype=np.int32)766 def _set_bbox_range(self):767 self.bbox_min = np.copy(self.bbox_original)768 self.bbox_max = np.copy(self.bbox_original)769class Sample_1_M2_Real(Imdb):770 ''' Real images from Sample_1, task 2 '''771 def _set(self, kwargs):772 self.ignore_cache = True773 self.real_fake = 'real'774 self.subdir = 'Sample_1'775 self.anno_file = 'Sample_1_M2_bbox.csv'776 def _parse_annotation(self):777 root = os.path.join(self.cfg.data_path, 'RnDChallenge')778 with open(os.path.join(root, self.anno_file)) as f:779 imgpath = list()780 imgsize = list()781 bbox = list()782 rdr = csv.DictReader(f)783 for row in rdr:784 if row['real_fake'] != self.real_fake:785 continue786 imgpath.append(os.path.join('RnDChallenge', self.subdir, row['filename']))787 imgsize.append([int(row['h']), int(row['w'])])788 bbox.append([789 int(row['face_y']),790 int(row['face_x']),791 int(row['face_y']) + int(row['face_h']),792 int(row['face_x']) + int(row['face_w'])])793 self.imgpath = np.array(imgpath)794 self.imgsize = np.array(imgsize, dtype=np.int32)795 self.bbox_original = np.array(bbox, dtype=np.int32) 796 def _set_bbox_range(self):797 top, left, bottom, right = np.split(self.bbox_original, np.arange(1, 4), axis=1)798 h = bottom - top799 w = right - left800 size_min = np.max((h, w), 0) * 1.1801 size_max = np.max((h, w), 0) * 1.5802 cx = np.mean((left, right), 0)803 cy = np.mean((top, bottom), 0) - 0.1 * h804 self.bbox_min = np.hstack((805 cy - 0.5 * size_min,806 cx - 0.4 * size_min,807 cy + 0.5 * size_min,808 cx + 0.4 * size_min)).astype(np.int32)809 self.bbox_max = np.hstack((810 cy - 0.5 * size_max,811 cx - 0.5 * size_max,812 cy + 0.5 * size_max,813 cx + 0.5 * size_max)).astype(np.int32)814class Sample_1_M2_Syn(Sample_1_M2_Real):815 ''' Fake images from Sample_1, task 2 '''816 def _set(self, kwargs):817 self.ignore_cache = True818 self.real_fake = 'fake'819 self.subdir='Sample_1'820 self.anno_file = 'Sample_1_M2_bbox.csv'821class Sample_1_M1_GAN(Imdb):822 ''' 1~200 fake images from Sample_1, task 1 '''823 def _parse_annotation(self):824 with open(os.path.join(self.cfg.data_path, 'RnDChallenge', 'Sample_1_M1_imsize.csv'), 'r') as f:825 rdr = csv.DictReader(f)826 imgpath = list()827 bbox_original = list()828 imgsize = list()829 for row in rdr:830 idx = int(re.findall('\d{5}', row['filename'])[0])831 if idx <= 200:832 imgpath.append(os.path.join('RnDChallenge', 'Sample_1', row['filename']))833 h = int(row['h'])834 w = int(row['w'])835 bbox_original.append([0, 0, h, w])836 imgsize.append([h, w])837 838 self.imgpath = np.array(imgpath)839 self.bbox_original = np.array(bbox_original, dtype=np.int32)840 self.imgsize = np.array(imgsize, dtype=np.int32)841 def _set_bbox_range(self):842 self.bbox_min = np.copy(self.bbox_original)843 self.bbox_max = np.copy(self.bbox_original)844class Sample_2_GAN(Imdb):845 ''' 60 gan images from Sample_2 '''846 def _parse_annotation(self):847 root = os.path.join(self.cfg.data_path, 'RnDChallenge', 'Sample_2', 'gan_jpg')848 imglist = glob(os.path.join(root, 'gan_*.jpg'))849 imgpath = [os.path.join('RnDChallenge', 'Sample_2', 'gan_jpg', os.path.basename(_)) for _ in imglist]850 bbox_original = [[0, 0, 128, 128]] * len(imgpath)851 imgsize = [[0, 0, 128, 128]] * len(imgpath)852 assert len(imgpath) > 0853 self.imgpath = np.array(imgpath)854 self.bbox_original = np.array(bbox_original, dtype=np.int32)855 self.imgsize = np.ones((self.imgpath.shape[0], 2), dtype=np.int32) * 128856 def _set_bbox_range(self):857 self.bbox_min = np.copy(self.bbox_original)858 self.bbox_max = np.copy(self.bbox_original)859class Sample_2_Syn(Imdb):860 ''' Fake images from Sample_2 '''861 def _set(self, kwargs):862 self.ignore_cache = True863 def _parse_annotation(self):864 root = os.path.join(self.cfg.data_path, 'RnDChallenge', 'Sample_2', 'syn')865 imgpath = glob(os.path.join(root, '*.jpg'))866 imgpath = ['/'.join(_.split('/')[-4:]) for _ in imgpath]867 bbox_original = [[0, 0, 128, 128]] * len(imgpath)868 assert len(imgpath) > 0869 self.imgpath = np.array(imgpath)870 self.bbox_original = np.array(bbox_original, dtype=np.int32) 871 self.imgsize = np.ones((self.imgpath.shape[0], 2), dtype=np.int32) * 128872 def _set_bbox_range(self):873 self.bbox_min = np.copy(self.bbox_original)...
test_interfaceless.py
Source:test_interfaceless.py
...45 print(ts.to_json(True))46 assert 0 == len(ts.inputs)47 assert 0 == len(ts.outputs)48 # TODO: not tested on func and metadata because that is supposed to change to something else49def test__parse_annotation():50 assert dict(data_type=object, nullable=True) == _parse_annotation(None)51 assert dict(data_type=object, nullable=True) == _parse_annotation(52 inspect.Parameter.empty)53 assert dict(data_type=object, nullable=True) == _parse_annotation(Any)54 assert dict(data_type=int, nullable=False) == _parse_annotation(int)55 assert dict(data_type=dict, nullable=False) == _parse_annotation(Dict[str, Any])56 assert dict(data_type=object, nullable=True) == _parse_annotation(57 Optional[Any])58 assert dict(data_type=str, nullable=True) == _parse_annotation(59 Optional[str])60 assert dict(data_type=dict, nullable=True) == _parse_annotation(61 Optional[Dict[str, Any]])62 assert dict(data_type=dict, nullable=True) == _parse_annotation(63 Union[None, Dict[str, Any]])64 assert dict(data_type=dict, nullable=True) == _parse_annotation(65 Union[Dict[str, Any], None])66 assert dict(data_type=dict, nullable=True) == _parse_annotation(67 Union[Dict[str, Any], None, None])68 assert dict(data_type=dict, nullable=False) == _parse_annotation(69 Union[Dict[str, Any]])70 raises(TypeError, lambda: _parse_annotation(Union[Dict[str, Any], List[str]]))71 raises(TypeError, lambda: _parse_annotation(Union[Dict[str, Any], List[str], None]))72 raises(TypeError, lambda: _parse_annotation(Union[None]))73 raises(TypeError, lambda: _parse_annotation(Union[None, None]))74 raises(TypeError, lambda: _parse_annotation(type(None)))75def test__get_origin_type():76 assert _get_origin_type(Any) is object77 assert _get_origin_type(Dict[str, Any]) is dict78 assert _get_origin_type(List[str]) is list79 assert _get_origin_type(List[Any]) is list80 assert _get_origin_type(Tuple[int, str]) is tuple81 assert _get_origin_type(Union[int, str], False) is Union82 assert _get_origin_type(Union[None]) is type(None)83 assert _get_origin_type(int) is int84def f1(a: Optional[int], b: "int", c: str, d: "Optional[str]" = "x") -> "int":85 return a + b86def f2(a: int, b, c: int) -> "Tuple[int,Optional[str]]":87 return a + c88def f3(a: int, b: str, c: int):...
lidc_adaper.py
Source:lidc_adaper.py
...5from pydicom.pixel_data_handlers.numpy_handler import pack_bits6from .adapter_base import AdapterBase7class LIDCAdapter(AdapterBase):8 @classmethod9 def _parse_annotation(cls, dataset: Dataset, annotation_path: str) -> np.ndarray:10 tree = etree.parse(annotation_path, etree.XMLParser())11 root = tree.getroot()12 reading_session = root.find(path='readingSession', namespaces=root.nsmap)13 roi_list = reading_session.findall(14 f'unblindedReadNodule[characteristics]/roi[imageSOP_UID='15 f'"{dataset.SOPInstanceUID}"]', namespaces=root.nsmap)16 annotation_array = np.zeros([dataset.Rows, dataset.Columns])17 for roi in roi_list:18 edgemap_list = roi.findall('edgeMap', namespaces=root.nsmap)19 for edgemap in edgemap_list:20 xCoord = edgemap.find('xCoord', namespaces=root.nsmap).text21 yCoord = edgemap.find('yCoord', namespaces=root.nsmap).text22 annotation_array[int(yCoord), int(xCoord)] = 123 return annotation_array24 @classmethod25 def _get_overlay(cls, dataset: Dataset, annotation_path: str) -> Dataset:26 annotation = cls._parse_annotation(dataset, annotation_path)27 elem_overlay_type = pydicom.DataElement(0x60000040, VR='CS', value='GRAPHICS')28 dataset.add(elem_overlay_type)29 elem_overlay_rows = pydicom.DataElement(0x60000010, VR='US', value=dataset.Rows)30 dataset.add(elem_overlay_rows)31 elem_overlay_columns = pydicom.DataElement(0x60000011, VR='US', value=dataset.Columns)32 dataset.add(elem_overlay_columns)33 elem_overlay_bit_allocated = pydicom.DataElement(0x60000100, VR='US', value=1)34 dataset.add(elem_overlay_bit_allocated)35 elem_overlay_bit_position = pydicom.DataElement(0x60000102, VR='US', value=0)36 dataset.add(elem_overlay_bit_position)37 elem_overlay_origin = pydicom.DataElement(0x60000050, VR='SS', value=[1, 1])38 dataset.add(elem_overlay_origin)39 elem_overlay_data = pydicom.DataElement(0x60003000, VR='OW', value=pack_bits(annotation))40 dataset.add(elem_overlay_data)41 return dataset42 @classmethod43 def _get_pixel(cls, dataset: Dataset, annotation_path: str) -> Dataset:44 annotation = cls._parse_annotation(dataset, annotation_path)45 dataset.PixelData = annotation.tobytes()...
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