python - Extraction of Object Labels

Question

I'm trying to detect objects inside my video sequences, but instead of outputting these 'burnt in' to the video file I want to print these to the command line output as variables which I can later store and retrieve from (e.g. where I can do print(labelname, confidence) or similar. Have been searching for methods to do this (including on here) though the variables used don't seem to align to this specific implementation. Have been able to do this previously in OpenCV but as tf is largely unfamiliar to me it's a bit of a struggle! (switched due to GPU + OpenCV issues).

Here's an extract from my code of when we process each frame:

    while True:
        return_value, frame = vid.read()
        if return_value:
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            image = Image.fromarray(frame)
        else:
            print('Video has ended or failed, try a different video format!')
            break
    
        frame_size = frame.shape[:2]
        image_data = cv2.resize(frame, (input_size, input_size))
        image_data = image_data / 255.
        image_data = image_data[np.newaxis, ...].astype(np.float32)
        start_time = time.time()

        if FLAGS.framework == 'tflite':
            interpreter.set_tensor(input_details[0]['index'], image_data)
            interpreter.invoke()
            pred = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))]
            if FLAGS.model == 'yolov3' and FLAGS.tiny == True:
                boxes, pred_conf = filter_boxes(pred[1], pred[0], score_threshold=0.25,
                                                input_shape=tf.constant([input_size, input_size]))
            else:
                boxes, pred_conf = filter_boxes(pred[0], pred[1], score_threshold=0.25,
                                                input_shape=tf.constant([input_size, input_size]))
        else:
            batch_data = tf.constant(image_data)
            pred_bbox = infer(batch_data)
            for key, value in pred_bbox.items():
                boxes = value[:, :, 0:4]
                pred_conf = value[:, :, 4:]

        boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(
            boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),
            scores=tf.reshape(
                pred_conf, (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])),
            max_output_size_per_class=50,
            max_total_size=50,
            iou_threshold=FLAGS.iou,
            score_threshold=FLAGS.score
        )
        pred_bbox = [boxes.numpy(), scores.numpy(), classes.numpy(), valid_detections.numpy()]
        image = utils.draw_bbox(frame, pred_bbox)
        fps = 1.0 / (time.time() - start_time)
        print("FPS: %.2f" % fps)
        result = np.asarray(image)
        cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
        result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

The full code can be found on Github here for reference.

Thanks!

Answer 1

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