triple Mu
1 year ago
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commit
86f44fb997
38 changed files with 4095 additions and 5390 deletions
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import argparse |
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from pathlib import Path |
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import cv2 |
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import numpy as np |
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from config import COLORS, KPS_COLORS, LIMB_COLORS, SKELETON |
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from models.utils import blob, letterbox, path_to_list, pose_postprocess |
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def main(args: argparse.Namespace) -> None: |
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if args.method == 'cudart': |
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from models.cudart_api import TRTEngine |
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elif args.method == 'pycuda': |
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from models.pycuda_api import TRTEngine |
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else: |
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raise NotImplementedError |
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Engine = TRTEngine(args.engine) |
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H, W = Engine.inp_info[0].shape[-2:] |
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images = path_to_list(args.imgs) |
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save_path = Path(args.out_dir) |
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if not args.show and not save_path.exists(): |
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save_path.mkdir(parents=True, exist_ok=True) |
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for image in images: |
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save_image = save_path / image.name |
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bgr = cv2.imread(str(image)) |
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draw = bgr.copy() |
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bgr, ratio, dwdh = letterbox(bgr, (W, H)) |
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dw, dh = int(dwdh[0]), int(dwdh[1]) |
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rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) |
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tensor = blob(rgb, return_seg=False) |
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dwdh = np.array(dwdh * 2, dtype=np.float32) |
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tensor = np.ascontiguousarray(tensor) |
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# inference |
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data = Engine(tensor) |
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bboxes, scores, kpts = pose_postprocess(data, args.conf_thres, |
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args.iou_thres) |
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if bboxes.size == 0: |
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# if no bounding box |
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print(f'{image}: no object!') |
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continue |
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bboxes -= dwdh |
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bboxes /= ratio |
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for (bbox, score, kpt) in zip(bboxes, scores, kpts): |
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bbox = bbox.round().astype(np.int32).tolist() |
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color = COLORS['person'] |
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cv2.rectangle(draw, bbox[:2], bbox[2:], color, 2) |
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cv2.putText(draw, |
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f'person:{score:.3f}', (bbox[0], bbox[1] - 2), |
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cv2.FONT_HERSHEY_SIMPLEX, |
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0.75, [225, 255, 255], |
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thickness=2) |
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for i in range(19): |
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if i < 17: |
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px, py, ps = kpt[i] |
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if ps > 0.5: |
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kcolor = KPS_COLORS[i] |
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px = round(float(px - dw) / ratio) |
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py = round(float(py - dh) / ratio) |
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cv2.circle(draw, (px, py), 5, kcolor, -1) |
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xi, yi = SKELETON[i] |
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pos1_s = kpt[xi - 1][2] |
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pos2_s = kpt[yi - 1][2] |
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if pos1_s > 0.5 and pos2_s > 0.5: |
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limb_color = LIMB_COLORS[i] |
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pos1_x = round(float(kpt[xi - 1][0] - dw) / ratio) |
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pos1_y = round(float(kpt[xi - 1][1] - dh) / ratio) |
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pos2_x = round(float(kpt[yi - 1][0] - dw) / ratio) |
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pos2_y = round(float(kpt[yi - 1][1] - dh) / ratio) |
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cv2.line(draw, (pos1_x, pos1_y), (pos2_x, pos2_y), |
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limb_color, 2) |
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if args.show: |
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cv2.imshow('result', draw) |
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cv2.waitKey(0) |
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else: |
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cv2.imwrite(str(save_image), draw) |
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def parse_args(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--engine', type=str, help='Engine file') |
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parser.add_argument('--imgs', type=str, help='Images file') |
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parser.add_argument('--show', |
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action='store_true', |
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help='Show the detection results') |
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parser.add_argument('--out-dir', |
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type=str, |
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default='./output', |
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help='Path to output file') |
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parser.add_argument('--conf-thres', |
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type=float, |
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default=0.25, |
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help='Confidence threshold') |
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parser.add_argument('--iou-thres', |
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type=float, |
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default=0.65, |
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help='Confidence threshold') |
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parser.add_argument('--method', |
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type=str, |
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default='cudart', |
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help='CUDART pipeline') |
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args = parser.parse_args() |
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return args |
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if __name__ == '__main__': |
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args = parse_args() |
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main(args) |
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@ -0,0 +1,112 @@ |
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from models import TRTModule # isort:skip |
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import argparse |
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from pathlib import Path |
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import cv2 |
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import torch |
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from config import COLORS, KPS_COLORS, LIMB_COLORS, SKELETON |
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from models.torch_utils import pose_postprocess |
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from models.utils import blob, letterbox, path_to_list |
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def main(args: argparse.Namespace) -> None: |
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device = torch.device(args.device) |
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Engine = TRTModule(args.engine, device) |
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H, W = Engine.inp_info[0].shape[-2:] |
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images = path_to_list(args.imgs) |
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save_path = Path(args.out_dir) |
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if not args.show and not save_path.exists(): |
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save_path.mkdir(parents=True, exist_ok=True) |
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for image in images: |
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save_image = save_path / image.name |
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bgr = cv2.imread(str(image)) |
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draw = bgr.copy() |
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bgr, ratio, dwdh = letterbox(bgr, (W, H)) |
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dw, dh = int(dwdh[0]), int(dwdh[1]) |
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rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) |
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tensor = blob(rgb, return_seg=False) |
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dwdh = torch.asarray(dwdh * 2, dtype=torch.float32, device=device) |
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tensor = torch.asarray(tensor, device=device) |
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# inference |
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data = Engine(tensor) |
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bboxes, scores, kpts = pose_postprocess(data, args.conf_thres, |
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args.iou_thres) |
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if bboxes.numel() == 0: |
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# if no bounding box |
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print(f'{image}: no object!') |
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continue |
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bboxes -= dwdh |
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bboxes /= ratio |
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for (bbox, score, kpt) in zip(bboxes, scores, kpts): |
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bbox = bbox.round().int().tolist() |
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color = COLORS['person'] |
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cv2.rectangle(draw, bbox[:2], bbox[2:], color, 2) |
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cv2.putText(draw, |
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f'person:{score:.3f}', (bbox[0], bbox[1] - 2), |
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cv2.FONT_HERSHEY_SIMPLEX, |
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0.75, [225, 255, 255], |
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thickness=2) |
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for i in range(19): |
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if i < 17: |
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px, py, ps = kpt[i] |
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if ps > 0.5: |
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kcolor = KPS_COLORS[i] |
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px = round(float(px - dw) / ratio) |
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py = round(float(py - dh) / ratio) |
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cv2.circle(draw, (px, py), 5, kcolor, -1) |
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xi, yi = SKELETON[i] |
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pos1_s = kpt[xi - 1][2] |
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pos2_s = kpt[yi - 1][2] |
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if pos1_s > 0.5 and pos2_s > 0.5: |
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limb_color = LIMB_COLORS[i] |
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pos1_x = round(float(kpt[xi - 1][0] - dw) / ratio) |
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pos1_y = round(float(kpt[xi - 1][1] - dh) / ratio) |
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pos2_x = round(float(kpt[yi - 1][0] - dw) / ratio) |
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pos2_y = round(float(kpt[yi - 1][1] - dh) / ratio) |
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cv2.line(draw, (pos1_x, pos1_y), (pos2_x, pos2_y), |
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limb_color, 2) |
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if args.show: |
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cv2.imshow('result', draw) |
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cv2.waitKey(0) |
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else: |
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cv2.imwrite(str(save_image), draw) |
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def parse_args() -> argparse.Namespace: |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--engine', type=str, help='Engine file') |
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parser.add_argument('--imgs', type=str, help='Images file') |
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parser.add_argument('--show', |
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action='store_true', |
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help='Show the detection results') |
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parser.add_argument('--out-dir', |
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type=str, |
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default='./output', |
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help='Path to output file') |
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parser.add_argument('--conf-thres', |
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type=float, |
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default=0.25, |
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help='Confidence threshold') |
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parser.add_argument('--iou-thres', |
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type=float, |
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default=0.65, |
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help='Confidence threshold') |
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parser.add_argument('--device', |
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type=str, |
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default='cuda:0', |
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help='TensorRT infer device') |
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args = parser.parse_args() |
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return args |
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if __name__ == '__main__': |
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args = parse_args() |
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main(args) |
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