ce detector (%s) %s' % (FLAGS.face_detection_type, FLAGS.face_detection_model)) face_detect = face_detection_model(FLAGS.face_detection_type, FLAGS.face_detection_model) face_files, rectangles = face_detect.run(FLAGS.filename) print(face_files) files += face_files config = tf.ConfigProto(allow_soft_placement=True) with tf.Session(config=config) as sess: label_list = AGE_LIST if FLAGS.class_type == 'age' else GENDER_LIST nlabels = len(label_list) print('Executing on %s' % FLAGS.device_id) model_fn = select_model(FLAGS.model_type) with tf.device(FLAGS.device_id): images = tf.placeholder(tf.float32, [None, RESIZE_FINAL, RESIZE_FINAL, 3]) logits = model_fn(nlabels, images, 1, False) init = tf.global_variables_initializer() requested_step = FLAGS.requested_step if FLAGS.requested_step else None checkpoint_path = '%s' % (FLAGS.model_dir) model_checkpoint_path, global_step = get_checkpoint(checkpoint_path, requested_step, FLAGS.checkpoint) saver = tf.train.Saver() saver.restore(sess, model_checkpoint_path) softmax_output = tf.nn.softmax(logits) coder = ImageCoder() # Support a batch mode if no face detection model if len(files) == 0: if (os.path.isdir(FLAGS.filename)): for relpath in os.listdir(FLAGS.filename): abspath = os.path.join(FLAGS.filename, relpath) if os.path.isfile(abspath) and any([abspath.endswith('.' + ty) for ty in ('jpg', 'png', 'JPG', 'PNG', 'jpeg')]): print(abspath) files.append(abspath) else: files.append(FLAGS.filename) # If it happens to be a list file, read the list and clobber the files if any([FLAGS.filename.endswith('.' + ty) for ty in ('csv', 'tsv', 'txt')]): files = list_images(FLAGS.filename) writer = None output = None if FLAGS.target: print('Creating output file %s' % FLAGS.target) output = open(FLAGS.target, 'w') writer = csv.writer(output) writer.writerow(('file', 'label', 'score')) image_files = list(filter(lambda x: x is not None, [resolve_file(f) for f in files])) print(image_files) if FLAGS.single_look: classify_many_single_crop(sess, label_list, softmax_output, coder, images, image_files, writer) else: for image_file in image_files: classify_one_multi_crop(sess, label_list, softmax_output, coder, images, image_file, writer) if output is not None: output.close() if __name__ == '__main__': tf.app.run()
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参考资料: 《TensorFlow技术解析与实战》
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