demo/demo_spatiotemporal_det_onnx.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy as cp
import tempfile

import cv2
import mmcv
import mmengine
import numpy as np
import onnxruntime
import torch
from mmdet.structures.bbox import bbox2roi
from mmengine import DictAction

from mmaction.apis import detection_inference
from mmaction.utils import frame_extract, get_str_type

try:
    import moviepy.editor as mpy
except ImportError:
    raise ImportError('Please install moviepy to enable output file')

FONTFACE = cv2.FONT_HERSHEY_DUPLEX
FONTSCALE = 0.5
FONTCOLOR = (255, 255, 255)  # BGR, white
MSGCOLOR = (128, 128, 128)  # BGR, gray
THICKNESS = 1
LINETYPE = 1


def hex2color(h):
    """Convert the 6-digit hex string to tuple of 3 int value (RGB)"""
    return (int(h[:2], 16), int(h[2:4], 16), int(h[4:], 16))


plate_blue = '03045e-023e8a-0077b6-0096c7-00b4d8-48cae4'
plate_blue = plate_blue.split('-')
plate_blue = [hex2color(h) for h in plate_blue]
plate_green = '004b23-006400-007200-008000-38b000-70e000'
plate_green = plate_green.split('-')
plate_green = [hex2color(h) for h in plate_green]


def visualize(frames, annotations, plate=plate_blue, max_num=5):
    """Visualize frames with predicted annotations.



    Args:

        frames (list[np.ndarray]): Frames for visualization, note that

            len(frames) % len(annotations) should be 0.

        annotations (list[list[tuple]]): The predicted results.

        plate (str): The plate used for visualization. Default: plate_blue.

        max_num (int): Max number of labels to visualize for a person box.

            Default: 5.

    Returns:

        list[np.ndarray]: Visualized frames.

    """

    assert max_num + 1 <= len(plate)
    plate = [x[::-1] for x in plate]
    frames_out = cp.deepcopy(frames)
    nf, na = len(frames), len(annotations)
    assert nf % na == 0
    nfpa = len(frames) // len(annotations)
    anno = None
    h, w, _ = frames[0].shape
    scale_ratio = np.array([w, h, w, h])
    for i in range(na):
        anno = annotations[i]
        if anno is None:
            continue
        for j in range(nfpa):
            ind = i * nfpa + j
            frame = frames_out[ind]
            for ann in anno:
                box = ann[0]
                label = ann[1]
                if not len(label):
                    continue
                score = ann[2]
                box = (box * scale_ratio).astype(np.int64)
                st, ed = tuple(box[:2]), tuple(box[2:])
                cv2.rectangle(frame, st, ed, plate[0], 2)
                for k, lb in enumerate(label):
                    if k >= max_num:
                        break
                    text = abbrev(lb)
                    text = ': '.join([text, str(score[k])])
                    location = (0 + st[0], 18 + k * 18 + st[1])
                    textsize = cv2.getTextSize(text, FONTFACE, FONTSCALE,
                                               THICKNESS)[0]
                    textwidth = textsize[0]
                    diag0 = (location[0] + textwidth, location[1] - 14)
                    diag1 = (location[0], location[1] + 2)
                    cv2.rectangle(frame, diag0, diag1, plate[k + 1], -1)
                    cv2.putText(frame, text, location, FONTFACE, FONTSCALE,
                                FONTCOLOR, THICKNESS, LINETYPE)

    return frames_out


def load_label_map(file_path):
    """Load Label Map.



    Args:

        file_path (str): The file path of label map.

    Returns:

        dict: The label map (int -> label name).

    """
    lines = open(file_path).readlines()
    lines = [x.strip().split(': ') for x in lines]
    return {int(x[0]): x[1] for x in lines}


def abbrev(name):
    """Get the abbreviation of label name:



    'take (an object) from (a person)' -> 'take ... from ...'

    """
    while name.find('(') != -1:
        st, ed = name.find('('), name.find(')')
        name = name[:st] + '...' + name[ed + 1:]
    return name


def pack_result(human_detection, result, img_h, img_w):
    """Short summary.



    Args:

        human_detection (np.ndarray): Human detection result.

        result (type): The predicted label of each human proposal.

        img_h (int): The image height.

        img_w (int): The image width.

    Returns:

        tuple: Tuple of human proposal, label name and label score.

    """
    human_detection[:, 0::2] /= img_w
    human_detection[:, 1::2] /= img_h
    results = []
    if result is None:
        return None
    for prop, res in zip(human_detection, result):
        res.sort(key=lambda x: -x[1])
        results.append(
            (prop.data.cpu().numpy(), [x[0] for x in res], [x[1]
                                                            for x in res]))
    return results


def parse_args():
    parser = argparse.ArgumentParser(description='MMAction2 demo')
    parser.add_argument('video', help='video file/url')
    parser.add_argument('out_filename', help='output filename')
    parser.add_argument(
        '--config',
        default=('configs/detection/slowonly/slowonly_k700-pre'
                 '-r50_8xb8-8x8x1-10e_ava-kinetics-rgb.py'),
        help='spatialtemporal detection model config file path')
    parser.add_argument(
        '--onnx-file', help='spatialtemporal detection onnx file path')

    parser.add_argument(
        '--det-config',
        default='demo/demo_configs/faster-rcnn_r50_fpn_2x_coco_infer.py',
        help='human detection config file path (from mmdet)')
    parser.add_argument(
        '--det-checkpoint',
        default=('http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/'
                 'faster_rcnn_r50_fpn_2x_coco/'
                 'faster_rcnn_r50_fpn_2x_coco_'
                 'bbox_mAP-0.384_20200504_210434-a5d8aa15.pth'),
        help='human detection checkpoint file/url')
    parser.add_argument(
        '--det-score-thr',
        type=float,
        default=0.9,
        help='the threshold of human detection score')
    parser.add_argument(
        '--det-cat-id',
        type=int,
        default=0,
        help='the category id for human detection')
    parser.add_argument(
        '--action-score-thr',
        type=float,
        default=0.5,
        help='the threshold of human action score')
    parser.add_argument(
        '--label-map',
        default='tools/data/ava/label_map.txt',
        help='label map file')
    parser.add_argument(
        '--device', type=str, default='cuda:0', help='CPU/CUDA device option')
    parser.add_argument(
        '--short-side',
        type=int,
        default=256,
        help='specify the short-side length of the image')
    parser.add_argument(
        '--predict-stepsize',
        default=8,
        type=int,
        help='give out a prediction per n frames')
    parser.add_argument(
        '--output-stepsize',
        default=4,
        type=int,
        help=('show one frame per n frames in the demo, we should have: '
              'predict_stepsize % output_stepsize == 0'))
    parser.add_argument(
        '--output-fps',
        default=6,
        type=int,
        help='the fps of demo video output')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        default={},
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. For example, '
        "'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'")
    args = parser.parse_args()
    return args


def main():
    args = parse_args()

    tmp_dir = tempfile.TemporaryDirectory()
    frame_paths, original_frames = frame_extract(
        args.video, out_dir=tmp_dir.name)
    num_frame = len(frame_paths)
    h, w, _ = original_frames[0].shape

    # resize frames to shortside
    new_w, new_h = mmcv.rescale_size((w, h), (args.short_side, np.Inf))
    frames = [mmcv.imresize(img, (new_w, new_h)) for img in original_frames]
    w_ratio, h_ratio = new_w / w, new_h / h

    # Get clip_len, frame_interval and calculate center index of each clip
    config = mmengine.Config.fromfile(args.config)
    config.merge_from_dict(args.cfg_options)
    val_pipeline = config.val_pipeline

    sampler = [
        x for x in val_pipeline if get_str_type(x['type']) == 'SampleAVAFrames'
    ][0]
    clip_len, frame_interval = sampler['clip_len'], sampler['frame_interval']
    window_size = clip_len * frame_interval
    assert clip_len % 2 == 0, 'We would like to have an even clip_len'
    # Note that it's 1 based here
    timestamps = np.arange(window_size // 2, num_frame + 1 - window_size // 2,
                           args.predict_stepsize)

    # Load label_map
    label_map = load_label_map(args.label_map)
    try:
        if config['data']['train']['custom_classes'] is not None:
            label_map = {
                id + 1: label_map[cls]
                for id, cls in enumerate(config['data']['train']
                                         ['custom_classes'])
            }
    except KeyError:
        pass

    # Get Human detection results
    center_frames = [frame_paths[ind - 1] for ind in timestamps]

    human_detections, _ = detection_inference(args.det_config,
                                              args.det_checkpoint,
                                              center_frames,
                                              args.det_score_thr,
                                              args.det_cat_id, args.device)
    torch.cuda.empty_cache()
    for i in range(len(human_detections)):
        det = human_detections[i]
        det[:, 0:4:2] *= w_ratio
        det[:, 1:4:2] *= h_ratio
        human_detections[i] = torch.from_numpy(det[:, :4]).to(args.device)

    # Build STDET model
    session = onnxruntime.InferenceSession(args.onnx_file)

    predictions = []

    img_norm_cfg = dict(
        mean=np.array(config.model.data_preprocessor.mean),
        std=np.array(config.model.data_preprocessor.std),
        to_rgb=False)

    print('Performing SpatioTemporal Action Detection for each clip')
    assert len(timestamps) == len(human_detections)
    prog_bar = mmengine.ProgressBar(len(timestamps))
    for timestamp, proposal in zip(timestamps, human_detections):
        if proposal.shape[0] == 0:
            predictions.append(None)
            continue

        start_frame = timestamp - (clip_len // 2 - 1) * frame_interval
        frame_inds = start_frame + np.arange(0, window_size, frame_interval)
        frame_inds = list(frame_inds - 1)
        imgs = [frames[ind].astype(np.float32) for ind in frame_inds]
        _ = [mmcv.imnormalize_(img, **img_norm_cfg) for img in imgs]
        # THWC -> CTHW -> 1CTHW
        input_array = np.stack(imgs).transpose((3, 0, 1, 2))[np.newaxis]
        rois = bbox2roi([proposal])

        input_feed = {
            'input_tensor': input_array,
            'rois': rois.cpu().data.numpy()
        }
        outputs = session.run(['cls_score'], input_feed=input_feed)
        logits = outputs[0]
        scores = 1 / (1 + np.exp(-logits))

        prediction = []
        # N proposals
        for i in range(proposal.shape[0]):
            prediction.append([])
        # Perform action score thr
        for i in range(scores.shape[1]):
            if i not in label_map:
                continue
            for j in range(proposal.shape[0]):
                if scores[j, i] > args.action_score_thr:
                    prediction[j].append((label_map[i], scores[j, i].item()))
        predictions.append(prediction)
        prog_bar.update()

    results = []
    for human_detection, prediction in zip(human_detections, predictions):
        results.append(pack_result(human_detection, prediction, new_h, new_w))

    def dense_timestamps(timestamps, n):
        """Make it nx frames."""
        old_frame_interval = (timestamps[1] - timestamps[0])
        start = timestamps[0] - old_frame_interval / n * (n - 1) / 2
        new_frame_inds = np.arange(
            len(timestamps) * n) * old_frame_interval / n + start
        return new_frame_inds.astype(np.int64)

    dense_n = int(args.predict_stepsize / args.output_stepsize)
    frames = [
        cv2.imread(frame_paths[i - 1])
        for i in dense_timestamps(timestamps, dense_n)
    ]
    print('Performing visualization')
    vis_frames = visualize(frames, results)
    vid = mpy.ImageSequenceClip([x[:, :, ::-1] for x in vis_frames],
                                fps=args.output_fps)
    vid.write_videofile(args.out_filename)

    tmp_dir.cleanup()


if __name__ == '__main__':
    main()