from __future__ import annotations import pathlib import sys import huggingface_hub import numpy as np import torch import torch.nn as nn app_dir = pathlib.Path(__file__).parent submodule_dir = app_dir / "ViTPose" sys.path.insert(0, submodule_dir.as_posix()) from mmdet.apis import inference_detector, init_detector from mmpose.apis import ( inference_top_down_pose_model, init_pose_model, process_mmdet_results, vis_pose_result, ) class DetModel: MODEL_DICT = { "YOLOX-tiny": { "config": "mmdet_configs/configs/yolox/yolox_tiny_8x8_300e_coco.py", "model": "https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth", }, "YOLOX-s": { "config": "mmdet_configs/configs/yolox/yolox_s_8x8_300e_coco.py", "model": "https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth", }, "YOLOX-l": { "config": "mmdet_configs/configs/yolox/yolox_l_8x8_300e_coco.py", "model": "https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth", }, "YOLOX-x": { "config": "mmdet_configs/configs/yolox/yolox_x_8x8_300e_coco.py", "model": "https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth", }, } def __init__(self): self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") self._load_all_models_once() self.model_name = "YOLOX-l" self.model = self._load_model(self.model_name) def _load_all_models_once(self) -> None: for name in self.MODEL_DICT: self._load_model(name) def _load_model(self, name: str) -> nn.Module: d = self.MODEL_DICT[name] return init_detector(d["config"], d["model"], device=self.device) def set_model(self, name: str) -> None: if name == self.model_name: return self.model_name = name self.model = self._load_model(name) def detect_and_visualize(self, image: np.ndarray, score_threshold: float) -> tuple[list[np.ndarray], np.ndarray]: out = self.detect(image) vis = self.visualize_detection_results(image, out, score_threshold) return out, vis def detect(self, image: np.ndarray) -> list[np.ndarray]: image = image[:, :, ::-1] # RGB -> BGR out = inference_detector(self.model, image) return out def visualize_detection_results( self, image: np.ndarray, detection_results: list[np.ndarray], score_threshold: float = 0.3 ) -> np.ndarray: person_det = [detection_results[0]] + [np.array([]).reshape(0, 5)] * 79 image = image[:, :, ::-1] # RGB -> BGR vis = self.model.show_result( image, person_det, score_thr=score_threshold, bbox_color=None, text_color=(200, 200, 200), mask_color=None ) return vis[:, :, ::-1] # BGR -> RGB class AppDetModel(DetModel): def run(self, model_name: str, image: np.ndarray, score_threshold: float) -> tuple[list[np.ndarray], np.ndarray]: self.set_model(model_name) return self.detect_and_visualize(image, score_threshold) class PoseModel: MODEL_DICT = { "ViTPose-B (single-task train)": { "config": "ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py", "model": "models/vitpose-b.pth", }, "ViTPose-L (single-task train)": { "config": "ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_coco_256x192.py", "model": "models/vitpose-l.pth", }, "ViTPose-B (multi-task train, COCO)": { "config": "ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py", "model": "models/vitpose-b-multi-coco.pth", }, "ViTPose-L (multi-task train, COCO)": { "config": "ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_coco_256x192.py", "model": "models/vitpose-l-multi-coco.pth", }, } def __init__(self): self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") self.model_name = "ViTPose-B (multi-task train, COCO)" self.model = self._load_model(self.model_name) def _load_all_models_once(self) -> None: for name in self.MODEL_DICT: self._load_model(name) def _load_model(self, name: str) -> nn.Module: d = self.MODEL_DICT[name] ckpt_path = huggingface_hub.hf_hub_download("public-data/ViTPose", d["model"]) model = init_pose_model(d["config"], ckpt_path, device=self.device) return model def set_model(self, name: str) -> None: if name == self.model_name: return self.model_name = name self.model = self._load_model(name) def predict_pose_and_visualize( self, image: np.ndarray, det_results: list[np.ndarray], box_score_threshold: float, kpt_score_threshold: float, vis_dot_radius: int, vis_line_thickness: int, ) -> tuple[list[dict[str, np.ndarray]], np.ndarray]: out = self.predict_pose(image, det_results, box_score_threshold) vis = self.visualize_pose_results(image, out, kpt_score_threshold, vis_dot_radius, vis_line_thickness) return out, vis def predict_pose( self, image: np.ndarray, det_results: list[np.ndarray], box_score_threshold: float = 0.5 ) -> list[dict[str, np.ndarray]]: image = image[:, :, ::-1] # RGB -> BGR person_results = process_mmdet_results(det_results, 1) out, _ = inference_top_down_pose_model( self.model, image, person_results=person_results, bbox_thr=box_score_threshold, format="xyxy" ) return out def visualize_pose_results( self, image: np.ndarray, pose_results: list[np.ndarray], kpt_score_threshold: float = 0.3, vis_dot_radius: int = 4, vis_line_thickness: int = 1, ) -> np.ndarray: image = image[:, :, ::-1] # RGB -> BGR vis = vis_pose_result( self.model, image, pose_results, kpt_score_thr=kpt_score_threshold, radius=vis_dot_radius, thickness=vis_line_thickness, ) return vis[:, :, ::-1] # BGR -> RGB class AppPoseModel(PoseModel): def run( self, model_name: str, image: np.ndarray, det_results: list[np.ndarray], box_score_threshold: float, kpt_score_threshold: float, vis_dot_radius: int, vis_line_thickness: int, ) -> tuple[list[dict[str, np.ndarray]], np.ndarray]: self.set_model(model_name) return self.predict_pose_and_visualize( image, det_results, box_score_threshold, kpt_score_threshold, vis_dot_radius, vis_line_thickness )