# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy as cp
import os
import os.path as osp
import shutil
import cv2
import mmcv
import numpy as np
import torch
from mmcv import DictAction
from mmcv.runner import load_checkpoint
from mmaction.models import build_detector
from mmaction.utils import import_module_error_func
try:
from mmdet.apis import inference_detector, init_detector
except (ImportError, ModuleNotFoundError):
@import_module_error_func('mmdet')
def inference_detector(*args, **kwargs):
pass
@import_module_error_func('mmdet')
def init_detector(*args, **kwargs):
pass
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_ = 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_[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_
def parse_args():
parser = argparse.ArgumentParser(description='MMAction2 demo')
parser.add_argument(
'--config',
default=('configs/detection/ava/'
'slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb.py'),
help='spatio temporal detection config file path')
parser.add_argument(
'--checkpoint',
default=('https://download.openmmlab.com/mmaction/detection/ava/'
'slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb/'
'slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb'
'_20201217-16378594.pth'),
help='spatio temporal detection checkpoint file/url')
parser.add_argument(
'--det-config',
default='demo/faster_rcnn_r50_fpn_2x_coco.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(
'--action-score-thr',
type=float,
default=0.5,
help='the threshold of human action score')
parser.add_argument('--video', help='video file/url')
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(
'--out-filename',
default='demo/stdet_demo.mp4',
help='output filename')
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 frame_extraction(video_path):
"""Extract frames given video_path.
Args:
video_path (str): The video_path.
"""
# Load the video, extract frames into ./tmp/video_name
target_dir = osp.join('./tmp', osp.basename(osp.splitext(video_path)[0]))
os.makedirs(target_dir, exist_ok=True)
# Should be able to handle videos up to several hours
frame_tmpl = osp.join(target_dir, 'img_{:06d}.jpg')
vid = cv2.VideoCapture(video_path)
frames = []
frame_paths = []
flag, frame = vid.read()
cnt = 0
while flag:
frames.append(frame)
frame_path = frame_tmpl.format(cnt + 1)
frame_paths.append(frame_path)
cv2.imwrite(frame_path, frame)
cnt += 1
flag, frame = vid.read()
return frame_paths, frames
def detection_inference(args, frame_paths):
"""Detect human boxes given frame paths.
Args:
args (argparse.Namespace): The arguments.
frame_paths (list[str]): The paths of frames to do detection inference.
Returns:
list[np.ndarray]: The human detection results.
"""
model = init_detector(args.det_config, args.det_checkpoint, args.device)
assert model.CLASSES[0] == 'person', ('We require you to use a detector '
'trained on COCO')
results = []
print('Performing Human Detection for each frame')
prog_bar = mmcv.ProgressBar(len(frame_paths))
for frame_path in frame_paths:
result = inference_detector(model, frame_path)
# We only keep human detections with score larger than det_score_thr
result = result[0][result[0][:, 4] >= args.det_score_thr]
results.append(result)
prog_bar.update()
return results
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 main():
args = parse_args()
frame_paths, original_frames = frame_extraction(args.video)
num_frame = len(frame_paths)
h, w, _ = original_frames[0].shape
# resize frames to shortside 256
new_w, new_h = mmcv.rescale_size((w, h), (256, 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 = mmcv.Config.fromfile(args.config)
config.merge_from_dict(args.cfg_options)
val_pipeline = config.data.val.pipeline
sampler = [x for x in val_pipeline if 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, center_frames)
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)
# Get img_norm_cfg
img_norm_cfg = config['img_norm_cfg']
if 'to_rgb' not in img_norm_cfg and 'to_bgr' in img_norm_cfg:
to_bgr = img_norm_cfg.pop('to_bgr')
img_norm_cfg['to_rgb'] = to_bgr
img_norm_cfg['mean'] = np.array(img_norm_cfg['mean'])
img_norm_cfg['std'] = np.array(img_norm_cfg['std'])
# Build STDET model
try:
# In our spatiotemporal detection demo, different actions should have
# the same number of bboxes.
config['model']['test_cfg']['rcnn']['action_thr'] = .0
except KeyError:
pass
config.model.backbone.pretrained = None
model = build_detector(config.model, test_cfg=config.get('test_cfg'))
load_checkpoint(model, args.checkpoint, map_location='cpu')
model.to(args.device)
model.eval()
predictions = []
print('Performing SpatioTemporal Action Detection for each clip')
assert len(timestamps) == len(human_detections)
prog_bar = mmcv.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]
input_tensor = torch.from_numpy(input_array).to(args.device)
with torch.no_grad():
result = model(
return_loss=False,
img=[input_tensor],
img_metas=[[dict(img_shape=(new_h, new_w))]],
proposals=[[proposal]])
result = result[0]
prediction = []
# N proposals
for i in range(proposal.shape[0]):
prediction.append([])
# Perform action score thr
for i in range(len(result)):
if i + 1 not in label_map:
continue
for j in range(proposal.shape[0]):
if result[i][j, 4] > args.action_score_thr:
prediction[j].append((label_map[i + 1], result[i][j,
4]))
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.int)
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_frame_dir = osp.dirname(frame_paths[0])
shutil.rmtree(tmp_frame_dir)
if __name__ == '__main__':
main()
Datasets
Models
