def main(self): if self.hash != None: t = threading.Thread(target = self.check) t.start() t.join() answer = self.queue.get() if answer != "Result failed" and answer != "Server failed": if self.language in answer: t = threading.Thread(target = self.download) t.start() t.join() subtitles = self.queue.get() if subtitles != "Malformed request": imdb = Imdb(self.name) information = imdb.main() if information != None: #agregar informacion a subtitulos subtitles = "00:00:1,0 --> 00:00:20,0\nTitle: " + information.title + "\n Director:" + information.director + "\n Year:" + information.year + "\n \n" + subtitles movie = Movie(title=information.title, director=information.director, year=information.year, hash=self.hash) movie.save() else: movie = Movie(title=self.name, hash=self.hash) movie.save() try: f = open("addsubs.srt",'w') f.write(subtitles.encode("utf-8")) f.close() except IOError: return None return movie return None
# evaluation model with pascal/voc measures from __future__ import absolute_import, division, print_function import os from imdb import Imdb from network import Network data_dir = os.path.join(os.getcwd(), 'data') anno_dir = os.path.join(data_dir, 'eval_annotation') images_dir = os.path.join(data_dir, 'images') imdb = Imdb(anno_dir, images_dir, batch_size=1) net = Network(is_training=False) for images, gt_boxes, gt_cls in imdb.next_batch(): # batch_size is 1 box_pred, cls_inds, scores = net.predict(images)
import time from datetime import timedelta from imdb import Imdb from network import Network data_dir = os.path.join(os.getcwd(), 'data') anno_dir = os.path.join(data_dir, 'annotation') images_dir = os.path.join(data_dir, 'images') parser = argparse.ArgumentParser() parser.add_argument('--num_epochs', type=int, default=1) parser.add_argument('--batch_size', type=int, default=1) parser.add_argument('--learn_rate', type=float, default=1e-3) args = parser.parse_args() imdb = Imdb(anno_dir, images_dir, batch_size=args.batch_size) net = Network(is_training=True, lr=args.learn_rate) train_t = 0 step = 0 print('start training') for epoch in range(1, args.num_epochs + 1): epoch_t = time.time() for images, gt_boxes, gt_cls in imdb.next_batch(): step, bbox_loss, iou_loss, cls_loss = net.fit(images, gt_boxes, gt_cls) if step % 100 == 0: