def predict_in_directory(self, spath, fold, stage, cb, data, limit=-1, batchSize=32, ttflips=False): with tqdm.tqdm(total=len(generic.dir_list(spath)), unit="files", desc="segmentation of images from " + str(spath)) as pbar: for v in self.predict_on_directory(spath, fold=fold, stage=stage, limit=limit, batch_size=batchSize, ttflips=ttflips): b: imgaug.Batch = v for i in range(len(b.data)): id = b.data[i] entry = self.toEntry(b, i) cb(id, entry, data) pbar.update(batchSize)
def predict_in_directory(self, spath, fold, stage, cb, data, limit=-1, batchSize=32, ttflips=False): with tqdm.tqdm(total=len(generic.dir_list(spath)), unit="files", desc="segmentation of images from " + str(spath)) as pbar: for v in self.predict_on_directory(spath, fold=fold, stage=stage, limit=limit, batch_size=batchSize, ttflips=ttflips): b: imgaug.Batch = v for i in range(len(b.data)): id = b.data[i] orig = b.images[i] map = b.segmentation_maps_aug[i] scaledMap = imgaug.augmenters.Scale({ "height": orig.shape[0], "width": orig.shape[1] }).augment_segmentation_maps([map]) cb(id, scaledMap[0], data) pbar.update(batchSize)
def predict_to_directory(self, spath, tpath, fold=0, stage=0, limit=-1, batchSize=32, binaryArray=False, ttflips=False): generic.ensure(tpath) with tqdm.tqdm(total=len(generic.dir_list(spath)), unit="files", desc="segmentation of images from " + str(spath) + " to " + str(tpath)) as pbar: for v in self.predict_on_directory(spath, fold=fold, stage=stage, limit=limit, batch_size=batchSize, ttflips=ttflips): b: imgaug.Batch = v for i in range(len(b.data)): id = b.data[i] entry = self.toEntry(b, i) if isinstance(tpath, datasets.ConstrainedDirectory): tp = tpath.path else: tp = tpath p = os.path.join(tp, id[0:id.index('.')] + ".npy") save(p, entry) pbar.update(batchSize)
def predict_to_directory(self, spath, tpath, fold=0, stage=0, limit=-1, batchSize=32, binaryArray=False, ttflips=False): generic.ensure(tpath) with tqdm.tqdm(total=len(generic.dir_list(spath)), unit="files", desc="segmentation of images from " + str(spath) + " to " + str(tpath)) as pbar: for v in self.predict_on_directory(spath, fold=fold, stage=stage, limit=limit, batch_size=batchSize, ttflips=ttflips): b: imgaug.Batch = v for i in range(len(b.data)): id = b.data[i] orig = b.images[i] map = b.segmentation_maps_aug[i] scaledMap = imgaug.augmenters.Scale({ "height": orig.shape[0], "width": orig.shape[1] }).augment_segmentation_maps([map]) if isinstance(tpath, datasets.ConstrainedDirectory): tp = tpath.path else: tp = tpath if binaryArray: np.save(os.path.join(tp, id[0:id.index('.')]), scaledMap[0].arr) else: imageio.imwrite( os.path.join(tp, id[0:id.index('.')] + ".png"), (scaledMap[0].arr * 255).astype(np.uint8)) pbar.update(batchSize)