# 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.apis import inference_recognizer
from mmaction.datasets.pipelines import Compose
from mmaction.models import build_detector, build_model, build_recognizer
from mmaction.utils import import_module_error_func
try:
from mmdet.apis import inference_detector, init_detector
from mmpose.apis import (init_pose_model, inference_top_down_pose_model,
vis_pose_result)
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
@import_module_error_func('mmpose')
def init_pose_model(*args, **kwargs):
pass
@import_module_error_func('mmpose')
def inference_top_down_pose_model(*args, **kwargs):
pass
@import_module_error_func('mmpose')
def vis_pose_result(*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))
PLATEBLUE = '03045e-023e8a-0077b6-0096c7-00b4d8-48cae4'
PLATEBLUE = PLATEBLUE.split('-')
PLATEBLUE = [hex2color(h) for h in PLATEBLUE]
PLATEGREEN = '004b23-006400-007200-008000-38b000-70e000'
PLATEGREEN = PLATEGREEN.split('-')
PLATEGREEN = [hex2color(h) for h in PLATEGREEN]
def visualize(frames,
annotations,
pose_results,
action_result,
pose_model,
plate=PLATEBLUE,
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 spatio-temporal
detection results.
pose_results (list[list[tuple]): The pose results.
action_result (str): The predicted action recognition results.
pose_model (nn.Module): The constructed pose model.
plate (str): The plate used for visualization. Default: PLATEBLUE.
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])
# add pose results
if pose_results:
for i in range(nf):
frames_[i] = vis_pose_result(pose_model, frames_[i],
pose_results[i])
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]
# add action result for whole video
cv2.putText(frame, action_result, (10, 30), FONTFACE, FONTSCALE,
FONTCOLOR, THICKNESS, LINETYPE)
# add spatio-temporal action detection results
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:])
if not pose_results:
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(
'--rgb-stdet-config',
default=('configs/detection/ava/'
'slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb.py'),
help='rgb-based spatio temporal detection config file path')
parser.add_argument(
'--rgb-stdet-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='rgb-based spatio temporal detection checkpoint file/url')
parser.add_argument(
'--skeleton-stdet-checkpoint',
default=('https://download.openmmlab.com/mmaction/skeleton/posec3d/'
'posec3d_ava.pth'),
help='skeleton-based 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(
'--pose-config',
default='demo/hrnet_w32_coco_256x192.py',
help='human pose estimation config file path (from mmpose)')
parser.add_argument(
'--pose-checkpoint',
default=('https://download.openmmlab.com/mmpose/top_down/hrnet/'
'hrnet_w32_coco_256x192-c78dce93_20200708.pth'),
help='human pose estimation checkpoint file/url')
parser.add_argument(
'--skeleton-config',
default='configs/skeleton/posec3d/'
'slowonly_r50_u48_240e_ntu120_xsub_keypoint.py',
help='skeleton-based action recognition config file path')
parser.add_argument(
'--skeleton-checkpoint',
default='https://download.openmmlab.com/mmaction/skeleton/posec3d/'
'posec3d_k400.pth',
help='skeleton-based action recognition checkpoint file/url')
parser.add_argument(
'--rgb-config',
default='configs/recognition/tsn/'
'tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py',
help='rgb-based action recognition config file path')
parser.add_argument(
'--rgb-checkpoint',
default='https://download.openmmlab.com/mmaction/recognition/'
'tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/'
'tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth',
help='rgb-based action recognition checkpoint file/url')
parser.add_argument(
'--use-skeleton-stdet',
action='store_true',
help='use skeleton-based spatio temporal detection method')
parser.add_argument(
'--use-skeleton-recog',
action='store_true',
help='use skeleton-based action recognition method')
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=0.4,
help='the threshold of action prediction score')
parser.add_argument(
'--video',
default='demo/test_video_structuralize.mp4',
help='video file/url')
parser.add_argument(
'--label-map-stdet',
default='tools/data/ava/label_map.txt',
help='label map file for spatio-temporal action detection')
parser.add_argument(
'--label-map',
default='tools/data/kinetics/label_map_k400.txt',
help='label map file for action recognition')
parser.add_argument(
'--device', type=str, default='cuda:0', help='CPU/CUDA device option')
parser.add_argument(
'--out-filename',
default='demo/test_stdet_recognition_output.mp4',
help='output filename')
parser.add_argument(
'--predict-stepsize',
default=8,
type=int,
help='give out a spatio-temporal detection prediction per n frames')
parser.add_argument(
'--output-stepsize',
default=1,
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=24,
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]))
# target_dir = osp.join('./tmp','spatial_skeleton_dir')
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 pose_inference(args, frame_paths, det_results):
model = init_pose_model(args.pose_config, args.pose_checkpoint,
args.device)
ret = []
print('Performing Human Pose Estimation for each frame')
prog_bar = mmcv.ProgressBar(len(frame_paths))
for f, d in zip(frame_paths, det_results):
# Align input format
d = [dict(bbox=x) for x in list(d)]
pose = inference_top_down_pose_model(model, f, d, format='xyxy')[0]
ret.append(pose)
prog_bar.update()
return ret
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 expand_bbox(bbox, h, w, ratio=1.25):
x1, y1, x2, y2 = bbox
center_x = (x1 + x2) // 2
center_y = (y1 + y2) // 2
width = x2 - x1
height = y2 - y1
square_l = max(width, height)
new_width = new_height = square_l * ratio
new_x1 = max(0, int(center_x - new_width / 2))
new_x2 = min(int(center_x + new_width / 2), w)
new_y1 = max(0, int(center_y - new_height / 2))
new_y2 = min(int(center_y + new_height / 2), h)
return (new_x1, new_y1, new_x2, new_y2)
def cal_iou(box1, box2):
xmin1, ymin1, xmax1, ymax1 = box1
xmin2, ymin2, xmax2, ymax2 = box2
s1 = (xmax1 - xmin1) * (ymax1 - ymin1)
s2 = (xmax2 - xmin2) * (ymax2 - ymin2)
xmin = max(xmin1, xmin2)
ymin = max(ymin1, ymin2)
xmax = min(xmax1, xmax2)
ymax = min(ymax1, ymax2)
w = max(0, xmax - xmin)
h = max(0, ymax - ymin)
intersect = w * h
union = s1 + s2 - intersect
iou = intersect / union
return iou
def skeleton_based_action_recognition(args, pose_results, num_frame, h, w):
fake_anno = dict(
frame_dict='',
label=-1,
img_shape=(h, w),
origin_shape=(h, w),
start_index=0,
modality='Pose',
total_frames=num_frame)
num_person = max([len(x) for x in pose_results])
num_keypoint = 17
keypoint = np.zeros((num_person, num_frame, num_keypoint, 2),
dtype=np.float16)
keypoint_score = np.zeros((num_person, num_frame, num_keypoint),
dtype=np.float16)
for i, poses in enumerate(pose_results):
for j, pose in enumerate(poses):
pose = pose['keypoints']
keypoint[j, i] = pose[:, :2]
keypoint_score[j, i] = pose[:, 2]
fake_anno['keypoint'] = keypoint
fake_anno['keypoint_score'] = keypoint_score
label_map = [x.strip() for x in open(args.label_map).readlines()]
num_class = len(label_map)
skeleton_config = mmcv.Config.fromfile(args.skeleton_config)
skeleton_config.model.cls_head.num_classes = num_class # for K400 dataset
skeleton_pipeline = Compose(skeleton_config.test_pipeline)
skeleton_imgs = skeleton_pipeline(fake_anno)['imgs'][None]
skeleton_imgs = skeleton_imgs.to(args.device)
# Build skeleton-based recognition model
skeleton_model = build_model(skeleton_config.model)
load_checkpoint(
skeleton_model, args.skeleton_checkpoint, map_location='cpu')
skeleton_model.to(args.device)
skeleton_model.eval()
with torch.no_grad():
output = skeleton_model(return_loss=False, imgs=skeleton_imgs)
action_idx = np.argmax(output)
skeleton_action_result = label_map[
action_idx] # skeleton-based action result for the whole video
return skeleton_action_result
def rgb_based_action_recognition(args):
rgb_config = mmcv.Config.fromfile(args.rgb_config)
rgb_config.model.backbone.pretrained = None
rgb_model = build_recognizer(
rgb_config.model, test_cfg=rgb_config.get('test_cfg'))
load_checkpoint(rgb_model, args.rgb_checkpoint, map_location='cpu')
rgb_model.cfg = rgb_config
rgb_model.to(args.device)
rgb_model.eval()
action_results = inference_recognizer(rgb_model, args.video,
args.label_map)
rgb_action_result = action_results[0][0]
return rgb_action_result
def skeleton_based_stdet(args, label_map, human_detections, pose_results,
num_frame, clip_len, frame_interval, h, w):
window_size = clip_len * frame_interval
assert clip_len % 2 == 0, 'We would like to have an even clip_len'
timestamps = np.arange(window_size // 2, num_frame + 1 - window_size // 2,
args.predict_stepsize)
skeleton_config = mmcv.Config.fromfile(args.skeleton_config)
num_class = max(label_map.keys()) + 1 # for AVA dataset (81)
skeleton_config.model.cls_head.num_classes = num_class
skeleton_pipeline = Compose(skeleton_config.test_pipeline)
skeleton_stdet_model = build_model(skeleton_config.model)
load_checkpoint(
skeleton_stdet_model,
args.skeleton_stdet_checkpoint,
map_location='cpu')
skeleton_stdet_model.to(args.device)
skeleton_stdet_model.eval()
skeleton_predictions = []
print('Performing SpatioTemporal Action Detection for each clip')
prog_bar = mmcv.ProgressBar(len(timestamps))
for timestamp in timestamps:
proposal = human_detections[timestamp - 1]
if proposal.shape[0] == 0: # no people detected
skeleton_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)
num_frame = len(frame_inds) # 30
pose_result = [pose_results[ind] for ind in frame_inds]
skeleton_prediction = []
for i in range(proposal.shape[0]): # num_person
skeleton_prediction.append([])
fake_anno = dict(
frame_dict='',
label=-1,
img_shape=(h, w),
origin_shape=(h, w),
start_index=0,
modality='Pose',
total_frames=num_frame)
num_person = 1
num_keypoint = 17
keypoint = np.zeros(
(num_person, num_frame, num_keypoint, 2)) # M T V 2
keypoint_score = np.zeros(
(num_person, num_frame, num_keypoint)) # M T V
# pose matching
person_bbox = proposal[i][:4]
area = expand_bbox(person_bbox, h, w)
for j, poses in enumerate(pose_result): # num_frame
max_iou = float('-inf')
index = -1
if len(poses) == 0:
continue
for k, per_pose in enumerate(poses):
iou = cal_iou(per_pose['bbox'][:4], area)
if max_iou < iou:
index = k
max_iou = iou
keypoint[0, j] = poses[index]['keypoints'][:, :2]
keypoint_score[0, j] = poses[index]['keypoints'][:, 2]
fake_anno['keypoint'] = keypoint
fake_anno['keypoint_score'] = keypoint_score
skeleton_imgs = skeleton_pipeline(fake_anno)['imgs'][None]
skeleton_imgs = skeleton_imgs.to(args.device)
with torch.no_grad():
output = skeleton_stdet_model(
return_loss=False, imgs=skeleton_imgs)
output = output[0]
for k in range(len(output)): # 81
if k not in label_map:
continue
if output[k] > args.action_score_thr:
skeleton_prediction[i].append(
(label_map[k], output[k]))
skeleton_predictions.append(skeleton_prediction)
prog_bar.update()
return timestamps, skeleton_predictions
def rgb_based_stdet(args, frames, label_map, human_detections, w, h, new_w,
new_h, w_ratio, h_ratio):
rgb_stdet_config = mmcv.Config.fromfile(args.rgb_stdet_config)
rgb_stdet_config.merge_from_dict(args.cfg_options)
val_pipeline = rgb_stdet_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']
assert clip_len % 2 == 0, 'We would like to have an even clip_len'
window_size = clip_len * frame_interval
num_frame = len(frames)
timestamps = np.arange(window_size // 2, num_frame + 1 - window_size // 2,
args.predict_stepsize)
# Get img_norm_cfg
img_norm_cfg = rgb_stdet_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.
rgb_stdet_config['model']['test_cfg']['rcnn']['action_thr'] = .0
except KeyError:
pass
rgb_stdet_config.model.backbone.pretrained = None
rgb_stdet_model = build_detector(
rgb_stdet_config.model, test_cfg=rgb_stdet_config.get('test_cfg'))
load_checkpoint(
rgb_stdet_model, args.rgb_stdet_checkpoint, map_location='cpu')
rgb_stdet_model.to(args.device)
rgb_stdet_model.eval()
predictions = []
print('Performing SpatioTemporal Action Detection for each clip')
prog_bar = mmcv.ProgressBar(len(timestamps))
for timestamp in timestamps:
proposal = human_detections[timestamp - 1]
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 = rgb_stdet_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)): # 80
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()
return timestamps, predictions
def main():
args = parse_args()
frame_paths, original_frames = frame_extraction(args.video)
num_frame = len(frame_paths)
h, w, _ = original_frames[0].shape
# Get Human detection results and pose results
human_detections = detection_inference(args, frame_paths)
pose_results = None
if args.use_skeleton_recog or args.use_skeleton_stdet:
pose_results = pose_inference(args, frame_paths, human_detections)
# 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
# Load spatio-temporal detection label_map
stdet_label_map = load_label_map(args.label_map_stdet)
rgb_stdet_config = mmcv.Config.fromfile(args.rgb_stdet_config)
rgb_stdet_config.merge_from_dict(args.cfg_options)
try:
if rgb_stdet_config['data']['train']['custom_classes'] is not None:
stdet_label_map = {
id + 1: stdet_label_map[cls]
for id, cls in enumerate(rgb_stdet_config['data']['train']
['custom_classes'])
}
except KeyError:
pass
action_result = None
if args.use_skeleton_recog:
print('Use skeleton-based recognition')
action_result = skeleton_based_action_recognition(
args, pose_results, num_frame, h, w)
else:
print('Use rgb-based recognition')
action_result = rgb_based_action_recognition(args)
stdet_preds = None
if args.use_skeleton_stdet:
print('Use skeleton-based SpatioTemporal Action Detection')
clip_len, frame_interval = 30, 1
timestamps, stdet_preds = skeleton_based_stdet(args, stdet_label_map,
human_detections,
pose_results, num_frame,
clip_len,
frame_interval, h, w)
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)
else:
print('Use rgb-based SpatioTemporal Action Detection')
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)
timestamps, stdet_preds = rgb_based_stdet(args, frames,
stdet_label_map,
human_detections, w, h,
new_w, new_h, w_ratio,
h_ratio)
stdet_results = []
for timestamp, prediction in zip(timestamps, stdet_preds):
human_detection = human_detections[timestamp - 1]
stdet_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)
output_timestamps = dense_timestamps(timestamps, dense_n)
frames = [
cv2.imread(frame_paths[timestamp - 1])
for timestamp in output_timestamps
]
print('Performing visualization')
pose_model = init_pose_model(args.pose_config, args.pose_checkpoint,
args.device)
if args.use_skeleton_recog or args.use_skeleton_stdet:
pose_results = [
pose_results[timestamp - 1] for timestamp in output_timestamps
]
vis_frames = visualize(frames, stdet_results, pose_results, action_result,
pose_model)
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
