mirror of https://github.com/open-mmlab/mmpose
286 lines
8.9 KiB
Python
286 lines
8.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import logging
|
|
import mimetypes
|
|
import os
|
|
import time
|
|
from argparse import ArgumentParser
|
|
|
|
import cv2
|
|
import json_tricks as json
|
|
import mmcv
|
|
import mmengine
|
|
import numpy as np
|
|
from mmengine.logging import print_log
|
|
|
|
from mmpose.apis import inference_topdown, init_model
|
|
from mmpose.registry import VISUALIZERS
|
|
from mmpose.structures import (PoseDataSample, merge_data_samples,
|
|
split_instances)
|
|
|
|
|
|
def parse_args():
|
|
parser = ArgumentParser()
|
|
parser.add_argument('config', help='Config file')
|
|
parser.add_argument('checkpoint', help='Checkpoint file')
|
|
parser.add_argument(
|
|
'--input', type=str, default='', help='Image/Video file')
|
|
parser.add_argument(
|
|
'--output-root',
|
|
type=str,
|
|
default='',
|
|
help='root of the output img file. '
|
|
'Default not saving the visualization images.')
|
|
parser.add_argument(
|
|
'--save-predictions',
|
|
action='store_true',
|
|
default=False,
|
|
help='whether to save predicted results')
|
|
parser.add_argument(
|
|
'--disable-rebase-keypoint',
|
|
action='store_true',
|
|
default=False,
|
|
help='Whether to disable rebasing the predicted 3D pose so its '
|
|
'lowest keypoint has a height of 0 (landing on the ground). Rebase '
|
|
'is useful for visualization when the model do not predict the '
|
|
'global position of the 3D pose.')
|
|
parser.add_argument(
|
|
'--show',
|
|
action='store_true',
|
|
default=False,
|
|
help='whether to show result')
|
|
parser.add_argument('--device', default='cpu', help='Device for inference')
|
|
parser.add_argument(
|
|
'--kpt-thr',
|
|
type=float,
|
|
default=0.3,
|
|
help='Visualizing keypoint thresholds')
|
|
parser.add_argument(
|
|
'--show-kpt-idx',
|
|
action='store_true',
|
|
default=False,
|
|
help='Whether to show the index of keypoints')
|
|
parser.add_argument(
|
|
'--show-interval', type=int, default=0, help='Sleep seconds per frame')
|
|
parser.add_argument(
|
|
'--radius',
|
|
type=int,
|
|
default=3,
|
|
help='Keypoint radius for visualization')
|
|
parser.add_argument(
|
|
'--thickness',
|
|
type=int,
|
|
default=1,
|
|
help='Link thickness for visualization')
|
|
|
|
args = parser.parse_args()
|
|
return args
|
|
|
|
|
|
def process_one_image(args, img, model, visualizer=None, show_interval=0):
|
|
"""Visualize predicted keypoints of one image."""
|
|
# inference a single image
|
|
pose_results = inference_topdown(model, img)
|
|
# post-processing
|
|
pose_results_2d = []
|
|
for idx, res in enumerate(pose_results):
|
|
pred_instances = res.pred_instances
|
|
keypoints = pred_instances.keypoints
|
|
rel_root_depth = pred_instances.rel_root_depth
|
|
scores = pred_instances.keypoint_scores
|
|
hand_type = pred_instances.hand_type
|
|
|
|
res_2d = PoseDataSample()
|
|
gt_instances = res.gt_instances.clone()
|
|
pred_instances = pred_instances.clone()
|
|
res_2d.gt_instances = gt_instances
|
|
res_2d.pred_instances = pred_instances
|
|
|
|
# add relative root depth to left hand joints
|
|
keypoints[:, 21:, 2] += rel_root_depth
|
|
|
|
# set joint scores according to hand type
|
|
scores[:, :21] *= hand_type[:, [0]]
|
|
scores[:, 21:] *= hand_type[:, [1]]
|
|
# normalize kpt score
|
|
if scores.max() > 1:
|
|
scores /= 255
|
|
|
|
res_2d.pred_instances.set_field(keypoints[..., :2].copy(), 'keypoints')
|
|
|
|
# rotate the keypoint to make z-axis correspondent to height
|
|
# for better visualization
|
|
vis_R = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]])
|
|
keypoints[..., :3] = keypoints[..., :3] @ vis_R
|
|
|
|
# rebase height (z-axis)
|
|
if not args.disable_rebase_keypoint:
|
|
valid = scores > 0
|
|
keypoints[..., 2] -= np.min(
|
|
keypoints[valid, 2], axis=-1, keepdims=True)
|
|
|
|
pose_results[idx].pred_instances.keypoints = keypoints
|
|
pose_results[idx].pred_instances.keypoint_scores = scores
|
|
pose_results_2d.append(res_2d)
|
|
|
|
data_samples = merge_data_samples(pose_results)
|
|
data_samples_2d = merge_data_samples(pose_results_2d)
|
|
|
|
# show the results
|
|
if isinstance(img, str):
|
|
img = mmcv.imread(img, channel_order='rgb')
|
|
elif isinstance(img, np.ndarray):
|
|
img = mmcv.bgr2rgb(img)
|
|
|
|
if visualizer is not None:
|
|
visualizer.add_datasample(
|
|
'result',
|
|
img,
|
|
data_sample=data_samples,
|
|
det_data_sample=data_samples_2d,
|
|
draw_gt=False,
|
|
draw_bbox=True,
|
|
kpt_thr=args.kpt_thr,
|
|
convert_keypoint=False,
|
|
axis_azimuth=-115,
|
|
axis_limit=200,
|
|
axis_elev=15,
|
|
show_kpt_idx=args.show_kpt_idx,
|
|
show=args.show,
|
|
wait_time=show_interval)
|
|
|
|
# if there is no instance detected, return None
|
|
return data_samples.get('pred_instances', None)
|
|
|
|
|
|
def main():
|
|
args = parse_args()
|
|
|
|
assert args.input != ''
|
|
assert args.show or (args.output_root != '')
|
|
|
|
output_file = None
|
|
if args.output_root:
|
|
mmengine.mkdir_or_exist(args.output_root)
|
|
output_file = os.path.join(args.output_root,
|
|
os.path.basename(args.input))
|
|
if args.input == 'webcam':
|
|
output_file += '.mp4'
|
|
|
|
if args.save_predictions:
|
|
assert args.output_root != ''
|
|
args.pred_save_path = f'{args.output_root}/results_' \
|
|
f'{os.path.splitext(os.path.basename(args.input))[0]}.json'
|
|
|
|
# build the model from a config file and a checkpoint file
|
|
model = init_model(
|
|
args.config, args.checkpoint, device=args.device.lower())
|
|
|
|
# init visualizer
|
|
model.cfg.visualizer.radius = args.radius
|
|
model.cfg.visualizer.line_width = args.thickness
|
|
|
|
visualizer = VISUALIZERS.build(model.cfg.visualizer)
|
|
visualizer.set_dataset_meta(model.dataset_meta)
|
|
|
|
if args.input == 'webcam':
|
|
input_type = 'webcam'
|
|
else:
|
|
input_type = mimetypes.guess_type(args.input)[0].split('/')[0]
|
|
|
|
if input_type == 'image':
|
|
# inference
|
|
pred_instances = process_one_image(args, args.input, model, visualizer)
|
|
|
|
if args.save_predictions:
|
|
pred_instances_list = split_instances(pred_instances)
|
|
|
|
if output_file:
|
|
img_vis = visualizer.get_image()
|
|
mmcv.imwrite(mmcv.rgb2bgr(img_vis), output_file)
|
|
|
|
elif input_type in ['webcam', 'video']:
|
|
|
|
if args.input == 'webcam':
|
|
cap = cv2.VideoCapture(0)
|
|
else:
|
|
cap = cv2.VideoCapture(args.input)
|
|
|
|
video_writer = None
|
|
pred_instances_list = []
|
|
frame_idx = 0
|
|
|
|
while cap.isOpened():
|
|
success, frame = cap.read()
|
|
frame_idx += 1
|
|
|
|
if not success:
|
|
break
|
|
|
|
# topdown pose estimation
|
|
pred_instances = process_one_image(args, frame, model, visualizer,
|
|
0.001)
|
|
|
|
if args.save_predictions:
|
|
# save prediction results
|
|
pred_instances_list.append(
|
|
dict(
|
|
frame_id=frame_idx,
|
|
instances=split_instances(pred_instances)))
|
|
|
|
# output videos
|
|
if output_file:
|
|
frame_vis = visualizer.get_image()
|
|
|
|
if video_writer is None:
|
|
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
|
|
# the size of the image with visualization may vary
|
|
# depending on the presence of heatmaps
|
|
video_writer = cv2.VideoWriter(
|
|
output_file,
|
|
fourcc,
|
|
25, # saved fps
|
|
(frame_vis.shape[1], frame_vis.shape[0]))
|
|
|
|
video_writer.write(mmcv.rgb2bgr(frame_vis))
|
|
|
|
if args.show:
|
|
# press ESC to exit
|
|
if cv2.waitKey(5) & 0xFF == 27:
|
|
break
|
|
|
|
time.sleep(args.show_interval)
|
|
|
|
if video_writer:
|
|
video_writer.release()
|
|
|
|
cap.release()
|
|
|
|
else:
|
|
args.save_predictions = False
|
|
raise ValueError(
|
|
f'file {os.path.basename(args.input)} has invalid format.')
|
|
|
|
if args.save_predictions:
|
|
with open(args.pred_save_path, 'w') as f:
|
|
json.dump(
|
|
dict(
|
|
meta_info=model.dataset_meta,
|
|
instance_info=pred_instances_list),
|
|
f,
|
|
indent='\t')
|
|
print_log(
|
|
f'predictions have been saved at {args.pred_save_path}',
|
|
logger='current',
|
|
level=logging.INFO)
|
|
|
|
if output_file is not None:
|
|
input_type = input_type.replace('webcam', 'video')
|
|
print_log(
|
|
f'the output {input_type} has been saved at {output_file}',
|
|
logger='current',
|
|
level=logging.INFO)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|