from __future__ import annotations import os import pathlib import shlex import subprocess import sys import json def run_command(command): try: subprocess.run(shlex.split(command), check=True, capture_output=True, text=True) except subprocess.CalledProcessError as e: print(f"Error running command '{command}':") print(f"Exit code: {e.returncode}") print(f"stdout: {e.stdout}") print(f"stderr: {e.stderr}") raise if os.getenv("SYSTEM") == "spaces": try: # Update pip first run_command("pip install --upgrade pip") # Install openmim version 0.3.9 run_command("pip install openmim==0.3.9") # Install specific version of click run_command("pip install click==8.0.4") from mmcv.utils import install_module, uninstall_module uninstall_module("mmcv-full") install_module("mmcv-full==1.5.0") run_command("pip uninstall -y opencv-python") run_command("pip uninstall -y opencv-python-headless") run_command("pip install opencv-python-headless==4.5.5.64") except Exception as e: print(f"An error occurred during setup: {str(e)}") sys.exit(1) 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, ) HF_TOKEN = os.getenv("HF_TOKEN") 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: dic = self.MODEL_DICT[name] return init_detector(dic["config"], dic["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, outjson = self.detect(image) vis = self.visualize_detection_results(image, out, score_threshold) return out, vis, outjson def detect(self, image: np.ndarray) -> list[np.ndarray]: image = image[:, :, ::-1] # RGB -> BGR out = inference_detector(self.model, image) # Convert numpy arrays to lists out2 = [arr.tolist() for arr in out] # Convert output to JSON out_json = json.dumps(out2) return out, out_json 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": "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": "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": "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": "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: dic = self.MODEL_DICT[name] ckpt_path = huggingface_hub.hf_hub_download( "taesiri/ViTPose", dic["model"], use_auth_token=HF_TOKEN ) model = init_pose_model(dic["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, outjson = 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, outjson 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 out_for_json = [ { k: (v.tolist() if isinstance(v, np.ndarray) else v) for k, v in person.items() } for person in out ] # Convert output to JSON outjson = json.dumps(out_for_json) return out, outjson 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, )