You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
117 lines
4.1 KiB
117 lines
4.1 KiB
1 year ago
|
import argparse
|
||
|
|
from pathlib import Path
|
||
|
|
|
||
|
|
import cv2
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from config import COLORS, KPS_COLORS, LIMB_COLORS, SKELETON
|
||
|
|
from models.utils import blob, letterbox, path_to_list, pose_postprocess
|
||
|
|
|
||
|
|
|
||
|
|
def main(args: argparse.Namespace) -> None:
|
||
|
|
if args.method == 'cudart':
|
||
|
|
from models.cudart_api import TRTEngine
|
||
|
|
elif args.method == 'pycuda':
|
||
|
|
from models.pycuda_api import TRTEngine
|
||
|
|
else:
|
||
|
|
raise NotImplementedError
|
||
|
|
|
||
|
|
Engine = TRTEngine(args.engine)
|
||
|
|
H, W = Engine.inp_info[0].shape[-2:]
|
||
|
|
|
||
|
|
images = path_to_list(args.imgs)
|
||
|
|
save_path = Path(args.out_dir)
|
||
|
|
|
||
|
|
if not args.show and not save_path.exists():
|
||
|
|
save_path.mkdir(parents=True, exist_ok=True)
|
||
|
|
|
||
|
|
for image in images:
|
||
|
|
save_image = save_path / image.name
|
||
|
|
bgr = cv2.imread(str(image))
|
||
|
|
draw = bgr.copy()
|
||
|
|
bgr, ratio, dwdh = letterbox(bgr, (W, H))
|
||
|
|
dw, dh = int(dwdh[0]), int(dwdh[1])
|
||
|
|
rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
|
||
|
|
tensor = blob(rgb, return_seg=False)
|
||
|
|
dwdh = np.array(dwdh * 2, dtype=np.float32)
|
||
|
|
tensor = np.ascontiguousarray(tensor)
|
||
|
|
# inference
|
||
|
|
data = Engine(tensor)
|
||
|
|
|
||
|
|
bboxes, scores, kpts = pose_postprocess(data, args.conf_thres,
|
||
|
|
args.iou_thres)
|
||
|
|
if bboxes.size == 0:
|
||
|
|
# if no bounding box
|
||
|
|
print(f'{image}: no object!')
|
||
|
|
continue
|
||
|
|
bboxes -= dwdh
|
||
|
|
bboxes /= ratio
|
||
|
|
|
||
|
|
for (bbox, score, kpt) in zip(bboxes, scores, kpts):
|
||
|
|
bbox = bbox.round().astype(np.int32).tolist()
|
||
|
|
color = COLORS['person']
|
||
|
|
cv2.rectangle(draw, bbox[:2], bbox[2:], color, 2)
|
||
|
|
cv2.putText(draw,
|
||
|
|
f'person:{score:.3f}', (bbox[0], bbox[1] - 2),
|
||
|
|
cv2.FONT_HERSHEY_SIMPLEX,
|
||
|
|
0.75, [225, 255, 255],
|
||
|
|
thickness=2)
|
||
|
|
for i in range(19):
|
||
|
|
if i < 17:
|
||
|
|
px, py, ps = kpt[i]
|
||
|
|
if ps > 0.5:
|
||
|
|
kcolor = KPS_COLORS[i]
|
||
|
|
px = round(float(px - dw) / ratio)
|
||
|
|
py = round(float(py - dh) / ratio)
|
||
|
|
cv2.circle(draw, (px, py), 5, kcolor, -1)
|
||
|
|
xi, yi = SKELETON[i]
|
||
|
|
pos1_s = kpt[xi - 1][2]
|
||
|
|
pos2_s = kpt[yi - 1][2]
|
||
|
|
if pos1_s > 0.5 and pos2_s > 0.5:
|
||
|
|
limb_color = LIMB_COLORS[i]
|
||
|
|
pos1_x = round(float(kpt[xi - 1][0] - dw) / ratio)
|
||
|
|
pos1_y = round(float(kpt[xi - 1][1] - dh) / ratio)
|
||
|
|
|
||
|
|
pos2_x = round(float(kpt[yi - 1][0] - dw) / ratio)
|
||
|
|
pos2_y = round(float(kpt[yi - 1][1] - dh) / ratio)
|
||
|
|
|
||
|
|
cv2.line(draw, (pos1_x, pos1_y), (pos2_x, pos2_y),
|
||
|
|
limb_color, 2)
|
||
|
|
if args.show:
|
||
|
|
cv2.imshow('result', draw)
|
||
|
|
cv2.waitKey(0)
|
||
|
|
else:
|
||
|
|
cv2.imwrite(str(save_image), draw)
|
||
|
|
|
||
|
|
|
||
|
|
def parse_args():
|
||
|
|
parser = argparse.ArgumentParser()
|
||
|
|
parser.add_argument('--engine', type=str, help='Engine file')
|
||
|
|
parser.add_argument('--imgs', type=str, help='Images file')
|
||
|
|
parser.add_argument('--show',
|
||
|
|
action='store_true',
|
||
|
|
help='Show the detection results')
|
||
|
|
parser.add_argument('--out-dir',
|
||
|
|
type=str,
|
||
|
|
default='./output',
|
||
|
|
help='Path to output file')
|
||
|
|
parser.add_argument('--conf-thres',
|
||
|
|
type=float,
|
||
|
|
default=0.25,
|
||
|
|
help='Confidence threshold')
|
||
|
|
parser.add_argument('--iou-thres',
|
||
|
|
type=float,
|
||
|
|
default=0.65,
|
||
|
|
help='Confidence threshold')
|
||
|
|
parser.add_argument('--method',
|
||
|
|
type=str,
|
||
|
|
default='cudart',
|
||
|
|
help='CUDART pipeline')
|
||
|
|
args = parser.parse_args()
|
||
|
|
return args
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
args = parse_args()
|
||
|
|
main(args)
|