yangxue0827 / RotationDetection

This is a tensorflow-based rotation detection benchmark, also called AlphaRotate.
https://rotationdetection.readthedocs.io/
Apache License 2.0
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Test_mlt res output #73

Open Testbild opened 2 years ago

Testbild commented 2 years ago

Hello,

I have a question regarding the "res" in the test_mlt_base.py . The result_queue is initialized withresult_queue = Queue(500), which to my understanding creates a Queue object with length 500.

Then the res = result_queue.get() is called and somehow this creates boxes and scales in the resvariable.

I do not really understand, why the res = result_queue.get() results in me having boxes and scales, that I can draw on an image. Where are the predictions generated?

I would love to hear your explanation to better understand the code. Thank you very much and best regards

    def test_mlt(self, det_net, real_test_img_list, txt_name):

        save_path = os.path.join('./test_mlt', self.cfgs.VERSION)
        tools.makedirs(save_path)

        nr_records = len(real_test_img_list)
        pbar = tqdm(total=nr_records)
        gpu_num = len(self.args.gpus.strip().split(','))

        nr_image = math.ceil(nr_records / gpu_num)
        result_queue = Queue(500)
        procs = []

        for i, gpu_id in enumerate(self.args.gpus.strip().split(',')):
            start = i * nr_image
            end = min(start + nr_image, nr_records)
            split_records = real_test_img_list[start:end]
            proc = Process(target=self.worker, args=(int(gpu_id), split_records, det_net, result_queue))
            print('process:%d, start:%d, end:%d' % (i, start, end))
            proc.start()
            procs.append(proc)

        for i in range(nr_records):
            res = result_queue.get()
            tools.makedirs(os.path.join(save_path, 'mlt_res'))
            if res['boxes'].shape[0] == 0:
                fw_txt_dt = open(os.path.join(save_path, 'mlt_res', 'res_{}.txt'.format(
                    res['image_id'].split('/')[-1].split('.')[0].split('ts_')[1])), 'w')
                fw_txt_dt.close()
                pbar.update(1)

                fw = open(txt_name, 'a+')
                fw.write('{}\n'.format(res['image_id'].split('/')[-1]))
                fw.close()
                continue
            x1, y1, x2, y2, x3, y3, x4, y4 = res['boxes'][:, 0], res['boxes'][:, 1], res['boxes'][:, 2], res['boxes'][:, 3],\
                                             res['boxes'][:, 4], res['boxes'][:, 5], res['boxes'][:, 6], res['boxes'][:, 7]

            x1, y1 = x1 * res['scales'][0], y1 * res['scales'][1]
            x2, y2 = x2 * res['scales'][0], y2 * res['scales'][1]
            x3, y3 = x3 * res['scales'][0], y3 * res['scales'][1]
            x4, y4 = x4 * res['scales'][0], y4 * res['scales'][1]

            boxes = np.transpose(np.stack([x1, y1, x2, y2, x3, y3, x4, y4]))