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Zero
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# 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()
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