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.
116 lines
4.1 KiB
116 lines
4.1 KiB
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)
|
|
|