[深度学习][原创]使用labelImg+yolov5完成所有slowfast时空动作检测项目-开山篇

slowfast项目地址：GitHub - facebookresearch/SlowFast: PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

网上其实就只有一个仁兄（计算机视觉-杨帆）搞slowfast自定义数据集训练的，出了很多教程，专门照顾小白学者和学生，但是里面有很多细节没有说清楚，不过像我这样子老司机看过之后直呼简单。虽然说起来容易但是做起来却是很难。其实如果你想自定义ava action数据集训练，你必须要搞清楚ava数据集每个文件具体含义才行。在计算机视觉-杨帆这个博主的csdn中，写的其实还算详细，其中有2个地方不得不提：

（1）ava2.1和ava2.2中所有基本csv文件是差不多的，只不过ava_train_2.2.csv中多了一个追踪ID，这个追踪ID其实要在自定义数据集中去标注难度就很大了。既然我们做时空动作检测，其实没必要标注这个追踪ID，即使有必要也是很简单的，只要在labelImg中加上ID就可以了。我训练自己的数据集其实没有用追踪ID。

（2）在person_box_67091280_iou90/ava_detection_train_boxes_and_labels_include_negative_v2.2.csv这个文件中，仔细看下图

最后一列有个概率值，我看很多同学不清楚怎么来的这些值，这些值其他博客都没有怎么解释，其实这就是bbox概率值，如果你用目标检测会有一个置信度值你完全可以写在这列，因此完全可以设置一个固定值。我设定0.99是没问题的,当然你也可以放在voc的name里面这样后面写个脚本在对应放进。

网上统一的自定义ava数据集训练自己的模型步骤如下：

（1）采集自己的视频文件

（2）对视频文件进行抽帧，按30fps抽取

（3）将抽取的图片采用mmdetection中的模型或者faster-rcnn模型进行预标注，并转化为via或者via3(一个网页版本的标注工具)

（4）手工矫正标注的框，个人感觉via3虽然好用，但是感觉比较复杂，至少比labelImg复杂一点，而且保存的标注一大坨。只有通过json.load才能看的清楚

（5）对标注进行代码分析转化为需要ava格式的文件

   (6)送入slowfast训练，然后测试结果

这回我的自定义训练数据集ava的流程为

（1）采集自己的视频文件

（2）对视频文件进行抽帧，按30fps抽取

（3）将抽取的图片采用yolov5模型进行预标注，并转化pascal VOC格式数据集

（4）手工矫正标注的框，使用labelImg矫正

（5）对标注进行代码分析转化为需要ava格式的文件

   (6)送入slowfast训练，然后测试结果

其中labelImg需要解决的关键技术有：

1、同一个人多种行为怎么标注，采用逗号分隔，比如一个人有talk还有stand则可以标注为talk,stand，如果需要追踪，则可以每个标注后面加个追踪ID号

2、负样本怎么标注，采用逗号分隔，标注动作名称只要不在自己动作名称就可以，比如talk,stand,neg这样转化解析时候将neg设置-1这样就解决了负样本标注问题

3、转化ava_predicted_box.csv数据集时候，最后一列的score可以设置为统一值

4、ava默认是从902帧开始，我们可以从第1帧开始，不过需要修改代码，位置在slowfast/slowfast/datasets/ava_helper.py，这里我门要求视频1-40秒，即视频文件时长需要>40s

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.

import logging
import os
from collections import defaultdict

from slowfast.utils.env import pathmgr

logger = logging.getLogger(__name__)

FPS = 30
SECONDS=40
AVA_VALID_FRAMES = range(1, FPS*SECONDS)


def load_image_lists(cfg, is_train):
    """
    Loading image paths from corresponding files.

    Args:
        cfg (CfgNode): config.
        is_train (bool): if it is training dataset or not.

    Returns:
        image_paths (list[list]): a list of items. Each item (also a list)
            corresponds to one video and contains the paths of images for
            this video.
        video_idx_to_name (list): a list which stores video names.
    """
    list_filenames = [
        os.path.join(cfg.AVA.FRAME_LIST_DIR, filename)
        for filename in (
            cfg.AVA.TRAIN_LISTS if is_train else cfg.AVA.TEST_LISTS
        )
    ]
    image_paths = defaultdict(list)
    video_name_to_idx = {}
    video_idx_to_name = []
    for list_filename in list_filenames:
        with pathmgr.open(list_filename, "r") as f:
            f.readline()
            for line in f:
                row = line.split()
                # The format of each row should follow:
                # original_vido_id video_id frame_id path labels.
                assert len(row) == 5
                video_name = row[0]

                if video_name not in video_name_to_idx:
                    idx = len(video_name_to_idx)
                    video_name_to_idx[video_name] = idx
                    video_idx_to_name.append(video_name)

                data_key = video_name_to_idx[video_name]

                image_paths[data_key].append(
                    os.path.join(cfg.AVA.FRAME_DIR, row[3])
                )

    image_paths = [image_paths[i] for i in range(len(image_paths))]

    logger.info(
        "Finished loading image paths from: %s" % ", ".join(list_filenames)
    )

    return image_paths, video_idx_to_name


def load_boxes_and_labels(cfg, mode):
    """
    Loading boxes and labels from csv files.

    Args:
        cfg (CfgNode): config.
        mode (str): 'train', 'val', or 'test' mode.
    Returns:
        all_boxes (dict): a dict which maps from `video_name` and
            `frame_sec` to a list of `box`. Each `box` is a
            [`box_coord`, `box_labels`] where `box_coord` is the
            coordinates of box and 'box_labels` are the corresponding
            labels for the box.
    """
    gt_lists = cfg.AVA.TRAIN_GT_BOX_LISTS if mode == "train" else []
    pred_lists = (
        cfg.AVA.TRAIN_PREDICT_BOX_LISTS
        if mode == "train"
        else cfg.AVA.TEST_PREDICT_BOX_LISTS
    )
    ann_filenames = [
        os.path.join(cfg.AVA.ANNOTATION_DIR, filename)
        for filename in gt_lists + pred_lists
    ]
    ann_is_gt_box = [True] * len(gt_lists) + [False] * len(pred_lists)

    detect_thresh = cfg.AVA.DETECTION_SCORE_THRESH
    # Only select frame_sec % 4 = 0 samples for validation if not
    # set FULL_TEST_ON_VAL.
    boxes_sample_rate = (
        4 if mode == "val" and not cfg.AVA.FULL_TEST_ON_VAL else 1
    )
    all_boxes, count, unique_box_count = parse_bboxes_file(
        ann_filenames=ann_filenames,
        ann_is_gt_box=ann_is_gt_box,
        detect_thresh=detect_thresh,
        boxes_sample_rate=boxes_sample_rate,
    )
    logger.info(
        "Finished loading annotations from: %s" % ", ".join(ann_filenames)
    )
    logger.info("Detection threshold: {}".format(detect_thresh))
    logger.info("Number of unique boxes: %d" % unique_box_count)
    logger.info("Number of annotations: %d" % count)

    return all_boxes


def get_keyframe_data(boxes_and_labels):
    """
    Getting keyframe indices, boxes and labels in the dataset.

    Args:
        boxes_and_labels (list[dict]): a list which maps from video_idx to a dict.
            Each dict `frame_sec` to a list of boxes and corresponding labels.

    Returns:
        keyframe_indices (list): a list of indices of the keyframes.
        keyframe_boxes_and_labels (list[list[list]]): a list of list which maps from
            video_idx and sec_idx to a list of boxes and corresponding labels.
    """

    def sec_to_frame(sec):
        """
        Convert time index (in second) to frame index.
        0: 900
        30: 901
        """
        return (sec - 900) * FPS

    keyframe_indices = []
    keyframe_boxes_and_labels = []
    count = 0
    for video_idx in range(len(boxes_and_labels)):
        sec_idx = 0
        keyframe_boxes_and_labels.append([])
        for sec in boxes_and_labels[video_idx].keys():
            if sec not in AVA_VALID_FRAMES:
                continue

            if len(boxes_and_labels[video_idx][sec]) > 0:
                keyframe_indices.append(
                    (video_idx, sec_idx, sec, sec_to_frame(sec))
                )
                keyframe_boxes_and_labels[video_idx].append(
                    boxes_and_labels[video_idx][sec]
                )
                sec_idx += 1
                count += 1
    logger.info("%d keyframes used." % count)

    return keyframe_indices, keyframe_boxes_and_labels


def get_num_boxes_used(keyframe_indices, keyframe_boxes_and_labels):
    """
    Get total number of used boxes.

    Args:
        keyframe_indices (list): a list of indices of the keyframes.
        keyframe_boxes_and_labels (list[list[list]]): a list of list which maps from
            video_idx and sec_idx to a list of boxes and corresponding labels.

    Returns:
        count (int): total number of used boxes.
    """

    count = 0
    for video_idx, sec_idx, _, _ in keyframe_indices:
        count += len(keyframe_boxes_and_labels[video_idx][sec_idx])
    return count


def parse_bboxes_file(
    ann_filenames, ann_is_gt_box, detect_thresh, boxes_sample_rate=1
):
    """
    Parse AVA bounding boxes files.
    Args:
        ann_filenames (list of str(s)): a list of AVA bounding boxes annotation files.
        ann_is_gt_box (list of bools): a list of boolean to indicate whether the corresponding
            ann_file is ground-truth. `ann_is_gt_box[i]` correspond to `ann_filenames[i]`.
        detect_thresh (float): threshold for accepting predicted boxes, range [0, 1].
        boxes_sample_rate (int): sample rate for test bounding boxes. Get 1 every `boxes_sample_rate`.
    """
    all_boxes = {}
    count = 0
    unique_box_count = 0
    for filename, is_gt_box in zip(ann_filenames, ann_is_gt_box):
        with pathmgr.open(filename, "r") as f:
            for line in f:
                row = line.strip().split(",")
                # When we use predicted boxes to train/eval, we need to
                # ignore the boxes whose scores are below the threshold.
                #if not is_gt_box:
                 #   score = float(row[7])
                   # if score < detect_thresh:
                       # continue

                video_name, frame_sec = row[0], int(row[1])
                if frame_sec>=FPS*SECONDS:
                    continue
                if frame_sec % boxes_sample_rate != 0:
                    continue

                # Box with format [x1, y1, x2, y2] with a range of [0, 1] as float.
                box_key = ",".join(row[2:6])
                box = list(map(float, row[2:6]))
                label = -1 if row[6] == "" else int(row[6])

                if video_name not in all_boxes:
                    all_boxes[video_name] = {}
                    for sec in AVA_VALID_FRAMES:
                        all_boxes[video_name][sec] = {}

                if box_key not in all_boxes[video_name][frame_sec]:
                    all_boxes[video_name][frame_sec][box_key] = [box, []]
                    unique_box_count += 1

                all_boxes[video_name][frame_sec][box_key][1].append(label)
                if label != -1:
                    count += 1

    for video_name in all_boxes.keys():
        for frame_sec in all_boxes[video_name].keys():
            # Save in format of a list of [box_i, box_i_labels].
            all_boxes[video_name][frame_sec] = list(
                all_boxes[video_name][frame_sec].values()
            )

    return all_boxes, count, unique_box_count

下一篇将介绍如何训练自己数据集具体流程

参考文献：
1、https://blog.csdn.net/WhiffeYF/article/details/115581800
2、https://blog.csdn.net/qq_45672807/article/details/123294954
3、https://github.com/futureflsl/self-made_ava_dataset_tool
4、https://blog.csdn.net/Bluemoon17/article/details/123238573